John Oliver, succinctly sums up the state of where we are

It's often the case that more sense is spoken in satire than in countless papers and column inches. A case in point is John Oliver summing up where we are at with the AI hype. It is a well paced piece, getting into the heart of the many issues faced after around 15 minutes. The TayTweets example was both funny and alarming, showing that AI can easily fall for populism/fascism. This is going to be an issue with alignment, as whose values will alignment actually embrace? Human values are still a contested topic, and will remain so, giving national interests. The three laws of robotics will not suffice.

'The right to human review of decisions made fully by computers should not be removed ‘while AI is still in its infancy’, the professional body for IT has warned. Regulation needs to go far beyond this.

I've noted that GPT5 is now going to be an incremental roll out, so by the end of this year, or early next year GPT 4.2, rather than 5 may be rolled out, giving rise to the '6 months freeze' as has been called for, in appearance at least. We should all know from experience that 6 months is akin to mere minutes given the pace of reactive legislation.

An added bonus, but this is about Sydney:

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The AI Dilemma and "Gollem-Class" AIs

From the Center for Humane Technology Tristan Harris and Aza Raskin discuss how existing A.I. capabilities already pose catastrophic risks to a functional society, how A.I. companies are caught in a race to deploy as quickly as possible without adequate safety measures, and what it would mean to upgrade our institutions to a post-A.I. world. This presentation is from a private gathering in San Francisco on March 9th with leading technologists and decision-makers with the ability to influence the future of large-language model A.I.s. This presentation was given before the launch of GPT-4. One of the more astute critics of the tech industry, Tristan Harris, who has recently given stark evidence to Congress. It is worth watching both of these videos, as the Congress address gives a context of PR industry and it's regular abuses. "If we understand the mechanisms and motives of the group mind, it is now possible to control and regiment the masses according to our will without their

Beware the Orca, the challenge to ChatGPT and Palm2 is here

So Google's 'we have no moat' paper was correct. If you train an LLM wisely then it's cost effective and cheap to produce a small LLM that is able to compete or even beat established, costly LLMs, as Microsoft has just found. It's another excellent video from AI Explained, who goes through some of the training procedures, which I won't get into here. Orca, is a model that learns from large foundation models (LFMs) like GPT-4 and ChatGPT by imitating their reasoning process. Orca uses rich signals such as explanations and complex instructions to improve its performance on various tasks. Orca outperforms other instruction-tuned models and achieves similar results to ChatGPT on zero-shot reasoning benchmarks and professional and academic exams. The paper suggests that learning from explanations is a promising way to enhance model skills. Smaller models are often overestimated in their abilities compared to LFMs, and need more rigorous evaluation methods. Explana

What is happening inside of the black box?

Neel Nanda is involved in Mechanistic Interpretability research at DeepMind, formerly of AnthropicAI, what's fascinating about the research conducted by Nanda is he gets to peer into the Black Box to figure out how different types of AI models work. Anyone concerned with AI should understand how important this is. In this video Nanda discusses some of his findings, including 'induction heads', which turn out to have some vital properties. Induction heads are a type of attention head that allows a language model to learn long-range dependencies in text. They do this by using a simple algorithm to complete token sequences like [A][B] ... [A] -> [B]. For example, if a model is given the sequence "The cat sat on the mat," it can use induction heads to predict that the word "mat" will be followed by the word "the". Induction heads were first discovered in 2022 by a team of researchers at OpenAI. They found that induction heads were present in

Charting the emergence of AGI?

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