The hottest Deep Learning Substack posts right now

And their main takeaways
Category
Top Technology Topics

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.

Two years later, deep learning is still faced with the same fundamental challenges

Marcus on AI • 61 HN points • 10 Mar 24
🕹 Technology AI Deep Learning Artificial Intelligence
  1. Deep learning still faces fundamental challenges after two years - progress made, but not in all areas.
  2. Obstacles to general intelligence persist despite advancements like GPT-4 and Sora.
  3. Scaling in deep learning hasn't solved issues like genuine comprehension; there's acknowledgment of a potential plateau in AI innovation.

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.
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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.

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.

Artificial Intelligence: The importance of temporal validity for financial AI

The Fintech Blueprint • 78 implied HN points • 09 Jan 24
🕹 Technology Artificial Intelligence Financial Services Machine Learning Deep Learning Neural Networks
  1. Understanding time series data can give a competitive edge in the financial markets.
  2. Fintech's future relies on building better AI models with temporal validity.
  3. AI in finance involves LLMs, generative AI, machine learning, deep learning, and neural networks.

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.

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.

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.

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.

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.

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.

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.

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).

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.

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.

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.

Evals are all we need

Dubverse Black • 58 implied HN points • 26 Oct 23
🕹 Technology AI Voice Cloning Deep Learning
  1. Evaluations are crucial for advancing voice cloning technology
  2. Open-source community is making strides in developing Large Language Models
  3. Mean Opinion Score (MOS) and proposed evals like Speaker Similarity and Intelligibility are important for evaluating voice cloning technology

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.

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

Technology Made Simple • 99 implied HN points • 11 Jul 23
🕹 Technology AI Deep Learning Neural Networks Machine Learning Natural Language Processing
  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.

What CEOs, leaders, and investors need to know about the different meanings of AI

Mike Talks AI • 78 implied HN points • 27 Jul 23
🕹 Technology AI Analytics Deep Learning Machine Learning Algorithms
  1. The term AI can mean different things and understanding those meanings is crucial for clear communication, better decisions, and addressing concerns.
  2. Different definitions of AI include AGI or artificial general intelligence, deep learning for solving complex problems, and tools like ChatGPT for tasks like writing and summarizing.
  3. CEOs, leaders, and investors should explore opportunities in AGI, deep learning, ChatGPT, and practical AI to stay relevant and make informed decisions.

How the field of "AI" got like this

Apperceptive (moved to buttondown) • 20 implied HN points • 02 Nov 23
🕹 Technology AI Machine Learning Computer Science Deep Learning Models
  1. The field of AI can be hostile to individuals who are not white men, which hinders progress and innovation.
  2. The history of AI showcases past failures and the subsequent shift towards more practical, engineering-focused approaches like machine learning.
  3. Success in the AI field is heavily reliant on performance advancements on known benchmarks, emphasizing practical engineering solutions.

Why I'm allergic to AI hype

More is Different • 7 implied HN points • 06 Jan 24
🕹 Technology AI Healthcare Research Deep Learning Generative AI
  1. Data science jobs may not be as glamorous as they seem, often involving mundane tasks and not much intellectual excitement.
  2. Efforts to create AGI have faced challenges, with ambitious projects like Mindfire encountering skepticism and practical difficulties.
  3. AI in healthcare, such as for radiology, has seen startups struggle and face issues like lack of affordability, deployment challenges, and unpredictability in performance.

Are all hard technical problems AI problems now?

Perceptions • 35 implied HN points • 17 Feb 23
🕹 Technology AI Deep Learning Automation Programming Tech Ethics
  1. AI has made significant progress in solving complex technical problems in various domains.
  2. Many technical problems can be boiled down to optimization/minimization challenges, which AI is well-equipped to handle.
  3. The advancement in AI technology raises questions about the future of work, centralization, and the impact on different professions.