The hottest Neural Networks Substack posts right now

And their main takeaways
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Top Technology Topics

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.
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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.

Grounding Large Language Models in a Cognitive Foundation

The Gradient • 20 implied HN points • 15 Apr 23
🕹 Technology AI Robotics Neural Networks Speech Recognition
  1. Intelligent robots have struggled commercially due to the challenge of having meaningful conversations with them.
  2. Recent advancements in AI, speech recognition, and large language models like ChatGPT and GPT-4 have opened up new possibilities.
  3. For robots to effectively interact in the physical world, they need to quickly adapt to context and be localized in their knowledge.

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
  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.

The Past and Present of Computer-Augmented Hypothesis Generation

FreakTakes • 11 implied HN points • 10 Aug 23
🔬 Science Chemistry Machine Learning Neural Networks
  1. Computer-augmented hypothesis generation is a promising concept that can help uncover new and valuable ideas from existing data.
  2. Looking at old research in a new light can lead to significant breakthroughs, as seen with Don Swanson's and Sharpless' work in different fields.
  3. Tools like LLMs can assist researchers in finding connections between disparate data points, potentially unlocking new avenues for scientific discovery.

Neural Network Diffusion

Gonzo ML • 1 HN point • 26 Feb 24
🕹 Technology Neural Networks Model Evaluation
  1. Hypernetworks involve one neural network generating weights for another - still a relatively unknown but promising concept worth exploring further.
  2. Diffusion models involve adding noise (forward) and removing noise (reverse) gradually to reveal hidden details - a strategy utilized effectively in the study.
  3. Neural Network Diffusion (p-diff) involves training an autoencoder on neural network parameters to convert and regenerate weights, showing promising results across various datasets and network architectures.

LLMs, anthropocentric thinking, accuracy, and self-driving

Apperceptive (moved to buttondown) • 16 implied HN points • 16 Feb 23
🕹 Technology AI Machine Learning Neural Networks UX
  1. Large language models are different from earlier neural network models in architecture and scale of training data.
  2. Large language models exploit the anthropomorphic fallacy, making people interpret them as conscious beings.
  3. The illusion of cognitive depth in machine learning systems like large language models can lead to misunderstandings and challenges in applications like autonomous cars.

Update #48: Generative AI in Law & Art and Promptable Vision Models

The Gradient • 11 implied HN points • 25 Apr 23
🕹 Technology AI Generative models Neural Networks Research
  1. Generative AI is transforming fields like Law and Art, raising ethical and legal questions about ownership and bias.
  2. Recent models allow users to specify vision tasks through flexible prompts, enabling diverse applications in image segmentation and visual tasks.
  3. Advances in promptable vision models and generative AI pose challenges and opportunities, from disrupting professions to potential ethical and legal implications.

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.

Update #43: Propaganda Deepfakes and Transformers get Loopy

The Gradient • 11 implied HN points • 14 Feb 23
🕹 Technology AI Deepfakes Neural Networks Language Models Research
  1. Deepfakes were used for spreading state-aligned propaganda for the first time, raising concerns about the spread of misinformation.
  2. Transformers embedded in loops can function like Turing complete computers, showing their expressive power and potential for programming.
  3. As generative models evolve, it becomes crucial to anticipate and address the potential misuse of technology for harmful or misleading content.

Why Do A.I. Image Generators Have Problems Creating Hands?

I'll Keep This Short • 5 implied HN points • 14 Aug 23
🕹 Technology AI Neural Networks Image Generation Machine Learning Interpretability
  1. A.I. image generators struggle with creating hands due to the complexity of hand shapes and poses
  2. Neural networks power image generators through mathematical transforms
  3. Efforts are being made to improve A.I. image generation by addressing challenges like hand creation through interpretability of neural networks

Why We Won't Achieve AGI Until Memory Is A Core Architectural Component

Am I Stronger Yet? • 3 HN points • 09 Aug 23
🕹 Technology AI Memory Neural Networks
  1. Memory is central to almost everything we do, and different types of memory are crucial for complex tasks.
  2. Current mechanisms for equipping LLMs with memory have limitations, such as static model weights and limited token buffers.
  3. To achieve human-level intelligence, a breakthrough in long-term memory integration is necessary for AIs to undertake deep work.

ChatGPT Learns Fintech

Chaos Engineering • 5 implied HN points • 24 Feb 23
🕹 Technology AI Fintech Machine Learning Neural Networks Data Modeling
  1. ChatGPT can learn some superficial aspects of finance but needs explicit training to become a financial expert.
  2. For ChatGPT to learn fintech, a hybrid architecture combining its pretrained model with a specific ML model optimized for financial tasks is necessary.
  3. Improving ChatGPT's understanding of finance requires training it on structured financial data and updating its architecture to process dense, numeric data.

Why are neural networks so powerful?

The Palindrome • 1 implied HN point • 11 Sep 23
🔬 Science Machine Learning Neural Networks Mathematics
  1. Neural networks are powerful due to their ability to closely approximate almost any function.
  2. Machine learning involves finding a function that approximates the relationship between data points and their ground truth.
  3. Approximation theory seeks to find a simple function close enough to a complex one by determining the right function family and precise approximation within that family.

Deep Learning Is Better Than Linear Regression

As Clay Awakens • 2 HN points • 19 Mar 23
🕹 Technology Deep Learning Machine Learning Data science Neural Networks
  1. Linear regression is a reliable, stable, and simple technique with a long history of successful applications.
  2. Deep learning, especially non-linear regression, has shown significant advancements over the past decade and can outperform linear regression in many real-world tasks.
  3. Deep learning models have the ability to automatically learn and discover complex features, making them advantageous over manually engineered features in linear regression.

What is an embedding, anyways?

Simplicity is SOTA • 2 HN points • 27 Mar 23
🕹 Technology Machine Learning Neural Networks Embeddings Data Transformation
  1. The concept of 'embedding' in machine learning has evolved and become widely used, replacing terms like vectors and representations.
  2. Embeddings can be applied to various types of data, come from different layers in a neural network, and are not always about reducing dimensions.
  3. Defining 'embedding' has become challenging due to its widespread use, but the essence is about learned transformations that make data more useful.

The Four Fundamental Quantities of LLMs: Part Zero - 📜 What are Large Language Models?

Intuitive AI • 1 HN point • 21 May 23
🕹 Technology Machine Learning Neural Networks Training Data Large Language Models
  1. Large language models (LLMs) are neural networks with billions of parameters trained to predict the next word using large amounts of text data.
  2. LLMs use parameters learned during training to make predictions based on input data during the inference stage.
  3. Training an LLM involves optimizing the model to predict the next token in a sentence by feeding it billions of sentences to adjust its parameters.

Papers I’ve read this week: Image generation

Artificial Fintelligence • 1 HN point • 11 Apr 23
🕹 Technology Image Generation Neural Networks Artificial Intelligence
  1. CLIP focuses on aligning text and image embeddings, showcasing its utility for various applications like search, image generation, and zero-shot classification.
  2. DALL-E introduces a large-scale autoregressive transformer model for text-to-image generation, revolutionizing image generation beside the prevalent GAN models.
  3. GLIDE employs a 3.5B parameter diffusion model to convert text embeddings into images, exploring guiding methods like CLIP and classifier-free guidance.