The hottest Neural Networks Substack posts right now

And their main takeaways
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Top Technology Topics

Address Not Found (Part 1)

Cybernetic Forests • 79 implied HN points • 11 Jun 23
🕹 Technology AI Communication Algorithm Neural Networks Media
  1. The organization of information shapes the world by prioritizing what is relevant and categorizing discourse, leading to challenges and social movements.
  2. Digital mediation of communication alters the intended recipient and how messages are perceived by algorithms like Twitter, causing misunderstanding and lack of context.
  3. AI systems should be viewed as communication networks, translating and re-encoding human discourse, but currently function as closed, noisy systems with weighted biases that limit new ideas.

Exphormer(Graph Neural Networks)

MLOps Newsletter • 39 implied HN points • 04 Feb 24
🕹 Technology Machine Learning Neural Networks Optimization Deep Learning Library
  1. Graph transformers are powerful for machine learning on graph-structured data but face challenges with memory limitations and complexity.
  2. Exphormer overcomes memory bottlenecks using expander graphs, intermediate nodes, and hybrid attention mechanisms.
  3. Optimizing mixed-input matrix multiplication for large language models involves efficient hardware mapping and innovative techniques like FastNumericArrayConvertor and FragmentShuffler.

What is inner alignment?

Musings on the Alignment Problem • 259 implied HN points • 08 May 22
🕹 Technology Machine Learning Artificial Intelligence Neural Networks Deep Learning
  1. Inner alignment involves the alignment of optimizers learned by a model during training, separate from the optimizer used for training.
  2. In rewardless meta-RL setups, the outer policy must adjust behavior between inner episodes based on observational feedback, which can lead to inner misalignment by learning inaccurate representations of the training-time reward function.
  3. Auto-induced distributional shift can lead to inner alignment problems, where the outer policy may cause its own inner misalignment by changing the distribution of inner RL problems.

Language is a SimCity

Cybernetic Forests • 59 implied HN points • 02 Jul 23
🔬 Science Language Statistics AI Generative models Neural Networks
  1. Language can be seen as a dynamic city, shaped by collective contributions that form its intricate structure.
  2. Generative AI models, like GPT4, rely on statistics and random selection to produce text, often betraying a lack of true understanding.
  3. Human communication involves a choice between shallow, statistically-driven speech, like that of machines, and deeper, intent-driven speech that seeks to convey personal truths.
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Catechizing the Bots, Part 2: Reinforcement Learning and Fine-Tuning With RLHF

jonstokes.com • 206 implied HN points • 10 Jun 23
🕹 Technology AI Machine Learning Neural Networks Reinforcement Learning Language Models
  1. Reinforcement Learning is a technique that helps models learn from experiencing pleasure and pain in their environment over time.
  2. Human feedback plays a crucial role in fine-tuning language models by providing ratings that indicate how a model's output impacts users' feelings.
  3. To train models effectively, a preference model can be used to emulate human responses and provide feedback without the need for extensive human involvement.

John C. Dvorak on Intel's First Neural Network Chip

The Chip Letter • 95 HN points • 21 Feb 24
🕹 Technology Chips Neural Networks Machine Learning
  1. Intel's first neural network chip, the 80170, achieved the theoretical intelligence level of a cockroach, showcasing a significant breakthrough in processing power.
  2. The Intel 80170 was an analog neural processor introduced in 1989, making it one of the first successful commercial neural network chips.
  3. Neural networks like the 80170 aren't programmed but trained like a dog, opening up unique applications for analyzing patterns and making predictions.

Twitter open-sourced their recommendation algorithm

MLOps Newsletter • 39 implied HN points • 09 Apr 23
🕹 Technology Algorithms Machine Learning Open Source Neural Networks Data science
  1. Twitter has open-sourced their recommendation algorithm for both training and serving layers.
  2. The algorithm involves candidate generation for in-network and out-network tweets, ranking models, and filtering based on different metrics.
  3. Twitter's recommendation algorithm is user-centric, focusing on user-to-user relationships before recommending tweets.

For the Love of PyTorch

Sector 6 | The Newsletter of AIM • 39 implied HN points • 04 Sep 23
🕹 Technology AI Software Neural Networks Deep Learning Data science
  1. PyTorch is a key player in the development of AI, particularly large language models (LLMs). Its flexibility makes it great for deep learning experiments.
  2. The framework supports GPUs really well and allows for easy updates to computation graphs during programming.
  3. In 2022, PyTorch had a significant edge on platforms like Hugging Face, with 92% of models being PyTorch-exclusive compared to just 8% for TensorFlow.

An “AI Breakthrough” on Systematic Generalization in Language?

AI: A Guide for Thinking Humans • 47 HN points • 07 Jan 24
🔬 Science AI Language Neural Networks Generalization Machine Learning
  1. Compositionality in language means the meaning of a sentence is based on its individual words and how they are combined.
  2. Systematicity allows understanding and producing related sentences based on comprehension of specific sentences.
  3. Productivity in language enables the generation and comprehension of an infinite number of sentences.

Fear of AI is Profitable

Cybernetic Forests • 39 implied HN points • 02 Apr 23
🕹 Technology AI Data Ethics Art Neural Networks
  1. Fear of AI can be profitable through marketing strategies that capitalize on existential threats from AI.
  2. There is skepticism about the narratives surrounding powerful AI systems being motivated by fear of sentient AI surpassing humans.
  3. Prioritizing speculative future AI risks can distract from addressing the immediate impacts of AI technology on society and real-world problems.

A simple test for AI comprehension

Logos • 19 implied HN points • 21 Jan 24
🕹 Technology AI Machine Learning Neural Networks Cognition Child development
  1. The author tests AI's understanding using a guessing game. The AI struggled and often made mistakes, which leads to questions about their comprehension.
  2. LLMs act like children by mimicking language without true understanding. They can say the right words but might not grasp the ideas behind them.
  3. The argument suggests that while LLMs can analyze complex topics, their understanding is shallow compared to human comprehension.

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.

Key Components to Understand the LLM Models

The Beep • 19 implied HN points • 07 Jan 24
🕹 Technology AI Models Natural Language Machine Learning Neural Networks Data processing
  1. Large language models (LLMs) like Llama 2 and GPT-3 use transformer architecture to process and generate text. This helps them understand and predict words based on previous context.
  2. Emergent abilities in LLMs allow them to learn new tasks with just a few examples. This means they can adapt quickly without needing extensive training.
  3. Techniques like Sliding Window Attention help LLMs manage long texts more efficiently by breaking them into smaller parts, making it easier to focus on relevant information.

Speech Data Processing Takes Flight

Gradient Flow • 79 implied HN points • 15 Sep 22
🕹 Technology Data processing Neural Networks Open Source Podcasts Artificial Intelligence
  1. Interest in neural networks and deep learning has led to groundbreaking advancements in computer vision and speech recognition.
  2. Working with audio data historically posed challenges due to various formats, compression methods, and multiple channels.
  3. New open source projects are simplifying audio data processing, making it easier for data scientists and developers to incorporate audio data into their models.

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.

General Theory of Neural Networks

Rob Leclerc • 2 HN points • 10 Jul 24
🔬 Science Neural Networks
  1. Universal Activation Networks (UANs) span various systems from gene regulatory networks to artificial neural networks, emphasizing evolvability and generative open-endedness.
  2. Identifying a network's critical topology is crucial as it dictates function, not implementation details, leading to efficient and adaptable systems.
  3. Extreme pruning of networks reveals necessary and sufficient circuit topology, enhancing performance by reducing noise and increasing efficiency.

What's Going on Under the Hood of LLMs

The End of Reckoning • 19 implied HN points • 21 Feb 23
🔬 Science Artificial Intelligence Cognitive Science Neural Networks Interpretability Philosophy
  1. Transformer models, like LLMs, are often considered black boxes, but recent work is shedding light on the internal processes and interpretability of these models.
  2. Induction heads in transformer models help with in-context learning and the ability to predict information based on the sequence of tokens seen before.
  3. By analyzing hidden states and conducting memory-based experiments, researchers are beginning to understand how transformer models store and manipulate information, providing insights into how these models may represent truth internally.

A brief history of speech to text + how it actually works

Mythical AI • 19 implied HN points • 08 Mar 23
🕹 Technology AI Models Neural Networks Machine Learning Evaluation Metrics
  1. Speech to text technology has a long history of development, evolving from early systems in the 1950s to today's advanced AI models.
  2. The process of converting speech to text involves recording audio, breaking it down into sound chunks, and using algorithms to predict words from those chunks.
  3. Speech to text models are evaluated based on metrics like Word Error Rate (WER), Perplexity, and Word Confusion Networks (WCNs) to measure accuracy and performance.

The unreasonable effectiveness of LLMs

John’s Contemplations • 19 implied HN points • 08 Mar 23
🕹 Technology AI Machine Learning Neural Networks Big Data Efficiency
  1. LLMs have displayed surprising reasoning abilities like solving math problems using words.
  2. LLMs can be trained to use tools to address their weaknesses and improve tasks like code generation.
  3. LLMs work well due to the general nature of language, the breakdown of complex tasks into simpler steps, and the efficiency of neural networks like Transformers.

Explaining "Taking features out of superposition with sparse autoencoders"

From AI to ZI • 19 implied HN points • 16 Jun 23
🔬 Science Neural Networks Linear Algebra
  1. Explanations of complex AI processes can be simplified by using sparse autoencoders to reveal individual features.
  2. Sparse and positive feature activations can help in interpreting neural networks' internal representations.
  3. Sparse autoencoders can be effective in reconstructing feature matrices, but finding the right hyperparameters is important for successful outcomes.

A Book to Make You Less Afraid of AI: Melanie Mitchell's "Artificial Intelligence"

Mike Talks AI • 19 implied HN points • 14 Jul 23
🔬 Science AI Neural Networks Natural Language Processing Algorithms
  1. The book 'Artificial Intelligence' by Melanie Mitchell eases fears about AI and provides education.
  2. It covers the history of AI, details on algorithms, and a discussion on human intelligence.
  3. The book explains how deep neural networks and natural language processing work in an understandable way.

Why the Abstraction and Reasoning Corpus is interesting and important for AI

AI: A Guide for Thinking Humans • 60 HN points • 01 Mar 23
🕹 Technology AI Machine Learning Neural Networks Problem Solving Research
  1. Forming and abstracting concepts is crucial for human intelligence and AI.
  2. The Abstraction and Reasoning Corpus is a challenging domain that tests AI's ability to infer abstract rules.
  3. Current AI struggles with ARC tasks, showing limitations in solving visual and spatial reasoning problems.

The Dragonfly Project

R&D Reflections • 2 HN points • 13 Jun 24
🕹 Technology Neural Networks Model Training
  1. Multi-Layer Perceptrons (MLPs) in neural networks consist of interconnected nodes that perform simple mathematical operations, revealing complexity in how they compute results.
  2. MLPs can be used to approximate equations and discover underlying patterns in experimental data, but may not efficiently solve known mathematical functions unless they memorize data.
  3. Analyzing MLP parameters can reveal insights, improve model training, and potentially lead to the discovery of unknown equations or constants in scientific research.

Two AIs talking to one another

philsiarri • 22 implied HN points • 18 Mar 24
🕹 Technology Artificial Intelligence Neural Networks Language processing Robotics
  1. Researchers developed an artificial neural network that can understand tasks based on instructions and describe them in language to other AI systems.
  2. The AI model S-Bert, with 300 million artificial neurons, was enhanced to simulate brain regions involved in language processing, achieving linguistic communication between AI systems.
  3. This breakthrough enables machines to communicate using language, paving the way for collaborative interactions in robotics.

Car-GPT: Could LLMs finally make self-driving cars happen?

The Gradient • 20 implied HN points • 08 Mar 24
🕹 Technology AI Self-driving cars Neural Networks
  1. Self-driving cars are traditionally built with separate modules for perception, localization, planning, and control.
  2. New approach of End-To-End learning involves a single neural network for steering and acceleration, but it can create a black box problem.
  3. The article explores the potential role of Large Language Models (LLMs) like GPT in revolutionizing autonomous driving by replacing traditional modules.

A primer on sparse autoencoders

Nick’s Substack • 1 HN point • 03 Jul 24
🕹 Technology Machine Learning Artificial Intelligence Neural Networks Data science Computer Science
  1. Sparse autoencoders are tools that help us understand how language models work by breaking down their process into simpler parts. They help identify important features in the model that contribute to its outputs.
  2. The idea of sparsity means only a few features are needed to describe something, while superposition lets a lot of different features exist in a small space. This makes learning and processing more efficient for the model.
  3. Using sparse autoencoders opens up new ways to interact with language models. Instead of just inputting text and getting answers, we can manipulate features and explore the model's internal workings more creatively.

Why Deep Learning is everywhere [Math Mondays]

Technology Made Simple • 19 implied HN points • 25 Oct 22
🕹 Technology AI Machine Learning Neural Networks Mathematics Deep Learning
  1. Deep Learning is a subset of Machine Learning that uses Neural Networks with many layers, introducing non-linearity in functions which is crucial for its success.
  2. Deep Networks work well because they can approximate any continuous function by combining non-linear functions, allowing them to tackle complex problems.
  3. The widespread use of Deep Learning is driven by its trendiness and efficiency, appealing to many due to its ability to provide results without extensive data analysis or training.

How does batching work on modern GPUs?

Artificial Fintelligence • 8 implied HN points • 01 Mar 24
🕹 Technology Deep Learning Neural Networks
  1. Batching is a key optimization for modern deep learning systems, allowing for processing multiple inputs simultaneously without significant time overhead.
  2. Modern GPUs run operations concurrently, leading to no additional time needed as batch sizes increase up to a certain threshold.
  3. For convolutional networks, the advantage of batching is reduced compared to other models due to the reuse of weights across multiple instances.