The hottest Computational Models Substack posts right now

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A Visual Guide to Mixture of Experts (MoE)

Exploring Language Models • 3289 implied HN points • 07 Oct 24
🕹 Technology Artificial Intelligence Machine Learning Data science Neural Networks Computational Models
  1. Mixture of Experts (MoE) uses multiple smaller models, called experts, to help improve the performance of large language models. This way, only the most relevant experts are chosen to handle specific tasks.
  2. A router or gate network decides which experts are best for each input. This selection process makes the model more efficient by activating only the necessary parts of the system.
  3. Load balancing is critical in MoE because it ensures all experts are trained equally, preventing any one expert from becoming too dominant. This helps the model to learn better and work faster.

LLMs and World Models, Part 1

AI: A Guide for Thinking Humans • 247 implied HN points • 13 Feb 25
🕹 Technology AI Machine Learning Neural Networks Natural Language Processing Computational Models
  1. In the past, AI systems often used shortcuts to solve problems rather than truly understanding concepts. This led to unreliable performance in different situations.
  2. Today’s large language models are debated to either have learned complex world models or just rely on memorizing and retrieving data from their training. There’s no clear agreement on how they think.
  3. A 'world model' helps systems understand and predict real-world behaviors. Different types of models exist, with some capable of capturing causal relationships, but it's unclear how well AI systems can do this.

Nobel Prize to the Statistical Physics of artificial neural networks

Complexity Thoughts • 379 implied HN points • 08 Oct 24
🔬 Science Physics Machine Learning Artificial Intelligence Computational Models Statistical Methods
  1. John J. Hopfield and Geoffrey E. Hinton won the Nobel Prize for their work on artificial neural networks. Their research helps us understand how machines can learn from data using ideas from physics.
  2. Hopfield's networks use energy minimization to recall memories, similar to how physical systems find stable states. This shows a connection between physics and how machines learn.
  3. Boltzmann machines, developed by Hinton, introduce randomness to help networks explore different configurations. This randomness allows for better learning from data, making these models more effective.

Evaluating Consciousness and Reasoning in Abstract Strategic Games (I)

Encyclopedia Autonomica • 19 implied HN points • 20 Oct 24
🕹 Technology AI Game Theory Machine Learning Computational Models
  1. Tic Tac Toe is a simple game that can be played on bigger boards. The larger boards lead to more complex strategies and reduce the first-move advantage that smaller boards often have.
  2. Different player types can be implemented in the game, such as random players and those using reinforcement learning. These players can have various strengths and weaknesses based on their strategies.
  3. As players compete, the performance of agents like the Cognitive ReAct agent is evaluated. Analyzing how these agents think and make moves helps understand their reasoning and decision-making processes.

LCM: Large Concept Model

Gonzo ML • 189 implied HN points • 04 Jan 25
🕹 Technology Artificial Intelligence Machine Learning Natural Language Processing Data science Computational Models
  1. The Large Concept Model (LCM) aims to improve how we understand and process language by focusing on concepts instead of just individual words. This means thinking at a higher level about what ideas and meanings are being conveyed.
  2. LCM uses a system called SONAR to convert sentences into a stable representation that can be processed and then translated back into different languages or forms without losing the original meaning. This creates flexibility in how we communicate.
  3. This approach can handle long documents more efficiently because it represents ideas as concepts, making processing easier. This could improve applications like summarization and translation, making them more effective.
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The Super Weight in Large Language Models

Gonzo ML • 189 implied HN points • 29 Nov 24
🕹 Technology AI Research Machine Learning Data science Computational Models Tech Innovation
  1. There's a special weight in large language models called the 'super weight.' If you remove it, the model's performance crashes dramatically, showing just how crucial it is.
  2. Super weights are linked to what's called 'super activations,' meaning they help generate better text. Without them, the model struggles to create coherent sentences.
  3. Finally, researchers found ways to identify and protect these super weights during the model training and quantization processes. This makes the model more efficient and retains its quality.

Edge 441: SSMs Beyond Language

TheSequence • 119 implied HN points • 22 Oct 24
🕹 Technology AI Machine Learning Research Computational Models
  1. SSMs can be used in areas beyond just language, like audio processing. This makes them very useful for handling complex and irregular data.
  2. Meta AI is researching how SSMs can improve speech recognition, showing their potential in understanding spoken language better.
  3. The Llama-Factory framework helps in pretraining large language models, making them more efficient and powerful.

Can one black box explain another?

The Counterfactual • 39 implied HN points • 29 May 23
🕹 Technology AI Machine Learning Neural Networks Data science Computational Models
  1. Large language models (LLMs) like GPT-4 are often referred to as 'black boxes' because they are difficult to understand, even for the experts who create them. This means that while they can perform tasks well, we might not fully grasp how they do it.
  2. To make sense of LLMs, researchers are trying to use models like GPT-4 to explain the workings of earlier models like GPT-2. This involves one model generating explanations about the neuron activations of another model, aiming to uncover how they function.
  3. Despite the efforts, current methods only explain a small fraction of neurons in these LLMs, which indicates that more research and new techniques are needed to better understand these complex systems and avoid potential failures.