Artificial Intelligence (AI) is reshaping our world at a rapid pace. From optimizing business processes to supporting critical decisions, AI offers infinite potential, fostering a synergy between AI and humans. However, alongside its immense benefits, AI also poses significant challenges, especially concerning ethics. The question is no longer “How will AI develop?” but rather “How should AI develop responsibly?”. 

This is where AI ethics becomes a timely and urgent topic, not only for technology developers but also for regulators, businesses, and society as a whole. Let’s explore what AI ethics is with FPT.AI! 

What is AI Ethics? 

AI ethics is a set of principles, values, and guidelines that aim to ensure the transparent, fair, and responsible development, deployment, and use of artificial intelligence. According to UNESCO, AI ethics doesn’t just focus on what the technology can do, but also revolves around the questions of “What should AI do?” and “How should we use AI to benefit humanity, rather than cause harm?”. 

Why is AI Ethics Important?  

AI is no longer merely supporting technology. It’s a powerful tool capable of making decisions. According to Deputy Minister of Science and Technology, Mr. Bui The Duy, AI is completely different from any other technologies that humans have found before. While old technological products only follow existing instructions, AI can create its own directions, beyond the control of developers. 

Furthermore, AI has now become an indispensable partner for humans. A global study involving approximately 32,000 workers from 47 countries by The University of Melbourne showed that over 58% of employees actively use AI in their work, with one-third of them using AI weekly or daily. . 

Therefore, without ethical oversight, sometimes wrong decisions made by AI can have far-reaching impacts on individuals or even a nation. An algorithm could reject a job application simply because their resume came from a rural area, implicitly assuming they are “less promising.” A facial recognition system could misidentify people of color due to a lack of diverse data. A chatbot could learn discriminatory language from social media users if left unchecked. 

These examples highlight that if AI lacks ethics, the consequences will not be limited to technical errors but will also lead to social, legal, and humanitarian consequences. 

Core Values Shaping AI for the Future 

In the journey of artificial intelligence development, what’s important is not just how far technology advances, but which direction we are leading it. To ensure AI serves the common good, for people, society, and this planet, UNESCO has outlined four core values that act as guiding principles, including: 

• Respecting human rights and human dignity: Ensuring the respect, protection, and promotion of human rights, fundamental freedoms, and the dignity of each individual. 

• Building peaceful, just, and interconnected societies: Encouraging the development of societies where everyone can live in harmony, fairness, and connection. 

• Promoting diversity and inclusion: AI must be designed to serve everyone, excluding no one, fostering diversity and creating equal opportunities. 

• Protecting the environment and developing thriving ecosystems: AI technology needs to be environmentally responsible, contributing to the protection of the planet and natural ecosystems. 

These values are crucial compasses for guiding AI development in a positive, sustainable direction. 

Core Principles in AI Ethics

With the goal of “living safely” with AI, philosopher Luciano Floridi has distilled 5 core principles, becoming reliable beacons on the journey alongside AI: 

• Beneficence: AI should be developed and applied to improve the lives of humans and our planet. The ultimate goal is to create “AI for Social Good” (AI4SG), where AI is used to enhance societal well-being. 

• Nonmaleficence: The principle of “do no harm” is paramount, especially when AI has the potential to affect human existence. This includes avoiding harm to privacy, autonomy, and employment opportunities. 

• Autonomy: Human ability to act freely and independently must be preserved and promoted, while machine autonomy needs to be limited. 

• Justice: AI must be developed, designed, and deployed in a way that promotes justice, fairness, equality, and related values. This requires addressing issues like algorithmic bias and ensuring equitable access. 

• Explicability: To promote other principles, we need to understand the “how” and “why” behind AI systems and products. Accountability and transparency are key. 

What Should Businesses Do? 

For businesses, investing in AI technology cannot be separated from building an ethical foundation. According to Coursera, many global corporations like IBM, Google, and Microsoft have established internal ethics committees, built codes of conduct, and verification processes to ensure their AI products adhere to ethical standards from the outset. 

Some specific actions regarding AI ethics that businesses can take include: 

• Training personnel on AI ethics, data, and privacy. 

• Integrating ethical risk assessment into the product development process. 

• Consulting independent experts to review critical algorithms. 

• Being transparent about how data is collected, processed, and used. 

• Establishing internal ethics councils to verify products before launch. 

Not Just One Person’s Responsibility 

AI ethics is not solely a matter for the tech industry; it’s a shared responsibility of society as a whole: 

• Nations and government agencies need to create flexible, appropriate, and practical legal frameworks that keep pace with technological development while still safeguarding human rights. 

• Universities and research institutes need to integrate ethics topics into AI and data science curricula. 

• AI users also need to enhance their understanding to use AI intelligently and responsibly. 

Towards a Sustainable Technological Future 

AI ethics is not a barrier to innovation, but a solid foundation for the development of artificial intelligence. By prioritizing these core values, we are building a future where AI is not only exceptionally intelligent but also deeply humane, serving and enhancing human life. 

As AI becomes increasingly integrated into decision-making systems, AI ethics is no longer an option – it’s a prerequisite for building a just, transparent, and humane digital society. 

🤝 Build ethical AI today, create lasting trust tomorrow. 

Sources

UNESCO. (n.d.). Recommendation on the Ethics of Artificial Intelligence. https://www.unesco.org/en/artificial-intelligence/recommendation-ethics 

Coursera. (2023, July 26). What is AI ethics? Definition and examples. https://www.coursera.org/articles/ai-ethics 

IBM. (n.d.). AI ethics. IBM. https://www.ibm.com/think/topics/ai-ethics 

University of Texas at Austin. (n.d.). AI ethics. Ethics Unwrapped. https://ethicsunwrapped.utexas.edu/glossary/ai-ethics thics Unwrapped. https://ethicsunwrapped.utexas.edu/glossary/ai-ethics 

In the advanced age of artificial intelligence, every breakthrough technology marks a pivotal shift — and the latest buzz driving the tech world is the Model Context Protocol. 

Launched in late 2024 by Anthropic, the parent company of Claude, Model Context Protocol (MCP) has rapidly gained global attention. This new protocol is designed to revolutionize the way AI Agents and Large Language Models (LLMs) interact, enabling seamless communication and unlocking collaboration at an unprecedented scale. 

But what exactly is MCP? How is it being applied in real-world scenarios? And is it related to the familiar APIs we use today? Join FPT.AI as we dive into the fascinating world of Model Context Protocol and uncover the value it brings to the future of AI collaboration. 

 

What is MCP? 

MCP, or Model Context Protocol, is essentially a set of rules and standards that enables different AI Agents and Large Language Models (LLMs) to exchange information, share resources, and coordinate on complex tasks. MCP acts as the “USB-C” of the AI world — a universal connector that allows AI models to plug into tools, services, and external data sources, resulting in more accurate and context-aware responses for real-world business needs.  

MCP Architecture

To operate seamlessly, Model Context Protocol is built on three core components:  

Figure 2 – MCP Architecture

• MCP Host – A chatbot, IDE, or AI-powered tool that acts as the central coordinator. It manages sessions, controls access, and can initiate commands to MCP based on user requests or automated workflows. 
• MCP Client – A web or mobile application initiated by the Host. It connects to a single Server and facilitates two-way communication between the Host and the Server. 
• MCP Server – Acts as a bridge to external tools or data sources (e.g., Google Drive, Slack), enabling functions such as file retrieval, status updates, and data access: 
  • Prompt – Predefined instructions for LLMs, easily triggered via slash commands (/search), menus, or interactive UI elements. 
  • Source – Structured data (files, databases, histories) that provides essential context to AI models. 
  • Tool – Functional units that empower AI agents to take action, such as calling an API or writing data. 

Why MCP Matters?

Beyond just a connection protocol, MCP brings a host of powerful advantages that enhance flexibility, security, and scalability across AI systems: 

• Standardized Communication: MCP provides a structured framework that enables AI models to interact with diverse tools in a consistent and unified manner. 

• Real-time Tool Access & Integration: AI assistants can now leverage external tools to gather real-time data — boosting operational efficiency and reducing costs for businesses. 

• Enterprise-grade Security & Scalability: MCP enables secure and seamless integration with enterprise applications, supporting agile growth without compromising data protection. 

• Multi-modal Integration: MCP supports multiple communication methods — including STDIO, SSE (Server-Sent Events), and WebSocket — ensuring compatibility across different deployment environments. This makes it easy for businesses to expand their AI ecosystems simply by connecting new MCP Servers, without disrupting existing workflows. 

API vs MCP: Key Similarities and Differences 

To implement MCP effectively, businesses need to understand how it differs from traditional APIs.  

When to Use API vs MCP 

On the one hand, business can use APIs when precision, predictability, and strict control are paramount in a tightly scoped environment. 
For example: 

• Online Banking Applications – Tasks like checking balances or transferring funds demand high security and accuracy, with clearly defined operations. 

On the other hand, businesses will use MCP when you need dynamic, intelligent coordination between AI Agents and LLMs for complex, multi-step tasks. 
For example: 

• Business Trip Planning – Instead of integrating with calendar, flight, and email services separately, an AI Agent can interact with all via MCP in one seamless flow. 

• Smart IDEs – MCP simplifies connections with file systems and version control, enabling AI to better understand code context and make smarter suggestions. 

• Complex Data Analysis – AI platforms can automatically discover and interact with data sources and visualization tools via a unified MCP layer. 

A Major Leap Toward “Build Your Own AI Agents” journey 

Staying ahead of the curve, FPT AI Agents has integrated MCP Client Tools into its platform. This game-changing feature allows your AI Agents to connect and interact with external tools via MCP Servers — unlocking new levels of flexibility and intelligence. 

With this integration, users can: 

• Declare secure endpoints with flexible auth options (No Auth / Bearer Token). 

• Select or exclude specific tools from connected MCP Servers as needed. 

• Embed directly into business processes, allowing AI Agents to automate smart workflows powered by external capabilities. 

This upgrade empowers enterprise AI teams to become more agile, versatile, and impactful — enabling smarter collaboration across systems and unlocking greater business value. 

The Future of MCP in the AI-Driven World 

Although still emerging, MCP is already reshaping the way we interact with and scale artificial intelligence: 

• Standardizing AI Communication – Like HTTP standardized the web, MCP is poised to become the universal language for AI agents and tools to talk, collaborate, and co-create. 

• Enabling a New AI Economy – MCP fosters a thriving ecosystem where enterprises and indie developers alike can build and expand AI-powered products. 

• Accelerating the Evolution of AI Agents – With a consistent interaction framework, MCP makes intelligent, autonomous AI agents a practical reality. 

• Redefining Human-AI Collaboration – By allowing machines to understand and respond in more humanlike, contextual ways, MCP moves us closer to true augmented intelligence.

Conclusion 

MCP is more than just a protocol — it’s a dynamic gateway that enables AI tools to understand deeper context and deliver smarter, more accurate responses. With its unmatched advantages in scalability, flexibility, and performance, MCP is set to become a core trend shaping the future of AI. 

At FPT AI Agents, the adoption of MCP Client Tools empowers your business to harness this breakthrough — enabling smarter automation, cost savings, and enhanced productivity. 

Let’s build better, smarter AI Agents together — with FPT.AI! 

Sources: 

Base.vn. (n.d.). Model Context Protocol (MCP) là gì?. https://base.vn/blog/model-context-protocol-mcp-la-gi/ 

BitOnTree. (2024, March 19). Model Context Protocol vs API: What’s the difference and why it matters. https://www.bitontree.com/blog/model-context-protocol-vs-api 

Microsoft Tech Community. (2024, April 23). Unleashing the power of Model Context Protocol (MCP): A game-changer in AI integration. https://techcommunity.microsoft.com/blog/educatordeveloperblog/unleashing-the-power-of-model-context-protocol-mcp-a-game-changer-in-ai-integrat/4397564 

AI Agents are artificial intelligence systems that can interact with the environment and make decisions to achieve goals in the real world without any human guidance or intervention. This technology are shaping technology trends, with notable milestones such as the Google I/O 2023 event launching Astra or the emergence of GPT-4o.

Large corporations are pouring billions of dollars into AI Agents to take the lead in AI Era. In this article, FPT.AI will clarify how AI Agents are helping businesses improve processes, enhance customer experience and optimize operations.

What are AI Agents (Intelligent Agents)?

AI Agents are artificial intelligence systems that can interact with the environment and make decisions in the real world without any human guidance or intervention.

AI Agents can gather information from their surroundings, design their own workflows, use available tools, coordinate between different systems, and even work with other Agents to achieve goals without requiring user supervision or continuous new instructions.

With the development of Generative AI, Natural language processing, Foundation Models, and Large Language Models (LLMs), AI Agents can now simultaneously process multiple types of multimodal information such as text, voice, video, audio, and code. Advanced agent AI can learn and update their behavior over time, continuously experimenting with new solutions to problems until achieving optimal results. Notably, they can detect their own errors and find ways to correct them as they progress.

AI Agents can exist in the physical world (robots, autonomous drones, or self-driving cars) or operate within computers and software to complete digital tasks. The aspects, components, and interfaces of each agent AI can vary depending on its specific purpose. Encouragingly, even people without deep technical backgrounds can now build and use AI Agents through user-friendly platforms.

>>> READ NOW: What is Generative AI? Trends in Applying GenAI from 2024 to 2027

What are the key features of an AI Agent platform?

Key features of an AI Agent platform include:

• Autonomy: AI Agents can operate independently, make decisions, and take actions without continuous human supervision. For example, self-driving cars can adjust speed, change lanes, stop, or adjust routes based on real-time sensor data about road conditions and obstacles, without driver intervention.
• Reasoning Ability: AI agents use logic and analyze available information to draw conclusions and solve problems. They can identify patterns in data, evaluate evidence, and make decisions based on the current context, similar to human thinking processes.
• Continuous Learning: AI Agents continuously improve their performance over time by learning from data and adapting to changes in the environment. For instance, customer support chatbots can analyze millions of conversations to gain deeper understanding of common issues and improve the quality of proposed solutions.
• Environmental Observation: AI agents continuously collect and process information from their surroundings through techniques like computer vision, natural language processing, and sensor data analysis. This ability helps them understand the current context and make appropriate decisions.
• Action Capability: AI agents can perform specific actions to achieve goals. These actions can be physical (like a robot moving objects) or digital (like sending emails, updating data, or triggering automated processes).
• Strategic Planning: AI agents can develop detailed plans to achieve goals, including identifying necessary steps, evaluating alternatives, and selecting optimal solutions. This ability requires predicting future outcomes and considering potential obstacles.
• Proactivity and Reactivity: AI agents proactively anticipate and prepare for future changes. For example, Nest Thermostat learns the homeowner’s heating habits and proactively adjusts temperature before the user returns home, while quickly responding to unusual temperature fluctuations.
• Collaboration Ability: AI agents can work effectively with humans and other agents to achieve common goals. This collaboration requires clear communication, coordinated actions, and understanding the roles and objectives of other participants in the system.
• Self-Improvement: Advanced AI agents can self-evaluate and improve their operational performance. They analyze the results of previous actions, adjust strategies based on feedback, and continuously enhance their capabilities through machine learning techniques and optimization.

>>> READ NOW: 2 Ways to Classify Artificial Intelligence and 7 Common Types of AI

Differences between Agentic AI Chatbots and AI Chatbots

Below is a comparison table highlighting the distinctions between Agentic AI chatbots and AI Chatbots:

Criteria	Agentic AI Chatbots	Traditional AI Chatbots
Autonomy	Operate independently, perform complex tasks without continuous intervention	Require continuous guidance from users, only respond when prompted
Memory	Maintain long-term memory between sessions, remember user interactions and preferences	Limited or no memory storage capability, each session typically starts from scratch
Tool Integration	Use function calls to connect with APIs, databases, and external applications	Operate in closed environments with no ability to access external tools or data sources
Task Processing	Break down complex tasks into subtasks, execute them sequentially to achieve goals	Only process simple, individual requests without ability to decompose complex problems
Knowledge Sources	Combine existing knowledge with new information from external sources (RAG)	Rely solely on pre-trained data, unable to update with new information
Learning Capability	Continuously learn from interactions, improving accuracy and relevance over time	Do not learn or improve from user interactions, responses always follow fixed patterns
Operation Mode	Can perform multiple processing rounds for a single request, creating multi-step workflows	Operate on a single-turn basis (receive-process-respond), without multi-step capabilities
Planning Ability	Strategically plan and self-adjust when encountering new information or obstacles	No long-term planning capability or strategy adjustment
Personalization	Provide personalized experiences based on user history, preferences, and context	Deliver generalized responses, identical for all users
Response Process	Analyze intent, access relevant information, create plan, execute actions, and evaluate results	Recognize patterns, search for appropriate responses in existing database, reply
Error Handling	Recognize errors, self-correct, and find alternative solutions when problems arise	Often fail to recognize errors or lack ability to recover when encountering off-script situations
User Interaction	Proactively ask clarifying questions, suggest options, and track progress	Passive, only directly respond to what users explicitly ask
Workflow	Use threads to store all information, connect with tools, execute function calls when needed	Simple processing according to predefined scripts, no workflow extension capability
Practical Applications	Complex customer support, data analysis, process automation, personal assistance	Primarily for FAQs, basic customer support, simple conversations
Intent Detection	Accurately identify users’ underlying intents, even when not explicitly stated	Only react to specific keywords or patterns, often missing true intentions
System Integration	Easily integrate with multiple systems and applications through APIs	Limited integration capabilities, often requiring custom solutions
Development Requirements	Can be developed on no-code platforms, without requiring in-depth programming knowledge	Typically require programming knowledge to build and maintain

Agentic AI chatbots mark a significant evolution in conversational AI, powered by LLMs but extending well beyond them. Operating on thread-based architecture, they store complete conversation histories, files, and function call results. These advanced chatbots activate via various triggers (scheduled events, database changes, or manual inputs) to analyze requests, interpret intentions, and execute actions autonomously.

Five key innovations drive this technology:

• RAG integration for context-aware responses with higher accuracy
• Function calling to interact with external systems
• Advanced memory systems for continuous learning and adaptation
• Tool evaluation to assess resources and fill information gaps
• Subtask generation to break down complex goals independently

Unlike traditional chatbots’ single-turn model (receive-process-respond), agentic chatbots process multiple turns per prompt, queue actions strategically, and dynamically select appropriate tools based on user intent. They can search connected knowledge bases, call external APIs, or generate responses from core training when external tools aren’t needed. Critically, no-code platforms have democratized their development, accelerating adoption across industries by enabling businesses of all sizes to implement sophisticated AI without significant technical investment.

>>> READ MORE: What is Agentic RAG? Difference between Agentic RAG and RAG

Key Components of AI Agents

AI Agents are composed of multiple components working together as a unified system, similar to how the human body functions with senses, muscles, and brain. Each component in AI Agent Architecture plays a specific role in helping the agent sense, think, and interact with the surrounding world.

Sensors

Sensors help AI Agents collect information (percepts) from the surrounding environment to understand the context and current situation. In physical robots, sensors might be cameras for “seeing,” microphones for “hearing,” or thermal sensors for “feeling” temperature. For software agents running on computers, sensors might be web search functions to gather online information, or file reading tools to process data from PDF documents, CSV files, or other formats.

>>> EXPLORE MORE: How to build an AI Agent and train it successfully?

Actuators

If sensors are how agents receive information, actuators are how they affect the world. Actuators are components that allow agents to perform specific actions after making decisions. In physical robots, actuators might be wheels for movement, mechanical arms for lifting objects, or speakers for producing sound. For software agents, actuators might be the ability to create new files, send emails, control other applications, or modify data in systems.

Brain

Processors, Control Systems, and Decision-Making Mechanisms form the “brain” of the AI Agents, where information is processed and decisions are made. Processors analyze raw data from sensors and convert it into meaningful information. Control systems coordinate the agent’s activities, ensuring all parts work harmoniously. Decision-making mechanisms are the most important part, where the agent “thinks” about processed information, evaluates different action options, and selects the most optimal action based on goals and existing knowledge.

>>> EXPLORE: Applications of AI Agents in Personalized Marketing

Learning and Knowledge Base Systems

These are the memory and learning capabilities of AI Agents, allowing them to improve performance over time. Knowledge base systems store information the agent already knows: data about the world, rules of action, and experiences from previous interactions. This might be a database of locations, events, or problems the agent has encountered along with corresponding solutions.

Learning systems allow the agent to learn from experience, recognize patterns, and improve decision-making abilities. An agent with learning capabilities will continuously update its knowledge base, helping it better cope with new situations or changes in the environment.

The complexity level of these components depends on the tasks the AI Agent performs. A smart thermostat might only need simple temperature sensors, a basic control system, and actuators to turn heating systems on/off. In contrast, a self-driving car needs to be equipped with all components at high complexity levels: diverse sensors to observe roads and other vehicles, powerful processors to handle large amounts of real-time data, sophisticated decision-making systems for safe navigation, precise actuators to control the vehicle, and continuous learning systems to improve driving capabilities through each experience.

>>> EXPLORE: What Are Intelligent Agents? The Difference Between AI Agents and Intelligent Agents

How do AI Agents Work?

When receiving a command (goal) from a user (Prompt), AI Agents immediately initiate the goal analysis process, transferring the prompt to the core AI model (typically a Large Language Model) and beginning to plan actions. The Agent will break down complex goals into specific tasks and subtasks, with clear priorities and dependencies. For simple tasks, the Agent may skip the planning stage and directly improve responses through an iterative process.

During implementation, thanks to Sensors, AI agents collect information (transaction data, customer interaction history) from various sources (including external datasets, web searches, APIs, and even other agents). During this collection process, the AI Agent continuously updates its knowledge base, self-adjusts, and corrects errors if necessary.

The Processors of AI Agents use algorithms, Deep Neural Networks, machine learning models, and artificial intelligence to analyze information and calculate necessary actions.

Throughout this process, the agent’s Memory continuously stores information (such as history of decisions made or rules learned). Additionally, AI Agents also use feedback from users, feedback from other Agents, and Human-in-the-loop (HITL) to self-compare, adjust, and improve performance over time, avoiding repetition of the same errors.

Finally, through Actuators, AI Agents perform actions based on their decisions. For robots, actuators might be parts that help them move or manipulate objects. For software agents, this might be sending information or executing commands on systems.

To illustrate this process, imagine a user planning their vacation. They ask an AI Agent to predict which week of the coming year will have the best weather for surfing in Greece. Since the large language model that underpins the agent is not specialized in weather forecasting, the agent must access an external database that contains daily weather reports in Greece over the past several years.

Even with historical data, the agent cannot yet determine the optimal weather conditions for surfing. Therefore, it must communicate with a surf agent to learn that ideal surfing conditions include high tides, sunny weather, and low or no rainfall.

With the newly gathered information, the agent combines and analyzes the data to identify relevant weather patterns. Based on this, it predicts which week of the coming year in Greece is most likely to have high tides, sunny weather, and low rainfall. The final result is then presented to the user.

>>> READ NOW: RPA vs AI Agents: Is RPA Still Relevant in the Age of AI?

Common Types of AI Agents

There are 5 primary types of AI Agents: Simple Reflex Agents,  Goal-Based AI Agents, Model-Based Reflex Agents, Utility-Based Agents, Learning Agents. Each suited to specific tasks and applications:

• Simple Reflex Agents: Simple Reflex Agents operate on the “condition-action” principle and respond to their environment based on simple pre-programmed rules, such as a thermostat that turns on the heating system at exactly 8pm every night. The agent does not retain any memory, does not interact with other agents without information, and cannot react appropriately if faced with unexpected situations.
• Model-Based Reflex Agents: Model-Based Reflex Agents use their cognitive abilities and memory to create an internal model of the world around them. By storing information in memory, these agents can operate effectively in changing environments but are still constrained by pre-programmed rules. For example, a robot vacuum cleaner can sense obstacles when cleaning a room and adjust its path to avoid collisions. It also remembers areas it has cleaned to avoid unnecessary repetition.
• Goal-Based AI Agents: Goal-Based Agents are driven by one or more specific goals. They look for appropriate courses of action to achieve the goal and plan ahead before executing them. For example, when a navigation system suggests the fastest route to your destination, it analyzes different paths to find the most optimal one. If the system detects a faster route, it updates and suggests an alternative route.
• Utility-Based Agents: Utility-Based Agents evaluate the outcomes of decisions in situations with multiple viable paths. They employ utility functions to measure the usefulness that each action might bring. Evaluation criteria typically include progress toward goals, time requirements, or implementation complexity. This evaluation system helps identify the ideal choice: Is the best option the cheapest? The fastest? The most efficient? For example, a navigation system considers factors such as fuel economy, reduced travel time, and toll costs to select and recommend the most favorable route for the user.
• Learning Agents: Learning Agents learn through concepts and sensors, while utilizing feedback from the environment or users to improve performance over time. New experiences are automatically added to the Learning Agent’s initial knowledge base, helping the agent operate effectively in unfamiliar environments. For example, e-commerce websites use Learning Agents to track user activity and preferences, then recommend suitable products and services. The learning cycle repeats each time new recommendations are made, and user activities are continuously stored for learning purposes, helping Agents improve the accuracy of their suggestions over time.

>>> EXPLORE: What is an LLM Agent? How it works, advantages, and disadvantages

What are the outstanding benefits of using AI Agents?

AI Agents for businesses deliver a consistent experience to customers across multiple channels, with the following 4 outstanding benefits:

• Improve productivity: AI Agents help automate repetitive and time-intensive tasks, freeing up human resources from manual work so that businesses can focus on more strategic, creative and high-value initiatives, fostering innovation. For more complex issues, AI Agents can intelligently escalate cases to human agents. This seamless collaboration ensures smooth operations, even during periods of high demand.
• Reduce costs: By optimizing processes and minimizing human errors, AI personnel help businesses cut operating costs. Complex tasks are handled efficiently by AI Agents without the need for constant human intervention.
• Make informed decisions: AI Agents use machine learning (ML) technologies to help managers collect and analyze data (product demand or market trends) in real time, making faster and more accurate decisions.
• Improve customer experience: AI agents significantly enhance customer satisfaction and loyalty by offering round-the-clock support and personalized interactions. Their prompt and precise responses effectively address customer needs, ensuring a smooth and engaging service experience. Lenovo leveraged AI agents to streamline product configuration and customer service, integrating them into key systems like inventory tracking. By building a knowledge database from purchase data, product details, and customer profiles, AI agents help Lenovo cut setup time from 12 minutes to 2 minutes, boosting sales productivity and customer experience. This led to a 12% improvement in order delivery KPIs (within 17 days) and generated $5.88 million in one year, according to Gartner.

>>> Read more about: AI Agents at Work – Foundation for Productivity Breakthrough

Is ChatGPT an AI Agent?

ChatGPT is not an AI Agent. It is a large language model (LLM) designed to generate human-like responses based on received input, with some components similar to AI Agents:

• Simple sensors that receive text input
• Actuators that generate text, images, or audio
• Control system based on transformer architecture
• Knowledge base system from pre-training data and fine-tuning.

However, these elements are not sufficient to make ChatGPT a genuine Agent. The most important difference between AI Agents and ChatGPT is autonomy. ChatGPT cannot set its own goals, make plans, or take independent actions. When you ask ChatGPT to write an email, it can create content but cannot send the email itself or evaluate whether sending an email is the best action in a specific situation.

Additionally, ChatGPT cannot directly interact with external systems or adjust its behavior based on real-time feedback. Updates like plugins, extended frameworks, APIs, and prompt engineering can improve ChatGPT’s functionality, but still don’t create a complete Agent. ChatGPT also lacks the ability to maintain long-term memory between sessions. It doesn’t “remember” you or previous conversations unless specifically programmed to do so in certain applications.

>>> READ NOW: What is a Multi Agent System (MAS)?

Practical Applications of AI Agents

Imagine a future workplace where every employee, manager, and leader not only works together, but is also equipped with a team of AI teammates to support them in every task and at every moment of the workday. With these AI teammates, we will become 10x more productive, achieve better results, create higher quality products, and of course, become 10x more creative.

You may be wondering, “When will this future come?” The answer from FPT is: The future is now. Here are four stories that demonstrate how AI is already impacting businesses.

Revolutionizing Insurance Claims Processing

Imagine you go to the hospital for a health check-up, buy medicine, and file an insurance claim. Typically, the insurance company’s document processing will take at least 20 minutes. With integrated AI Agents, insurers can process all documents through rapid assessment tools, risk assessment tools, and fraud detection tools, returning results in just 2 minutes.

This represents an incredible leap in productivity, improving the customer experience and creating new competitive value for the business.

>>> READ NOW: Blockchain, Deepseek & AI Agents Reshape the AI ​​Race

Transforming the Customer Contact Center

The second story focuses on customer service. Several FPT.AI customers have deployed AI systems for inbound and outbound communications. These systems provide human-like customer support, handling requests, resolving issues, and providing excellent service.

For some customers, AI Agents are now handling 70% of customer requests, completing 95% of received tasks, and achieving a customer satisfaction rating of 4.5/5. Currently, FPT’s customer service AI Agents manage 200 million user interactions per month.

Empowering pharmacists with AI Mentor

At Long Chau, the largest pharmacy chain in Vietnam, more than 14,000 pharmacists work every day to advise customers. To ensure they stay updated with knowledge and work effectively, FPT.AI has developed an AI Mentor that interacts with more than 16,000 pharmacists across 2,000 pharmacies every day.

This AI Mentor identifies strengths and weaknesses, provides insights, and personalizes conversations to help them improve. The results are:

• Pharmacists’ competencies improved by 15%.
• Productivity increased by 30%.

Within the first nine months of the year, the pharmacy chain recorded a revenue growth of 62%, reaching VND 18.006 trillion, accounting for 62% of FRT’s total revenue and completing 85% of its 2024 plan. More importantly, we pride ourselves on helping pharmacists become the best versions of themselves while continuously improving.

>>> READ NOW: Understanding AI Agents in KYC

From a cost center to a profit center

FPT.AI’s AI Innovation Lab works with customers to identify opportunities, deploy pilots, and scale solutions. For example, one of our clients transformed their customer service center from a cost center to a profit center.

Using AI, they detected when customers were happy and immediately suggested appropriate products or services to upsell credit cards, cross-sell overdrafts, activate new customers to sign up, and reactivate existing customers. This approach helped the customer service center contribute about 6% of total revenue.

The four stories above are just a small part of the countless ways AI can transform businesses. AI, as a new competitive factor, is opening up a blue ocean of innovation. Every company and organization will need to reinvent their operations and build a strong foundation to compete in the future, leveraging the advances of AI.

>>> EXPLORE: What is Agentic AI? The differences between Generative AI and Agentic AI

Challenges in Deploying AI Agents

AI Agents are still in their early stages of development and face many major challenges. According to Kanjun Qiu, CEO and founder of AI research startup Imbue, the development of AI Agents today can be compared to the race to develop self-driving cars 10 years ago. Although AI Agents can perform many tasks, they are still not reliable enough and cannot operate completely autonomously.

One of the biggest problems that AI Agents face is the limitation of logical thinking. According to Qiu, although AI programming tools can generate code, they often write wrong or cannot test their own code. This requires constant human intervention to perfect the process.
Dr. Fan also commented that at present, we have not achieved an AI Agent that can fully automate daily repetitive tasks. The system still has the ability to “go crazy” and not always follow the exact user request.

Another major limitation is the context window – the ability of AI models to read, understand, and process large amounts of data. Dr. Fan explains that models like ChatGPT can be programmed, but have difficulty processing long and complex code, while humans can easily follow hundreds of lines of code without difficulty.
Companies like Google have had to improve the ability to handle context in their AI models, such as with the Gemini model, to improve performance and accuracy.

For “physical” AI Agents such as robots or virtual characters in games, training them to perform human-like tasks is also a challenge. Currently, training data for these systems is very limited and research is just beginning to explore how to apply generative AI to automation.

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Continue writing the future with AI Agents with FPT.AI

In the digital economy, competition between companies and countries is no longer based solely on core resources, technology and expertise. Organizations, from now on, will need to compete with a new important factor: AI Companions or AI Agents.

It is expected that by the end of 2025, there will be about 100,000 AI Agents accompanying businesses in customer care, operations and production. Each AI Agent will undertake a number of tasks such as programming, training, customer care… Thanks to that, employees are more empowered, businesses increase operational productivity, improve customer experience, and make more accurate decisions based on data analysis.

FPT AI Agents – a platform that allows businesses to develop, build and operate AI Agents in the simplest, most convenient and fastest way. The main advantages of FPT AI Agents include:

• Easy to operate and use natural language.
• Flexible integration with enterprise knowledge sources.
• AI models are optimized for each task and language.

Currently, FPT AI Agents supports 4 languages: English, Vietnamese, Japanese and Indonesian. In particular, AI Agents have the ability to self-learn and improve over time.

AI Agents are all operated on FPT AI Factory – an ecosystem established with the mission of empowering every organization and individual to build their own AI solutions, using their data, supplementing their knowledge and adapting to their culture. This differentiation fosters a completely new competitive edge among enterprises and extends to building AI sovereignty among nations.

With more than 80 cloud services and 20 AI products, FPT AI Factory helps accelerate AI applications by 9 times thanks to the use of the latest generation GPUs, such as H100 and H200, while saving up to 45% in costs. These factories are fully compatible with the NVIDIA AI Enterprise platform and architectural blueprints, ensuring seamless integration and operation.

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Agentic RAG is a method that combines the power of Retrieval-Augmented Generation (RAG) with AI Agents, creating intelligent, proactive, and flexible information retrieval and generation systems. Compared to traditional RAG, Agentic RAG can actively determine when, how, and what needs to be retrieved from various diverse data sources.

In this article, FPT.AI will introduce in detail the nature, operating mechanism, and comprehensive differences of Agentic RAG compared to traditional RAG. Through this, readers will clearly understand the potential as well as the limitations of this technology, thereby making the right decisions when choosing solutions suitable for specific business needs. References are also listed at the end of our articles (in case you would like to look for deeper insights).

What is Agentic RAG?

Agentic RAG is a method that combines the power of Retrieval-Augmented Generation (RAG) with AI Agents to enhance content creation and decision-making capabilities in artificial intelligence systems. While traditional RAG systems supplement large language models with information from external sources according to fixed retrieval strategies, Agentic RAG can actively decide which information is relevant, which information should be prioritized, and how to adjust the content creation process to suit contexts or needs that change in real-time.

Agentic RAG opens new potential for AI applications that require both accurate information retrieval and complex decision-making. By combining the power of RAG and AI Agents, Agentic RAG not only enhances the quality of retrieved information but also optimizes how that information is used in the content creation process.

>>> EXPLORE: How to build an AI Agent and train it successfully?

How does Agentic RAG Work?

Unlike traditional RAG, which uses Retrievers and Generators operating separately, Agentic RAG integrates one or more types of AI Agents into the RAG system (Multi-Agent Framework). These AI Agents collaborate to process complex queries together.

For example, an Agentic RAG system can combine multiple information retrieval Agents, each specializing in a specific domain or type of data source. For instance, one Agent might focus on querying External Databases while another searches emails or web results. This task allocation creates a high level of specialization in the information processing.

>>> EXPLORE: What is a Multi Agent System (MAS)?

Single-Agent RAG (Router)

An Agentic RAG system can include AI Agent types such as:

• Routing Agents: Routing Agents determine which knowledge sources and tools will be used to process user queries. They process prompts and select the appropriate RAG pipeline to create optimal responses. In single-Agent RAG systems, the Routing Agent will select the data source that needs to be retrieved.
• Query planning Agents: Query planning agents act as task managers in the RAG pipeline. They break complex queries into smaller steps and distribute them to other agents. After receiving results from specialized Agents, Query Planning Agents combine the responses into a comprehensive, complete result. This mechanism, called AI Orchestration, allows the system to efficiently process complex multi-dimensional queries.
• ReAct Agents: ReAct (reasoning and action) is an agent framework that helps create multi-agent systems capable of reasoning and acting step by step. Notably, ReAct Agents can determine the appropriate tool for each specific task. Based on step-by-step results, ReAct agents can flexibly adjust subsequent steps.
• Plan-and-execute Agents: This is an advanced version of ReAct agents that can perform multi-step processes without needing to return to the primary agent. This mechanism helps reduce processing costs and increase system efficiency. Since this Agent must develop a comprehensive plan from the beginning, the task completion rate and result quality are usually higher than other Agent types.

Frameworks that can be found on GitHub such as LangChain, LlamaIndex, and the LangGraph Orchestration Framework help simplify the implementation of Agentic RAG. Using open-source models like Granite™ or Llama-3 also helps reduce costs and increase observability.

>>> EXPLORE: What is an LLM Agent? How it works, advantages, and disadvantages

What is RAG?

Retrieval Augmented Generation is an artificial intelligence (AI) technique that enhances the performance of large language models (LLMs) by connecting Generative AI models with an External Knowledge Base. Instead of relying solely on available training data, RAG helps AI models access real-time data through APIs and other connections to data sources.

A standard RAG pipeline consists of two main components:

1. Information retrieval component (Retriever): Typically an Embedding Model combined with a Vector Database containing data to be retrieved. Retrievers usually search for information relevant to the input query in huge datasets or document repositories.
2. Generation component (Generator): Usually an LLM like GPT, BERT, or similar architectures. The Generator processes the query and retrieved documents to create coherent and contextually appropriate responses.

When receiving a natural language query, the Embedding Model converts the query into a Vector Embedding, then retrieves similar data from the Knowledge Base. The AI system combines the retrieved data with the user query to create contextually appropriate responses.

The main advantage of RAG lies in its ability to reference updated information or specialized data that may not have been included in the model’s training phase. This minimizes the hallucination problem, where language models provide information that seems reasonable but is inaccurate, while ensuring higher factual accuracy. RAG allows LLMs to operate more accurately in specialized contexts without needing fine-tuning.

RAG is widely applied in fields requiring accuracy and contextual relevance in content creation such as:

• Customer support: RAG provides accurate responses by retrieving relevant information from product manuals, FAQs, or customer databases.
• Medicine and research: RAG enhances language models to create deep insights by retrieving and referencing academic articles or research datasets.
• AI Chatbots: Specialized chatbots are significantly improved by RAG, ensuring that responses are informed by a broader dataset than what was used in the initial training process.

>>> EXPLORE: RPA vs AI Agents: Is RPA Still Relevant in the Age of AI?

What are AI Agents?

AI Agents are types of AI that can interact with the environment, process input information, and perform a sequence of actions based on specified inputs or goals without human intervention. Most current Agents are large language models (LLMs) with Function Calling capabilities, meaning they can call tools to perform tasks.

The main roles of Agents are to automate tasks, optimize processes, and make intelligent decisions in dynamic environments, particularly suitable for complex decision-making tasks. Theoretically, AI Agents are LLMs with three prominent characteristics:

1. Possessing both short-term and long-term memory, able to reference previous tasks to plan and execute complex subsequent tasks.
2. Having the ability to route queries, plan step by step, and make decisions. AI Agents have memorization capabilities to retain information and outline actions appropriate for complex queries.
3. Having the ability to call tools through APIs. More advanced Agents can even actively choose appropriate tools to optimize the user response process.

The Agent workflow (Agentic Workflow) can include a single AI Agent or a system of multiple Agents working together. Agents can vary in complexity, from simple rule-based systems to complex models leveraging Deep Learning.

Based on characteristics and functions, AI Agents can be classified into several groups. Reactive Agents operate based on the current state of the environment, following predetermined rules or responses without storing or using past experiences.

Cognitive Agents are more advanced with the ability to store past experiences, analyze patterns, and make decisions based on memory, often used in systems requiring learning from previous interactions. Collaborative Agents interact with other Agents or systems to achieve common goals, commonly found in multi-Agent systems where multiple Agents collaborate, share information, or coordinate actions.

In terms of architecture and communication, Agents rely on various architectures, including decision-making models, Neural Networks, and rule-based systems. Communication between Agents is typically conducted through protocols such as message passing, event triggering, or interactions based on complex networks, particularly important in distributed systems.

Agents can be organized according to centralized models, where all decisions are made by a single controlling entity, or distributed, where each Agent operates autonomously but still contributes to a larger goal.

>>> EXPLORE: Applications of AI Agents in Personalized Marketing

Differences between Agentic RAG and Traditional RAG

See the detailed comparison table between Agentic RAG and traditional RAG:

Criteria	Traditional RAG	Agentic RAG
Operating mechanism	Passive information retrieval, only when requested	Adds a decision-making layer through autonomous Agents, actively decides when, how, and what needs to be retrieved
Flexibility	Connects LLM with a single dataset	Can retrieve data from multiple External Knowledge Bases and use external tools
Adaptability	Reactive data retrieval tool, does not adapt to changing contexts, requires prompt engineering to achieve optimal results	Solves problems intelligently and flexibly, Agents coordinate and check each other
Accuracy	Does not self-verify or optimize results	Can iterate the process to optimize results over time
Scalability	Limited due to connection with a single data source	Higher thanks to a network of Agents working together, accessing multiple data sources, and using Tool-Calling
Multimodality	Usually limited to text processing	Leverages Multimodal LLMs to process diverse data such as images and audio
Cost	Lower due to using fewer tokens	Higher because it needs more Agents and tokens
Latency	Lower	Higher because LLMs need time to generate responses
Reliability	Depends on the quality of source data	May fail depending on complexity and type of Agent used

Thus, the most fundamental difference between Agentic RAG and traditional RAG lies in proactivity and decision-making ability. Traditional RAG operates as a passive tool, only retrieving information when requested and based on a rigid process established in advance. In contrast, Agentic RAG integrates intelligent Agents capable of actively deciding the process of searching, processing, and synthesizing information.

While traditional RAG is like an employee strictly following given instructions, Agentic RAG operates like a team of autonomous experts, not only performing assigned tasks but also having the ability to store and reference previous query sets, contexts, and results (through Semantic Caching), analyze problems, coordinate with each other, and provide creative solutions.

However, Agentic RAG is not always better than traditional RAG. Having multiple AI Agents means higher costs, as more tokens are needed. Additionally, LLMs can create latency because they take time to generate responses. Moreover, Agentic RAG still fails in complex tasks, competing for resources, leading to conflicts. And even the best RAG systems cannot completely eliminate the possibility of “hallucination.”

Therefore, businesses should only choose Agentic RAG when they need to solve complex problems requiring multiple data sources, need high flexibility in searching and processing information, or want systems capable of self-improving accuracy over time. With limited budgets, needing quick response solutions with simple tasks and clearly defined data sources, traditional RAG remains an effective and cost-efficient choice.

>>> EXPLORE: What is Agentic AI? The differences between GenAI and Agentic AI

Notable Applications of Agentic RAG

Agentic RAG can be used in most applications of traditional RAG, but due to higher computational demands, it is more suitable in situations requiring queries across multiple data sources. Some applications include:

• Real-time question answering and decision support: In situations requiring rapid data analysis such as stock market analysis or medical diagnosis, businesses deploy AI chatbots, virtual assistants, or FAQ systems using RAG to provide accurate, updated information to employees and customers.
• Automated support: With the ability to retrieve content relevant to ongoing conversations and automate customer service with personalized and contextually appropriate content, businesses can use Agentic RAG to handle simple support requests and forward more complex issues to human staff.
• Data management: RAG systems help quickly retrieve information in internal databases, reducing employees’ manual search needs.
• Multi-Agent collaboration systems: Agentic RAG shows great potential in distributed AI systems where multiple Agents need to coordinate work on large datasets or process complex queries, creating an intelligent network with superior information processing capabilities.

In conclusion, Agentic RAG marks a significant advancement in artificial intelligence by combining the power of retrieval-generation and intelligent multi-Agent systems. The choice between Agentic RAG and traditional RAG needs to be carefully considered based on specific requirements, available resources, and the complexity of the task.

In the future, with the continuous development of large language models and Agent technology, Agentic RAG promises to become increasingly refined, overcoming current limitations and expanding its application scope in various fields of life and business.

References:

• Weaviate. (n.d.). What is Agentic RAG. Retrieved April 20, 2025, from https://weaviate.io/blog/what-is-agentic-rag
• IBM. (n.d.). What is Agentic RAG? Retrieved April 20, 2025, from https://www.ibm.com/think/topics/agentic-rag
• LeewayHertz. (n.d.). Agentic RAG: What it is, its types, applications and implementation. Retrieved April 20, 2025, from https://www.leewayhertz.com/agentic-rag/

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• What Are Intelligent Agents? The Difference Between AI Agents and Intelligent Agents
• Why Gen AI Agents are the future prospect of Generative AI?

AI Chatbot is a computer program that uses artificial intelligence to simulate human-to-human conversation, capable of understanding and responding to user requests naturally and accurately. In this article, FPT.AI will present in-depth information about the core technologies, types of AI Chatbot, and their popular applications. The article also provides detailed reviews of the features and advantages of the top 9 AI chatbot online free that you can leverage to level up your performance.

What is an AI Chatbot?

AI Chatbot is a computer program designed to simulate human conversation, interacting with users through text or voice. AI Chatbot combine artificial intelligence, Machine Learning, and Natural Language Processing (NLP) to understand and analyze user requests, then apply Natural Language Generation (NLG) techniques to provide appropriate, accurate, and natural human-like responses.

In reality, you may have already interacted with chatbots through various activities, from smart speakers in your home, popular messaging applications like SMS, WhatsApp, and Facebook Messenger, to work platforms like Slack. These bots simulate natural conversation and try to resolve your queries before transferring to human representatives if necessary.

With the ability to automate conversations, AI Chatbot help organizations and businesses flexibly scale, personalize, and improve communication capabilities in many activities – from internal workflows and customer service to DevOps management.

Review of TOP 9 AI Chatbot online free

AI Chatbot	Developer	Service Plans	Key Strengths	Special Features	Best For
FPT AI Chat	FPT Smart Cloud (Vietnam)	Business service package, contact for detailed consultation	Superior Vietnamese language processing (>90% accuracy); Multi-platform integration	Rating Agent; User Reaction; Typing Suggestion	Vietnamese businesses seeking customer service automation
ChatGPT	OpenAI	Free to $200/month	Globally popular; Continuously updated features	DALL-E 3 (image generation); Deep Research; Advanced Voice Mode; Sora (AI video)	Individuals and businesses needing comprehensive AI solutions
Claude	Anthropic	Free to $20/month	Large context memory (150,000 words); Pleasant conversational style	Artifacts (interactive data dashboards); Computer use (experimental)	Long document analysis and natural conversations
Deepseek	DeepSeek (China)	Completely free (open source)	Strong logical reasoning; Can run on personal computers	DeepSeek R1 comparable to ChatGPT o3	Developers and small organizations needing free AI
Microsoft Copilot	Microsoft	Free to paid	Deep integration with Microsoft Office	Unlimited image generation; Personalized experience	Microsoft Office users (Word, Excel, PowerPoint)
Gemini	Google	Free	Integration with the entire Google ecosystem	Direct Python code writing/execution; Gems (customizable like GPTs)	Gmail, Google Drive and Google services users
Meta AI	Meta	Completely free	Direct integration with Meta social networks	Developed on LLaMA 3 model (70 billion parameters), can create animated images and videos	Facebook, Instagram, Messenger and WhatsApp users
Perplexity	Perplexity AI	Free to $20/month	Results displayed with source citations	Purpose-specific searches; Conversation sharing	Research and information searches requiring clear sources
Grok	xAI (Elon Musk)	Completely free	Real-time updates; Humorous style	DeepSearch; Brainstorm; Analyze Data; Create Images	Users needing quick information updates and creativity

FPT.AI Chat

FPT.AI Chat is one of the leading AI chatbot platforms in Vietnam, developed by FPT Smart Cloud – a member of FPT Technology Group. This artificial intelligence solution is designed to help businesses automate customer care, optimize consultation processes, and increase business efficiency. Not only possessing superior Vietnamese natural language processing technology, FPT.AI Chat also stands out with its deep and flexible integration capabilities, suitable for many different industries.

FPT.AI Chat uses Generative AI technology and Deep Learning, helping the chatbot correctly understand the intent and context of user questions. The accurate response rate can reach over 90%. This allows the chatbot to quickly and accurately handle common questions related to products, prices, promotions, or purchasing processes, thereby reducing repetitive work for consultants, helping them focus on situations requiring higher expertise.

Additionally, FPT.AI Chat has multi-platform integration capabilities, from websites and mobile applications to popular channels such as Facebook Messenger, Zalo, Viber, Instagram, WhatsApp, and even the business social network GapoWork. Businesses only need to build a chatbot once and can deploy it across multiple channels, effectively expanding customer reach.

Besides the main features, FPT.AI Chat is continuously updated with add-ons based on actual needs:

• Rating Agent: Allows customers to rate their satisfaction with consultants or with the chatbot itself after each conversation session, helping businesses measure support quality and improve services.
• User Reaction: Users can respond with emoji reactions to chatbot answers, creating a more natural and interesting conversation experience. These interactions are compiled to improve response quality.
• Typing Suggestion: Suggests questions for users based on keywords being typed, saving time and directing the conversation more effectively.
• Copy Model: Useful in the testing and deployment phases of chatbots, allowing the copying of model structures from a test bot to an official bot, ensuring consistency and accuracy when put into actual operation.

Since July 2024, LPBank has officially deployed FPT.AI Chat on their website, Zalo OA, and fanpages to automate 24/7 customer care processes. This banking chatbot can handle a range of common issues such as account registration, product information, interest rate consultation… and is ready to connect with consultants when customers need in-depth support. This brings a seamless, modern experience that aligns with the trend of digitizing banking services.

According to representatives from FPT Smart Cloud and LPBank, the FPT.AI chatbot is not just a support tool but an intelligent interaction bridge, helping the bank maintain engagement with customers across multiple channels. This is a powerful “made in Vietnam” AI chatbot solution, suitable for Vietnamese businesses looking to effectively digitally transform, enhance customer care quality, and optimize operations with advanced AI technology.

ChatGPT

ChatGPT, developed by OpenAI, has become a global phenomenon and is currently the most popular free AI chat software. This AI Chatbot uses Deep Learning technology to generate natural, intelligent, and contextually accurate responses.

Users can ask questions and communicate via text, request tasks such as writing paragraphs, writing programming code, and composing emails as if conversing with a real person. ChatGPT also supports many different languages, including Vietnamese, ensuring it meets the learning and working needs of Vietnamese users.

ChatGPT provides many outstanding features such as:

• Content management
• Programming support
• Processing and analyzing big data to provide detailed reports
• Predicting business trends and supporting strategic decision-making
• Creating images from text through DALL-E 3
• Analyzing and summarizing data

With the latest version, OpenAI has added many creative features such as:

• Search tool to find information on the web and cite sources
• Deep Research acts as an assistant that reads and analyzes all search results to create in-depth reports
• Projects allows uploading documents and setting up system guidelines to direct how ChatGPT responds
• Canvas is a new output mode that helps users write together with ChatGPT
• Advanced Voice Mode allows real-time voice interaction
• Operator AI Agent can perform tasks by browsing the web
• Sora, OpenAI’s AI video model (currently only available to Pro users in the United States)

Regarding service packages, ChatGPT offers several options:

• ChatGPT 3.5: Free, but with limited uses per day
• ChatGPT Plus: $20/month, unlocks many advanced features and unlimited use
• ChatGPT Pro: $200/month
• ChatGPT Team: $20-30/user/month, designed for small teams
• ChatGPT Enterprise: Variable pricing depending on needs

To use ChatGPT, users only need to access the website https://chatgpt.com/ and log in with an existing account or register a new one. When starting a conversation, you just need to enter content in the box at the bottom of the screen. When results are displayed, a new item will be created in the left menu, helping you easily manage and interact with previous conversations.

However, the biggest limitations of the free version of ChatGPT that you should note include:

• Limited usage per day, which can cause interruptions in your workflow.
• Does not provide source citations and has no indicators of the accuracy of answers.

Claude

Claude AI is an artificial intelligence integrated chatbot developed by Anthropic – a technology startup based in San Francisco, USA, notable for its direction of developing AI that is “safe, ethical, and transparent.” Although still unfamiliar to most Vietnamese users, Claude AI is increasingly highly rated and considered one of the smartest chatbots available today, supporting users with:

• Answering questions
• Creating content based on requests
• Writing emails
• Translating between multiple languages, including Vietnamese
• Summarizing long text files and website content
• Processing images or scanned documents, recognizing handwritten text with high accuracy, even when the content is in Vietnamese

A superior advantage of Claude is its ability to remember large contexts – up to 150,000 words in a conversation. This is ideal for uploading long documents like PDF files, analyzing and discussing them in depth without “forgetting” information like many other chatbots. Claude also saves conversation history, allowing users to review previous queries – very convenient for those who frequently analyze data or work with repetitive information.

Especially recently, this AI Chatbot launched the “Artifacts” feature, allowing users to create visual, interactive data dashboards that can be edited right in the conversation, such as personal budget planning tables or simple physics games with just a few command lines. Everything is updated in real-time without having to leave the chat interface.

Claude currently has three AI model versions, each suitable for different needs:

• Claude Haiku: Fast, concise, suitable for user-oriented applications and big data processing.
• Claude Sonnet: More advanced, capable of reasoning and writing complex code. This version is currently testing the “computer use” feature, allowing AI to operate like a real user on a computer.
• Claude Opus: The strongest, excelling in natural language understanding, content creation, and programming. Although stronger than Sonnet, the processing speed is still comparable.

Claude is often rated as having a more pleasant, emotional conversational style compared to ChatGPT. Therefore, this AI Chatbot is very suitable for business environments or when communicating with customers. Additionally, Claude also has an active community on Reddit, where users share experiences, discuss, and support each other during use.

To use Claude AI, users only need to access the website claude.ai/login to register or log in with a Gmail account. It’s worth noting that in the free package, the number of messages per day from Claude may vary depending on overall usage levels. If you want to experience all features, you can upgrade to the Pro package for $20/month, unlocking additional utilities such as integration with Zapier to automate workflows.

Deepseek

DeepSeek is an artificial intelligence model developed in China, with two main versions: DeepSeek V3 and DeepSeek R1. Of these, DeepSeek R1 is the latest version, specially designed to handle tasks related to logical reasoning and problem-solving. Many users rate DeepSeek R1 as comparable to powerful OpenAI models like ChatGPT o3.

Notably, DeepSeek R1 is an open-source AI Chatbot and completely free to use. Users can access the model through a web application or download it to run on personal computers or servers, as long as the device is powerful enough to process it. This opens up many opportunities for AI access to individuals, small organizations, or developers wanting to integrate AI without licensing costs.

The interface of the DeepSeek application – whether on the web or mobile devices – is quite basic, focusing only on the main function of text input. Users can send questions, request web searches, upload text documents for content extraction, and review the history of previous conversations.

However, DeepSeek doesn’t understand images, only reading the text portions of documents. Additionally, another notable disadvantage to consider is the data security issue. Because DeepSeek application servers are located in China, the storage and processing methods of user data remain unclear. This may be concerning for those who prioritize personal information privacy.

Microsoft Copilot

Built on OpenAI’s AI platform, Microsoft Copilot is not just an AI Chatbot but a comprehensive virtual assistant, helping to optimize workflows, increase productivity, and deliver a seamless user experience for both individuals and businesses. The greatest strength of Microsoft Copilot is its deep integration into Microsoft Office flagship products such as Word, Excel, PowerPoint, Teams, the Edge browser, and the Windows 11 operating system.

In Word, you can ask Copilot to draft text from simple suggestions; in Excel, the tool can analyze data and create charts; and in PowerPoint, it helps build professional presentation slides with just a few lines of guidance. This is a major advantage over standalone chatbots like ChatGPT, especially for users already familiar with the Microsoft work environment.

Users can quickly access it from the taskbar to ask questions, search the Internet, suggest ideas, or quickly open installed applications without downloading additional software. If you don’t see the Copilot icon, simply search for the term “Copilot” in the Windows search menu to activate it.

Not only supporting office work, Copilot also impresses with its ability to create unlimited images, serving well for illustration needs, simple design, or creating visual content. This is an outstanding point compared to many current AI chatbots that don’t support image creation.

Another interesting aspect of Copilot is its ability to customize and personalize the user experience. On first use, Copilot may ask your name to personalize the conversation. Responses from Copilot not only answer questions but often end with expansion suggestions, inviting users to continue interacting. Additionally, Microsoft’s large user community, from official forums to Reddit groups, provides a helpful and dynamic support ecosystem.

Microsoft offers two versions: a free version with basic features and Copilot Pro, allowing access to the latest AI models, prioritized processing, and integration of more in-depth features. Furthermore, Microsoft is developing specialized versions for specific fields such as sales, finance, or cybersecurity – suitable for large organizations wanting to use AI to improve operational efficiency.

However, some users feel that responses from Copilot are sometimes not deep enough or lack updated elements such as analyzing modern trends (e.g., changes due to digitization, streaming…). Additionally, compared to competitors like ChatGPT, some versions of Copilot are rated as not “sharp” enough, feeling like a “condensed” version.

Gemini

Gemini is an AI chatbot developed by Google with the ability to deeply integrate with the entire Google ecosystem – from Gmail, Drive, Docs, Sheets to services like Maps, Flights, or YouTube. Users only need to log in once to access it directly from Gmail, Calendar, or Docs without switching between platforms, maximizing the use of familiar tools in work and daily life. This transforms Gemini into a truly intelligent assistant tied to Google usage habits.

You can ask Gemini to search for emails in your Gmail inbox, summarize document content in Drive, check flight or hotel prices in real-time, and even plan vacations and suggest packing lists for trips.

Beyond convenience, Gemini is also highly rated for its ability to understand deep context and remember long conversation information (this AI Chatbot can store content equivalent to the entire Harry Potter series). This allows Gemini to process complex queries, analyze in-depth content, and respond naturally, like having a real conversation with a knowledgeable person.

Not limited to text, Gemini can generate multimedia content such as videos, images, and music. Additionally, users can write and run Python code directly within the interface – a very useful feature for programmers or highly technical users.

Another special feature is Gems – a feature that allows personalizing the experience similar to GPTs in ChatGPT. Users can create customized Gemini versions by adding specific instructions for the chatbot to respond according to their own style or purpose.

However, Gemini’s response quality is sometimes inconsistent – with the same query, you might receive different answers if you try again later. Therefore, when using it for important tasks, users should carefully check information before applying it. Google is also transparent about this, displaying warnings that Gemini may make mistakes and encouraging users to verify content.

Meta AI

Meta AI is an AI chatbot software developed by Meta – the parent company of Facebook – and integrated directly into familiar applications such as Facebook, Instagram, Messenger, and WhatsApp. The introduction of Meta AI marks a step forward in bringing artificial intelligence closer to social media users by leveraging the very platforms that millions of people use daily.

Meta AI uses the LLaMA 3 language model, the latest version, with over 70 billion parameters. This is one of the most powerful large language models currently available, capable of understanding and processing language well and supporting users in various tasks such as chatting, content creation, information searching, and even creating images, short videos, or animations – completely free.

Meta AI also supports Vietnamese language, making it very friendly and accessible to Vietnamese users. Thanks to direct integration into Facebook, Instagram, and Messenger, you only need to log in with your social media account to start chatting with AI right within the application you’re using, without downloading additional apps or switching platforms.

Meta AI also has the ability to create images and animations right in the conversation. Users can request AI to create illustrative images based on descriptions, even simple short videos.

Meta AI still has some weaknesses:

• Web information search capability is not as strong as ChatGPT or Gemini; users need to verify information from sources for important content
• Limited extensibility, not yet suitable for deep customization or expansion to specialized business needs.

However, if you are a developer, Meta offers a great opportunity with its open licensing policy for LLaMA models. You can use this model to build your own AI applications without paying licensing fees, as long as revenue doesn’t exceed a very high threshold. This helps individuals and small businesses easily access powerful AI technology without financial barriers.

Perplexity

Perplexity is an AI chatbot notable for its ability to conduct in-depth information searches and provide clear source citations. Unlike traditional AI tools that often just provide answers, Perplexity is an intelligent search tool that can display search results with a list of sources, helping users access, verify, and continue to explore content easily and visually.

You can continue expanding your search by entering additional questions or selecting from a list of related search suggestions. Responses will be stacked in a scrolling format, allowing you to easily follow the information flow throughout the conversation.

Perplexity also supports purpose-specific search modes, such as product suggestions, healthy cooking recipes, or finding hotels through TripAdvisor. You just need to check “Copilot” in the search bar, and this AI Chatbot will present a series of sub-questions to clarify your intention, then compile the most accurate and complete content.

Perplexity also has the ability to share conversations: recipients can continue the conversation from where you left off, while you can see the number of views, likes, and follow-up questions from the community. This AI Chatbot also has a “Discover” section – a place that compiles popular searches into short, easy-to-understand, and engaging articles. Technologically, Perplexity uses many powerful AI models such as OpenAI GPT, Claude, and DeepSeek, while also leveraging Wolfram Alpha to process real-time data or solve complex problems.

The free version of Perplexity allows users to use some basic features, including 3 Pro queries per day. Meanwhile, the Perplexity Pro package ($20/month) expands access to up to 300 Pro queries/day, along with the ability to analyze files, display data in image format, and choose AI models at will.

Regarding the interface, Perplexity is rated as clean and easy to use, with help sections, guides, and shortcuts clearly arranged. The system always operates stably, and the user community on Discord as well as Perplexity’s blog regularly share knowledge and effective usage tips.

Grok

Grok is an AI chatbot developed by xAI, Elon Musk’s company. The name “Grok” comes from an English word meaning “to understand intuitively,” and true to its name, Grok was built to become an AI Chatbot with deep reasoning abilities and intelligent responses.

Initially, Grok was only available within the X social media platform (formerly Twitter) and required users to pay. However, currently, you can use Grok for free through web and mobile applications, making it more accessible to many people. The latest version of this AI Chatbot is Grok 3, released in February 2025, quickly entering the top chatbot rankings, surpassing many other well-known competitors.

This AI Chatbot has a diverse system of functions, with options serving many learning, work, and entertainment needs such as:

• DeepSearch: Dive deep into a specific topic
• Brainstorm: Support creative idea generation
• Analyze Data: Aanalyze documents, data
• Create Images: Create images on request (for example, “an otter playing ukulele”)
• Code: Programming support

Regarding response style, Grok has a friendly approach with a touch of light humor, not too serious but also not excessively comedic. Some users describe Grok’s style as “slightly rebellious” compared to GPT-4o, but overall it maintains coherence and professionalism when needed.

Another significant advantage of Grok is its ability to update and respond in real-time. Because it’s deeply integrated with the X platform – which frequently updates trends and news – Grok can answer questions about hot topics quickly and flexibly.

However, Grok has also been involved in controversies related to accuracy, especially when handling topics with political elements. This requires users to verify information in sensitive cases or those of high importance.

Outstanding benefits of AI Chatbot

AI Chatbot brings clear benefits to both businesses and customers:

• 24/7 Customer Support: Before chatbots, all customer inquiries required direct human response. This was especially difficult when issues arose outside working hours, on weekends, or holidays. Maintaining a 24/7 support team requires high costs and is challenging to manage. With AI Chatbot, businesses can solve human resource issues, ensuring timely support, eliminating waiting times, and ensuring customers always receive immediate responses.
• Reduced Operating Costs: Implementing chatbots can help save up to 30% of operating costs, reducing the need for customer support center staffing, while minimizing time and costs related to training employees to answer repetitive queries.
• Enhanced Customer Experience and Loyalty:The ability of chatbots to respond quickly and accurately creates an excellent user experience. Satisfied customers are more likely to show loyalty to the brand and complete purchase transactions.
• Improved Human Resource Management: Chatbots automate workflows and free employees from repetitive tasks, allowing them to focus on more complex issues. Businesses can enhance staff efficiency to meet increased demands.

• Generate Leads and Increase Conversion Rates: Chatbots can assist customers in the purchasing process by answering questions about products or services on the spot, helping customers move toward making a purchase. For complex transactions, chatbots can assess lead quality and connect them with sales agents.
• Continuous Learning and Improvement: With Deep Learning technology and Natural Language Processing (NLP), AI chatbot continuously learns from previous interactions to improve response quality, becoming smarter and more flexible in handling complex queries.
• Internal Administration Support: AI Chatbot integrates into internal management systems, helping automatically retrieve and synthesize data from digital document repositories. Departments such as human resources, accounting, or operations management can quickly access information, create reports, and make decisions based on real-time data.
• Multi-channel Integration and Technology Ecosystem: Chatbots easily integrate with many communication platforms such as websites, social media, and mobile applications. This flexibility helps businesses reach customers at various touchpoints while creating a seamless technology ecosystem to effectively collect and analyze customer data.

Notable Applications of AI Chatbot

Common use cases for chatbots include:

• Personalized E-commerce: In online retail, e-commerce chatbots provide personalized product recommendations based on purchase history and browsing behavior, helping increase conversion rates and improve shopping experiences.
• Conversational Marketing Strategies: Marketers use Marketing chatbots to promote products, services, and collect insights about customer interaction and purchasing patterns, creating engaging and personalized conversations across web and messaging channels.
• Office Process Automation: In finance and healthcare fields, AI chatbot helps identify fields in forms, capture customer information, and schedule appointments for healthcare offices, minimizing processing time and errors.
• Employee Self-service: IT and HR teams use chatbots to allow employees to solve common issues themselves, such as resetting passwords, accessing company policy information, or asking benefits-related questions.
• Intelligent Call Management: At contact centers, chatbots function as Interactive Voice Response (IVR) systems, streamlining incoming communications and directing customers to the right resources, along with the ability to seamlessly transfer to support staff when necessary.
• Smart Personal Assistants: Chatbots can provide automatic reminders for time or location-based tasks, helping users manage schedules and work more effectively.
• Advanced Conversational Analytics: Chatbot technology also helps extract valuable information from natural language communications between customers and businesses, providing deep insights to improve services and products.

Chatbot interaction interfaces are also very diverse, from social media messaging applications, independent messaging platforms, website integration and proprietary applications, to phone calls. This helps businesses easily reach customers across multiple channels while creating a consistent and seamless experience.

How is AI Chatbot Different from Traditional Chatbots and Virtual Assistants?

Chatbot technology has advanced tremendously since its inception in the 1960s. From interactive FAQ programs based on a limited set of common questions with fixed scripted answers requiring users to choose from simple keywords and phrases to continue the conversation, today we have intelligent AI chatbots with many impressive capabilities such as:

• Understanding complex contexts in conversations
• Overcoming everything from typing errors to translation issues (Machine Translation)
• Mapping meaning with specific intentions that users want chatbots to perform
• Creating natural, diverse, and appropriate responses for each user
• Communicating in many different languages
• Self-improving performance through each interaction without requiring programmer intervention

Although the initial cost to deploy AI chatbot is higher than traditional chatbots, businesses will save many long-term costs because they don’t need to maintain large customer care teams to handle complex requests or frequently upgrade systems when business needs change.

The latest advancement in Chatbot software is chatbots using Generative AI. They are called “intelligent virtual assistants,” “Virtual Agents,” or “Virtual Assistants,” with famous representatives like Siri, Alexa, Gemini, and ChatGPT.

Besides the ability to understand and respond to user questions through Natural Language Processing (NLP) and Machine Learning, Virtual Assistants combine Conversational AI and Robotic Process Automation (RPA) to proactively perform actions on behalf of users.

For example, when asked about tomorrow’s weather, a Virtual Assistant doesn’t just answer that “it will rain” but also proactively suggests setting an earlier alarm to avoid delays due to rain. Additionally, thanks to Large Language Models (LLMs), Gen AI Chatbot can also:

• Create entirely new content (text, images, sound)
• Automatically answer questions outside the script scope based on the organization’s knowledge base without pre-programming
• Learn from previous conversations to continuously improve how conversation flows are routed
• Adapt to each user’s communication style
• Express empathy in responses

In other words, AI Chatbot are like helpful friends who know a lot of information, while Virtual Assistants are like actual personal assistants who can act on your behalf.

According to the CEO’s Guide to Generative AI report from the IBM Institute for Business Value (IBV), 85% of executives believe that Gen AI Chatbot will interact directly with customers in the next two years. Enterprise-level chatbots can also integrate into software like Microsoft Teams, creating efficient work centers, and handling from simple tasks to complex processes across different applications, helping businesses automate services and enhance user experiences.

In summary, AI Chatbot is intelligent virtual assistant, capable of learning and continuously developing to deliver optimal user experiences. This technology is changing how businesses and customers communicate while optimizing workflows and supporting management. With constant advances in Machine Learning, NLP, and Generative AI, in the future, AI Chatbot will become increasingly sophisticated, bringing convenience and improving service quality for users and businesses.

 

Text to Speech (TTS) is a technology that converts digital text into natural-sounding audio, allowing computers to read content aloud in a voice that closely resembles human speech. Demand for this technology is growing rapidly, with a projected CAGR of 13.7% between 2024 and 2029. According to Markets and Markets, the TTS market is expected to reach USD 7.6 billion by 2029. 

 

In this article, FPT.AI explores the development, working mechanism, real-world applications, limitations, and future trends of Text-to-Speech technology. 

What is Text-to-Speech? 

TTS is also known as “speech synthesis” or “computer-generated voice technology”. Most TTS services are offered as APIs, allowing developers to easily integrate voice capabilities into apps, websites, or digital services. 

Originally designed to assist individuals with visual impairments or dyslexia, TTS has evolved into a foundational technology powering virtual assistants, automated call centers, and GPS navigation systems. Today, it plays a key role in human-machine interaction and is making the digital world more accessible for everyone.

The Evolution of Text-to-Speech Technology 

The first electronic speech synthesizer appeared around the 1930s and marked the beginning of TTS development. These early devices had minimal capabilities and were primarily used for research. 

In the late 1950s, with the advent of computers, developers began experimenting with algorithms that matched audio files to text components. These early systems produced robotic and unnatural voices. 

A major breakthrough came in the 2000s when Deep Learning and neural networks entered the scene. Instead of piecing together pre-recorded sounds, developers began modeling sound waves using real voice recordings. 

This shift led to much more realistic, high-quality synthetic voices. At the same time, there were advancements in Automatic Speech Recognition (ASR) and Natural Language Processing (NLP), which laid the groundwork for modern TTS systems. 

In the past decade, AI and Machine Learning have further enhanced voice realism—making synthetic speech nearly indistinguishable from human voices. However, this progress also introduces ethical concerns, particularly around audio deepfakes, which mimic real voices without consent. To combat this, tech companies are developing real-time voice detection tools to identify deepfakes and ensure the responsible growth of TTS technology. 

 

How Does Text-to-Speech Work? 

TTS involves both linguistic analysis and speech synthesis. Deep learning models help TTS systems understand how words relate to their audio characteristics and generate realistic AI voices. 

Linguistic Analysis 

When given a text input, the TTS model first analyzes it using deep neural networks. It examines words, punctuation, and sentence structure to understand intonation, pitch, rhythm, and volume. The system also expands abbreviations, calculates word lengths, determines proper pronunciation, and maps prosody (intonation patterns) across sentences. 

Speech Synthesis 

Once the text has been processed, the model converts it into speech using two main steps: 

Generate audio features: The model transforms the text into time-aligned features like mel spectrograms, which map changes in sound frequency over time. These features capture detailed characteristics of speech, including pronunciation and emphasis. 

 

 

Convert to sound waves: A vocoder model, such as WaveNet or WaveGlow, transforms the spectrogram into an actual audio sound wave that sounds natural. Some TTS systems also allow users to adjust pitch, volume, speed, language, accent, or speaking style

TTS systems are built into many devices, such as smartphones, and are available via software, browser extensions, websites, or downloadable apps. 

Real-World Applications of Text-to-Speech 

TTS is a key component of Conversational AI, especially in applications using Automatic Speech Recognition (ASR) and Natural Language Processing (NLP). It’s a game-changer for people who want to access content hands-free in a fast-paced world. 

Here are some major use cases of TTS: 

• Audio Content: TTS reads digital text, books, lessons, and instructions aloud. News organizations use TTS to convert articles into audio formats for more flexible content access. 

• Education & Learning: TTS supports students by helping them follow along with text, improve pronunciation, and retain information. It’s especially helpful for people with visual impairments or learning disabilities like dyslexia. 

• Virtual Assistants & Chatbots: Virtual assistants like Siri, Google Assistant, and Alexa use both TTS and STT (Speech to Text) to create natural, responsive interactions. They can read messages, make announcements, assist while driving, and offer 24/7 customer support. 

• GPS Navigation & Maps: TTS enables real-time spoken directions, helping drivers stay focused. It reads street names, traffic alerts, and alternate routes for safer travel. 
• Multilingual Communication & Language Learning: Apps like Google Translate use TTS to help users understand and pronounce foreign words. It also powers voice-overs for video content in different languages. 

• Media & Entertainment: TTS creates narration for games, voices for animated characters, and transforms written books into audiobooks, reducing production costs and expanding content accessibility. 
• Healthcare: TTS reads medical documents, device instructions, and prescriptions to patients. It reminds patients of appointments and medication schedules, especially useful for those with visual or speech impairments. 

• Marketing & Advertising: TTS generates voice content for ads without the need for voice actors. It enhances personalization in campaigns via voice chatbots and email marketing. 
• IoT & Smart Homes: TTS is built into smart speakers, watches, and home security systems. Devices can speak alerts, schedules, or weather updates, offering seamless, voice-based interaction. 

• Customer Service & IVR Systems: TTS powers automated phone systems that answer calls and provide spoken options. When paired with voice recognition, these systems can handle complex queries and deliver voice responses, replacing traditional call center agents. 

Challenges in Implementing Text-to-Speech 

Despite its progress, TTS still faces some limitations: 

• Voice quality still sounds robotic: Some TTS systems still generate flat, machine-like voices that lack natural flow and can hinder listener engagement. 

• Lack of emotional tone: TTS struggles to convey emotions like happiness, sadness, or surprise, making it less suitable for expressive content like storytelling or film dubbing. 

• Mispronunciation of special terms: TTS often misreads names, slang, foreign words, or technical terms, leading to confusion in fields like healthcare, finance, or technology. 

• Incorrect context interpretation: Unlike humans, TTS systems often fail to understand context, which affects rhythm, pauses, and emphasis. 

• Inconsistent handling of abbreviations: TTS may pronounce the same abbreviation in different ways within a single document. 

• Incomplete multilingual support: While many TTS systems support multiple languages, they often struggle with mixed-language texts, mispronouncing foreign terms. 

• Inconsistent tone in long texts: TTS voices can lose consistency across long passages, leading to abrupt changes in tone. 

• Poor sentence pacing and emphasis: TTS often places pauses and pitch changes in unnatural spots—especially problematic for tonal languages like Vietnamese, Chinese, Korean, or Japanese. 

• High hardware requirements: Modern AI-based TTS systems require significant computing resources, making them harder to implement on low-power or mobile devices. 

• Limited voice personalization: While some systems allow basic voice customization, fully cloning or personalizing a unique voice is still a major challenge. 

 

Future Trends of TTS Technology 

•  
• Here’s what the future holds for TTS: 
• AI integration to improve voice quality: Advanced AI models like Transformers, WaveNet, and Tacotron are making synthetic voices more human-like. These models can better understand context, adjust tone, and pronounce words accurately across different languages and cultures. 
• Voice Cloning: This enables TTS to replicate a specific individual’s voice. It’s great for personalized audiobooks, virtual assistants, or customer service bots, making user interaction feel more authentic. 
• AI Dubbing: This innovation syncs speech with lip movements in videos. It revolutionizes dubbing for films, educational content, and online media by making translations more accurate and lifelike. 
• Voice Conversion: This allows you to convert one person’s voice into another without re-recording. It’s especially useful in gaming, animation, or podcast production, offering flexible voice creation without additional effort. 

In conclusion, Text to Speech technology has become an essential technology in many fields from education, healthcare, marketing to route navigation, virtual assistants, and smart homes. Although there are still some limitations, TTS is constantly improving significantly. The strong growth of the global TTS market reflects the increasingly important role of this technology in improving human-to-machine communication and building a more accessible digital world for everyone. 

Digital transformation is spreading strongly around the world, businesses are increasingly facing pressure to optimize operations, AI Agents have become a workforce that promotes innovation activities from internal processes, demonstrating a strategic role in the transformation of modern businesses.

In particular, FPT AI Agents, the platform for creating “AI Human Resources” of FPT Corporation, has been opening a new era for domestic Vietnamese enterprises with outstanding potential in integrating, operating and developing internal automation solutions, meeting domestic market needs, while competing internationally.

Innovating  internal business operations by implementing AI Agents for business

Businesses around the world have quickly recognized the importance of internal automation to minimize repetitive tasks, minimize errors and optimize resources. AI Agents systems are now widely applied in process automation, helping businesses handle work quickly and accurately. According to reports from McKinsey and Gartner, the application of automation technologies can help businesses reduce internal process processing time by 50% to 80%, while cutting personnel costs by 30% to 50%. Pioneering applications such as JPMorgan Chase’s internal system (reducing 360,000 labor hours annually) or IBM Watson integrated into the internal management system have demonstrated the outstanding effectiveness of AI in improving productivity and business efficiency. These achievements not only open up great cost savings opportunities but also facilitate businesses to make decisions based on accurate and fast data.

In the global context, 2025 marks the time when businesses will be ready to comprehensively transform with the support of AI Agents. In particular, the Vietnamese market – with its strong digital transformation policy, investment in IT infrastructure and human resource development – is becoming a potential place to apply these automation solutions, helping domestic businesses compete in the international arena. In May 2024, FPT became a strategic partner with NVIDIA through a $200 million investment in the development of an AI Factory. At the Tech Day 2024 event, FPT officially launched FPT AI Factory – a set of solutions to support the development of the entire AI process. In this ecosystem, FPT AI Agents is positioned as the core platform that allows the construction and operation of “AI personnel” quickly and flexibly. FPT AI Agents not only “Vietnamizes” the interface and content to suit the local culture but is also built on an advanced technology platform, allowing businesses to quickly deploy and customize according to business requirements. As a result, businesses in Vietnam can achieve flexibility, improve service quality and increase productivity, opening up opportunities to reach the international market.

FPT AI Agents is developed by FPT.AI based on advanced Generative AI technology and large language models (LLM) combined with Deep Learning and Reinforcement Learning. This platform allows businesses to easily create a multilingual AI team (including Vietnamese, English, Indonesian and Japanese) without requiring in-depth programming or technical knowledge. Users only need to interact in natural language to control and communicate with the system, thereby empowering human employees, revolutionizing customer experience and breakthrough business productivity. These technologies help FPT AI Agents not only perform tasks accurately but also automatically learn, update new knowledge, create contextually appropriate responses and remember previous conversations.

>>> EXPLORE: How to build an AI Agent and train it successfully?

Applying FPT AI Agents in internal operations

AI Agents – Optimizing Administrative – Human Resources Operations

The administrative department plays a fundamental role in the operations of every business. From managing records, processing text documents, planning and scheduling, supporting departments to ensure smooth business operations. However, with a huge amount of work, the dependence on manual processes makes administrative work often time-consuming, error-prone and unable to keep up with the pace of modern business development.

The emergence of AI Agents – artificial intelligence assistants – has opened a new turning point in administrative management. In particular, FPT AI Agents can automate repetitive tasks, improve work productivity, reduce administrative workload and optimize operating costs. According to McKinsey’s report, the application of AI Agents in the administrative department reduces 50-70% of manual workload, increases employee productivity by 40%, cuts up to 30% of administrative operating costs, and helps employees focus on strategic and creative tasks.

AI Agents are capable of handling key operational and human resources tasks professionally:

• Document management and automatic text processing: AI Agents can scan and extract information from documents (PDF, Word, images); automatically classify and store documents for easy searching; convert paper documents into digital data with OCR (Optical Character Recognition), reduce document processing time by about 60%, increasing data entry accuracy by 90%. In particular, AI Agents can manage personnel records, helping to track labor contracts, and periodically update social insurance.
• Manage work schedules, meetings and meeting rooms: AI Agents automatically check available schedules and schedule meetings that suit everyone; Send invitations, schedule reminders via email, chatbot or text message; Record meeting minutes, summarize main content. This can help reduce 50% of the time to organize meetings, limit scheduling conflicts between departments
• Optimize reporting and data analysis processes: AI Agents automatically aggregate data from multiple sources, analyze, create reports and suggest improvements, display visual data on dashboards, helping to reduce about 80% of reporting time, increase 45% accuracy in data analysis.
• Smart recruitment process: automatically screen thousands of profiles based on pre-defined criteria, helping to reduce selection time and increase the ability to find suitable candidates, creating an effective recruitment process.
• Internal human resource training: AI Agents have the ability to support businesses in improving the quality of human resources by automating and personalizing the training process through the application of modern learning methods.

In Vietnam, FPT AI Mentor has become a powerful assistant accompanying all employees, answering all questions related to work, helping employees improve their knowledge and abilities, thereby increasing productivity and operational efficiency for the business. FPT AI Mentor will assess employees’ daily knowledge, personalize training content according to each individual’s strengths and weaknesses. Knowledge in the following days will focus on weak areas, combined with the spaced repetition method to maintain solid knowledge. In addition, employees can access knowledge anytime, anywhere thanks to diverse learning content formats such as text, images, audio and short videos, built on corporate knowledge sources. At the same time, FPT AI Mentor provides a professional reporting system for all levels. For employees, FPT AI Mentor will build a knowledge map based on the assessment results, helping employees understand their existing and needed knowledge and propose the most suitable learning path. Managers can quickly grasp the training quality of employees with summary reports by department and campaign, customized according to the needs of the business.

Many businesses applying FPT AI Agents in human resources have recorded a 50% reduction in recruitment time and an increase in productivity of up to 40% thanks to the ability to automate and personalize the training process. Banks and financial institutions have used FPT AI Agents to automate the process of processing invoices, preparing financial reports and reconciling transactions. This helps reduce errors and improve the speed of decision making based on real-time financial reports. Integrating FPT AI Agents into CRM and ERP systems has improved customer information processing, optimized data management and enhanced customer experience through the automation of multi-channel customer care tasks.

>>> EXPLORE: Why Gen AI Agents are the future prospect of Generative AI?

AI Agents in the Finance and Accounting Department: Transparent, accurate and fast

The finance and accounting department plays an important role in the operation and sustainable development of the enterprise. However, the large workload, high accuracy requirements along with risks of fraud, errors and cash flow management require businesses to continuously improve to optimize the finance and accounting process. Accounting operations require high accuracy with a large workload. According to PwC’s Report, AI Agents can help businesses reduce 80% of manual work in accounting, increase accuracy up to 99%, and save 30-50% of operating costs.

AI Agents are capable of processing invoices and documents by automatically scanning, extracting and storing information from invoices, contracts and financial documents, helping to reduce errors and increase processing speed by up to 80%. In particular, the system automatically synthesizes data from multiple sources, creates financial reports, real-time revenue and expenditure, supporting the management in decision making. In addition, AI Agents use analytical algorithms to compare transactions with standard samples, provide early warnings of abnormalities and risks, and detect fraud quickly and effectively. Application in the accounting department not only helps reduce operating costs but also increases data accuracy, thereby supporting businesses in making decisions based on updated and accurate information.

• Automate data entry and invoice processing: AI Agents help automatically extract information from invoices & contracts using OCR (Optical Character Recognition) technology; Assign tax codes, automatically account for and check for duplication; Synchronize with accounting & ERP systems for faster processing. This helps reduce accounting data entry time by about 70%, increase accuracy in invoice processing by 95%.
• Financial data analysis and trend forecasting: AI Agents help analyze financial data in real time, predict revenue, costs and market trends to help businesses make strategic decisions and assess financial risks, optimize budgets and allocate capital more effectively. As a result, AI Agents support a 40% increase in accurate cash flow forecasting and a 30% reduction in financial risks thanks to accurate data analysis.
• Fraud Detection and Risk Control: AI Agents help monitor unusual transactions, alert fraud immediately, compare transaction data with financial history to detect discrepancies, ensure compliance with financial standards, and minimize the risk of legal violations.
• Support for automatic financial reporting: AI Agents help automatically synthesize data from ERP & CRM systems, create financial reports according to international standards, help businesses make more accurate decisions and provide intuitive dashboards, helping managers easily monitor finances.

Applying AI Agents to finance – accounting not only helps businesses reduce manual work but also creates transparency, accuracy and speed in operations. With the support of FPT AI Agents, Vietnamese businesses can automate data entry, optimize cash flow, detect fraud and improve the efficiency of financial analysis.

>>> EXPLORE: Understanding AI Agents in KYC

AI Agents in Marketing and Sales: Winning Customers and Boosting Revenue in the Digital Age

In the modern business context, where customers increasingly expect personalized experiences and responsiveness, the sales & marketing department plays a central role in driving revenue and building sustainable customer relationships. However, businesses are still facing many challenges in optimizing internal processes, synchronizing data and improving work performance. These issues not only reduce sales efficiency but also waste resources, increase costs and reduce customer satisfaction.

In response to that need, FPT AI Agents – FPT’s advanced “AI human resources” creation platform – appears as a comprehensive solution, helping to automate sales and marketing processes, increase productivity and improve customer experience from within. FPT AI Agents has outstanding strengths such as multilingual support (Vietnamese, English, Japanese, Indonesian – suitable for domestic and international businesses), quick deployment (building AI Agents in just a few hours, easy integration with CRM, ERP systems), analyzing customer data to create interactions suitable for each subject, helping to personalize customers optimally. In particular, FPT AI Agents ensures information security issues, complies with international standards on data security. With support from the 200 million USD investment with NVIDIA and the FPT AI Factory platform, FPT AI Agents owns a powerful “super infrastructure”, allowing businesses to comprehensively improve sales & marketing performance.

FPT AI Agents is applied to internal sales and marketing operations such as:

• Managing customer data and optimizing CRM systems: Customer management is the core foundation in all sales & marketing activities. However, when data is scattered and not updated promptly, businesses easily lose opportunities to close deals and waste time on manual processing. FPT AI Agents helps automatically import and update data from the AI ​​system that automatically collects information from channels (email, social networks, websites) and updates it into CRM; intelligently classify customers based on behavior, purchase frequency and interaction, AI Agent groups customers to prioritize approach; track customer journey, support sales teams to grasp the status of each customer to develop appropriate strategies.
• Automate sales processes: Traditional sales processes often take a lot of time to schedule appointments, create quotes and track orders. FPT AI Agents helps schedule appointments and automatically remind, ensuring that the sales team does not miss opportunities to approach customers; create quotes and contracts quickly with the AI ​​system that supports creating and sending quotes in a short time; Personalize consulting with product/service suggestions that suit each customer’s needs.
• Support internal marketing campaigns: For a marketing campaign to be highly effective, there needs to be smooth coordination between the marketing and sales teams. FPT AI Agents supports automatic sending of emails and promotional messages with personalized content based on customer behavior; monitor campaign performance with AI Agents’ analysis of email open rates, clicks and conversions, helping the team adjust strategies in a timely manner. At the same time, AI Agents can make consumption trend forecasts from data analysis via social networks and customer behavior to make accurate predictions.
• Train and support new sales staff: Training new staff is time-consuming and costly. FPT AI Agents helps provide documents and automatic instructions, new staff can easily look up sales processes and product information; support direct situation handling, answer questions about products, sales policies immediately and make suggestions for development roadmaps based on work performance, AI suggests appropriate training.

FPT AI Agents not only automates processes but also brings a series of practical benefits, saving 50% of work processing time, increasing 35-40% conversion rate thanks to accurate data and quick response, shortening order closing time, helping revenue grow significantly. Thereby creating innovations in customer experience, supporting customers to make quick decisions.

2025 promises to be an important milestone marking the explosion of AI Agents solutions in businesses. With the global trend and strong development of AI technology, businesses will continue to digitally transform, integrate automation systems to optimize internal operations, thereby enhancing business efficiency and competitiveness. Applying AI Agents in internal operations is not only an inevitable trend of digital transformation but also a breakthrough that helps businesses optimize operations, minimize risks and costs, and improve productivity and employee experience. With FPT AI Agents, businesses in Vietnam can create a modern, flexible and creative working environment, thereby strengthening their competitive position in the domestic and international markets, opening up a sustainable development future in the new era.

>>> EXPLORE:

• Applications of AI Agents in Personalized Marketing
• What Are Intelligent Agents? The Difference Between AI Agents and Intelligent Agents

AI Agents are becoming the mainstream trend in 2025 with the ability to support and replace humans in performing many tasks, from simple to complex. According to many experts, the explosion of AI Agents in work is a “stepping stone” before humanity moves towards artificial general intelligence AGI.

Mr. Nguyen Hong Phuc, Chief Science Officer of Conductify AI, commented that artificial intelligence has a much faster and more comprehensive development speed than other technologies, such as biology or quantum computing. “On average, AI has a small breakthrough every two weeks, a big step forward every month, and in the past year, AI has made comprehensive changes,” he shared.

2024 will witness a strong expansion of AI, from the ability to generate text like ChatGPT in 2023, to new areas such as image, video, audio creation, content creation and learning support. However, the question is: How effectively can AI change the way people work?

Most AI models, such as OpenAI’s GPT and o, Google Gemini, Anthropic Claude, Meta Llama or DeepSeek, mainly help speed up productivity rather than completely change the way people work. People still do the same work as before, but at a faster speed and higher efficiency. The birth of AI Agents marks an important turning point, when AI not only supports but also has the ability to automate processes and perform work on behalf of humans more proactively.

In this article, FPT.AI will describe in detail the trend of applying AI Agents at work and why AI human resources are a prominent technology trend in 2025. Let’s explore.

What are AI Agents?

At the end of 2024, the term AI Agents began to be mentioned a lot by large technology corporations and scientists around the world. In fact, this concept has been around since 2023, referring to the ability of artificial intelligence to assist with repetitive and boring tasks.

“Imagine an AI in customer service that can predict a user’s needs before providing an answer, or an AI managing network connections that can detect potential problems and automatically handle them to ensure uninterrupted service,” Liz Centoni, Cisco’s Director of Customer Experience, shared with National Technology. “This will be an important trend in 2025, when AI becomes more autonomous, more automated, and integrated into personal devices, IoT, robots… to perform tasks in reality.”

According to Mr. Phuc, AI Agent can be understood as an artificial intelligence system that is capable of operating independently and making decisions based on specific goals set by humans.

“Unlike AI chatbots or virtual assistants that can only answer questions, AI Agents are designed to handle complex tasks, can interact with other systems and even operate continuously without human intervention. In other words, AI Agents at work not only respond but also proactively complete tasks and make decisions when necessary,” Mr. Phuc explained.

According to Mr. Tran The Trung, Director of FPT Technology Research Institute, in general, AI Agent is an automatic system that performs professional tasks in a similar way to humans or employees in the organization, but without support or intervention and still achieves high efficiency.

According to IBM’s definition, AI Agent is a system or software that can automatically perform many tasks on behalf of humans, by building the optimal operating order and taking advantage of available tools.

Amazon has also provided a similar definition, describing an AI Agent at work as a software program that can interact with its environment, collect data, and use that information to perform self-defined tasks to achieve a given goal. While humans set the goal, an autonomous AI Agent can make its own decisions about what actions to take to achieve that goal.

>>> EXPLORE: How to build an AI Agent and train it successfully?

Real-life applications of AI Agents work

According to Mr. Phuc, the application of AI Agent in work brings a breakthrough when compared to chatbots. Instead of just stopping at the question-and-answer function, AI personnel can automate work processes flexibly.

“We are getting closer to AI replacing humans in repetitive tasks, and that can start as early as 2025,” he commented.

Technology expert Duy Luan assessed that AI Agent, to some extent, can think for itself and use designated support tools. “This technology can be applied in many fields, typically controlling computers, retrieving and processing information according to input, supporting customers,…” Mr. Luan shared.

In the world, a number of large technology corporations have been considering using AI Agent in work to replace humans. In January 2025, Meta CEO Mark Zuckerberg said that AI Agents could take over the role of a mid-level engineer in his company as early as this year.

In December 2024, Salesforce, one of the leading cloud technology companies, announced that it would not hire any more software engineers in 2025, citing the significant improvements in productivity thanks to AI. The company has developed an automated AI Agent with flexible customization capabilities, directly connected to business data, and performing various tasks in the fields of sales, customer service, marketing, and commerce.

Meanwhile, OpenAI CEO Sam Altman said that virtual employees will start entering the labor market this year. In a blog post on January 6, he shared:

“2025 will be the year we witness the emergence of AI Agents, also known as virtual employees, in many businesses. They not only support but can also significantly improve work productivity. The gradual introduction of advanced AI tools into practice will bring about large and increasingly widespread impacts.”

In a report predicting 6 AI trends that will explode in 2025, Microsoft Vietnam assessed that AI Agents will reshape the way we work: “Thanks to advances in memory, reasoning and multimodal interaction, AI Agents can handle complex tasks more flexibly and effectively.”

Sharing the same view, Mr. Nguyen Nhu Dung, Managing Director of Cisco Vietnam, Laos and Cambodia, also predicted that AI will become an important part of the workforce, taking on many tasks instead of humans.

>>> EXPLORE: AI Agents for Business Internal Operations in Vietnam

In Vietnam, how is the trend of applying AI Agents at work taking place?

“Vietnam will have thousands of AI personnel, or in other words, thousands of mature AI models, widely applied in all areas of life. These assistants can support millions of people at the same time, helping to increase labor productivity many times over. When AI Agents are fully automated, AI Agents will perform work at a speed far surpassing that of humans” Mr. Truong Gia Binh shared about the future development of artificial intelligence in Vietnam in an interview with VnExpress in December 2024.

Two months earlier, FPT launched its first AI Agent at the Techday 2024 event. According to Mr. Vu Anh Tu, FPT’s Chief Technology Officer, FPT AI Agents is a platform researched and developed by the group’s experts to create and operate multilingual AI Agents, leveraging generative artificial intelligence (Generative AI) and large language models (LLM). This platform helps businesses build a team of AI personnel capable of collaborating effectively with humans.

Mr. Tu said: “AI personnel will appear everywhere, supporting people in many different tasks such as programming, processing documents or writing emails. We aim for every FPT employee to have an AI Agent at work to work more effectively, and each customer will also have at least one AI Agent to support them. This year, we will accelerate this process.”

According to Mr. Tran The Trung, Vietnam already has a strong computing infrastructure, creating favorable conditions for developing artificial intelligence algorithms to serve the development of AI Agents. He also emphasized that “Some units have collected and accumulated large amounts of data to train AI Agent models”.

In addition, he revealed that some domestic companies are implementing humanoid robot projects to combine with AI Agents. “Vietnam has the opportunity to catch up with the growing AI Agent trend in the world. However, this will depend on the level of awareness, action strategies of pioneering enterprises as well as support from the Government,” Mr. Trung commented.

In short, the appearance of AI Agents in work is creating important changes, not only helping to speed up work but also gradually automating complex processes. Major technology corporations in the world and Vietnam have been investing heavily in building infrastructure and developing AI Agent models, to seize the opportunity to optimize labor productivity.

Although there are still challenges in deployment and management, the potential of AI Agent in work is undeniable, bringing us closer to a completely new era of labor.

>>> EXPLORE:

• What Are Intelligent Agents? The Difference Between AI Agents and Intelligent Agents
• What is Agentic AI? The differences between GenAI and Agentic AI

In the era of Industry 4.0, automated communication technologies such as NLP chatbots and AI Agents have become an indispensable part of many industries. However, these two concepts are often confused or used interchangeably. Let’s find out with FPT.AI the difference between NLP chatbots and AI Agents, thereby helping you choose the right technology for your business.

What is NLP Chatbot?

NLP Chatbots are computer programs that integrate Natural Language Processing (NLP) technology, capable of recognizing, understanding the purpose and processing user queries based on pre-programmed scripts (list of questions and answers). They are often used in:

• Customer support: Answering common questions (FAQs).
• Product consulting: Introduce services or products, instructions for use, exchange/return policies
• Content automation: Send reminders, provide quick information.

NLP chatbot helps consultants reduce manual, repetitive, time-consuming and laborious tasks to focus on consulting customers on more in-depth issues. Machine Learning technology helps NLP bots continuously update new data, to become smarter and understand users better every day.

Some businesses also integrate Dialog Management technology, use Memory cards and manage variables for chatbots to remember information about products/services/time/location, etc. Thanks to that, Dialog Management Bot can give feedback to users according to the information that the conversation context is referring to, helping users not have to repeat the issue many times while the bot can still understand their intentions correctly.

However, if businesses expect AI chatbots to be multi-functional and bring customers a seamless and automatic two-way interactive experience, NLP Chatbot or Dialog Management Bot is still not the optimal choice for businesses. These chatbots can still completely misunderstand the user’s request, giving irrelevant answers that frustrate customers because they have to rephrase the question many times.

>>>> EXPLORE: What Are Intelligent Agents? The Difference Between AI Agents and Intelligent Agents

What are AI Agents?

AI Agents are intelligent systems powered by core technologies of generative AI and large language models (LLM). AI Agents have natural language processing, and understanding of context. AI Agents not only communicate but also perform complex tasks such as:

• Automated decision making: Based on collected data.
• Learning and adapting: Actively learning and improving efficiency on the enterprise knowledge base over time.
• Multi-system integration: Operating on multiple platforms.

AI Agents can perform complex tasks such as calculations, organizing routes, or predicting trends. They bridge the gap between conversation and action, making them a powerful tool for businesses looking to automate tedious, repetitive tasks or generate new creative insights based on large amounts of structured and unstructured data from various sources.

>>> EXPLORE: What is a Multi Agent System (MAS)?

Comparing NLP Chatbots and AI Agents

Criteria	NLP Chatbot	AI Agents
Communication Ability	Basic communication based on pre-defined scripts	Ability to compute, make decisions, and execute tasks from simple to complex
Context	Limited to predefined response frameworks	Understands context from simple to complex
Learning Capability	Requires manual updates by humans	Self-learning and improves over time
Integration Level	Basic, simple integration	High, capable of connecting with multiple systems

>>> EXPLORE: How to build an AI Agent and train it successfully?

When to choose AI Chatbot or AI Agents?

AI Chatbots are suitable for simple tasks such as answering frequently asked questions or providing quick product advice.

AI Agents are a good choice when businesses need an advanced system that can learn and calculate automatically, accompany human resources, and help create a breakthrough in labor productivity

Will AI Agents replace NLP chatbots?

As AI technology continues to develop, AI Agents are predicted to grow rapidly and reach the 100 Billion AI Agents mark by the end of 2025. Customers will have more intuitive interactions in many different forms such as: text, voice and images. In particular, improved contextual understanding will be a key factor in helping AI Agents provide increasingly personalized information over time.

While the evolution of traditional chatbots may not be as exciting as AI Agents, we will still see practical improvements in user experience through enhanced integration with other business systems and easier implementation of custom flows and responses. As we all navigate this rapidly changing AI landscape, understanding how both NLP chatbots and AI Agents will be critical to maximizing their impact – both now and in the future. Whether using AI chatbots, AI agents, or a hybrid approach, these tools are sure to play an increasingly important role in business operations, reshaping the way we interact with technology and each other.

You can experience AI Agents called OVA right on the FPT.AI website to experience the intelligence of these new generation AI Human Resources. OVA will support customers to resolve all questions, needs and provide advice on FPT.AI products and services that are most suitable for your business.

In short, in the era of rapid technological development, distinguishing and properly utilizing the features of NLP Chatbot and AI Agents is the key to helping businesses optimize business operations. NLP Chatbot is suitable for simple tasks, while AI Agents excel in their ability to self-learn and perform complex tasks. The combination of these two technologies promises to bring a more comprehensive and effective customer experience, opening up breakthrough opportunities in the future.

>>> EXPLORE:

• Why Gen AI Agents are the future prospect of Generative AI?
• What is Agentic RAG? Difference between Agentic RAG and RAG

Although still needing further technical development before being deployed in business, Gen AI Agents are rapidly gaining attention. In the past year alone, giants like Google, Microsoft, and Open AI have invested heavily in software libraries and frameworks to support Agentic functionality. LLM-powered applications like Microsoft Copilot, Amazon Q, and Google’s upcoming Project Astra are shifting from knowledge-based to more action-based. Companies and research labs like Adept, crewAI, and Imbue are also developing Agent-based models and multi-agent systems.

What is the reason behind this trend? Let’s explore the details with FPT.AI in the following article. (The article is translated from McKinsey’s latest research with specific sources cited at the end of the article).

How will Gen AI Agents change the way we interact?

Traditional Agentic AI systems are often difficult to implement, requiring laborious rule-based programming or very specific training for machine learning models. Generative AI changes that. Rather than just answering questions and generating content like AI Chatbots, Gen AI Agents can perform multi-step workflows such as:

• Automatically planning actions
• Using online tools to complete tasks
• Collaborating with other agents and humans
• Learning to improve performance

The strength of AI Agents is their ability to flexibly adapt to many situations (similar to how LLMs can intelligently respond to commands they have not been explicitly trained for) because they are built on foundational models that have been trained on extremely large and diverse unstructured data sets instead of rigid rules.

Instead of programming languages, users can use natural language to instruct the system to perform complex tasks. The multi-agent system then automatically organizes workflows, divides tasks, assigns them to specialized agents, and collaborates with humans to continuously improve the quality of its actions.

>>> EXPLORE: What is an LLM Agent? How it works, advantages, and disadvantages

What value can Gen AI Agents bring to businesses?

Generative AI enables AI Agents to automate complex processes with diverse inputs and outputs – currently unimaginable due to time and cost constraints. For example, a business trip requires coordinating multiple elements across multiple platforms such as: booking airline tickets, reviewing hotel loyalty programs, reserving restaurant tables, and arranging after-hours activities. Much of this work is still done manually because it is too complex to automate using traditional methods.

Gen AI Agents offer three key advantages:

• Diverse Scenario Handling: Unlike rule-based systems that are prone to “fragile” situations when faced with unexpected situations, AI Agents based on platform models can adapt to a variety of situations. They are capable of handling non-linear, variable-rich processes that require sophisticated judgment and real-time adaptation to complete specialized tasks.
• Allows users to control using natural language: Instead of having to translate processes into programming languages ​​(which is time-consuming, labor-intensive, and requires high technical expertise to set up rules and systematize systems), users can direct AI Agents using plain language. This allows even non-technical employees to create complex automated processes, enhancing collaboration between technical and non-technical teams.
• Allows employees to integrate with existing tools: AI Agents can work with a variety of software applications (such as drawing and charting tools), search the web for information, collect user feedback, and connect with other AI models. This capability avoids the need for manual system integration or aggregating output from multiple separate systems – tasks that are time-consuming and labor-intensive.

>>> EXPLORE: How to build an AI Agent and train it successfully?

How do Gen AI Agents Work?

Gen AI Agents can handle complex tasks across a wide range of industries, especially those that require time-consuming processes or in-depth qualitative and quantitative analysis. They work by breaking down complex work into smaller tasks based on specific instructions and data sources to achieve a desired goal. The Gen AI Agent workflow consists of four steps:

1. Receive instructions: The user makes a request in natural language, the system determines the expected course of action, and asks clarifying questions when necessary.
2. Plan and assign: The system translates the request into a workflow, breaking down tasks. The manager subagent assigns these tasks to specialized subagents, each equipped with the necessary knowledge and tools. These agents work together, using the enterprise’s data and systems to perform the work.
3. Improve Results: During the execution, the system may request additional information from the user to ensure accuracy. The agent then provides the results and continues to improve based on the feedback.
4. Action: The agent performs the necessary operations to complete the user’s request.

>>> EXPLORE: AI Agents at Work – Foundation for Productivity Breakthrough

When should Gen AI Agents be applied?

AI Agents can bring significant value to businesses through the following three typical cases:

Loan appraisal

Financial institutions often have to create credit risk reports to assess the risk of extending credit or loans to borrowers. This process requires compiling, analyzing, and reviewing a variety of information about borrowers and loans, which is time-consuming and requires the coordination of many parties (relationship managers must work with borrowers, stakeholders, and credit analysts to conduct specialized analyses that are then submitted to credit managers for review and additional expertise.).

A multi-agent system can handle this scenario with:

• Relationship management agent: Handles communication between borrowers and financial institutions
• Execution agent: Compiles and compiles necessary documentation
• Financial analysis agent: Checks debt from cash flow statements and calculates relevant financial ratios
• Review agent: Checks for errors and provides feedback. This analysis, refinement, and review process is repeated until the final credit report is completed.

With this approach, businesses can reduce review time by 20-60%, process data from multiple sources, quickly verify outputs, and simplify the entire report generation process.

>>> EXPLORE: Understanding AI Agents in KYC

Document and modernize code

Legacy software systems often pose security risks and hinder innovation. However, modernizing them requires engineers to understand the millions of lines of codebase in legacy databases as well as manual documentation of business logic, then translate this logic into an updated codebase and integrate it with other systems.

AI agents have the potential to significantly streamline this process with:

• Legacy software expert agents: Analyze legacy codebases and document and translate different code segments.
• Quality assurance agents: Review documentation and create test suites, helping the AI ​​system refine output and ensure compliance with organizational standards.

This process creates a flywheel effect as components are reused for multiple transformation projects, improving productivity and reducing development costs.

>>> EXPLORE: AI Agents for Business Internal Operations in Vietnam

Create an online marketing campaign

Designing, launching, and running an online marketing campaign requires the use of multiple tools and applications. To move from business goals to creative ideas, marketers must create relevant content for each segment and geography, then test campaigns with user groups across multiple platforms. Using different forms of software and transferring output from one tool to another is often tedious and time-consuming.

With the help of Generative AI, a marketer can describe their target audience, initial idea, intended channel, and other parameters in natural language to the agent system. The Multi Agent System – with the support of marketing experts – then helps develop, test and iterate on different campaign ideas with:

• Strategy Agents: Mining online surveys, analyzing customer relationship management solutions and market research platforms to gather information and using multi-modal platform models to build strategies
• Content and Design Agents: Writing ads, designing, building content that reviewers can review for brand alignment

These agents will work together to refine the output to optimize campaign impact while minimizing brand risk.

>>> EXPLORE: Applications of AI Agents in Personalized Marketing

How should business leaders prepare for the era of Gen AI Agents?

Business leaders should proactively learn about Gen AI Agents, identifying processes that can be accelerated by agent systems. Then, organizations can explore how to use APIs, toolkits, and libraries of leading AI Agents such as Microsoft Autogen, Hugging Face, and LangChain to understand what is involved.

To prepare for agent systems, organizations should focus on three key elements:

• Codify knowledge: Convert business processes into coded workflows to train AI Agents. At the same time, build expertise to guide AI Agents in natural language, simplifying complex processes.
• Strategic technology planning: Organize data and IT systems to ensure agents can effectively connect to existing infrastructure. Build a system to collect user feedback and ensure the ability to integrate new technologies without disrupting operations.
• Establish a human-involved control mechanism: Establish a control system to balance the autonomy of AI Agents and risks. Ensure the accuracy and fairness of output results, work with experts to maintain and scale the Agent system, and create a learning flywheel for continuous improvement.

According to a McKinsey survey, more than 72% of companies are implementing AI solutions, with growing interest in generative AI. Thus, it is not difficult to predict that businesses will soon integrate pioneering technology such as AI Agents into their AI plans. Automation based on AI agents promises to revolutionize many industries, bringing new breakthroughs in productivity.

However, this technology is still developing and needs time to perfect. Like new hires, Gen AI Agents need to be thoroughly vetted, trained, and coached before they can operate independently. However, we can already see the huge potential that this generation of virtual coworkers can bring.

Reference: McKinsey & Company. (2023). Why agents are the next frontier of generative AI. https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/why-agents-are-the-next-frontier-of-generative-ai

>>> EXPLORE:

• What is Agentic AI? The differences between GenAI and Agentic AI
• What is a Multi Agent System (MAS)?

Developed by OpenAI, ChatGPT is not only an intelligent chatbot but also a breakthrough in natural language processing. Since its launch in November 2022, ChatGPT has quickly become a global phenomenon, attracting more than 100 million users in just 2 months, making it the fastest growing application in history. In this article, FPT AI will introduce you in detail what ChatGPT is, the reason why it is causing a global fever, and guide you on how to create a free ChatGPT account.

What is ChatGPT?

What is ChatGPT? ChatGPT (Generative Pre-training Transformer) is a type of Artificial Intelligence (AI) capable of understanding and generating text with natural language created by OpenAI. ChatGPT is developed on the Large Language Model (LLM) platform – a model capable of reading, summarizing, and translating text. Thanks to that, this chatbot can predict the next word in a sentence, as naturally as a human communicates.

Why is ChatGPT causing a “fever” all over the world? The answer is the outstanding progress in natural language processing.

Unlike traditional language models, which are often limited by the amount of training data and rely only on simple statistical techniques to predict the next word in a sequence, ChatGPT uses the Transformer architecture. This architecture allows for parallel processing of large amounts of data, helping ChatGPT learn more about language and its nuances, understanding and generating text with a higher degree of naturalness.

The superiority of ChatGPT is also reflected in its ability to integrate into solutions such as chatbots, virtual assistants, machine translation systems, text summarization tools, etc. ChatGPT has changed the way of working in areas such as customer support, content creation, programming and education, helping millions of people save time and improve work efficiency.

In addition, ChatGPT can also handle a variety of text, images and sounds. The latest updates have allowed ChatGPT to interact with users in an increasingly natural and intelligent way, opening up application opportunities in complex areas such as science, medicine, and big data research.

With these achievements, it is no wonder that ChatGPT has easily taken the lead in the AI ​​race worldwide. Currently, the number of users of the ChatGPT tool developed by Open AI is approximately 100 million people / month. This is the fastest growing consumer app in history, just two months after its public launch.

What are the highlights of ChatGPT?

With the continuous advancement of AI technology, ChatGPT is increasingly appreciated for its ability to generate rich and contextually-variable answers. Here are 2 outstanding benefits of this AI Chatbot:

• Naturality: ChatGPT is created by a Large Language Model. Therefore, every sentence generated by this AI Chatbot creates the feeling of communicating with a real person, convincing, attractive, enough to attract users to enjoy long-term communication with the computer.
• Innovation: The Large Language Model applies statistical probability models to create a certain level of randomness in the results. It is this randomness, combined with the ability to understand context and conversation history, that helps ChatGPT respond and create different answers, even when the questions are the same.

How does ChatGPT work? What is the algorithm behind ChatGPT?

ChatGPT currently has a huge storage space of up to 570GB and has been trained on over 300 billion words. The large text dataset (from articles, conversations, documents, etc.) helps this chatbot learn and learn sample sentences and structures of the language. Once it has learned enough, ChatGPT can create its own text based on a specific suggestion or topic.

ChatGPT’s learning method is Reinforcement Learning from Human Feedback (RLHF). This allows ChatGPT to not only understand but also generate more accurate answers with each interaction. For example, when you ask “How many satellites does the Sun have?”, if the answer is wrong, the model will automatically update the data and provide a more accurate response next time.

The algorithm ChatGPT uses is a Transformer – A computer program designed to mimic the way the human brain works by training a neural network on a large amount of data. This allows it to analyze and understand large amounts of text data, and use this understanding to generate texts that resemble human conversation.

What can we use ChatGPT for?

ChatGPT has many features beyond answering simple questions. It can write essays, chat philosophy, do math, and even write code. Some other practical applications include creating to-do lists, shopping lists, and “to-do” lists that help users improve their daily productivity.

ChatGPT applications:

• Write essays
• Create apps
• Write programming code
• Build resumes
• Write Excel formulas
• Summary
• Write cover letters
• Start a business on Etsy
• Create charts and tables
• Browse the web
• Create custom AI assistants
• Analyze PDF documents
• Digitize handwritten notes
• Write Arduino drivers

Is ChatGPT free? How to create a free ChatGPT account

Since November 2, 2023, ChatGPT has officially been available in Vietnam. Users in Vietnam can easily register and use ChatGPT for free with functions such as writing and encoding through the website chat.openai.com, or download the ChatGPT application on Android and iOS.

Here is how to create a free ChatGPT account from the website. Specifically, you need to perform the following 6 steps:

Step 1: Access the website chat.openai.com, select “Sign Up” in the right corner of the screen

Step 2: Enter your email and click continue

Step 3: Enter the password you want to create in the password box and click continue. Remember that your password must be more than 12 characters.

Step 4: Then you will be sent an email asking you to confirm the email sent, click Open Gmail and find the newly received email and confirm the account in it.

Step 5: Click Confirm Email to successfully create an account.

On the phone, to create a free ChatGPT account, you also need to go through 6 steps. Here are instructions on how to create a ChatGPT account on your phone:

Step 1: Download the ChatGPT app from Google Play Store (Android) or App Store (iOS)

Step 2: Select Sign up with Email

Step 3: Enter the Email address used to register for your personal ChatGPT account, then click Continue

Step 4: Set your account password, remember it is over 12 characters and then click Continue

Step 5: Click Open Email App to verify your account

Step 6: Enter your personal information and your main phone number in Vietnam then click Continue to verify your account

Step 7: Enter the 6-digit code sent to your phone number to complete ChatGPT account verification

In addition, ChatGPT also offers the ChatGPT Plus package for $20 per month. If you purchase this package, you will have priority access to GPT-4o, DALL-E 3, unlimited image generation, Canvas, Voice Mode and the latest upgrades.

Latest Chat GPT Updates

Throughout its development, OpenAI has continuously upgraded and updated ChatGPT to bring a better user experience. Below is a summary of the ChatGPT versions up to now:

• ChatGPT-1: Released in 2018, this is the first version of the GPT (Generative Pre – Trained Transformer) language model with 117 million parameters. ChatGPT 1 has many limitations but is a solid foundation for the next versions.
• ChatGPT-2: Released in 2019, ChatGPT 2 has 1.5 billion parameters with the ability to generate a coherent text or long text.
• ChatGPT-3: Released by Open AI in 2020, this version has 175 billion parameters. This helps improve the accuracy and naturalness of the response.
• Chat GPT 3.5: This version is an upgraded version of ChatGPT 3, with many improvements in performance, context retention, and response quality.
• Chat GPT 4.o: The latest version updated on May 13, 2024. This version has about 10 trillion parameters and performs much better than Chat GPT 3.5 with the ability to respond almost perfectly.

Below is a detailed comparison table of the differences between Chat GPT 3.5 and Chat GPT 4.0:

	Chat GPT 3.5	Chat GPT 4.0
Goals	Suitable for natural language and multimodal tasks	Focus on reasoning and complex problem solving
Multimodal capabilities	Supports text, voice and images	Text only supported
Efficiency	Good at natural language and multimodal tasks	Excels at reasoning, problem solving, and programming tasks
Cost and speed	Faster and more cost efficency	Higher cost and slower speed due to complex calculations
Ideal applications	Suitable for content synthesis, machine translation, and chatbot tasks	Ideal for math, programming and scientific research tasks
Limits	Limited ability to reason deeply	No support for multimodality and simple natural language tasks

Most recently, on September 12, 2024, OpenAI introduced a new family of AI models called o1. ChatGPT-o1-Preview is designed to enhance reasoning and solve complex problems in areas such as science, programming, and mathematics.

New updates on OpenAI o1:

• OpenAI has increased the rate limit for o1-mini for Plus and Team users by 7x – from 50 messages per week to 50 messages per day.
• For o1-preview, the rate limit is increased from 30 to 50 messages per week.

What are the disadvantages of ChatGPT?

ChatGPT in particular and Large Language Models in general are models designed to generate content that mimics the way humans speak or write based on input information and context. However, there is no way to ensure that this content is accurate in the real world. Here are the three biggest limitations of ChatGPT:

Accuracy

The biggest drawback of ChatGPT is that the results and sentences it generates, although very natural like the way humans communicate, are not accurate. Open AI, the developer and publisher of ChatGPT, also confirmed that “ChatGPT sometimes gives answers that sound reasonable, but are actually incorrect or meaningless”. The reasons for this are:

• The input data contains incorrect, outdated or outdated information.
• ChatGPT confuses context, giving information that is correct in one context, but not suitable in another context.
• ChatGPT learns from incomplete data sets, giving accurate but incomplete results.

More dangerously, ChatGPT itself cannot recognize that the information it gives is wrong, and sometimes overconfidently affirms false information to users. This is harmful to those who do not have accurate information but completely trust ChatGPT. Once the risk leads to real consequences, in addition to customer dissatisfaction, it also leads to legal problems and damage to the business.

Reasoning ability

ChatGPT has the ability to grasp semantic structures, extract entities and their values ​​in sentences, as well as the relationships between entities. However, it is still limited in logical reasoning.

ChatGPT cannot be required to solve complex problems. Sometimes, even questions that require simple reasoning, ChatGPT can still give high wrong answers.

Business flow integration, on-premises deployment & Security and privacy issues

Currently, ChatGPT cannot be directly integrated into enterprise information systems because the results are not easily converted into a structure suitable for performing computer operations. To effectively apply ChatGPT in business processes, it is necessary to combine it with other information technologies and AI.

In addition, ChatGPT also has some limitations such as high cost and slow response speed. In particular, if a business wants to deploy this system on its own infrastructure (on-premise), ChatGPT cannot meet the requirements because it only operates on OpenAI’s cloud platform. Although it can be deployed on-premise, ChatGPT also requires powerful infrastructure, high cost and specialized technicians to install and manage.

In addition, OpenAI requires access to almost all communication data between users and ChatGPT. This can cause concerns for businesses in ensuring the security and privacy of user information.

What are the advantages of FPT AI Chat compared to ChatGPT?

FPT AI Chat is an AI chatbot applying Artificial Intelligence (AI) developed by FPT Smart Cloud on the FPT.AI artificial intelligence platform. Compared to ChatGPT, FPT AI Chat is customized to easily integrate with enterprise systems through APIs, allowing businesses to connect chatbots to popular messaging channels such as Livechat on websites, Facebook Messenger, Zalo, Viber, etc. Businesses only need to build a single chatbot and deploy it simultaneously on multiple channels.

In addition, with a powerful cloud platform, FPT AI Chat can also scale to serve thousands to millions of customers at the same time, while still ensuring stable performance. This is very important when businesses grow and need to support a large number of customers without interrupting service.

In particular, while ChatGPT focuses on general conversations, FPT AI Chat is designed to support specific activities such as sales and customer care. FPT AI’s chatbot also has the ability to automatically send information about promotions, manage conversations with customers according to their shopping journey, thereby helping businesses optimize marketing strategies and increase conversion rates.

FPT AI Chat also helps businesses save costs and optimize human resources by automatically processing simple and repetitive queries, reducing the workload for employees and saving up to 29% – 46% of salary funds. In addition, FPT AI Chat ensures the security of customer information and data, helping businesses feel secure when applying AI technology.

Below is a summary of other differences between FPT AI Chat and ChatGPT that you can refer to:

On Wednesday, September 25, 2024, Mira Murati, Chief Technology Officer of OpenAI, announced her departure from the company. This event shocked the technology industry and raised many speculations about the future of the platform. Employees and the online community questioned whether ChatGPT would still maintain its leading position in the field of artificial intelligence, especially when competition is increasingly fierce.

Hopefully, through the above article, you have answered the question of what is ChatGPT and know how to create a free ChatGPT account. If you need detailed advice and customize an AI solution suitable for your business to optimize operations and increase customer interaction, contact us immediately via Hotline: 1900 638 399 or visit FPT.AI for the earliest support!

With the ability to reason and adapt on par with or surpass humans, AGI makes humanity both curious and fearful. This is not only a technological destination but also a symbol of economic ambition, business strategy and the race for power in the field of AI.

This superintelligence is valued at up to 100 billion USD by 2 technology giants: OpenAI (a pioneering laboratory promoting AI for the benefit of humanity founded by CEO Sam Altman, billionaire Elon Musk and his associates) and Microsoft (the world’s largest software manufacturer co-founded by Bill Gates and Paul Allen).

So what is AGI and why is this a step forward that can change the way we interact with technology in the future? Let’s explore with FPT.AI!

What is AGI? Is ChatGPT AGI?

AGI stands for Artificial General Intelligence, roughly translated as general artificial intelligence. This is a cognitive superintelligence system that can learn, interact flexibly with the environment, and solve any human task.

An ideal AGI system would possess superior intelligence, be able to learn from experience, develop new skills, and even create new ideas and solutions without being limited by how they are set up.

ChatGPT, while impressive and useful, is still just a large language model (LLM), which does not meet the standards of Artificial General Intelligence (AGI). ChatGPT can only process language and solve problems based on trained data. It does not truly understand or “know” what it says, but only responds based on probabilistic and contextual models.

However, the development of ChatGPT is still an essential part of the path to conquering artificial superintelligence. o3 and o3-mini – two models recently announced at the “12 Days of OpenAI” event in December are said to have the ability to approach AGI.

With leaps and bounds in processing complex queries, generating coherent narratives, and solving problems in real time, o3 marks a new chapter in the journey towards artificial superintelligence, bringing us closer to the future of artificial general intelligence.

What is the difference between Artificial Narrow Intelligence (ANI) and AGI?

Artificial Narrow Intelligence (ANI) and Artificial General Intelligence (AGI) represent two different levels of AI technology. ANI is designed to perform specific, pre-programmed tasks, such as image recognition, natural language processing, or trend prediction. This type of AI cannot adapt to new situations and requires supervised human training to ensure the best results.

In contrast, AGI is capable of learning from new data, automatically adjusting and improving performance over time without human intervention. Superintelligence can handle complex situations and make smart decisions in real time thanks to its powerful computing power. In addition, with the ability to synthesize and apply knowledge widely, Artificial General Intelligence also has the potential to contribute greatly to research and development in fields such as medicine or computer science.

What are the capabilities of AGI superintelligence?

The capabilities that promise to bring great potential in the future of artificial general intelligence include:

• Learning from experience and adjusting behavior based on new information to adapt to different situations. AGI can understand natural human language and present them in text or voice form
• Understanding context and meaning to process information from many different sources, recognizing color, depth and 3D space in static images
• Making hypotheses, finding new solutions to complex problems and even creating new ideas and products.
• Simulating human thinking processes, analyzing, evaluating, making decisions and reasoning flexibly.

Benefits of Artificial General Intelligence

So with the above capabilities, what are the benefits of AGI? Here are the roles and practical applications of AGI:

• Medical: Artificial General Intelligence can make accurate diagnoses and help doctors develop appropriate treatment plans by analyzing large volumes of medical data and recognizing complex patterns in medical images and patient data.
• Manufacturing: In the manufacturing sector, artificial general intelligence can analyze data from production lines, predict problems and automatically adjust processes to optimize production processes, reduce waste and increase efficiency.
• Automation: AGI has the ability to automate complex tasks, minimize human intervention, optimize workflows, save time and reduce errors, and improve productivity.
• Customer Service: In the customer service sector, superintelligence can improve the customer experience through intelligent chatbots that can understand and respond quickly to context.
• Education: AGI can personalize learning for each student, offering learning programs that match each person’s abilities and interests. This helps improve the effectiveness of education and encourage student development.
• Research and development: Artificial General Intelligence generates new ideas and helps researchers develop new products or solutions quickly and efficiently by analyzing and finding relationships in data.

OpenAI and Microsoft have valued the potential of AGI at a minimum profit of $100 billion. This shows that the potential for creating economic value from comprehensive intelligent systems is not only large but also sustainable.

What are the technologies driving AGI research?

General artificial intelligence is receiving a lot of attention from scientists and engineers. Technologies driving the development of AGI include:

Deep Learning

Deep Learning is a subfield of artificial intelligence that focuses on using multi-layered neural networks to analyze and extract information from large data sets. By automatically recognizing complex patterns and features, this technology creates models that can understand the context in different types of data (audio, images, text, etc.).

For example, Amazon SageMaker is used by developers to design and deploy deep learning models for Internet of Things applications, helping to optimize the performance of connected devices.

Generative AI

Generative AI enables super-intelligent artificial systems to generate new and innovative content from the data they have learned. This technology not only helps automate the content production process but also provides natural interaction with users.

For example, organizations use models such as AI21 Labs and Cohere to develop applications that can generate text, audio, and images, thanks to the support of the Amazon Bedrock cloud platform.

Natural Language Processing (NLP)

Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on helping computers understand and process human language. This technology helps convert natural language into data that can be analyzed and interpreted.

For example, Amazon Lex allows businesses to build intelligent chatbots that automatically interact and support customers through natural language, enhancing the user experience.

Computer Vision

Computer Vision involves enabling machines to analyze and understand images and videos. This technology is key to developing automation applications in many fields.

A prime example is Amazon Rekognition, which allows engineers to automate image analysis processes, helping with object recognition and real-time monitoring.

Robotics

Robotics combines engineering and artificial intelligence, allowing the construction of autonomous systems capable of performing physical tasks. These robots can operate independently and interact with their environment, which is important in the development of AGI.

What are the concerns about superintelligence AGI?

The superior learning and adaptability of Artificial General Intelligence has many experts and scientists concerned about the ability of humans to control artificial superintelligence.

Tom Everitt, an AGI safety researcher at DeepMind, once emphasized that “AGI will solve any human task without being limited by the way it is set up, for example developing a cure for disease or discovering new forms of renewable energy.” Stephen Hawking also warned that “Artificial intelligence could be the worst thing in human history. Sooner or later it will be uncontrollable.”

However, Jacques Attali, a French economist and sociologist, emphasized at the Viva Tech event that the goodness or danger of AGI is entirely up to human choice. “If AI is used to develop weapons, the consequences will be horrifying. But if it is applied to health, education, culture, we will open up a promising future,” he shared.

How to control the risks of Artificial General Intelligence?

“I used to think it would take 20-50 years for humans to achieve AGI, but now the pace of development has exceeded expectations. The urgent problem now is how to control them,” said Geoffrey Hinton, a Turing Award-winning professor and the “father of AI.”

However, controlling artificial general intelligence cannot be as simple as the way humans control each other based on cognition and emotion. AGI superintelligence has the potential to go far beyond those limits, making controlling it a complex challenge.

To minimize the risks of AGI and ensure that it serves human interests, researchers can focus on the following important factors:

• Establish a system of auditing, monitoring source code, and regularly evaluating performance to detect early signs of AGI going out of control and intervene promptly when necessary.
• Develop clear standards and regulations to guide developers in building AGI. These regulations include requirements for safety, transparency and accountability to ensure AGI operates as intended.
• Develop policies to protect public interests, such as retraining workers affected by automation and ensuring equitable distribution of benefits from superintelligence.
• Limit the concentration of power in the hands of a few organizations to avoid the abuse of AGI.

Hopefully, the information shared by FPT.AI above will help you answer the question of what AGI is, the risks when applying and solutions to control this type of AI. The development speed of artificial superintelligence is increasing rapidly, requiring close coordination between experts, governments and the community to ensure this technology is developed for the common benefit of humanity.