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The illusion of AI consciousness: why gpt-4o and other chatbots are not conscious.

• Shannon Vallor, an AI expert and contributor to DeepMind, discusses the latest developments in generative AI, particularly OpenAI’s GPT-4o model, and warns of the dangers of the illusion of artificial consciousness.


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.


Join the discussion on this paper page.

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

Salesforce has been actively researching and developing AI since 2014, introduced our first AI functionalities into our products in 2016, and established our office of ethical and human use of technology in 2018. Trust is our top value. That’s why our AI offerings are founded on trust, security and ethics. Like many technologies, there’s more than one use for AI. Many people are already familiar with large language models (LLMs) via consumer-facing apps like ChatGPT. Salesforce is leading the development of AI tools for businesses, and our approach differentiates between consumer-grade LLMs and what we classify as enterprise AI.

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?