Latest in Artificial Intelligence
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Understanding the 4 Main Approaches to LLM Evaluation (From Scratch)
The article outlines four primary methods for evaluating Large Language Models: multiple-choice benchmarks, verifiers, leaderboards, and LLM judges, each with distinct advantages for assessing model performance. These evaluation approaches range from standardized testing frameworks to using other LLMs as judges, providing different perspectives on model capabilities. The piece includes code examples to illustrate how each evaluation method works in practice.
A guide to understanding AI as normal technology
And a big change for this newsletter
Understanding and Implementing Qwen3 From Scratch
A Detailed Look at One of the Leading Open-Source LLMs
From GPT-2 to gpt-oss: Analyzing the Architectural Advances
And How They Stack Up Against Qwen3
Could AI slow science?
Confronting the production-progress paradox
AGI is not a milestone
The article argues that Artificial General Intelligence (AGI) should not be viewed as a discrete milestone with a clear threshold, but rather as a gradual continuum of increasing capabilities. Rather than a sudden breakthrough moment, AGI development will likely involve incremental improvements that create impacts over time, challenging the common narrative of a singular transformative event.
AI as Normal Technology
A new paper that we will expand into our next book
Is AI progress slowing down?
Making sense of recent technology trends and claims
We Looked at 78 Election Deepfakes. Political Misinformation is not an AI Problem.
Researchers examined 78 election-related deepfakes and concluded that the proliferation of political misinformation stems from deliberate human choices rather than technological capability alone. The study suggests that addressing election misinformation requires focusing on human intent and media literacy rather than treating it purely as an AI problem.
Does the UK’s liver transplant matching algorithm systematically exclude younger patients?
A UK liver transplant matching algorithm may be systematically disadvantaging younger patients due to seemingly minor technical design choices in how it prioritizes recipients. The article examines how algorithmic decisions that appear neutral can actually have significant real-world consequences for patient outcomes and fairness. This raises important questions about the need for transparency and equity audits in medical algorithms.
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