The hottest Neural Networks Substack posts right now

And their main takeaways
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Top Technology Topics

What Is SwiGLU? How to Implement It? And Why Does it Work?

Aziz et al. Paper Summaries • 59 implied HN points • 13 Mar 24
🕹 Technology Neural Networks
  1. SwiGLU is a type of activation function used in deep learning. It's a mix of two parts: the Swish function and Gated Linear Units, which helps models learn better patterns.
  2. To implement SwiGLU, you can use a straightforward code in Pytorch that combines linear transformations with the Swish function. This makes it easier for neural networks to handle complex data.
  3. The exact reason why SwiGLU works so well is not fully understood yet. Researchers are still exploring why this approach gives better results in certain models.

Moving Past RLHF: In 2025 We Will Transition from Preference Tuning to Reward Optimization in Foundation Models

TheSequence • 189 implied HN points • 29 Dec 24
🕹 Technology Neural Networks
  1. Artificial intelligence is moving from preference tuning to reward optimization for better alignment with human values. This change aims to improve how models respond to our needs.
  2. Preference tuning has its limits because it can't capture all the complexities of human intentions. Researchers are exploring new reward models to address these limitations.
  3. Recent models like GPT-o3 and Tülu 3 showcase this evolution, showing how AI can become more effective and nuanced in understanding and generating language.

Towards a systems semiotics

johan’s substack • 19 implied HN points • 05 Jun 24
🔬 Science Neural Networks
  1. Engaging with AI involves a unique process of language generation, bridging the gap between human and synthetic realms.
  2. Humans navigate the Sociosemioscape, a network of speech acts that shape communication and understanding in language, culture, and social interactions.
  3. Venturing into the Semioscape, through the creation and exploration of neologisms, leads to a fluid and transformative experience where meaning shifts and new patterns emerge.

10 ways to estimate SHAP

Mindful Modeler • 119 implied HN points • 18 Jul 23
🕹 Technology Neural Networks
  1. SHAP values are estimated using various methods due to computational constraints
  2. Estimation methods include exact explainer, sampling explainer, permutation explainer, and more to attribute model predictions to features
  3. The `shap` package implements multiple estimation methods, with defaults based on the type of data and model
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Console #191 -- Interview with Bernhard of ACID Chess - Chess computer for nerds, by nerds

Console • 472 implied HN points • 07 Jan 24
🕹 Technology Neural Networks
  1. ACID Chess is a chess computer program written in Python that can analyze the movements of pieces on a chessboard through image recognition.
  2. The creator of ACID Chess balanced working on the project with a full-time job by dedicating time in evenings and weekends while finding it to be a good balance.
  3. The creator of ACID Chess believes AI will simplify various aspects of software development, and open-source software will continue to thrive with challenges in monetization for small developers.

Pay Attention to Small Weights

Gonzo ML • 63 implied HN points • 06 Jul 25
🕹 Technology Neural Networks
  1. Small weight updates during model training can lead to better results, especially since large weights might hold key features that we don't want to change.
  2. Using a method called NanoAdam, we can focus on smaller weights, which allows for more efficient memory usage and better performance during fine-tuning.
  3. It seems that large gradients often come from small weights, suggesting that sometimes it’s smarter to update these smaller weights instead of the larger ones.

Understanding the Different Types of Transformers in AI [Math Mondays]

Technology Made Simple • 99 implied HN points • 11 Jul 23
🕹 Technology Neural Networks
  1. There are three main types of transformers in AI: Sequence-to-Sequence Models excel at language translation tasks, Autoregressive Models are powerful for text generation but may lack deeper understanding, and Autoencoding Models focus on language understanding and classification by capturing meaningful representations of input data.
  2. Transformers with different training methodologies influence their performance and applicability, so understanding these distinctions is crucial for selecting the most suitable model for specific use cases.
  3. Deep learning with transformer models offers a diverse range of capabilities, each catering to unique needs: mapping sequences between languages, generating text, or focusing on language understanding and classification.

Links for 2024-01-11

Axis of Ordinary • 58 implied HN points • 11 Jan 24
🕹 Technology Neural Networks
  1. Researchers are exploring AI's ability to analyze massive amounts of data for surveillance purposes.
  2. Scientists are connecting human brain cells to interfaces to recognize sounds.
  3. Political updates include Trump's stance on helping Europe, Russia's view of Trump's presidency, and international support for Ukraine.

Address Not Found (Part 1)

Cybernetic Forests • 79 implied HN points • 11 Jun 23
🕹 Technology Neural Networks
  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 Neural Networks
  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.

Edge 443: EVERYTHING you Need to Know About State Space Models

TheSequence • 133 implied HN points • 29 Oct 24
🕹 Technology Neural Networks
  1. State space models (SSMs) are a promising alternative to transformers for processing data. They handle long sequences more efficiently without losing important information.
  2. SSMs are designed to be computationally efficient, scaling linearly with context windows unlike transformers which scale quadratically. This makes them better for tasks needing a lot of information.
  3. Recent models like Mamba show that SSMs can outperform transformers in performance and efficiency, especially for tasks that require understanding long contexts.

Make softmax great again

Gonzo ML • 126 implied HN points • 06 Nov 24
🕹 Technology Neural Networks
  1. Softmax is widely used in machine learning, especially in transformers, to turn numbers into probabilities. However, it struggles when dealing with new kinds of data that the model hasn't seen before.
  2. The sharpness of softmax can fade when there's a lot of input data. This means it sometimes can't make clear predictions about which option is best in bigger datasets.
  3. To improve softmax, researchers suggest using 'adaptive temperature.' This idea helps make the predictions sharper based on the data being processed, leading to better performance in some tasks.

What is inner alignment?

Musings on the Alignment Problem • 259 implied HN points • 08 May 22
🕹 Technology Neural Networks
  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 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.

The Most Valuable Problem in AI

How the Hell • 313 implied HN points • 30 Aug 23
🕹 Technology Neural Networks
  1. In AI, there's a shift to being able to throw any amount of compute power at problems
  2. We are approaching a world where we can solve any intellectual problem by allocating money as a compute budget to AI agents
  3. Solving the problem of efficient compute allocation can lead to building the most valuable company of the century

Not All Layers Are Equal

Gonzo ML • 63 implied HN points • 31 Jan 25
🕹 Technology Neural Networks
  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.

🤘ACDC (not that one)

Gonzo ML • 63 implied HN points • 29 Jan 25
🕹 Technology Neural Networks
  1. The paper introduces a method called ACDC that automates the process of finding important circuits in neural networks. This can help us better understand how these networks work.
  2. Researchers follow a three-step workflow to study model behavior, and ACDC fully automates the last step which helps identify connections that matter for a specific task.
  3. While ACDC shows promise, it isn't perfect. It may miss some important connections and needs adjustments for different tasks to improve its accuracy.

The beginner’s guide to AI model architectures

Technically • 20 implied HN points • 05 Aug 25
🕹 Technology Neural Networks
  1. AI models are like blueprints, guiding how models are built and designed. Choosing the right design is key to solving specific problems effectively.
  2. Neurons mimic real brain functions and are the basic units that help AI learn patterns from data. They work by performing simple math repeatedly across many layers.
  3. There are many ways to connect these neurons, forming various network types, like feedforward or recurrent networks. Each type is good for different tasks, like language or vision.

Twitter open-sourced their recommendation algorithm

MLOps Newsletter • 39 implied HN points • 09 Apr 23
🕹 Technology Neural Networks
  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 Neural Networks
  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.

Fear of AI is Profitable

Cybernetic Forests • 39 implied HN points • 02 Apr 23
🕹 Technology 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 Neural Networks
  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 Neural Networks
  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.

Catechizing the Bots, Part 2: Reinforcement Learning and Fine-Tuning With RLHF

jonstokes.com • 206 implied HN points • 10 Jun 23
🕹 Technology Neural Networks
  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.

Key Components to Understand the LLM Models

The Beep • 19 implied HN points • 07 Jan 24
🕹 Technology Neural Networks
  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 Neural Networks
  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 Neural Networks
  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.

Why AI Needs to Forget

Nano Thoughts • 1 implied HN point • 14 Jan 26
🕹 Technology Neural Networks
  1. Memory is organized as a graph not to store everything, but so edges can decay and useless paths are forgotten; forgetting is an intentional feature, not a bug.
  2. What gets remembered depends on the agent’s goals, so memory must be filtered by a utility function before or during encoding; a single universal context that keeps everything will produce noise not useful memory.
  3. Current AI systems are mostly search/archives, not true memory; real memory needs valuation-driven, lossy compression (e.g., reinforcing repetition or preserving surprise) to avoid overfitting and enable useful prediction.

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

The Chip Letter • 95 HN points • 21 Feb 24
🕹 Technology Neural Networks
  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.

What metastructures might LLMs have?

How the Hell • 68 implied HN points • 29 Jun 24
🕹 Technology Neural Networks
  1. LLMs have different layers, like humans do. Lower layers handle basic language, while higher layers form more complex ideas.
  2. These models might develop their own unique structures for understanding visuals, since they don't see like humans do.
  3. There could be even higher layers that aren't just about language but add more complexity. It's still unclear how we might study these structures.

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 Neural Networks
  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 Neural Networks
  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.