About this Blog, why?

Introduction and Welcome

I began writing this blog on the April 8th, 2003. I say that, but it's inaccurate. What I did, in fact was start a series of 'prompts' in ChatGPT via the Edge Browser to generate text, also in Dall-E image creator to formulate the graphics. I only began my own content the day after, here. The examples of Tools I've used and Speculations about how certain tasks will change is far from exclusive. For instance I will not comment on the medical implications of AI, though fascinating (as GPT4 has passed medical exams - without taking a hypocritic oath) but instead confine myself to tasks I have had to undertake in my recent work career.

So welcome, this is a simple blog that aims to document the next 6 months of some of the AI occurrences, tools, news and opinions to document:

Will the legislators respond to the call for a 6 month halt to a ChatGPT5 occur?
Given the unprecedented speed of development, just 4 months ago ChatGPT could only muster a 40% pass rate at a bar exam and now it is passing at a 90% rate, what will happen in this next crucial time period.

There was a time, at the beginning of the 1980's and throughout the 1990's where I could, with a fair amount of accuracy, predict the development of IT, as it was then known, out to an 18 month or so period into the future. This gap became increasingly smaller circa 1997. I have still retained an interest, primarily as a user and low level developer of communication technologies. I write simple code for websites, CRM's and DAMs and customise them to suit a purpose. I began with code for typesetting in the late 1970's.

All of this may seem that my views are perhaps archaic, the fact is that if people haven't studied what's happened last week, their views too are soon to be archaic. Where it once took 6 -18 months for major breakthroughs in technological applications to occur, it is now weeks, and very soon it will be counted in days, such is the transformative growth we are witnessing.

So, I aim to complete this blog in October. I have no idea how much time I can spend upon it yet, but I do hope that the content I begin with will provide some utility.

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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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