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.

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.

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.

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.

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 in 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.

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.

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.

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?

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.

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

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.

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.

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.

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.

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.

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.

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.

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

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.

In the era of rapid technological development, consumers are no longer unfamiliar with image-creating technologies such as visual effects (VFX) and computer-generated imagery (CGI). Now, a new technology is making waves in the content industry – generative AI. In this article, FPT.AI will help you learn about the core technologies and operating mechanisms of generative AI, thereby helping you discover how to leverage the power of artificial intelligence in image creation.

What is AI image generator?

AI image generator is a technology that uses generative artificial intelligence (Generative AI) to create completely new images from input text. This technology is based on pre-trained artificial neural networks, often using large amounts of image data and accompanying descriptions. When receiving a text description, AI will analyze and create an image based on the characteristics and content learned from the training data.

Image-generating AI models can generate a wide range of creative images, from landscapes, objects to artistic images. With this technology, users can create entirely new images from just a simple description, opening up many possibilities for digital art, content creation, and other image-related fields.

What technologies does AI image generator use?

Image generation is the result of a combination of many advanced technologies in the field of artificial intelligence. Here are four core technologies, each of which plays an important role in generating images from text descriptions:

Natural Language Processing (NLP)

Natural Language Processing (NLP) is a technology that helps AI understand and process input text to create suitable images. NLP models such as Contrastive Language-Image Pre-training (CLIP) encode text into vectors of numbers, with each value in the vector representing an attribute of the text. NLP plays a role in determining the content and key elements that the image should represent, helping AI understand the context and layout of the image.

Generative Adversarial Networks (GANs)

GANs are a type of machine learning model that consists of two neural networks that work in opposition to each other: a Generator and a Discriminator. The Generator creates fake images based on the input data, while the Discriminator tries to distinguish between real and fake images.

This process is continuous, with the Generator creating more and more realistic images to fool the Discriminator, and the Discriminator becoming “smarter” at detecting fake images. GANs help create realistic, vivid images, even images that are difficult for humans to distinguish from the original.

Diffusion Models

A diffusion model is an advanced form of generative model in machine learning that is notable for its ability to generate new data such as images or sounds. It works by adding random noise to the original data in a series of steps, which helps to reconstruct the original data from the noisy state.

The process starts with the model receiving an original image. It then gradually adds Gaussian noise, a common type of random noise. This happens through a Markov chain, where at each step, the data becomes less recognizable than the original image. The model learns to reconstruct the original data from the noisy images.

Once the training is complete, the model is able to remove noise and restore the details of the image. As a result, it can create new images that are completely similar to the original image but still provide high detail and uniqueness. This technology has proven to be superior in creating colorful and vivid works of art, highlighting human creativity in the use of artificial intelligence.

Neural Style Transfer – NST

Neural Style Transfer is a prominent technology in the field of deep learning, allowing users to transfer the artistic style from one photo to another easily. This technology uses a trained neural network to separate the content of one image and the style of another image.

This process creates a new image that combines the desired content and the characteristic artistic style. The content image retains the main components of the original photo, while the style image brings in unique textures and patterns.

To ensure content and style consistency, NST uses metrics such as content loss to measure content differences and style loss to evaluate style differences between images.

The optimization process will help to minimize the aggregation of these errors, thereby creating a unique work of art. The newly generated image will transform an ordinary photo into a work of art similar to that of famous painters, opening up many creative opportunities for artists and content creators.

How does AI Image Generative Work?

Image Generative AI uses advanced machine learning algorithms, especially Artificial Neural Networks (ANN), to generate new images based on text descriptions. This process begins by training the AI ​​on a large amount of data including millions of pairs of images and accompanying text descriptions.

Through this, AI learns to recognize elements such as color, shape, object, and artistic style, as well as understand the relationships between these elements and how they are depicted in the text. This allows AI to generate images based not only on the description content but also on the requested context and style.

When a user enters a text description, AI uses Natural Language Processing (NLP) technology to convert that text into a numerical representation in the form of a vector. Each value in this vector represents an attribute of the text such as an object, color, or style.

For example, given the description “a yellow dog running in a field”, AI will analyze the components “dog”, “yellow”, and “field”. This helps the image-generating AI determine how to arrange the elements in the image and capture the exact content that the user wants.

After analyzing the text, the AI ​​begins to generate images from the original signals. One of the common techniques used in this process is Diffusion Models. This model starts by generating an image filled with random noise.

Then, through many editing steps, the AI ​​gradually removes the noise and adds details, making the image clearer and more consistent with the original description. This process is similar to looking at a cloud and imagining the shape of an animal, but the AI ​​is able to continue to refine it to make the image more specific and vivid.

To ensure image quality, the AI ​​also uses Generative Adversarial Networks (GAN) architecture. GANs consist of two neural networks that work in opposition: a Generator that generates images, and a Discriminator that determines whether the image is real or not.

This confrontation process helps the Generator improve its image quality, while the Discriminator continuously “challenges” the Generator’s ability to distinguish between fake and real images. Over many iterations, the generated images become more realistic and sharp, meeting the user’s expectations.

Finally, after going through the optimization and testing process, the AI ​​will create a complete image based on the original text description. This image can reflect any style from realistic, abstract, to artistic, depending on how the AI ​​is trained and the specific requirements from the user.

Thanks to its fast processing capabilities, AI can generate images within seconds, opening up many potential applications in graphic design, advertising, and content creation.

Detailed reviews of TOP 5 best AI image generation tools

Below is a summary and detailed reviews of the top 5 AI image generation tools. Each tool has its own advantages, suitable for different usage needs, from easy image creation, to high-quality images or using commercial-safe images.

Tool	Description	Key Advantages	How to Access	Price	Parent Company
DALLE·3	AI tool for generating images integrated directly into ChatGPT Plus, allowing users to create images during the conversation.	Easy to use	ChatGPT Plus, Enterprise; Bing AI Copilot; API	Free for 2 images/day; $20/month with ChatGPT Plus	OpenAI
Midjourney	Top choice for those who want sharp, high-quality images with great colors and textures.	High-quality results	Discord, web app	From $10/month for ~200 images/month and commercial use	Midjourney
Adobe Firefly	AI image generation tool for professional designers, integrating AI tools into photo editing software to support rapid image development.	AI integration into real photos	Adobe.com, Photoshop, Express	Free 25 credits/month; from $4.99/month for 100 credits	Adobe
Generative AI by Getty	Generative AI by Getty provides copyright-compliant images, integrated with iStock and uses NVIDIA Picasso technology.	Safe for commercial use, avoids legal risks	iStock	From $14.99 for 100 image generations	Getty (using NVIDIA Picasso)
Stable Diffusion	An open-source AI tool providing high customization and control, allowing users to fine-tune as desired.	High customization and control	NightCafe, Tensor.Art, Civitai, or download and edit on a private server	Depends on platform	Stability AI

Image generation AI has been opening up endless potential for the creative field, from graphic design, art to marketing campaigns. FPT.AI hopes that this article has provided you with a deeper insight into how this new generation of AI works and the core technology for quick application in practice.

Artificial intelligence is increasingly developing and becoming the ultimate weapon of businesses on the digital transformation journey. Join FPT.AI to learn about 2 ways to classify artificial intelligence and the huge benefits that AI technology application solutions bring to businesses in the following article.

How many ways to classify artificial intelligence?

There are many different ways to classify artificial intelligence. Common types of AI are often distinguished based on factors such as intelligence level, flexibility or similarity to human intelligence. However, below are two main classifications of artificial intelligence:

Classification of artificial intelligence based on capacity: Classification of AI based on the ability to perform tasks and the level of intelligence of AI.

Classification of artificial intelligence based on function: Classification of AI based on the ability to simulate human intelligence, behavior and emotions.

TOP 3 ways to classify artificial intelligence based on capabilities

Artificial Narrow Intelligence (ANI)

ANI, also known as Weak AI, is the most popular type of AI today. This type of artificial intelligence is programmed to replace humans in performing one or several specific tasks with superior productivity. However, because it only focuses on a certain job, Narrow AI cannot perform tasks for which it has not been trained.

Apple’s Siri and Amazon’s Alexa are virtual assistants that use ANI to analyze search requests, respond to users, and provide information support services. Tesla also applies ANI in self-driving car technology, helping the car to recognize the surrounding environment and make automatic decisions based on sensor and camera data.

Artificial General Intelligence (AGI)

What is AGI? AGI stands for artificial general intelligence, which is capable of performing a wide range of human-like tasks, including learning, thinking, and making decisions without being pre-programmed for specific tasks. This type of artificial intelligence can learn and adapt to different situations.

Currently, AGI does not really exist and is only a theory. However, advances in research are helping us gradually develop Partial AGI systems.

In fact, OpenAI aims to develop AGI to create a type of artificial intelligence that can contribute to the common good of humanity. They are developing AI models that can handle many different tasks, but currently these models are still at the ANI level.

Artificial Super Intelligence (ASI)

Artificial Superintelligence is the highest level of artificial intelligence, far beyond AGI and ANI, with the ability to self-aware and excel in all aspects, from memory to processing and decision making. This type of artificial intelligence not only has the ability to learn and adapt, but also has the ability to continuously develop and improve itself to surpass humans.

Currently, ASI has not been developed and only exists in science fiction movies. Visions of ASI often raise questions about the control and threat of artificial intelligence to humans.

In movies like “The Terminator” or “Her”, ASI can improve itself and develop to the point where humans are no longer able to control it. However, in reality, this type of intelligence is still a distant goal of AI research.

How to classify artificial intelligence based on function

Reactive machine

Reactive AI is the simplest type of artificial intelligence. It does not have the ability to remember or learn from the past, but can only react, process and make decisions with current situations based on immediate stimuli.

IBM’s Deep Blue is a famous example of a Reactive machine in life. Deep Blue was developed in the 1990s and defeated chess grandmaster Gary Kasparov. Although it cannot remember or learn, Deep Blue has the ability to calculate and choose moves based on the rules of the game of chess.
Limited memory

Limited Memory AI has the ability to remember and store data for a short time, analyze and make decisions based on past data and learn from experiences to improve performance.

Tesla’s self-driving car uses AI with limited memory to analyze traffic data from cameras. This type of artificial intelligence helps self-driving cars recognize lanes, signs, or pedestrians to make safe decisions.

AI with limited memory

Limited Memory AI has the ability to remember and store data for a short period of time, analyze and make decisions based on past data, and learn from experiences to improve performance.

Tesla’s self-driving cars use limited memory AI to analyze traffic data from cameras. This type of artificial intelligence helps self-driving cars recognize lanes, signs, or pedestrians to make safe decisions.

Theory of Mind

A Theory of Mind AI is capable of perceiving human emotions, psychology, and is capable of simulating social behaviors. The goal of this type of AI is to help machines understand and respond to human emotions and intentions to improve communication and interaction.

Hanson Robotics’ Sophia robot is an early example of AI that has evolved along the lines of Theory of Mind. Sophia is equipped to recognize human facial expressions and respond with appropriate responses. However, Sophia is still limited, and Theory of Mind AI is currently in research and development.

Self-awareness

Self-aware AI is the final and most complex development of AI. This type of artificial intelligence not only understands humans but also has the ability to perceive itself, has emotions, beliefs and a sense of its own existence.

Skynet in the movie “The Terminator” is a fictional example of an AI system that is self-aware, even wanting to protect itself from threats from humans. However, in reality, this type of AI still only appears in science fiction works and we have not yet developed AI that is self-aware.

How does FPT.AI help businesses apply artificial intelligence to optimize operations?

FPT AI Engage virtual assistant is deployed in the customer care switchboard, capable of automatically receiving incoming calls (Inbound calls), simultaneously making thousands of outgoing calls (Outbound calls). Thanks to that, businesses can proactively approach customers, bring better customer experiences and improve customer care efficiency.

Specifically, this Voicebot allows businesses to build automated scripts to answer simple, repetitive calls. It also integrates Speech Recognition, Speech Synthesis and Natural Language Processing (NLP) technology, which can understand and analyze the content of the conversation to respond to customers naturally and quickly.

In addition, with FPT.AI’s conversation management platform, all calls are automatically scored and analyzed according to criteria set by the business, helping to save time and improve representativeness in quality assessment. No more random assessment, FPT.AI Engage allows businesses to promptly monitor and improve service quality, thereby providing appropriate solutions.

For more information about FPT.AI Virtual Assistant and how it can help your business improve operational efficiency, please visit https://fpt.ai/ or contact us via hotline: 1900 638 399. Let FPT.AI become a powerful arm to help your business go further on the digital transformation journey!

In short, understanding how to classify artificial intelligence not only helps us gain a deeper insight into the development of technology but also helps businesses apply AI most effectively. With FPT.AI’s advanced solutions, businesses can leverage the power of artificial intelligence to optimize operations, enhance customer experience and improve overall performance.

In the digital age, content automation has become an inevitable trend to meet the need for fast and accurate information. Natural Language Generation (NLG) is the key to unlocking new possibilities in automatically generating text, improving efficiency and optimizing the content creation process. Let’s learn more about this technology with FPT.AI through the article below.

What is Natural Language Generation? Common Applications of NLG in Practice

Currently, Natural Language Generation (NLG) is becoming an important tool in many different fields such as media, entertainment, education, e-commerce, business and finance, etc. According to a report by MarketsandMarkets, the NLG market is expected to reach $1.35 billion by 2024, with a compound annual growth rate (CAGR) of 20.2% from 2019 to 2024.

This development is driven by the increasing demand for automation in content creation, from financial reports, news articles to customer support applications. So, fully understand, what is NLG?

Natural Language Generation (NLG) is a branch of artificial intelligence that allows computers to automatically generate text similar to what humans write. The technology uses algorithms and language models to convert data and information into natural language, generating structured, understandable text at speeds of up to thousands of pages per second.

The Relationship Between NLG, NLU, and NLP

Many users are confused between NLG (Natural Language Generation), NLU (Natural Language Understanding), and NLP (Natural Language Processing). This confusion arises because all three concepts are in the field of artificial intelligence (AI) and are often used in language communication systems. However, there are significant differences between them in terms of function, input, and output.

Here is a detailed comparison table between NLG, NLU and NLP:

Criteria	NLG (Natural Language Generation)	NLU (Natural Language Understanding)	NLP (Natural Language Processing)
Definition	The process of generating natural text from structured data	A branch of artificial intelligence focused on understanding and interpreting the semantic meaning of text or language data	A combined field of AI and linguistics that helps computers understand, analyze, and generate natural language from text or speech
Main Function	Generate text from data	Understand and interpret language	Process and analyze language
Input	Structured data (spreadsheets, databases, etc.)	Natural language text	Natural text or speech
Output	Natural language text (reports, summaries, descriptions)	Analyzed meaning, intent, and information	Processed language (parsing, sentence segmentation, part-of-speech tagging, etc.)
Applications	– Automatically generate financial reports based on business data. – Write product descriptions on e-commerce sites. – Automatically generate news from sports event data.	– AI chatbots that understand questions and provide appropriate answers. – Virtual assistants like Siri or Alexa interpret user voice commands. – Sentiment analysis on social media posts or customer surveys.	– Analyze language on websites. – Convert speech to text. – Automatic translation tools like Google Translate.

To conclude, NLG, NLU and NLP are closely related, together creating an effective natural language processing system. NLP processes and analyzes language, NLU helps understand semantic meaning, and NLG transforms data into meaningful text, creating a complete chain of operations.

How Natural Language Generation Works

Natural Language Generation generates text in a six-step process:

• Data Ingestion: The Natural Language Generation system begins by ingesting organized data from various sources, such as spreadsheets, databases, or text files. In this step, the system picks out key themes in the source data and evaluates the relationships between them.
• Data Understanding: Natural Language Processing (NLP) is used to analyze and understand the data, identify appropriate sentence structures, and perform syntactic analysis.
• Document Structure Generation: Through Natural Language Understanding (NLU), the system identifies the underlying meaning and relationships in the data and then constructs an initial rough text structure.
• Content Creation: The system starts to deploy each sentence in the content, then considers how to organize the sentences and paragraphs to restructure them appropriately.
• Grammatical Structure: At this step, the system will create understandable text using the grammar rules of natural language.
• Presentation and completion: Finally, the system will give a complete output text in any form that the user requires such as reports, emails, customer feedback, etc.

The importance of Natural Language Generation to businesses

In the context of increasing competition, Natural Language Generation (NLG) is becoming an important tool to help businesses optimize operations and improve customer experience. Here are three key benefits that NLG brings to businesses:

Improve data analytics

Natural Language Generation helps businesses automate the process of generating reports from large data sets. This not only saves businesses time but also improves the accuracy and speed of decision making thanks to clear data analysis.

Improve customer response efficiency

With NLG, businesses have the ability to create automated responses to frequently asked questions from customers. This saves time for customer support teams, while improving user experience and retaining customers better thanks to quick and accurate answers.

Improve customer relationships

Another extremely important role of Natural Language Generation is to improve customer relationships. NLG allows businesses to personalize content based on customer preferences and behaviors, creating better experiences and increasing customer loyalty.

Bloomberg, one of the world’s leading financial companies, has applied Natural Language Generation to automate the process of generating financial reports.

With NLG, Bloomberg can produce four types of automated news and speed up data analysis. Ted Merz, Director of News Products at Bloomberg, said that the readership rate of NLG-generated articles has increased from 0 to 7% in just two to three years. Natural Language Generation has helped the company not only improve the speed of response but also improve customer relationships, trust and engagement with accurate, personalized reports.

How to apply Natural Language Generation to business?

The application of Natural Language Generation to business is opening up many new opportunities for businesses to optimize processes and improve customer experience. Below are some outstanding applications of NLG in the modern business environment:

• AI Chatbot: NLG plays an important role in developing chatbots to automate communication with customers. For example, FPT AI Chat by FPT.AI integrates two technologies, NLG and NLP, to analyze semantics, understand the intent and context in customers’ questions to create natural and coherent answers. This helps reduce the workload for the customer support team, while improving the user experience by providing information quickly and accurately.
• Voice Assistant: NLG application solutions such as Siri, Alexa, and Google Assistant support call notes, automatically send emails, or translate important information and then convert text to speech naturally, helping businesses improve the process of interacting and responding to customers.
• Content Creation: NLG is capable of creating content in many different fields such as: Creating humorous explanations for acronyms (HAHAcronym), Developing algorithms to automatically create textbooks, crosswords and poems (Phillip Parker), Creating word puzzles for children (JAPE system), …
• Automated reporting: NLG automates the process of creating reports from big data, allowing businesses to get information quickly and accurately. One of the first applications was the FoG system, deployed by Environment Canada in the early 1990s to create weather forecasts in both French and English. FoG’s success paved the way for the Los Angeles Times’ automated earthquake reporting, which quickly provided detailed information within three minutes of an event. Today, Natural Language Generation is also used to summarize financial data or generate content for e-commerce sites.
• Sentiment Analysis: NLG can help analyze customer sentiment from social media responses, reviews, and surveys. An integrated NLG system can not only identify positive or negative emotions but also provide insights into the reasons behind those emotions, helping companies adjust their marketing strategies and improve customer service.
• Hyper-personalization: With NLG, businesses can create personalized content and experiences for users based on their preferences and behaviors. Netflix has integrated Natural Language Generation technology to create customized movie descriptions based on users’ viewing history, making it easier for them to find content that matches their interests. Similarly, Amazon applies NLG to create personalized product recommendations, thereby increasing engagement and increasing sales.

In summary, Natural Language Generation (NLG) is proving to be an important role in improving business performance, from data analysis to customer experience personalization. With diverse applications such as automatic content generation and sentiment analysis, NLG helps businesses optimize processes and improve services. In case you are looking for a superior customer care tool that effectively combines NLG and NLP, FPT.AI’s chatbot is a great choice.

For more information and advice on solutions, please contact FPT.AI via:

• Hotline: 1900 638 399
• Website: FPT.AI

Large Language Models (LLMs) are becoming a breakthrough trend in automating and optimizing customer interaction processes. In this article, FPT.AI will introduce you to how large language models work, practical applications in many different fields, along with the advantages and disadvantages to note when applying this advanced technology.

What is a Large Language Model (LLM)?

Large Language Models (LLMs) are machine learning models trained on huge text data sets. The model data comes from information sources such as books, articles, websites and other documents on the internet, with the number of parameters of the models up to billions, even trillions.

The largest and most powerful LLMs today are built on the Transformer architecture, which is capable of unsupervised learning and capturing the semantics of text without needing specifically labeled data. They can process data in parallel instead of sequentially like Recurrent Neural Networks (RNNs), leveraging the power of GPUs to improve the speed of natural language processing.

Notable features of large language models:

• Learning ability: LLMs can learn from data, continuously improving their language processing ability over time.
• Generalization ability: This model can generalize knowledge from data and flexibly apply it to new situations.
• Creativity ability: LLMs can generate new content, translate languages, write creatively, and answer questions intelligently based on the context of the question.

The architecture of Large Language Models

The architecture of LLM consists of multiple layers of neural networks that work together to process input text and generate output predictions. The main components include:

• Embedding Layer: Converts each word or token in the input text into a high-dimensional vector representation. These vectors help the model capture the semantic and syntactic information that makes up the word, sentence, or token to understand the context of the text.
• Feedforward Layers: Consists of multiple interconnected layers that apply non-linear transformations to extract abstract information from the embedding vectors.
• Recurrent Layers: Designed to process text sequentially, maintaining a continuously updated hidden state, helping the model understand the relationships between words in a sentence.
• Attention Layers: Important components that allow the model to focus on the most relevant parts of the input text. This mechanism helps to improve prediction accuracy and better understand context.

How do Large Language Models Work?

A large language model consists of two main components: an encoder and a decoder. The encoder extracts features from the input, and the decoder uses this information to predict or generate output.

When given a prompt, the LLM uses multidimensional vectors, also known as word embeddings, to represent words that have similar meanings or close contextual relationships in a vector space. This helps the model understand the semantics of individual words, the relationships between words and sentences in the text, and respond logically.

During training, the large language model learns to accurately predict the next token in the input data sequence by continuously adjusting its parameters through self-learning techniques. The ultimate goal of the LLM is to generate coherent and contextually relevant text.

What are the applications of large language models?

Large language models are used in various fields, including:

• Creating AI Chatbots and AI assistants that respond like humans to help businesses improve customer service and experience.
• Classifying customer comments, searching for documents, and organizing text data based on semantics.
• Improving the accuracy of search results by providing more direct and contextual answers.
• Analyzing protein, molecule, DNA, and RNA sequences in biological research.
• Converting natural language prompts into programming code, automating software development.
• Analyzing financial transactions to detect anomalies and protect consumers.
• Creating transcripts of important meetings or summarizing content from calls.
• Creating articles, poems, scripts, or songs.

ChatGPT, based on the GPT-3 model (175 billion parameters), is one of the most typical and popular applications of LLM. Developed by OpenAI, ChatGPT is capable of conversing with users naturally and interacting intelligently based on the context of the conversation. Individuals and businesses can use ChatGPT to translate, summarize text or create new content.

Some other notable large language models:

• Claude 2 (Anthropic): Although the exact number of parameters of Claude 2 is not announced, this model is capable of receiving input of up to 100,000 tokens, allowing it to read and process hundreds of pages of technical documents or even entire books.
• Jurassic-1 (AI21 Labs): Jurassic-1 is one of the largest LLM models today with 178 billion parameters. It has a token vocabulary of 250,000 elements and is capable of generating very natural, human-like text.
• Command (Cohere): Cohere’s Command model is also an LLM with the ability to operate in more than 100 different languages.
• Paradigm (LightOn): Paradigm is a platform that provides LLM models with features that are announced to be superior to GPT-3.

In the digital era, applications that support large language models (LLMs) such as chatbots, virtual assistants and sentiment analysis tools are leading the trend of automating customer interactions. FPT AI Engage is a typical example, successfully applying LLMs to the field of automatic switchboards, helping businesses automate call handling and customer interactions

FPT AI Engage successfully applies LLM to the field of automatic switchboards

At the AI ​​Awards 2022 organized by VnExpress, FPT AI Engage was honored in the Top 5 best projects. This voicebot solution is designed to automate outgoing calls (Outbound calls), receive incoming calls (Inbound calls) and intelligently forward calls (Smart IVR).

Thanks to its ability to understand and interact naturally with an accuracy of up to 92%, FPT AI Engage helps to effectively handle simple tasks, answer customer questions, and reduce the workload for switchboard operators, allowing them to focus on more complex tasks.

FPT AI Engage has been trusted by large enterprises such as VIB, SeABank, FWD and HomeCredit Vietnam. In particular, HomeCredit Vietnam with more than 5 million calls/month has saved 50% of operating costs and improved call center performance by up to 40% thanks to this solution.

FPT AI Engage is an important step in applying artificial intelligence to optimize call center operations and enhance customer experience. To learn more about FPT.AI’s Large Language Model application solution or receive detailed advice, please contact hotline 1900 638 399 or visit the website.

The rapid development of artificial intelligence (AI) has brought powerful data analysis tools, helping to optimize the process of collecting, processing and visualizing information. This article will introduce the top 6 AI data analysis tools today, helping businesses and organizations make accurate strategic decisions. Let’s explore with FPT.AI!

PowerDrill AI

PowerDrill AI is an AI data analysis platform that applies advanced machine learning technology. With a customizable dashboard, PowerDrill AI helps professionals and businesses process data quickly and accurately predict trends in real time. This tool can seamlessly integrate with existing systems and promote collaboration through an active user community on Discord.

Advantages:

• Provides fast data analysis through an intuitive conversational interface.
• Ensures data privacy according to GDPR, ISO and AICPA standards.
• Integrates seamlessly with existing IT systems for immediate productivity.
• Has an active user community and extensive support documentation.
• Supports multiple file formats such as XLSX, XLS, TSV, and CSV

Disadvantages:

• Requires a stable internet connection to work.
• May have an initial learning curve.

Microsoft Power BI

Microsoft Power BI is a business intelligence platform that allows users to organize and visualize data for insights. It supports importing data from almost any source and allows for instant creation of reports and dashboards. Power BI integrates seamlessly with applications in the Microsoft ecosystem, such as Excel, Microsoft 365, and Azure Machine Learning.

Advantages:

• Seamless integration with existing Microsoft applications.
• Create personalized dashboards.
• Helps publish reports securely.
• No memory or speed limitations.
• Multiple free plan options
• Offers new features like Events, Conditional Formatting, and Azure Map Publishing

Disadvantages:

• May require basic knowledge of the Microsoft ecosystem to get the most out of it.
• There is a learning curve for complex features like DAX and Power Query.

Tableau

Tableau is a no-code, interactive, and easy-to-use AI data analytics toolkit. With powerful dashboards, it supports data visualization at any scale. Tableau offers a limited free trial and paid plans for each software such as Tableau Creator, Explorer, and Viewer.

Advantages:

• No coding required, easy to use.
• Powerful data visualization support.
• Can handle large amounts of data.
• Integrated AI/ML and data management.
• Has a large community of over a million members
• Offers new features such as Custom Themes and VizQL Data Service API

Disadvantages:

• Paid plans can be expensive (e.g., Tableau Creator is $70/month).
• Requires individual software licenses.
• May have limitations on real-time collaboration and formatting.

MonkeyLearn

MonkeyLearn is a no-code platform that uses AI to analyze and visualize data. It provides text analysis tools that help classify and extract data automatically, saving time on manual processing. MonkeyLearn has a simple, intuitive interface, and offers a free trial and paid plans for teams and businesses.

Advantages:

• No coding required, easy to use.
• Automatic text classification and extraction.
• Save time on manual data processing
• Offers a free trial and flexible paid plans.
• Provides templates and integrates with other tools via API.

Disadvantages:

• Focuses primarily on text analysis.
• Business plans can be expensive ($299/month).

Polymer

Polymer is a no-code data analysis AI application that helps organize raw data into meaningful data. It has an intuitive interface, fast processing speed, and allows for easy dashboard sharing. Polymer is popular in the social media and digital marketing space, and is affordable ($10/month for the Starter plan).

Advantages:

• No coding required, easy to use.
• Organize raw data into a logical database.
• Intuitive interface and fast processing speed.
• Affordable.
• Offers a variety of charts and interactive features.

Disadvantages:

• May not be suitable for large or complex businesses.
• Limited in-depth analysis features.

Akkio

Akkio is a data analytics AI tool that requires no programming knowledge, ideal for beginners. It allows users to load data and select a variable to predict, build a neural network around that variable. Akkio evaluates the accuracy of the model and visualizes the data with charts. It offers paid plans starting at $49/month.

Advantages:

• No coding required, suitable for beginners.
• Build neural networks for predictions.
• Evaluate model accuracy.
• API integration and fast processing speed.
• Offers features like Chat Explore and Generative Reports

Disadvantages:

• Focuses on predictive analytics and may not be suitable for other analytics needs.
• Enterprise plans can be expensive ($999/month).

FAQs about AI for data analytics

What is AI for data analytics?

AI in data analytics is the use of artificial intelligence to detect trends and patterns in large data sets, make predictions, identify opportunities for success, and improve business processes. AI data analytics is a powerful, efficient, and accessible tool for processing data, giving users a holistic view by bringing data into a holistic solution.

How is AI applied in data analytics?

AI data analytics is especially useful in fields such as communications, marketing, and economics. AI coding assistants such as DataLab, Anaconda, AI Jupyter, and GitHub Copilot can automatically complete code based on queries and debugging, saving users time when analyzing data or building machine learning models.

AI can scan data, identify correlations, automatically format data, and create reports that visualize data points, helping users quickly grasp important information without spending time creating charts manually.

AI can automatically collect and clean data from multiple sources, standardize and manage them to create aggregated data. AI applications also have the ability to automatically fill in missing values in data accurately and quickly, helping to ensure a complete aggregated data file.

Advantages of data analysis using AI

Criteria	Traditional Data Analysis	AI Data Analysis
Analysis Process	– Manual – Relies on data analysts to process and search for patterns in the data – Time-consuming – Requires significant human intervention	– Automated – Uses AI algorithms and machine learning to analyze data – Faster and more accurate – Minimizes human intervention
Ability to Handle Large Data	Struggles with handling large data due to limitations in processing speed and storage capacity	Can handle vast amounts of data quickly with superior analysis speed compared to traditional analysis
Type of Data	Primarily processes structured data, such as spreadsheets or data organized in rows and columns	Can process both structured and unstructured data (e.g., text, images, audio, video)
Accuracy	– Prone to human error – Results may be inconsistent	– Higher accuracy – Consistent results – Reduces human error
Cost	– High labor costs – Requires many analysts	– High initial investment cost – Lower operational costs in the long run
Scalability	– Difficult to scale up – Needs additional staff as workload increases	– Easy to scale up – Can increase processing capacity without needing to hire additional personnel
Analysis Time	– Time-consuming – Takes hours to months depending on complexity	– Fastc – Can analyze in minutes or seconds
Predictive Ability	– Primarily descriptive and diagnostic analysis – Limited in predicting future outcomes	– Predictive analysis and recommendations – Can forecast trends and provide recommendations

In short, with the continuous development of technology, AI data analysis tools have become a powerful assistant for businesses in making quick and accurate decisions. However, businesses need to carefully consider the needs and scale of the organization to choose the right tool.