Following on from Part 1 of this series on AGI which set out the need for an AI systems's ability to build comprehensive abstractions, this post reviews different levels of abstraction and how the strengths of LLMs and symbolic AI could be combined into even more capable systems than we have today, that are not only amazing at approximating but also capable of precise, step-by-step reasoning. https://open.substack.com/pub/bockster/p/abstracting-fast-and-slow?r=ionsk&utm_campaign=post&utm_medium=web

Quite interesting coverage of Nvidia's hardware choices: "Instead of one big piece of silicon, Blackwell consists of two advanced new Nvidia processors and numerous memory components joined in a single, delicate mesh of silicon, metal and plastic. The manufacturing of each chip has to be close to perfect: Serious defects in any one part can spell disaster, and with more components involved, there is a greater chance of that happening. What’s more, the heat generated by all those pieces risks warping different materials in the package at different rates." https://www.wsj.com/tech/nvidias-future-relies-on-chips-that-push-technologys-limits-bd3839fc

Generative AI agents are transforming industries by mimicking human behavior in interactive and believable ways. Here are the key points: 1. Definition and Functionality: These agents can observe, act, retrieve, reflect and interact with others, enhancing customer service, healthcare and cybersecurity. 2. Impact on Customer Service: They personalise interactions, remember past conversations, and anticipate needs, improving service quality and productivity. 3. Applications and Challenges: Data quality issues and employee distrust are important to consider. 4. Future Potential: As technology advances, these agents will automate processes and augment human capabilities, potentially transforming work across various industries.

You probably have seen / read about the control centres that direct self-driving cars in critical situations. What surprised me that in some cases this number is 1.5 ppl per car. more than if these taxis has drivers. Maybe its just the cost of advancing technology, but its certainly a good illustration of some of the problems that continue to plague AI technology: dealing with out-of-distribution events. https://www.nytimes.com/interactive/2024/09/03/technology/zoox-self-driving-cars-remote-control.html PS: is it fair to put this under 'How it works'? 😅

GPT-4 passed the Bar Exam and the SAT, and AI models crush benchmarks with every new release. All, but one: on ARC-AGI you would score 5-10x as well as your favourite AI model. You can actually try it and might be surprise how current AI models could struggle with it as much as they do. To see how ARC-AGI is different and find out why there is a US$1m prize on cracking it, head to Guaranteed Dissent! https://open.substack.com/pub/bockster/p/arc-agi-one-to-rule-them-all?r=ionsk&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true