The hottest Deep Learning Substack posts right now

And their main takeaways
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Five ways in which the last 3 months — and especially the DeepSeek era — have vindicated “Deep learning is hitting a wall"

Marcus on AI • 7074 implied HN points • 09 Feb 25
🕹 Technology AI Machine Learning Deep Learning Data science
  1. Just adding more data to AI models isn't enough to achieve true artificial general intelligence (AGI). New techniques are necessary for real advancements.
  2. Combining neural networks with traditional symbolic methods is becoming more popular, showing that blending approaches can lead to better results.
  3. The competition in AI has intensified, making large language models somewhat of a commodity. This could change how businesses operate in the generative AI market.

Five things most people don't seem to understand about DeepSeek

Marcus on AI • 12133 implied HN points • 28 Jan 25
🕹 Technology AI Deep Learning Software Economics Geopolitics
  1. DeepSeek is not smarter than older models. It just costs less to train, which doesn't mean it's better overall.
  2. It still has issues with reliability and can be expensive to run if you want it to 'think' for longer.
  3. DeepSeek may change the AI market and pose challenges for companies like OpenAI, but it doesn't bring us closer to achieving artificial general intelligence (AGI).

An AI rumor you won’t want to miss

Marcus on AI • 7153 implied HN points • 10 Nov 24
🕹 Technology AI Deep Learning Scaling Data Computing
  1. The belief that more scaling in AI will always lead to better results might be fading. It's thought we might have reached a limit where simply adding more data and computing power is no longer effective.
  2. There are concerns that scaling laws, which have worked before, are just temporary trends, not true laws of nature. They don’t actually solve issues like AI making mistakes or hallucinations.
  3. If rumors are true about a major change in the AI landscape, it could lead to a significant loss of trust in these scaling approaches, similar to a bank run.

The Weekly Kaitchup #63

The Kaitchup – AI on a Budget • 119 implied HN points • 18 Oct 24
🕹 Technology AI Machine Learning Deep Learning Software Development Robotics
  1. There's a new fix for gradient accumulation in training language models. This issue had been causing problems in how models were trained, but it's now addressed by Unsloth and Hugging Face.
  2. Several new language models have been released recently, including Llama 3.1 Nemotron 70B and Zamba2 7B. These models are showing different levels of performance across various benchmarks.
  3. Consumer GPUs are being tracked for price drops, making them a more affordable option for fine-tuning models. This week highlights several models for those interested in AI training.

DeepSeek moment

Gonzo ML • 441 implied HN points • 27 Jan 25
🕹 Technology AI Models Machine Learning Open Source Deep Learning
  1. DeepSeek is a game-changer in AI, trained models at a much lower cost compared to its competitors like OpenAI and Meta. This makes advanced technology more accessible.
  2. They released new models called DeepSeek-V3 and DeepSeek-R1, which offer impressive performance and reasoning capabilities similar to existing top models. These require advanced setups but show promise for future development.
  3. Their multimodal model, Janus-Pro, can work with both text and images, and it reportedly outperforms popular models in generation tasks. This indicates a shift toward more versatile AI technologies.
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Weekly Top Picks #95

The Algorithmic Bridge • 191 implied HN points • 20 Jan 25
🕹 Technology Artificial Intelligence Open Source Deep Learning Public Perception
  1. DeepSeek-R1 shows that open-source AI models can compete with OpenAI's offerings, proving that smaller and cheaper options are just as effective.
  2. OpenAI's partnership with EpochAI raises questions about fairness, as they had exclusive access to important tools like FrontierMath.
  3. Writers are starting to recognize AI's writing abilities, a change they need to accept, even if it feels challenging at first.

R1 is reasoning for the masses

Artificial Ignorance • 176 implied HN points • 22 Jan 25
🕹 Technology AI Models Deep Learning Open Source Geopolitics Research
  1. DeepSeek's new AI model, R1, is making waves in the tech community. It can solve tough problems and is much cheaper to use than existing models.
  2. The research behind R1 is very transparent, showing how it was developed using common methods. This could help other researchers create similar models in the future.
  3. R1's success signals a shift in the AI race, especially with a Chinese company achieving this level of performance. It raises questions about the future of global AI competition.

OpenAI's Reinforcement Finetuning and RL for the masses

Democratizing Automation • 427 implied HN points • 11 Dec 24
🕹 Technology Artificial Intelligence Machine Learning Deep Learning Data science API Development
  1. Reinforcement Finetuning (RFT) allows developers to fine-tune AI models using their own data, improving performance with just a few training samples. This can help the models learn to give correct answers more effectively.
  2. RFT aims to solve the stability issues that have limited the use of reinforcement learning in AI. With a reliable API, users can now train models without the fear of them crashing or behaving unpredictively.
  3. This new method could change how AI models are trained, making it easier for anyone to use reinforcement learning techniques, not just experts. This means more engineers will need to become familiar with these concepts in their work.

Cracking biological emergence with mechanistic interpretability

Trevor Klee’s Newsletter • 597 implied HN points • 26 Nov 24
🔬 Science Biology Emergence Deep Learning
  1. Emergent properties in biology can be hard to connect, kind of like trying to understand a car by randomly taking it apart. Even as we learn about proteins and genes, connecting them to actual biological traits remains a challenge.
  2. Deep learning models like Alpha Fold are changing the game by revealing connections between micro and macro biological features, even if we don't fully understand how they do it. It's like having a model that can assemble a car based on its parts without exactly knowing how all those parts work together.
  3. Recently, there's been exciting work in mechanistic interpretability, which helps us understand how these deep learning models make sense of biology. This could lead to new insights and even virtual experiments that help us learn about cell behavior and gene interactions.

Import AI 372: Gibberish jailbreak; DeepSeek's great new model; Google's soccer-playing robots

Import AI • 399 implied HN points • 13 May 24
🕹 Technology AI Research Language Models Deep Learning Simulation Ethics
  1. DeepSeek released a powerful language model called DeepSeek-V2 that surpasses other models in efficiency and performance.
  2. Research from Tsinghua University shows how mixing real and synthetic data in simulations can improve AI performance in real-world tasks like medical diagnosis.
  3. Google DeepMind trained robots to play soccer using reinforcement learning in simulation, showcasing advancements in AI and robotics;

The Sequence Opinion #480: What is GPT-o1 Actually Doing?

TheSequence • 161 implied HN points • 30 Jan 25
🕹 Technology AI Machine Learning Deep Learning Software Development Data science
  1. GPT models are becoming more advanced in reasoning and problem-solving, not just generating text. They are now synthesizing programs and refining their results.
  2. There's a focus on understanding how these models work internally through ideas like hypothesis search and program synthesis. This helps in grasping the real innovation they bring.
  3. Reinforcement learning is a key technique used by newer models to improve their outputs. This shows that they are evolving and getting better at what they do.

Visualizing Sound, Lorenz in Blender, and Painterly Shaders

Generative Arts Collective • 92 implied HN points • 09 Nov 24
🕹 Technology Creative Coding Generative Arts 3D Modeling Deep Learning
  1. Using technology like deep learning can help identify nature sounds, like birds, which can be both fun and scientific.
  2. Blender and Python are great tools for visualizing complex concepts, like the Lorenz attractor, in a visually appealing way.
  3. Creating artistic effects in 3D, such as painterly shaders, allows artists to bring unique styles and expressions to their digital work.

Import AI 337: Why I am confused about AI; penguin dataset; and defending networks via RL with CYBERFORCE

Import AI • 718 implied HN points • 21 Aug 23
🕹 Technology AI Development Deep Learning Reinforcement Learning Cybersecurity
  1. Debate on whether AI development should be centralized or decentralized reflects concerns about safety and power concentration
  2. Discussion on the importance of distributed training and finetuning versus dense clusters highlights evolving AI policy and governance ideas
  3. Exploration of AI progress without needing 'black swan' leaps raises questions about the need for heterodox strategies and societal permissions for AI developers

Advanced Prompt Engineering

Deep (Learning) Focus • 609 implied HN points • 08 May 23
🕹 Technology AI/ML Deep Learning Prompt engineering Information Retrieval
  1. LLMs can solve complex problems by breaking them into smaller parts or steps using CoT prompting.
  2. Automatic prompt engineering techniques, like gradient-based search, provide a way to optimize language model prompts based on data.
  3. Simple techniques like self-consistency and generated knowledge can be powerful for improving LLM performance in reasoning tasks.

Practical Prompt Engineering (Part One)

Deep (Learning) Focus • 373 implied HN points • 01 May 23
🕹 Technology AI Machine Learning Prompt engineering Chatbots Deep Learning
  1. LLMs are powerful due to their generic text-to-text format for solving a variety of tasks.
  2. Prompt engineering is crucial for maximizing LLM performance by crafting detailed and specific prompts.
  3. Techniques like zero and few-shot learning, as well as instruction prompting, can optimize LLM performance for different tasks.

RAG Survey & Available Research

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 39 implied HN points • 27 Jun 24
🕹 Technology AI Machine Learning Natural Language Data science Deep Learning
  1. Retrieval-Augmented Generation (RAG) mixes retrieval methods with learning systems to help large language models use real-time data.
  2. RAG can enhance the accuracy of language models by incorporating current information, avoiding wrong answers that might come from outdated knowledge.
  3. The framework of RAG includes steps like pre-retrieval, retrieval, post-retrieval, and generation, each contributing to better outputs in language processing tasks.

So... what is the lottery ticket hypothesis [Math Mondays]

Technology Made Simple • 159 implied HN points • 05 Feb 24
🕹 Technology Deep Learning Neural Networks Mathematics AI Machine Learning
  1. The Lottery Ticket Hypothesis proposes that within deep neural networks, there are subnetworks capable of achieving high performance with fewer parameters, leading to smaller and faster models.
  2. Successful application of the Lottery Ticket Hypothesis relies on iterative magnitude pruning strategies, with potential benefits like faster learning and higher accuracy.
  3. The hypothesis works due to factors like favorable gradients, implicit regularization, and data alignment, but challenges like scalability and interpretability remain towards practical implementation.

Edge 459: Quantization Plus Distillation

TheSequence • 77 implied HN points • 24 Dec 24
🕹 Technology Machine Learning AI Models Data science Model optimization Deep Learning
  1. Quantized distillation helps make deep neural networks smaller and faster by combining two techniques: knowledge distillation and quantization.
  2. This method transfers knowledge from a high-precision model (teacher) to a low-precision model (student) without losing much accuracy.
  3. Using soft targets from the teacher model can reduce problems that often come with using simpler models, keeping performance strong.

Chain of Thought Prompting for LLMs

Deep (Learning) Focus • 294 implied HN points • 24 Apr 23
🕹 Technology AI Deep Learning Language Models Reasoning Prompting
  1. CoT prompting leverages few-shot learning in LLMs to improve their reasoning capabilities, especially for complex tasks like arithmetic, commonsense, and symbolic reasoning.
  2. CoT prompting is most beneficial for larger LLMs (>100B parameters) and does not require fine-tuning or extensive additional data, making it an easy and practical technique.
  3. CoT prompting allows LLMs to generate coherent chains of thought when solving reasoning tasks, providing interpretability, applicability, and computational resource allocation benefits.

Imitation Models and the Open-Source LLM Revolution

Deep (Learning) Focus • 294 implied HN points • 19 Jun 23
🕹 Technology Deep Learning Natural Language Processing Open Source
  1. Creating imitation models of powerful LLMs is cost-effective and easy but may not perform as well as proprietary models in broader evaluations.
  2. Model imitation involves fine-tuning a smaller LLM using data from a more powerful model, allowing for behavior replication.
  3. Open-source LLMs, while exciting, may not close the gap between paid and open-source models, highlighting the need for rigorous evaluation and continued development of more powerful base models.

Get the Most Out of Your Custom LLMs

Gradient Flow • 279 implied HN points • 15 Jun 23
🕹 Technology AI Machine Learning Deep Learning Data Analysis Tools
  1. Custom Large Language Models (LLMs) and Custom Foundation Models can enhance accuracy, data privacy, and security in specialized fields like healthcare, law, and finance.
  2. Training custom models involves crucial stages like Pre-training, Supervised Fine-Tuning, Reward Modeling, and Reinforcement Learning.
  3. WeightWatcher is an open-source tool that helps analyze and improve the performance of deep learning models, aiding in conserving resources, detecting model saturation, and enhancing model quality.

How to AI

The Intersection • 277 implied HN points • 19 Sep 23
🕹 Technology AI Generative AI Machine Learning Deep Learning Digital Design
  1. History often repeats itself in the adoption of new technologies, as seen with the initial skepticism towards digital marketing and now with AI.
  2. Brands are either cautiously experimenting with AI for PR purposes or holding back due to concerns like data security, plagiarism, and unforeseen outcomes.
  3. AI's evolution spans from traditional artificial intelligence to the current era dominated by generative AI, offering operational efficiency, creative enhancements, and transformative possibilities.

Beyond LLaMA: The Power of Open LLMs

Deep (Learning) Focus • 275 implied HN points • 17 Apr 23
🕹 Technology Open Source Deep Learning Language Models Chatbots
  1. LLMs are becoming more accessible for research with the rise of open-source models like LLaMA, Alpaca, Vicuna, and Koala.
  2. Smaller LLMs, when trained on high-quality data, can perform impressively close to larger models like ChatGPT.
  3. Open-source models like Alpaca, Vicuna, and Koala are advancing LLM research accessibility, but commercial usage restrictions remain a challenge.

Understanding Large Language Models

Startup Pirate by Alex Alexakis • 216 implied HN points • 12 May 23
🕹 Technology AI Neural Networks Deep Learning Language Models AGI
  1. Large Language Models (LLMs) revolutionized AI by enabling computers to learn language characteristics and generate text.
  2. Neural networks, especially transformers, played a significant role in the development and success of LLMs.
  3. The rapid growth of LLMs has led to innovative applications like autonomous agents, but also raises concerns about the race towards Artificial General Intelligence (AGI).

Program-Aided Language Models

Deep (Learning) Focus • 196 implied HN points • 22 May 23
🕹 Technology AI Programming Reasoning Language Models Deep Learning
  1. LLMs can struggle with tasks like arithmetic and complex reasoning, but using an external code interpreter can help them compute solutions more accurately.
  2. Program-Aided Language Models (PaL) and Program of Thoughts (PoT) techniques leverage both natural language and code components to enhance reasoning capabilities of LLMs.
  3. Decoupling reasoning from computation within LLMs through techniques like PaL and PoT can significantly improve performance on complex numerical tasks.

Model merging lessons in The Waifu Research Department

Democratizing Automation • 209 implied HN points • 29 Jan 24
🕹 Technology AI Deep Learning Machine Learning Robotics
  1. Model merging is a way to blend two model weights to create a new model, useful for experimenting with large language models.
  2. Model merging is popular in creating anime models by merging Stable Diffusion variants, allowing for unique artistic results.
  3. Weight averaging techniques in model merging aim to find more robust solutions by creating models centered in flat regions of the loss landscape.

Language Models and Friends: Gorilla, HuggingGPT, TaskMatrix, and More

Deep (Learning) Focus • 176 implied HN points • 05 Jun 23
🕹 Technology Deep Learning API Integration Fine-tuning Models
  1. Specialized models are hard to beat in performance compared to generic foundation models.
  2. Combining language models with specialized deep learning models by calling their APIs can lead to solving complex AI tasks.
  3. Empowering language models with access to diverse expert models via APIs brings us closer to realizing artificial general intelligence.

Teaching Language Models to use Tools

Deep (Learning) Focus • 176 implied HN points • 29 May 23
🕹 Technology AI Machine Learning APIs Models Deep Learning
  1. Teaching LLMs to use tools can help them overcome limitations like arithmetic mistakes, lack of current information, and difficulty with understanding time.
  2. Giving LLMs access to external tools can make them more capable in solving complex tasks by delegating subtasks to specialized tools.
  3. Different forms of learning for LLMs include pre-training, fine-tuning, and in-context learning, which all contribute to enhancing the model's performance and capability.

A State-of-the-Art Survey of Text-to-Speech Technology 2023

Dubverse Black • 157 implied HN points • 24 Oct 23
🕹 Technology AI Generative AI Text-to-Speech Deep Learning
  1. The latest innovation in Generative AI focuses on Speech Models that can produce human-like voices, even in songs.
  2. Self-Supervised Learning is revolutionizing Text-to-Speech technology by allowing models to learn from unlabelled data for better quality outcomes.
  3. Text-to-Speech systems are structured in three main parts, utilizing models like TORTOISE and BARK to produce expressive and high-quality audio.

The Sequence Knowledge #463: Wrapping Up our Series About Knowledge Distillation: Pros and Cons

TheSequence • 35 implied HN points • 07 Jan 25
🕹 Technology Machine Learning Artificial Intelligence Data science Deep Learning Research
  1. Knowledge distillation is a method where a smaller model learns from a larger, more complex model. This helps make the smaller model efficient while retaining essential features.
  2. The series covered different techniques and challenges in knowledge distillation, highlighting its importance in machine learning and AI development. Understanding these can help when deciding if this approach is suitable for your projects.
  3. It's useful to be aware of both the benefits and drawbacks of knowledge distillation. This helps in figuring out the best way to implement it in real-world applications.

T5: Text-to-Text Transformers (Part One)

Deep (Learning) Focus • 157 implied HN points • 27 Mar 23
🕹 Technology Deep Learning NLP Model Training
  1. Transfer learning is powerful in deep learning, involving pre-training a model on one dataset then fine-tuning it on another for better performance.
  2. After BERT's breakthrough in NLP with transfer learning, T5 aims to analyze and unify various approaches that followed, improving effectiveness.
  3. T5 introduces a text-to-text framework for structuring tasks uniformly, simplifying how language tasks are converted to input-output text formats for models.

Modular Deep Learning

MLOps Newsletter • 78 implied HN points • 27 Jan 24
🕹 Technology Deep Learning Generative models Classes
  1. Modular Deep Learning proposes splitting models into smaller, independent modules for specific subtasks.
  2. Modularity in AI development can lead to collaborative and efficient ecosystem and democratize AI development.
  3. PyTorch 2.0 introduces performance gains such as faster inference and training speeds, autotuning, quantization, and improved memory management.

RLHF progress: Scaling DPO to 70B, DPO vs PPO update, Tülu 2, Zephyr-β, meaningful evaluation, data contamination

Democratizing Automation • 213 implied HN points • 22 Nov 23
🕹 Technology AI Deep Learning Research Data Analysis Models
  1. Reinforcement learning from human feedback (RLHF) is a technology that is still unknown and undocumented.
  2. Scaling DPO to 70B parameters showed strong performance by directly integrating the data and using lower learning rates.
  3. DPO and PPO have differences in their approaches, with DPO showing potential for enhancing chat evaluations and happy users of Tulu and Zephyr models.

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 AI Machine Learning Software Development Neural Networks Deep Learning
  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.