NVIDIA, they are changing everything, especially manufacturing

Anyone that's been following the AI race will know that there's one clear winner so far, NVIDIA. They are currently more or less unopposed in producing hardware for AI and that's not all, their software is rather sophisticated to. NVIDIA have just released this video. If you want to understand where AI is heading over the next year, it's well worth a close watch.

In five years we improved computer Graphics by 1,000 times in five years using artificial intelligence and accelerated Computing Moore's Law is probably currently running at about two times a thousand times in five years a thousand times. In five years is one million times in ten we're doing the same thing in artificial intelligence now question is what can you do when your computer is one million times faster.'

It's pretty outstanding, and it's far from just graphics, NVIDIA gives us a glimpse of what a MultiModal experience is like, and what will shortly be commonplace. Every factory in the future will be 'digital', will have AI and robotic capability, it's claimed.

The important thing is this we now have a software capability to learn the structure of almost any information we can learn the structure of text sound images there's structure in all of this physics proteins, DNA, chemicals. Anything that has structure we can learn that language and then the next breakthrough came generative AI once you can learn the language once you can learn the language of certain information.

Comments

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?

Search This Blog