On October 9, 2024, the Royal Swedish Academy of Sciences decided to award half of the 2024 Nobel Prize in Chemistry to Demis Hassabis and John Jumper for their development of AlphaFold2 in 2020, a model capable of predicting the structure of almost all 200 million proteins discovered by researchers. Here is the official scientific background: They have revealed proteins’ secrets through […]
Traditional biological research, often characterized by labor-intensive experiments, struggles to reveal the intricate mechanisms behind protein folding and function. The advent of large neural networks offers a transformative approach by uncovering hidden patterns and making accurate predictions, particularly in protein biology—life’s fundamental code. Biology is fundamentally programmable. Every living organism shares the same genetic code
Revolutionizing Protein Research with ESM3 Read More »
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Currently, models like OpenAI’s GPT, Anthropic’s Claude, and Meta AI’s LLaMA have achieved over 90% accuracy on the GSM8K dataset. But how do they accomplish this? Is it through memorization of data and problems, or do they truly understand the content of the questions? GSM8K, short for “Grade School Math 8K,” comprises 8,000 math problems
Grade-School Math and the Hidden Reasoning Process Read More »
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Have you ever wondered how AI tools like ChatGPT, powered by large language models (LLMs), manage to answer nearly any question posed by users, especially in open-domain queries that require extensive knowledge or up-to-date facts? Relying solely on traditional LLMs to generate answers can be incredibly challenging. Here’s why: 1. Knowledge Limitations: LLMs are trained
Over the past year, the explosion of generative AI has flooded the world with AI-generated content. As we move forward, training future models on this machine-generated data seems inevitable. A recent study explores the potential issues when large language models are trained using data generated by other models. The researchers found that as training progresses
Averting Model Collapse: The Importance of Human Data in AI Training Read More »
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As Large Language Models (LLMs) continue to evolve, their increasing complexity has brought a new set of challenges. One significant issue is the generation of outputs that are often vague, ambiguous, or logically inconsistent. These issues make it difficult for users to interpret and trust the AI’s reasoning. In response, OpenAI has introduced a novel
Enhancing AI Output: Understanding Prover-Verifier Games Read More »
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This research paper shows that TTT-Linear outperforms Mamba and Transformer in handling contexts as long as 32k. (See the card below) A self-supervised loss function on each test sequence reduces the likelihood of information forgetting in long sequences. Will the Test-Time Training(TTT) solve the problem of forgetting information in long sequences? As for the algorithm:
Test-Time Training — Is It an Alternative to Transformer? Read More »
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