When Less Really Is More
We've been told for years that bigger is better in AI. Want smarter models? Add more parameters. Need better reasoning? Scale up the compute. Build bigger data centers. Train on more GPUs. The message has been clear: if you want cutting-edge AI, you need cutting-edge infrastructure.
Enter the Tiny Recursive Model (TRM), Alexia Jolicoeur-Martineau, a revolutionary approach to AI reasoning that's turning the "bigger is better" dogma on its head. With just 7 million parameters—a fraction of a percent of what modern large language models use—TRMs are achieving state-of-the-art performance on complex reasoning tasks. Even more remarkably, they can be trained on a standard laptop CPU, no expensive GPU required.
This isn't just an incremental improvement. It's a fundamental rethinking of how we approach AI reasoning, and it has profound implications for the democratization of artificial intelligence research and development.
The Reasoning Gap in Modern AI
Large Language Models like GPT-4 and Claude are undeniably impressive. They can write poetry, code software, and engage in nuanced conversations about philosophy. But there's a dirty little secret in the AI world: these massive models often struggle with tasks that require rigorous, step-by-step logical reasoning.
Give an LLM a complex Sudoku puzzle, ask it to find the optimal path through a maze, or present it with abstract reasoning challenges like those in the ARC-AGI benchmark, and you'll see their limitations. Despite having hundreds of billions of parameters and being trained on vast swaths of human knowledge, they falter on problems that require deep, sequential deduction rather than pattern matching.
This phenomenon, known as the "reasoning gap," has been one of the most vexing challenges in AI research. The traditional solution has been to throw more compute at the problem—bigger models, more training data, longer context windows. But as we've pushed this approach to its limits, we've hit diminishing returns, especially on tasks requiring genuine multi-step reasoning.
A Different Approach: Thinking Recursively
The breakthrough insight behind recursive reasoning models is deceptively simple: instead of making models bigger, make them think deeper. Rather than processing information in a single, linear pass through billions of parameters, these models iterate on their answers, recursively refining their reasoning like a human checking their work multiple times.
The concept draws inspiration from how we actually solve difficult problems. When you're working on a challenging math problem or trying to solve a complex puzzle, you don't just write down the first answer that comes to mind. You work through it step by step, check your reasoning, catch mistakes, and refine your approach. You think recursively.
This is exactly what Tiny Recursive Models do, but with a crucial innovation that makes them dramatically more effective than previous attempts: deep supervision.
Deep Supervision: The Secret Ingredient
Here's how it works: instead of training the model to get the right answer in one shot, TRM trains it to progressively refine its answer through multiple supervision steps—typically 8 to 16 iterations. At each step, the model:
1. Recursively updates its internal reasoning state
2. Proposes a refined answer based on this updated understanding
3. Receives immediate feedback on whether it's getting closer to the correct solution
4. Uses this feedback to update its parameters before the next iteration
This process is like having a patient tutor who doesn't just tell you the final answer, but guides you through the reasoning process, helping you catch and correct errors along the way. The model learns not just what the right answer is, but how to arrive at it through iterative refinement.
Analysis by the ARC Prize Foundation found that deep supervision provided a 20% accuracy improvement over single-step training, while the recursive processing itself contributed about 3% improvement. The real magic isn't just in thinking recursively—it's in learning recursively.
The David vs. Goliath Results
The empirical results are stunning. With only 7 million parameters, TRM achieves:
- 45% accuracy on ARC-AGI-1 (compared to 40% for larger hierarchical models)
- 8% accuracy on ARC-AGI-2 (compared to 5% for previous approaches)
- 87% accuracy on Sudoku-Extreme puzzles (compared to 55% for competing models)
- 85% accuracy on challenging maze navigation tasks
To put this in perspective, TRM outperforms models like Deepseek R1, o3-mini, and Gemini 2.5 Pro on these reasoning benchmarks while using less than 0.01% of their parameters. It's the AI equivalent of a high school debate team consistently beating teams of Supreme Court justices.
The key insight is that reasoning capability isn't solely determined by model size, but by the depth of computational processing achieved through recursion. A small model that thinks deeply can outperform a giant model that thinks shallowly.
The Laptop Revolution
Perhaps the most revolutionary aspect of TRM isn't just its performance—it's where you can run it. The TRMlaptop implementation (available at https://github.com/alessoh/TRMlaptop) brings state-of-the-art AI reasoning to consumer hardware.
That's right: you can train a model that beats billion-parameter LLMs on complex reasoning tasks using nothing more than a standard Windows laptop with a CPU. No expensive GPU required. No cloud computing costs. No need to be affiliated with a well-funded research lab or tech giant.
This democratization has profound implications:
For Researchers: Individual scientists and small research groups can now experiment with cutting-edge AI without requiring massive computational resources. The barriers to entry have dropped dramatically.
For Educators: Universities and schools can teach advanced AI concepts using hardware they already have, making AI education accessible to institutions that couldn't previously afford GPU clusters.
For Developing Nations: Countries without extensive AI infrastructure can participate in frontier AI research, leveling the playing field in global AI development.
For Practitioners: Developers can prototype and deploy intelligent reasoning systems on edge devices, enabling new applications that don't require cloud connectivity.
How It Works
At its core, TRM maintains three key components during reasoning:
x - The input question or problem, embedded into a form the model can process
y - The current predicted answer, which gets progressively refined with each iteration
z - A latent reasoning trace, essentially the model's internal "chain of thought"
Unlike traditional models that use complex hierarchies of networks, TRM employs a single tiny network—often just 2 layers—that recursively processes these three components. The architecture is intentionally minimal, yet it achieves remarkable performance through the power of iterative refinement.
The training process uses what's called "latent recursion" where the model performs multiple recursive updates to its reasoning state, then updates its answer, and this entire cycle repeats across multiple supervision steps. After each supervision step, the model detaches its internal states from the computational graph—a clever trick that allows the model to learn from its reasoning process without accumulating gradients across all iterations, which would be computationally expensive.
Real-World Implications
The implications of this work extend far beyond academic benchmarks. Recursive reasoning models represent a fundamental shift in how we think about AI capabilities:
Energy Efficiency: Training large language models consumes enormous amounts of energy. TRM's ability to achieve competitive performance with a fraction of the parameters means dramatically lower energy consumption and carbon footprint.
Privacy: Running sophisticated AI reasoning locally on consumer hardware means sensitive data doesn't need to be sent to cloud servers, addressing privacy concerns in healthcare, legal, and financial applications.
Accessibility: Breaking the monopoly that well-funded organizations have on cutting-edge AI research creates opportunities for innovation from unexpected sources.
Scientific Understanding: Smaller models that we can thoroughly analyze help us better understand what makes AI systems work, rather than treating them as inscrutable black boxes.
The Road Ahead
This is still early days for recursive reasoning models. Current implementations primarily focus on supervised learning tasks where there's a clear right answer. Extending these approaches to open-ended generation, handling multiple valid solutions, and scaling to even more complex reasoning tasks remain active areas of research.
There's also exciting potential in hybrid architectures that combine the infinite context capabilities of large language models with the precise logical reasoning of TRMs. Imagine a system where an LLM handles broad information retrieval and natural language understanding, while delegating specific logical sub-tasks to specialized TRM modules.
The theoretical understanding of why recursion helps so dramatically compared to simply using larger networks is still incomplete. While researchers hypothesize it relates to implicit regularization and overfitting prevention, a comprehensive theory remains to be developed.
A New Chapter in AI
The emergence of Tiny Recursive Models marks a pivotal moment in artificial intelligence research. For the first time in years, we're seeing a credible alternative to the "scale at all costs" paradigm that has dominated the field.
The message is clear: the future of AI reasoning isn't just about making models bigger—it's about making them think deeper. And critically, this future is accessible to anyone with a laptop and the curiosity to explore.
The reasoning gap is being closed not by making models bigger, but by making them better. That's a revolution worth paying attention to.
Resources
"Less is More: Recursive Reasoning with Tiny Networks," Alexia Jolicoeur-Martineau
https://github.com/lucidrains/tiny-recursive-model
"https://www.researchgate.net/publication/397847259_Tiny_Recursive_Models_Achieving_State-of-the-Art_Performance_with_Deep_Supervision_on_CPhttps://ajolicoeur.wordpress.com/about/U-Only_Systems Preprint: "
"Tiny Recursive Models: Achieving State-of-the-Art Performance with Deep Supervision on CPU-Only Systems" H. Peter Alesso
- TRMlaptop GitHub Repository: https://github.com/alessoh/TRMlaptop
AI Hive
Friday, January 23, 2026
Monday, September 22, 2025
The Death and Rebirth of Book Discovery: Why Everything Changed When Readers Started Talking to Machines
In 2019, if you wanted a book recommendation, you had three choices: ask a friend, browse a bookstore, or search Amazon. By 2025, millions of readers have adopted a fourth option that's rapidly becoming the first: asking an AI assistant to understand their exact reading desire and synthesize perfect recommendations from the entire history of human discussion about books.
This shift represents more than a new marketing channel. It fundamentally changes the relationship between books and readers. When someone types "books about complicated grief that aren't depressing" into ChatGPT, they're not searching for keywords or filtering by categories. They're having a conversation about human experience, expecting intelligence rather than algorithms to respond.
The implications ripple through every assumption about book marketing. Traditional SEO taught us to optimize for keywords that readers might search. Amazon optimization focused on categories, also-boughts, and velocity-driven visibility. Both assume readers know what they're looking for and need help finding it. But AI-mediated discovery assumes readers know what they feel, what they need, what they wonder about—and want help translating those human experiences into specific books.
This isn't just about technology; it's about the evolution of how humans navigate infinite choice. The 100,000 books published each month create a paradox of abundance where having every option available makes choosing any single option overwhelming. AI assistants solve this by understanding context, synthesizing discussions, and matching books to readers based on actual reader experience rather than metadata.
Book Discovery: AI Optimization by H. Peter Alesso
In 2019, if you wanted a book recommendation, you had three choices: ask a friend, browse a bookstore, or search Amazon. By 2025, millions of readers have adopted a fourth option that's rapidly becoming the first: asking an AI assistant to understand their exact reading desire and synthesize perfect recommendations from the entire history of human discussion about books.
This shift represents more than a new marketing channel. It fundamentally changes the relationship between books and readers. When someone types "books about complicated grief that aren't depressing" into ChatGPT, they're not searching for keywords or filtering by categories. They're having a conversation about human experience, expecting intelligence rather than algorithms to respond.
The implications ripple through every assumption about book marketing. Traditional SEO taught us to optimize for keywords that readers might search. Amazon optimization focused on categories, also-boughts, and velocity-driven visibility. Both assume readers know what they're looking for and need help finding it. But AI-mediated discovery assumes readers know what they feel, what they need, what they wonder about—and want help translating those human experiences into specific books.
This isn't just about technology; it's about the evolution of how humans navigate infinite choice. The 100,000 books published each month create a paradox of abundance where having every option available makes choosing any single option overwhelming. AI assistants solve this by understanding context, synthesizing discussions, and matching books to readers based on actual reader experience rather than metadata.
Book Discovery: AI Optimization by H. Peter Alesso
Monday, June 3, 2024
The Cloud Computing Arms Race: A Comprehensive Analysis of Google, Amazon, and Microsoft's Data Center Infrastructure
Introduction
In the rapidly evolving landscape of cloud computing, three tech giants - Google, Amazon Web Services (AWS), and Microsoft Azure - have emerged as the dominant players, holding a significant share of the global data center market. As businesses and individuals increasingly rely on cloud services for their computing needs, these companies have invested heavily in building and maintaining state-of-the-art data center infrastructures to support the growing demand. This article presents a comprehensive analysis ofthese companies' data centers' estimated scale, costs, and computing powers, drawing upon the insights from three sources to provide a well-rounded perspective.
Data Center Footprint and Costs
Google, AWS, and Microsoft Azure have established extensive networks of data centers worldwide to ensure high availability, low latency, and reliable service delivery to their customers.
Google: Estimated to have 25-30 data centers globally as of 2023, with each facility costing between $600 million to $1.2 billion to build and maintain. Google's annual capital expenditure for data centers is approximately $10-15 billion.
AWS: Estimated to have 110-120 data centers worldwide as of 2023, with an annual capital expenditure of around $25-30 billion dedicated to data center infrastructure.
Microsoft Azure: Estimated to have 60-70 data centers globally as of 2023, with an annual capital expenditure of approximately $15-20 billion for data center infrastructure.
Server Count and Computational Power
The immense computational power of these cloud providers is driven by the vast number of servers housed within their data centers, along with specialized hardware such as GPUs and custom-designed accelerators.
Google: Estimated to have over 2 million servers worldwide, with larger data centers housing more than 100,000 servers each. Google's total computational power is estimated to be in the range of several exaFLOPS.
AWS: Believed to have the largest server fleet, with an estimated 5-6 million servers worldwide. AWS's computational power is likely in the exaFLOPS range, supporting a wide array of workloads.
Microsoft Azure: Estimated to have around 2-3 million servers worldwide, with a total computational power in the high petaFLOPS to low exaFLOPS range.
Specialized Hardware To cater to the growing demand for AI, machine learning, and high-performance computing workloads, Google, AWS, and Microsoft Azure have invested in specialized hardware such as GPUs, TPUs, and custom-designed accelerators.
Google: Utilizes both GPUs and custom-designed Tensor Processing Units (TPUs) extensively, with an estimated hundreds of thousands of each deployed across its data centers.
AWS: Leverages GPUs and custom-designed chips, such as the Graviton and Inferentia processors, with an estimated hundreds of thousands of GPUs and specialized chips in use.
Microsoft Azure: Employs GPUs and FPGAs to deliver high-performance computing and AI services, with an estimated hundreds of thousands of GPUs and tens of thousands of specialized chips, such as FPGAs for AI/ML workloads.
Market Share and Future Projections
As of 2023, AWS holds the largest share of the global data center market at approximately 32%, followed by Microsoft Azure at 21%, and Google Cloud at 10-12%. However, these market shares are subject to change as the companies continue to invest and innovate in their data center infrastructures. Looking ahead to 2024 and 2025, all three companies are expected to maintain strong growth and investment in their data center infrastructure:
Google: Projected annual growth of 10-15% in server count, 20-30% in computational capacity (FLOPS), and an estimated annual capital expenditure of $12-18 billion.
AWS: Projected annual growth of 15-20% in server count, 25-35% in computational capacity (FLOPS), and an estimated annual capital expenditure of $28-35 billion.
Microsoft Azure: Projected annual growth of 15-20% in server count, 25-35% in computational capacity (FLOPS), and an estimated annual capital expenditure of $18-25 billion.
Key Initiatives and Innovations
As the cloud computing arms race intensifies, Google, AWS, and Microsoft Azure are investing in various initiatives and innovations to stay ahead of the competition and cater to the evolving needs of their customers.
Google: Focusing on quantum computing, AI research and development, and sustainability initiatives such as renewable energy and carbon-neutral operations.
AWS: Developing custom silicon, expanding edge computing capabilities, and increasing investments in renewable energy and sustainable practices.
Microsoft Azure: Expanding hybrid cloud offerings, investing in AI and quantum computing research and infrastructure, and committing to carbon-negative operations by 2030.
Comparative Analysis
When comparing the data center infrastructures of Google, AWS, and Microsoft Azure, it is evident that AWS has the most extensive network of data centers and the largest server fleet, followed closely by Microsoft Azure and Google. All three companies invest heavily in their data center infrastructure, with annual capital expenditures ranging from $10 billion to $30 billion.
In terms of computational power, all three cloud providers operate in the exaFLOPS range, leveraging vast numbers of servers and specialized hardware to support the growing demand for high-performance computing, AI, and machine learning workloads.
While AWS currently holds the largest market share, Google and Microsoft Azure are rapidly expanding their presence and investing in innovative technologies to bridge the gap. As the cloud computing market continues to grow and evolve, these companies are expected to maintain their strong growth trajectories and continue investing heavily in their data center infrastructures.
Evaluation and research into AI areas is available at AI Hive
Introduction
In the rapidly evolving landscape of cloud computing, three tech giants - Google, Amazon Web Services (AWS), and Microsoft Azure - have emerged as the dominant players, holding a significant share of the global data center market. As businesses and individuals increasingly rely on cloud services for their computing needs, these companies have invested heavily in building and maintaining state-of-the-art data center infrastructures to support the growing demand. This article presents a comprehensive analysis ofthese companies' data centers' estimated scale, costs, and computing powers, drawing upon the insights from three sources to provide a well-rounded perspective.
Data Center Footprint and Costs
Google, AWS, and Microsoft Azure have established extensive networks of data centers worldwide to ensure high availability, low latency, and reliable service delivery to their customers.
Google: Estimated to have 25-30 data centers globally as of 2023, with each facility costing between $600 million to $1.2 billion to build and maintain. Google's annual capital expenditure for data centers is approximately $10-15 billion.
AWS: Estimated to have 110-120 data centers worldwide as of 2023, with an annual capital expenditure of around $25-30 billion dedicated to data center infrastructure.
Microsoft Azure: Estimated to have 60-70 data centers globally as of 2023, with an annual capital expenditure of approximately $15-20 billion for data center infrastructure.
Server Count and Computational Power
The immense computational power of these cloud providers is driven by the vast number of servers housed within their data centers, along with specialized hardware such as GPUs and custom-designed accelerators.
Google: Estimated to have over 2 million servers worldwide, with larger data centers housing more than 100,000 servers each. Google's total computational power is estimated to be in the range of several exaFLOPS.
AWS: Believed to have the largest server fleet, with an estimated 5-6 million servers worldwide. AWS's computational power is likely in the exaFLOPS range, supporting a wide array of workloads.
Microsoft Azure: Estimated to have around 2-3 million servers worldwide, with a total computational power in the high petaFLOPS to low exaFLOPS range.
Specialized Hardware To cater to the growing demand for AI, machine learning, and high-performance computing workloads, Google, AWS, and Microsoft Azure have invested in specialized hardware such as GPUs, TPUs, and custom-designed accelerators.
Google: Utilizes both GPUs and custom-designed Tensor Processing Units (TPUs) extensively, with an estimated hundreds of thousands of each deployed across its data centers.
AWS: Leverages GPUs and custom-designed chips, such as the Graviton and Inferentia processors, with an estimated hundreds of thousands of GPUs and specialized chips in use.
Microsoft Azure: Employs GPUs and FPGAs to deliver high-performance computing and AI services, with an estimated hundreds of thousands of GPUs and tens of thousands of specialized chips, such as FPGAs for AI/ML workloads.
Market Share and Future Projections
As of 2023, AWS holds the largest share of the global data center market at approximately 32%, followed by Microsoft Azure at 21%, and Google Cloud at 10-12%. However, these market shares are subject to change as the companies continue to invest and innovate in their data center infrastructures. Looking ahead to 2024 and 2025, all three companies are expected to maintain strong growth and investment in their data center infrastructure:
Google: Projected annual growth of 10-15% in server count, 20-30% in computational capacity (FLOPS), and an estimated annual capital expenditure of $12-18 billion.
AWS: Projected annual growth of 15-20% in server count, 25-35% in computational capacity (FLOPS), and an estimated annual capital expenditure of $28-35 billion.
Microsoft Azure: Projected annual growth of 15-20% in server count, 25-35% in computational capacity (FLOPS), and an estimated annual capital expenditure of $18-25 billion.
Key Initiatives and Innovations
As the cloud computing arms race intensifies, Google, AWS, and Microsoft Azure are investing in various initiatives and innovations to stay ahead of the competition and cater to the evolving needs of their customers.
Google: Focusing on quantum computing, AI research and development, and sustainability initiatives such as renewable energy and carbon-neutral operations.
AWS: Developing custom silicon, expanding edge computing capabilities, and increasing investments in renewable energy and sustainable practices.
Microsoft Azure: Expanding hybrid cloud offerings, investing in AI and quantum computing research and infrastructure, and committing to carbon-negative operations by 2030.
Comparative Analysis
When comparing the data center infrastructures of Google, AWS, and Microsoft Azure, it is evident that AWS has the most extensive network of data centers and the largest server fleet, followed closely by Microsoft Azure and Google. All three companies invest heavily in their data center infrastructure, with annual capital expenditures ranging from $10 billion to $30 billion.
In terms of computational power, all three cloud providers operate in the exaFLOPS range, leveraging vast numbers of servers and specialized hardware to support the growing demand for high-performance computing, AI, and machine learning workloads.
While AWS currently holds the largest market share, Google and Microsoft Azure are rapidly expanding their presence and investing in innovative technologies to bridge the gap. As the cloud computing market continues to grow and evolve, these companies are expected to maintain their strong growth trajectories and continue investing heavily in their data center infrastructures.
Evaluation and research into AI areas is available at AI Hive
Monday, July 10, 2023
Important Meta AI Research Projects
Meta AI is one of the world's leading research organizations in artificial intelligence. It has been at the forefront of research into large language models (LLMs). LLMs are a type of AI that can process and understand large amounts of text data. Meta AI has developed several LLMs, including GPT-3, Jurassic-1 Jumbo, and Blenderbot.
The goals of Meta AI's LLM research is to develop AI that can understand and generate human language more naturally. LLMs have been shown to be able to generate realistic and coherent text, and they have been used to create a variety of applications, such as chatbots, text generators, and translation tools.
Another goal of Meta AI's LLM research is to develop AI that can be used to solve real-world problems. LLMs have been used to improve the performance of search engines, to generate realistic dialogue for virtual assistants, and to create more engaging content for social media. Meta AI is also doing important research in the field of computer vision, the field of AI that deals with the interaction between computers and the physical world. Meta AI has developed a number of computer vision models, including Detectron2, Mask R-CNN, and StyleGAN.
One of the goals of Meta AI's computer vision research is to develop AI that can see and understand the world in a more natural way. Computer vision models have been shown to be able to identify objects, track people, and understand scenes.
Another goal of Meta AI's computer vision research is to develop AI that can be used to solve real-world problems. Computer vision models have been used to improve the performance of self-driving cars, to create augmented reality applications, and to develop new medical imaging tools.
Comparison with Google AI
Both Meta AI and Google AI are leading research organizations in the field of artificial intelligence. Both teams are working on a wide range of projects, and they are both making significant progress.
One of the key differences between Meta AI and Google AI is their focus. Meta AI is focused on developing AI that can be used to solve real-world problems. Google AI is focused on developing new AI technologies.
Conclusion
Meta AI and Google AI are both leading research organizations in the field of artificial intelligence. Both teams are making significant progress, and they are both having a major impact on the field. Evaluation and research into AI areas is available at AI Hive
The goals of Meta AI's LLM research is to develop AI that can understand and generate human language more naturally. LLMs have been shown to be able to generate realistic and coherent text, and they have been used to create a variety of applications, such as chatbots, text generators, and translation tools.
Another goal of Meta AI's LLM research is to develop AI that can be used to solve real-world problems. LLMs have been used to improve the performance of search engines, to generate realistic dialogue for virtual assistants, and to create more engaging content for social media. Meta AI is also doing important research in the field of computer vision, the field of AI that deals with the interaction between computers and the physical world. Meta AI has developed a number of computer vision models, including Detectron2, Mask R-CNN, and StyleGAN.
One of the goals of Meta AI's computer vision research is to develop AI that can see and understand the world in a more natural way. Computer vision models have been shown to be able to identify objects, track people, and understand scenes.
Another goal of Meta AI's computer vision research is to develop AI that can be used to solve real-world problems. Computer vision models have been used to improve the performance of self-driving cars, to create augmented reality applications, and to develop new medical imaging tools.
Comparison with Google AI
Both Meta AI and Google AI are leading research organizations in the field of artificial intelligence. Both teams are working on a wide range of projects, and they are both making significant progress.
One of the key differences between Meta AI and Google AI is their focus. Meta AI is focused on developing AI that can be used to solve real-world problems. Google AI is focused on developing new AI technologies.
Conclusion
Meta AI and Google AI are both leading research organizations in the field of artificial intelligence. Both teams are making significant progress, and they are both having a major impact on the field. Evaluation and research into AI areas is available at AI Hive
Google AI Research Projects
Google AI is constantly working on new and innovative ways to apply AI to real-world problems.
Gemini is an open source large language model (LLM) trained on a massive dataset of text and code, and it can generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way to improve the accuracy of Google Translate or to generate more engaging content for Google Search.
Bert is specifically designed for natural language processing (NLP) tasks used for question answering, sentiment analysis, and text summarization.
Bard is similar to Gemini, but it is specifically designed for dialogue tasks, such as customer service, chatbots, and virtual assistants.
Lambda is a type of AI that can learn to perform tasks by observing human demonstrations. This means that Lambdas can learn to do things that they have never been explicitly programmed to do.
Palm 2 can understand and reason about the physical world. It can understand the properties of objects, the relationships between objects, and the actions that can be performed on objects. Palm 2 can be used to help people with tasks such as cooking, cleaning, and organizing.
Evaluation and research into AI areas is available at AI Hive
Gemini is an open source large language model (LLM) trained on a massive dataset of text and code, and it can generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way to improve the accuracy of Google Translate or to generate more engaging content for Google Search.
Bert is specifically designed for natural language processing (NLP) tasks used for question answering, sentiment analysis, and text summarization.
Bard is similar to Gemini, but it is specifically designed for dialogue tasks, such as customer service, chatbots, and virtual assistants.
Lambda is a type of AI that can learn to perform tasks by observing human demonstrations. This means that Lambdas can learn to do things that they have never been explicitly programmed to do.
Palm 2 can understand and reason about the physical world. It can understand the properties of objects, the relationships between objects, and the actions that can be performed on objects. Palm 2 can be used to help people with tasks such as cooking, cleaning, and organizing.
Evaluation and research into AI areas is available at AI Hive
Wednesday, July 5, 2023
Point on Differences between ChatGPT and Google
It is important to note that ChatGPT and Google Search serve different purposes and are not directly competing with each other. ChatGPT is a variant of the GPT-3 language model developed by OpenAI, which is designed to generate human-like text and engage in conversation. Google Search, on the other hand, is a search engine that helps users find information on the internet by displaying relevant websites and documents in response to a search query.
It is also worth noting that GPT-3 and its variants, including ChatGPT, have the potential to be used in a wide range of applications beyond search. These applications could include natural language processing, machine translation, summarization, and more. As AI technology continues to advance, it will be interesting to see how ChatGPT and other language models are used in the future. Evaluation and research into AI areas is available at AI Hive
It is also worth noting that GPT-3 and its variants, including ChatGPT, have the potential to be used in a wide range of applications beyond search. These applications could include natural language processing, machine translation, summarization, and more. As AI technology continues to advance, it will be interesting to see how ChatGPT and other language models are used in the future. Evaluation and research into AI areas is available at AI Hive
Saturday, June 17, 2023
Creating new specialized AI communities
Many new AI communities are emerging to support the broad and diverse interest in AI. AI Hive is an AI community for novices who are developing AI skills. This platform seeks to become a one-stop destination for the latest news, thought-provoking articles, and a range of tutorials on AI subjects. AI Hive seeks to help newcomers stay abreast of the fast-evolving AI landscape. It’s possible that other emerging communities will splinter into specialties.
Additionally, new AI videosoftware is being produced. Video Software Lab ALso, new AI writers are emerging. H. Peter Alesso What other new communities are out there?
Additionally, new AI videosoftware is being produced. Video Software Lab ALso, new AI writers are emerging. H. Peter Alesso What other new communities are out there?
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