In this issue, we dig into the question of how AI affects expertise and skills: how AI affects mastering a complex field like coding, preserving human expertise in the age of AI, and a conversation with Matt Beane about his book, The Skill Code.
AI's impact on skill development: Balancing productivity gains with long-term expertise. Explore how AI changes apprenticeships, challenges traditional learning, and affects industries. Learn strategies to integrate AI while preserving crucial human skills and mentorship.
A conversation with UC Santa Barbara Assistant Professor Matt Beane about his book "The Skill Code: How to Save Human Ability in an Age of Intelligent Machines."
Research suggests that LLMs are not demonstrating genuine reasoning abilities but are instead relying on pattern matching and retrieval based on the provided examples. We're still a ways off reliable performance of LLMs in reasoning and decision-making tasks.
LLMs are great at coming up with approximate knowledge and ideas for potential plans. But to actually use those ideas, you need to pair the LLM with external programs that can rigorously check the plans for errors. The key is to use them as part of a bigger system.
Create by mixing modes, varying both inputs and outputs, and perspectives. Part 6 in our How to Use Generative AI series.
Graph RAG doesn't necessarily replace knowledge graphs but can serve as a complementary tool, especially in scenarios where rapid, scalable, and dynamic summarization of large unstructured datasets is required.
This research from Anthropic on the inner workings of Claude constitutes a major milestone in AI interpretability. It's a first glimpse into the mind of an alien intelligence, one that we've created but are only beginning to understand.
An interview with Māori Data Sovereignty expert, Karaitiana Taiuru about indigenous peoples and AI.
Weave by Levering the Technology's Combinatorial Power. Part 5 in our How to Use Generative AI series.
We are witnessing the emergence of agentic and ubiquitous AI systems that will reshape the digital world. We will see vastly more machine content than human content: nothing will be comprehensible without a machine interpreting it for us. By machines, for machines is the new paradigm.
A recent paper entitled The Platonic Representation Hypothesis supports the idea that widespread use of singular algorithms might actually be simplifying hte world. Large-scale AI models, such as GPT-4, are all starting to learn the same representations of human digitized reality.
Join the conversation as we discuss the significance of Google's new Gemini, Project Astra, TPUs, and the implications across security and education.
Join the conversation as we explore the implications of ChatGPT4o's new features, data gathering, emotionally engaging interactions, and the potential risks associated with emotional recognition technology.
I believe that accountability becomes more pronounced in the era of AI. While the accountability dynamics between humans and machines may differ, humans will invariably assert that if someone had access to such advanced AI capabilities, they had the means to make more informed decisions.
The Artificiality Weekend Briefing: About AI, Not Written by AI
