QFM025: Machine Intelligence Reading List - July 2024

Source: Photo by Milad Fakurian on Unsplash

This month's Machine Intelligence Reading List delves into the evolving capabilities and challenges of AI, focusing on reasoning, data availability, ethical considerations, and economic impact.

Multi-step reasoning and efficiency are explored in articles like Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning and Language Models on the Command-line. Both articles highlight advancements in making large language models (LLMs) more efficient and accessible. The Q* framework introduces a novel approach to enhancing LLMs' reasoning abilities through deliberative planning, offering a plug-and-play solution that outperforms traditional methods without requiring extensive fine-tuning. Similarly, Simon Willison's utility 'LLM' showcases how command-line access can simplify interaction with these models, making sophisticated AI tools more user-friendly for developers and data scientists.

The theme of building digital minds is explored through A Model of a Mind and OpenAI working on new reasoning technology under code name 'Strawberry'. Tyler Neylon presents a conceptual framework for constructing digital minds inspired by human cognition, emphasizing elements like agency and introspection. Meanwhile, OpenAI's "Strawberry" project focuses on enhancing AI's ability to autonomously navigate complex tasks, pushing the boundaries of AI reasoning capabilities toward human-like intelligence. Both articles explore how AI can mimic human thought processes, each offering a unique perspective on the future of AI development.

Data availability becomes a significant concern in Will We Run Out of Data? Limits of LLM Scaling Based on Human-Generated Data and the General Theory of Neural Networks. The former article discusses the potential scarcity of human-generated data for training LLMs and explores strategies to optimize existing data resources, projecting when we might exhaust available text data. Rob Leclerc's discussion on Universal Activation Networks (UANs) adds to this by examining how network topology can contribute to more efficient AI models, thereby addressing some challenges of data limitations through innovative network design.

The ethical and economic implications of AI are explored in SITUATIONAL AWARENESS - The Decade Ahead and Gen AI: too much spend, too little benefit?. Ashenbrenner emphasizes the need for secure alignment of AI technologies to prevent misuse, underlining the international coordination necessary to manage superintelligence safely. In contrast, the Goldman Sachs article questions whether the hefty investments in generative AI will yield substantial returns, highlighting concerns about chip shortages and infrastructure strains. These articles address the dual challenges of managing AI's ethical risks and assessing its economic viability.

Finally, improving AI performance is a common theme in Non-Obvious Prompt Engineering Guide and Overcoming the limits of current LLM. Both pieces emphasize strategies for enhancing AI's effectiveness, with the former focusing on prompt engineering to guide LLM behavior and the latter exploring methods to tackle limitations such as hallucinations and lack of confidence estimates. These articles offer practical insights into overcoming the current challenges faced by AI models, ensuring they become more reliable and robust in various applications.

As always, the Quantum Fax Machine Propellor Hat Key will guide your browsing. Enjoy!

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Originally published on quantumfaxmachine.com. Also cross-published on Medium.