The trend of a large language model and who creates birth in 2025: Forecast and prospects

Has stepped 2025, Large language models and generative AI will flourish, with new trends constantly shaping how AI is applied in many fields. From breakthroughs in performance and multimodal capabilities (multimodal) to ethical challenges and changes in legal regulations, AI is gradually changing the way we interact and work. In this article, Let's explore the outstanding trends that will shape the year 2025!

I. Trends in developing AI models in 2025

1. Model Performance and Sustainability

• Smaller model, hiệu quả hơn: As AI increasingly develops, Energy demand also increases. According to forecasts by Goldman Sachs, The energy needs of data centers will increase 160% in 2030. This puts great pressure on companies to develop more compact AI models while still ensuring optimal performance..
• Green AI (Green me): Developing AI consumes huge resources, opens up opportunities for environmentally friendly green AI solutions. Green AI helps reduce energy consumption through methods such as smart grids (smart grids) and optimize the balance between electricity production and consumption demand.

2. Field-specific LLM models (Domain-Specific LLMs)

• Vertical AI solutions (Verticalized AI Solutions): As industries increasingly apply AI to specific needs, Specialized AI solutions will explode, especially in fields such as medical diagnostics, detect financial fraud or optimize the supply chain. Thanks to exploiting industry-specific data and understanding legal regulations, These models not only help improve performance but also ensure accuracy and regulatory compliance.
• Custom models (Customizable Models): One of the trends that will grow strongly in the near future is the ability to customize large language models (Llms) and AI created according to business characteristics. Instead of depending on general models, Businesses can provide data, industry-specific terminology and processes to create more suitable AI models, brings greater efficiency.

3. Enhanced Multimodal capabilities (Multimodal Capabilities)

• Not just text (Beyond Text): When models not only process text but also integrate multimodal capabilities such as images, audio and video, The need for AI to create more complex content will only increase. This development allows AI to understand and create rich forms of content, more complicated, thereby promoting innovative and groundbreaking applications.
• Multilingual and multidisciplinary capabilities: In the year 2025, AI is expected to be capable of working across multiple languages ​​and areas of expertise. These advances not only make AI translation more accurate, but it can also translate and apply complex concepts across different professions., Expand the application of AI in practice.

4. Developing AI responsibly and ethically

• Minimize bias (Bias Mitigation): Reducing bias in AI development will become important during the year 2025, to create more transparent models, fairer and safer. Techniques such as equity awareness training, “Clean” data collection and continuous monitoring will play an important role in developing more accurate models with diverse perspectives.
• Data security and privacy: Privacy and data security will become a top priority, as data responsibility demands from both consumers and regulatory agencies increase. Methods such as federated learning (federated learning), protect privacy through differential security (differential privacy) and secure multi-party computation (secure multi-party computation) will help AI models learn from data without violating the privacy of the individuals involved.

5. LLMs for real-time applications

• Real-time and conversational AI: To deploy these applications, Real-time requirements are critical and language models are large (Llms) will be key in providing contextual feedback in dynamic situations. From receiving customer requests, to user support or instant translation requests, LLMs will help optimize performance and minimize latency, provides almost instant feedback.

6. Advances in Training and Refinement Techniques

• Learn Few-shot and Zero-shot: Few-shot and Zero-shot learning will revolutionize AI by allowing models to perform tasks with little or no specialized training data.. These advanced techniques will help AI systems generalize across a wide range of tasks, reduces reliance on large labeled data sets and promotes faster deployment.
• Self-supervised and unsupervised learning (Self-supervised and unsupervised Learning): Self-supervised and unsupervised learning methods are expected to become mainstream as AI models will be trained from unlabeled data., Reduce reliance on costly and time-consuming manually labeled data sets. These approaches will enable performance training to be more efficient and scalable, allows AI to detect patterns, relationships and information from large amounts of unstructured data.

II. AI development trends in new fields

1. Virtual world created by AI

After the fever of created images (generative image) during the year 2023 and creation videos (generative video) during the year 2024, The expected next step is generative virtual worlds (generative virtual world).

This technology was introduced in May 2/2024 when Google DeepMind launched the generative model Genie, capable of turning a still image into a 2D side-scrolling game (side-scrolling platform game) with interactivity. Come month 12/2024, upgraded version Genie 2 announced, with the ability to grow from a starting image into an entire virtual world.

The most prominent application of this technology is most evident in the field of video games. AI-generated 3D simulations can help test game design ideas, thereby turning a simple sketch into an interactive game space immediately. Therefore, this technology opens up the potential to develop completely new game genres.

Don't stop there, This technology can also be applied in robot training. A technology company that is focusing on development spatial intelligence (so-called spatial intelligence), an ability that helps machines understand and interact with the real world. However, the lack of real-world data for training remains a major challenge. Creating virtual worlds and training robots in those worlds to learn through experimentation can help solve this problem..

2. Large language models that “reason”

When OpenAI launched the o1 model in May 9/2024, they introduced a completely new approach to how large language models work. Just two months later, o3 announced, takes this method to a new level, has the potential to reshape all technology.

Most current models, including OpenAI's GPT-4, give immediate feedback without double-checking. That answer may be right or wrong. However, OpenAI's new models are trained to process answers step by step, Break down complex problems into a series of simpler problems. If one approach doesn't work, they will go back and go the other way. This technique, called “reasoning” (reasoning), Helps technology become more accurate, especially with math questions, physics and logic.

This “reasoning” ability is especially important for agents (AI agents).

Month 12 vừa qua, Google DeepMind introduces an AI agent that can intelligently browse the web called Mariner. During the test performance, Mariner was asked to find a recipe for Christmas cookies that resembled the photo provided. Mariner found a recipe online and started adding ingredients to her online shopping cart .

However, it was "stopped" for the reason of not knowing which type of flour to choose. But instead of not giving feedback, This AI agent analyzed the next step itself in the chat window by replying: “I will use the “Back” button on my browser to review the recipe.”

Instead of stopping, The AI ​​agent broke down the task and came up with a reasonable solution – something only a human could do.. Deciding to hit the “Back” button sounds simple, but with a mindless bot, This is almost a giant leap forward. And it worked as Mariner returned to the formula, Confirm the type of powder needed, and continue to complete the task.

Google DeepMind is also developing the latest version of its large language model Gemini, Apply this step-by-step problem-solving approach, called Gemini 2.0 Flash Thinking.

3. AI brings great progress in science

One of the most exciting applications of AI is to rapidly accelerate the development of natural sciences. Có thể nói, The greatest recognition of the potential of AI in this field was demonstrated in October 10 last year, when the Royal Swedish Academy of Sciences (Royal Swedish Academy of Sciences) awarded the Nobel Prize in Chemistry to Demis Hassabis and John M. Jumper from Google DeepMind for AlphaFold tool development, tool that decodes how proteins fold, and David Baker with tools to support new protein design.

This trend is forecast to continue next year, with the advent of many data sets and models built specifically for scientific research. In there, Proteins are ideal targets for AI, because there are already high-quality datasets, creates a good foundation for training AI models.

Companies that develop AI models are also eager to promote their biological products as research tools for scientists.. OpenAI has allowed scientists to test its latest o1 model to measure the effectiveness of research aids, and the results are promising.

III. Impact on industries

Chăm sóc sức khỏe

Large language models (Llms) is promoting diagnosis, Personalized medicine and medical research by processing large amounts of medical data to discover important information, Predict outcomes and support decision making. In diagnosis, LLMs assist in analyzing medical records, Imaging reports and patient history to suggest possible pathologies.

Finance

Large language models (Llms) Analyze transaction patterns to detect real-time anomalies and assist in fraud detection, Identify suspicious behavior when it occurs. LLMs help create personalized, contextual interactions in customer service, Reduce response time and improve customer satisfaction. They also support processing large data sets, Create forecasts and automate reports in financial analysis.

Education

LLMs can analyze learners' learning progress, Personal study habits and areas that need improvement, and gain a better understanding of each individual,, thereby providing customized learning content, Answer questions in real time and provide personalized feedback. This will allow for the creation of flexible classes , suitable for each learner's learning speed and preferences.

Media and entertainment

LLMs will make a huge impact on the content creation sector, games and interactive media by creating dynamic experiences, Attractive and personalized. In content creation, LLMs can assist with writing, Generate and refine ideas, helps creators produce high-quality content quickly.

IV. Challenge

Data and calculation requirements

The complexity and scale of these models require huge amounts of data, diversity, High quality to ensure accuracy, stability and generalizability, while the computational resources required to process this data surpass the limits of current hardware, driving innovation in cloud infrastructure and specialized processing units such as GPUs and TPUs.

Social ethical impact

Large language models will soon replace many career fields, from customer service to content creation and even data analysis, The threat of AI replacing humans at work is increasing. Besides, it also leads to ethical issues related to developments made possible by AI, including deepfakes and misinformation.

Legal environment

Governments and organizations need to establish regulations, ensure transparency, trách nhiệm, data privacy and bias mitigation in AI. Frameworks may include mandatory impact assessments for AI systems, Clear instructions for responsible use, as well as international cooperation in the development of global standards.

V. Forecasting future trends

Technological innovation

Trong tương lai không xa, We will see the emergence of truly automated systems capable of thinking and reasoning like humans., possess emotional intelligence and the ability to interact more naturally. Furthermore, quantum computing (quantum computing) could truly revolutionize AI by expanding its processing capabilities by many orders of magnitude, enabling models to learn and adapt at incredible speeds and breakthroughs in brain-computer interfaces (Brain-Computer Interface)can enable a smooth combination of human perception and AI.

Long-term impact on society

With the widespread application of major language models (Llms), The global workforce and economy are on the threshold of major change. LLMs can automate tasks like content writing, Answer customer questions and analyze data, this may contribute to job displacement in some industries, So we need to start retraining and redesigning the workforce to adapt to these changes..

The evolving role of human-AI collaboration

In the next few years, as humans and AI increasingly collaborate in all fields, AI will become a powerful tool to enhance decision-making, sáng tạo và năng suất của con người. Với sự hỗ trợ của AI, các chuyên gia có thể khai thác thông tin trong thời gian thực và tự động hóa các quy trình thủ công, giúp con người tập trung vào việc ra quyết định phức tạp, trí tuệ cảm xúc và đánh giá đạo đức.