Lifeboat Foundation

OpenAI’s “next frontier model” is expected to replace the GPT-4 model powering ChatGPT.

LoGAH: Predicting 774-Million-Parameter Transformers using Graph HyperNetworks with 1/100 Parameters.

https://huggingface.co/papers/2405.

A good initialization of deep learning models is essential since it can help them converge better and faster.

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Synthetic biology has been game-changing and with generative artificial intelligence, generative biology holds immense potential; let’s just speed it up.

How can rapidly emerging #AI develop into a trustworthy, equitable force? Proactive policies and smart governance, says Salesforce.

These initial steps ignited AI policy conversations amid the acceleration of innovation and technological change. Just as personal computing democratized internet access and coding accessibility, fueling more technology creation, AI is the latest catalyst poised to unlock future innovations at an unprecedented pace. But with such powerful capabilities comes large responsibility: We must prioritize policies that allow us to harness its power while protecting against harm. To do so effectively, we must acknowledge and address the differences between enterprise and consumer AI.

Enterprise versus consumer AI

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At the South Korean tech giant’s headquarters, people can get a unique Starbucks experience with 100 robots serving customers daily.

This bio-inspired robot, with passive grippers, climbs rocks and walls effortlessly.

Researchers create a bio-inspired robot with passive grippers and optimized climbing ability for rough rocks and cinder block walls.

Mann, J., Meshkin, H., Zirkle, J. et al. Mechanism-based organization of neural networks to emulate systems biology and pharmacology models. Sci Rep 14, 12,082 (2024). https://doi.org/10.1038/s41598-024-59378-9

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Researchers at the Princeton Plasma Physics Laboratory are harnessing artificial intelligence and machine learning to enhance fusion energy production, tackling the challenge of controlling plasma reactions. Their innovations include optimizing the design and operation of containment vessels and using AI to predict and manage instabilities, significantly improving the safety and efficiency of fusion reactions. This technology has been successfully applied in tokamak reactors, advancing the field towards viable commercial fusion energy. Credit: SciTechDaily.com.

The intricate dance of atoms fusing and releasing energy has fascinated scientists for decades. Now, human ingenuity and artificial intelligence are coming together at the U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL) to solve one of humankind’s most pressing issues: generating clean, reliable energy from fusing plasma.

Unlike traditional computer code, machine learning — a type of artificially intelligent software — isn’t simply a list of instructions. Machine learning is software that can analyze data, infer relationships between features, learn from this new knowledge, and adapt. PPPL researchers believe this ability to learn and adapt could improve their control over fusion reactions in various ways. This includes perfecting the design of vessels surrounding the super-hot plasma, optimizing heating methods, and maintaining stable control of the reaction for increasingly long periods.

Less than two weeks after Google introduced “AI Overview” in its search engine, the feature is facing public criticism due to ‘nonsensical and inaccurate’ responses without an option for users to opt-out. Social media users have highlighted numerous instances where the tool has given incorrect, even controversial answers. Why is this happening?