The hottest Neural Networks Substack posts right now

And their main takeaways
Category
Top Technology Topics

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.

Data Science Weekly - Issue 79

Data Science Weekly Newsletter • 19 implied HN points • 28 May 15
🕹 Technology Data science Machine Learning Artificial Intelligence Neural Networks Image Recognition
  1. Recurrent Neural Networks (RNNs) are powerful tools that can generate surprisingly good text, like image descriptions, quickly and easily.
  2. AI, like IBM's Chef Watson, is being used in creative ways, such as suggesting meals based on available ingredients, showing how tech can help with daily tasks.
  3. Google is developing tech that can analyze food photos to count calories, highlighting how machine learning can be applied to health and nutrition.

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.

Data Science Weekly - Issue 46

Data Science Weekly Newsletter • 19 implied HN points • 09 Oct 14
🕹 Technology Data science Machine Learning Artificial Intelligence Neural Networks Programming
  1. Machine learning is now a central part of data science, similar to the role algorithms played in computing 15 years ago. It's becoming essential for many fields.
  2. Deep learning has made significant advancements, especially in tasks like speech recognition and handwriting recognition. This technology is becoming a go-to for complex pattern recognition.
  3. Data science is not just about numbers; it involves understanding human behavior and data that relates to people. Many data scientists focus on human data for their work.
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Sketch of a new English orthography

Perambulations • 0 implied HN points • 07 May 23
🕹 Technology Language Neural Networks Machine Learning Writing
  1. English spelling is complex due to its accumulation of bits and pieces of other languages.
  2. Efforts for English spelling reform have included developing custom scripts and simplified spelling movements.
  3. An ideal English writing system may balance phonetic fidelity with concision, embed emphasis information, address vowel complexity, and include characters for high-frequency sound combinations.

Two-tower models for ranking problems

Simplicity is SOTA • 0 implied HN points • 22 May 23
🕹 Technology Big Data Machine Learning Research Neural Networks Algorithm
  1. Two-tower models are a technique being used in academia to improve ranking systems by looking into how position and user behavior affects clicks.
  2. Critiques have been raised against the two-tower models, questioning if they effectively separate biases and relevance in ranking.
  3. A new method called GradRev is emerging as a potential improvement over the previous two-tower models, applying a different approach to address bias in learning-to-rank systems.

DeOldify.NET

Barn Lab • 0 implied HN points • 07 Jun 23
🕹 Technology Neural Networks Image Processing
  1. Colorization of black-and-white images involves using color spaces like Lab to represent colors digitally
  2. Neural networks have been trained on colorized image datasets to aid in the colorization process
  3. DeOldify.NET offers a user-friendly way to colorize old images using AI without needing complex tools or specialized websites

What do we mean by inductive bias and expressiveness?

Simplicity is SOTA • 0 implied HN points • 19 Jun 23
🕹 Technology Machine Learning Neural Networks Models
  1. Inductive bias in machine learning refers to how models make choices in their learning process.
  2. Restriction bias limits the types of hypotheses considered in a model, while preference bias favors certain hypotheses over others.
  3. Expressiveness of a model determines the types of relationships it can capture, and can be enhanced by adding relevant features or interactions.

Primer on Machine Learning Interviews for Software Engineers

ExpandAI Newsletter • 0 implied HN points • 30 Jun 23
🕹 Technology Machine Learning Software Engineering Mathematics Neural Networks Systems Engineering
  1. Software engineers in the future will likely require strong machine learning backgrounds.
  2. Machine learning interviews for software engineers cover software engineering, mathematics, and machine learning topics.
  3. Preparing for machine learning interviews should focus on optimizing for both software and machine learning skills.

How Far Are We From Being Able to Generate Whatever 3D Objects On the Fly?

I'll Keep This Short • 0 implied HN points • 17 Jul 23
🕹 Technology AI 3D Modeling Generative models Neural Networks Large Language Models
  1. AI-generated 3D objects are still far from being created instantly in real 3D
  2. Shap-E improves upon previous models by generating 3D objects using Neural Radiance Fields
  3. Although new technologies show promise, limitations like resource-intensive processes and lack of fine details still exist

Hot Topics #20 (Mar. 1, 2023)

The Merge • 0 implied HN points • 01 Mar 23
🔬 Science Deep Learning Neural Networks
  1. Protein design using deep learning techniques to create custom biocatalysts
  2. Efficient de novo protein design through relaxed sequence space for better computational efficiency
  3. Improving robotic learning with corrective augmentation through NeRF for better manipulation policies

Where Bayes Falls Short

As Clay Awakens • 0 implied HN points • 30 May 23
🔬 Science Statistics Machine Learning Neural Networks Deep Learning
  1. Deep learning algorithms are powerful for intelligence and learning, especially in contexts where Bayes' theorem falls short.
  2. Simpson's paradox shows how data separation can change conclusions based on initial beliefs.
  3. Deep learning approaches in regression tasks offer solutions without the need for ad-hoc choices, allowing for better predictions and generalization.

Beyond the Hype: Understanding the Science Behind AI and LLMs

The Novice • 0 implied HN points • 12 Nov 23
🕹 Technology AI Machine Learning Neural Networks Natural Language Processing Deep Learning
  1. Word2Vec created word associations in 3D space but didn't understand word meanings.
  2. Generative Pretrained Transformers (GPTs) improved upon Word2Vec by understanding word context and relationships.
  3. Chat GPT appears smart by storing and retrieving vast amounts of data quickly, but it's not truly intelligent.

Consciousness in one forward pass? An inconvenient thought experiment

Boris Again • 0 implied HN points • 07 Mar 24
🔬 Science Consciousness Neural Networks
  1. LLM, or large language models, like a calculator, perform sequential operations and don't have memories or reflections like humans do
  2. This thought experiment questions at what point a being loses consciousness when subjected to memory wipes and repetitive questions, similar to how LLM operates
  3. This experiment raises the question of when a rational being transitions to a machine-like 'calculator' state

Tiny Language Model

John Mayo-Smith's Substack • 0 implied HN points • 20 Apr 23
🕹 Technology AI Neural Networks Training Limitations Math
  1. The Tiny Language Model is a small functional language model that runs in your browser and learns based on a six-word customizable vocabulary, providing insights into more complex models like ChatGPT.
  2. The Tiny Language Model's training involves a compact 'corpus' from the vocabulary, showcasing a scaled-down version of the training process compared to models like ChatGPT, enhancing understanding through patterns in text.
  3. Observing the changes in weights (parameters) of the Tiny Language Model visually displays how the model is learning and can help identify areas for improvement in its training and performance.

Rust for Data Engineering

ingest this! • 0 implied HN points • 12 Mar 24
🕹 Technology Data Engineering Neural Networks Open Source Machine Learning
  1. Rust is reshaping data engineering by offering performance, safety, and concurrency, making it a strong contender alongside languages like Python.
  2. Learning Rust through 'The Rust Programming Language' book provides a solid foundation, with hands-on projects to enhance understanding.
  3. Mathesar is an open-source tool providing a spreadsheet-like interface to PostgreSQL databases, making data collaboration easier and more accessible.

How AI systems work, actually

Meaningness • 0 implied HN points • 06 Mar 23
🕹 Technology AI Algorithms Neural Networks
  1. Understanding AI systems requires more than just knowing they are neural networks trained with machine learning. It's important to grasp the specifics of how they work to understand their limitations and capabilities.
  2. Task-relevant, algorithmic understanding of AI systems is vital. This means comprehending the 'how' behind their operations in real-world situations, similar to understanding conventional database systems.
  3. Analysis of AI systems, like text generators, can reveal insights into human language use and understanding. Studying the patterns they exploit can shed light on how we process language, rather than just AI mechanisms.

Gradient Dissent

Meaningness • 0 implied HN points • 01 Mar 23
🕹 Technology Artificial Intelligence Engineering Neural Networks
  1. Neural networks are criticized for being expensive, unreliable, and potentially harmful, yet continue to be widely used without adequate safeguards.
  2. In the software industry, inferior designs can dominate better alternatives, leading to long-term use of buggy, slow, and complicated programs.
  3. Replacing neural networks with better alternatives is not only possible but important and urgent for creating a safer technological future.

Any Angel: Realness Scars

Do Not Research • 0 implied HN points • 15 Oct 22
🎨 Art & Illustration Neural Networks Illustrations Text Simulation
  1. The video essay 'Realness Scars' was written and illustrated by neural networks, with the script by OpenAI's GPT-3 and images by Midjourney.
  2. The text explores a landscape where representation is overshadowed by 'realness scars,' reflecting on traces of simulation absorbed by infrastructures.
  3. The collaboration between AI models like GPT-3 and artists like Midjourney can lead to innovative and thought-provoking creative projects.

10 Lesser-Known but Incredibly Useful Deep Learning Algorithms You Need to Master in 2024

AI Disruption • 0 implied HN points • 04 May 24
🕹 Technology Neural Networks Artificial Intelligence Machine Learning
  1. Deep learning algorithms like Word2vec, Variational Autoencoder, and Generative Adversarial Network have revolutionized machine learning applications with profound theories and elegant concepts.
  2. Graph Convolutional Network (GCN) advancements have simplified graph networks, leading to the development of powerful models in machine learning, like PointNet and Neural Radiance Field (NeRF) for 3D vision and modeling light behavior.
  3. Research in the era of large models focuses on technical advancements, diverse applications, theoretical foundations, and social impacts of AI, emphasizing the need for understanding the strengths and implications of utilizing large-scale models across various domains.

Introduction to Neurons, Backpropagation and Transformers

Rob Leclerc • 0 implied HN points • 10 Jul 24
🕹 Technology Neural Networks Transformers Artificial Intelligence Machine Learning
  1. Neurons process information through reception, transmission, integration, propagation, and communication, illustrating a fundamental understanding of neural dynamics.
  2. Backpropagation is a key algorithm in training neural networks, involving forward pass, error calculation, backward pass, and weight update to optimize network performance.
  3. Artificial neural networks have evolved from single-layer perceptrons to multi-layer perceptrons, showcasing the importance of hierarchical learning and specialized architectures for different tasks.

Newsletter #14: Adding Memory to LLMs

Decoding Coding • 0 implied HN points • 01 Jun 23
🕹 Technology AI Machine Learning Data science Neural Networks Innovation
  1. LLMs can forget information when they get too big, which makes their performance worse. Adding an internal memory can help them remember better and adapt to new tasks.
  2. The new framework, Decision Transformers with Memory (DT-Mem), uses a special memory module to identify and store important information effectively. This helps the model improve its decision-making.
  3. By using techniques like content-based addressing, DT-Mem can selectively add or erase information in its memory, making it smarter and more efficient in handling tasks.

Newsletter #5: Backprop from scratch

Decoding Coding • 0 implied HN points • 09 Mar 23
🕹 Technology Artificial Intelligence Machine Learning Programming Data science Neural Networks
  1. Derivatives show how small changes in inputs affect the output of a function. This is important for understanding how neural networks adjust to improve their predictions.
  2. In neural networks, understanding how changes in weights and inputs influence the output helps us optimize performance. By adjusting weights based on calculated gradients, we can make the network learn better.
  3. The chain rule is key when calculating how different layers of a neural network affect the final output. It allows us to connect changes in inputs through to the overall output, helping us to fine-tune the model.

Back to the Future

Sector 6 | The Newsletter of AIM • 0 implied HN points • 09 Jan 23
🕹 Technology AI Neural Networks Software Automation Machine Learning
  1. Scientists are still trying to create a machine that works like the human brain, but they haven't found a solution yet.
  2. Researchers are looking at older AI methods, called Good-Old-Fashioned Artificial Intelligence (GOFAI), to help machines understand like humans do.
  3. Symbolic AI can understand complex ideas and relationships better, while deep learning needs to be retrained often to learn new tasks.

Decoding Deep Learning with Yoshua Bengio

Sector 6 | The Newsletter of AIM • 0 implied HN points • 25 Dec 22
🕹 Technology AI Deep Learning Machine Learning Neural Networks Data science
  1. Yoshua Bengio discusses how understanding intelligence can help us create better AI, possibly even surpassing human intelligence. He believes that knowing the fundamental principles is crucial.
  2. He emphasizes that we have built advanced machines like airplanes that don't directly mimic birds. They can perform tasks that birds can't, showing that different systems excel in different areas.
  3. Bengio is skeptical about the term 'AGI' or Artificial General Intelligence. He thinks there is more to be explored beyond that label when discussing the potential of AI.

ChatGPT Revolution

The Future of Life • 0 implied HN points • 31 Mar 23
🕹 Technology AI Human-computer interaction Machine Learning Neural Networks Data processing
  1. ChatGPT and similar AI technologies are changing how we create and interact with content. It's hard to tell if something was made by a human or an AI now.
  2. Future versions of AI will get smarter and faster. They will be able to access real-time data and solve more complex problems.
  3. AI will become more specialized, like how humans have different areas of expertise in the brain. This means future AIs will be even better at understanding and creating unique content.

🤖 Some mind-blowing/scary facts about neural networks (used by ChatGPT) 🤯

The Future of Life • 0 implied HN points • 30 Mar 23
🕹 Technology AI Neural Networks Machine Learning Computing Research
  1. Neural networks can do the same tasks as any standard computer. Even just three neurons can handle basic math operations.
  2. GPT-4, like the human brain, relies on complex simulations to generate context-based responses. It has an incredible number of parameters that allow it to mimic human-like thinking.
  3. There's a lot of excitement in AI research, driven by the massive success of models like ChatGPT. However, rapid development raises important safety concerns that are often overlooked.

Book Review: Why Machines Will Never Rule the World

The Grey Matter • 0 implied HN points • 17 Jul 23
🕹 Technology AI Machine Learning Artificial Intelligence Complex Systems Neural Networks
  1. The book emphasizes that machines will never rule the world, as AGI is fundamentally impossible due to computational limitations.
  2. The definitions of intelligence and machine intelligence play a crucial role in the argument against AGI.
  3. Language, context-dependence, and complex systems are central themes analyzed in the book to challenge the possibility of AGI.

An Introduction to LSTM with Attention Model

Data Science Daily • 0 implied HN points • 01 Mar 23
🕹 Technology Machine Learning Neural Networks Artificial Intelligence Data science Applications
  1. LSTM models are good for handling input sequences of varied length like in language modeling and translation.
  2. Attention models help LSTM models focus on important parts of a sequence, improving accuracy.
  3. Combining LSTM with attention models can lead to better predictions and performance in tasks like natural language processing and image captioning.

🤓LSTM Networks: The Power of Long-Short-Term Memory and comparisons to ARIMA/XGBoost/Prophet

Data Science Daily • 0 implied HN points • 23 Feb 23
🕹 Technology Data science Neural Networks Machine Learning
  1. LSTM Networks can remember information for long periods and are great for processing sequential data.
  2. LSTMs can handle a wide variety of input and output types, making them flexible for real-world data.
  3. LSTMs are powerful for time series forecasting but can be computationally expensive, especially with large datasets.

AI Explainability Is About to Get Worse

The Grey Matter • 0 implied HN points • 21 Apr 23
🕹 Technology AI Neural Networks Language Models Training Data
  1. AI explainability for large language models like GPT models is becoming more challenging as these models advance.
  2. Examining the model, training data, and asking the model are the three main ways to understand these models' capabilities, each with its limitations.
  3. As AI capabilities advance, the urgency to develop better AI explainability techniques grows to keep pace with the evolving landscape.

OneClick Stable Diffusion Installer that comes prepackaged with SD1.5 and SD2.0

Barn Lab • 0 implied HN points • 25 Apr 23
🕹 Technology AI Software Installation Generative Art Neural Networks
  1. The OneClick Stable Diffusion Installer includes SD 1.5 and SD 2.0 models to simplify installation for users.
  2. The installer provides integrated model downloader to access famous models within the SD interface.
  3. For those interested in AI generative art, AUTOMATIC1111 is a feature-packed interface worth exploring after trying InvokeAI.