The hottest Computational Theory Substack posts right now

And their main takeaways
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⚡✨🕸️ The computational power of complex societies: A Quick Q&A with … physicist and computer scientist David Wolpert

Faster, Please! • 1188 implied HN points • 08 Oct 24
🔬 Science Computational Theory Energy Systems Information processing
  1. Societies grow in size and complexity when they get better at using energy and processing information. More energy and better information help societies do more things and support more people.
  2. Job specialization plays a key role in a society's complexity. When people focus on different jobs and communicate well, it allows for innovation and better organization.
  3. Viewing societies as computers can help us understand how they evolve over time. It highlights how energy use and information processing are closely linked in driving societal growth.

Not All Layers Are Equal

Gonzo ML • 63 implied HN points • 31 Jan 25
🕹 Technology AI Research Machine Learning Data science Neural Networks Computational Theory
  1. Not every layer in a neural network is equally important. Some layers play a bigger role in getting the right results, while others have less impact.
  2. Studying how information travels through different layers can reveal interesting patterns. It turns out layers often work together to make sense of data, rather than just acting alone.
  3. Using methods like mechanistic interpretability can help us understand neural networks better. By looking closely at what's happening inside the model, we can learn which parts are doing what.

What if AI can already 'feel'?

State of the Future • 42 implied HN points • 23 Apr 25
🕹 Technology AI Machine Learning Robotics Computational Theory Digital ethics
  1. AI already has its own kind of 'body' based on digital processes, not physical sensations. This means that AI can experience things and develop understanding in ways that are different from humans.
  2. Wisdom isn't just about human experience; it's a set of skills that involves making good decisions from the information available. AI can potentially do this better by analyzing vast amounts of data without the limitations humans have.
  3. AI might create its own social hierarchies and status signals based on how efficiently they operate in their digital environment. These structures could be complex and different from human social dynamics, and we might not even notice them.

Intelligence and combinatorial complexity

Sunday Letters • 39 implied HN points • 27 Aug 23
🕹 Technology AI Research Machine Learning Cognitive Science Computational Theory Human-computer interaction
  1. More agents working together can create better intelligence than a single agent. This is surprising because we might think one advanced model is enough, but collaboration can enhance performance.
  2. Human-like patterns help improve AI performance. Just as we can review our work for errors, AI systems can use different modes to refine their outputs.
  3. Complex systems come with challenges like errors and biases. As AI gets more complicated, these issues tend to increase, similar to problems found in complex biological systems.

Claiming LLMs are merely "next token predictors" is a fundamental misunderstanding

The Future of Life • 19 implied HN points • 18 Jan 24
🕹 Technology AI Machine Learning Neural Networks Algorithms Computational Theory
  1. LLMs are more than just next-token predictors. They use complex internal algorithms that let them understand and create language beyond simple predictions.
  2. The process that powers LLMs, like token prediction, is just a tool that leads to their true capabilities. These systems can evolve and learn in many sophisticated ways.
  3. Understanding LLMs isn't easy because their full potential is still a mystery. What limits them could be anything from their training methods to the data they learn from.
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