The hottest Machine Learning Substack posts right now

And their main takeaways
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Why Graphs are great for Fraud Detection [Math Mondays]

Technology Made Simple • 639 implied HN points • 01 Jan 24
🕹 Technology Graphs Neural Networks Machine Learning Data Structures
  1. Graphs are efficient at encoding and representing relationships between entities, making them useful for fraud detection tasks.
  2. Graph Neural Networks excel at fraud detection due to their ability to visualize strong correlations among fraudulent activities that share common properties, adapt to new fraud patterns, and offer transparency in AI systems.
  3. Graph Neural Networks require less labeled data and feature engineering compared to other techniques, have better explainability, and work well with semi-supervised learning, making them a powerful tool for fraud detection.

Edge 371: Two-Step LLM Reasoning with Skeleton of Thoughts

TheSequence • 266 implied HN points • 20 Feb 24
🕹 Technology Artificial Intelligence Machine Learning
  1. The Skeleton-of-Thoughts (SoT) technique introduces a two-stage process for answer generation in Large Language Models (LLMs) by first creating a basic outline or 'skeleton' of the response and then elaborating on each point simultaneously.
  2. SoT was initially designed to reduce latency in end-to-end inference in LLMs but has significantly impacted the reasoning space by mimicking non-linear human thought patterns.
  3. Microsoft's original SoT paper and the Dify framework for building LLM apps are discussed in Edge 371, providing insights into the innovative techniques used in the field of Large Language Models.

How AI Will Be Used for Cyber Security in 2024

Rod’s Blog • 615 implied HN points • 29 Dec 23
🕹 Technology AI Cyber Security Machine Learning Data Protection
  1. Cyber security is crucial in today's digital era due to increasing complexity of attacks, making traditional defense methods inadequate.
  2. Artificial intelligence (AI) is becoming essential in fighting cyber threats by mimicking human intelligence in tasks like learning and decision-making.
  3. In 2024, AI will play a vital role in cyber security, aiding in threat detection, prevention, response, and recovery.
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Do Not Spend too Much Time "Getting Good" at Dealing with Current AML GPT LLMs

Brad DeLong's Grasping Reality • 169 implied HN points • 04 Mar 24
🕹 Technology Machine Learning Artificial Intelligence Data science Internet Software
  1. It's uncertain how current AML GPT LLMs will be most useful in the future, so spending too much time trying to master them may not be the best approach.
  2. Proper prompting is crucial when working with AML GPT LLMs as they can be capable of more than initially apparent. Good prompts can make tasks that seem impossible into achievable ones.
  3. Understanding the thought processes and effective way to prompt AML GPT LLMs is essential, as their responses can vary based on subtle changes or inadequate prompting.

Model commoditization and product moats

Democratizing Automation • 126 implied HN points • 13 Mar 24
🕹 Technology Artificial Intelligence Data Infrastructure Model Training Open Source Machine Learning
  1. Models like GPT4 have been replicated in many organizations, leading to a situation where moats are less significant in the language model space.
  2. The open LLM ecosystem is progressing, but there are challenges in data infrastructure and coordination, potentially leading to a gap between open and closed models.
  3. Despite some skepticism, Language Models have been consistently enhancing their reliability making them increasingly useful for various applications, with potential for new transformative uses.

Me and My AI Ugh Field

Atlas of Wonders and Monsters • 373 implied HN points • 25 Jan 24
🕹 Technology AI Machine Learning
  1. The author struggles with conflicting feelings about their career and education choices
  2. There's a concept of 'ugh fields' where the author subconsciously avoids tasks, even in their field of interest
  3. Despite challenges, the author believes in the importance of pursuing careers aligned with genuine excitement and passion

How I Taught Myself to Critique AI Research [Storytime Saturdays]

Technology Made Simple • 119 implied HN points • 10 Mar 24
🕹 Technology AI Research Writing Machine Learning System Design
  1. Writing allows you to store knowledge for future reference, spot cognitive blindspots, and engage with topics more deeply for better understanding.
  2. Challenges in self-learning writing include lack of contextual understanding, a defined learning path, and a peer network for feedback.
  3. Addressing challenges in self-learning involves finding strategies to gain clarity, identifying knowledge gaps, and seeking feedback from peers.

ByteDance's new fine-tuning technique boost LLMs for reasoning tasks

TechTalks • 314 implied HN points • 22 Jan 24
🕹 Technology AI Machine Learning Algorithm Research Training
  1. A new fine-tuning technique called Reinforced Fine-Tuning improves large language models for reasoning tasks.
  2. Reinforced Fine-Tuning combines supervised fine-tuning with reinforcement learning to enhance reasoning capabilities.
  3. ReFT helps models discover new reasoning paths without needing extra training data and outperforms traditional fine-tuning methods on reasoning benchmarks.

How to Use AI to Do Stuff: An Opinionated Guide

One Useful Thing • 1801 implied HN points • 15 Jul 23
🕹 Technology AI Machine Learning Programming Data Analysis Education
  1. Increasingly powerful AI systems are being released rapidly without proper user documentation.
  2. The major Large Language Models in use currently are GPT-3.5, GPT-4, Bard, Pi, and Claude 2.
  3. AI can assist with writing, generating images, coming up with ideas, making videos, and working with documents and data, but users must be cautious of biases and ethical concerns.

📝 Guest Post: Evaluating LLM Applications*

TheSequence • 91 implied HN points • 11 Mar 24
🕹 Technology AI Machine Learning Evaluation Development Monitoring
  1. Traditional software development practices like automation and testing suites are valuable when evaluating Large Language Models (LLMs) for AI applications.
  2. Different types of evaluations, including judgment return types and sources, are important for assessing LLMs effectively.
  3. A robust evaluation process for LLM applications involves interactive, batch offline, and monitoring online stages to support rapid iteration cycles and performance improvements.

Demystifying GPU Compute Architectures

The Chip Letter • 210 HN points • 04 Feb 24
🕹 Technology Machine Learning
  1. Understanding GPU compute architectures is crucial for maximizing their potential in machine learning and parallel computing.
  2. The complexity of GPU architectures stems from differences in terminology, architectural variations, legacy terminology, software abstractions, and specific dominance by CUDA.
  3. Examining the levels in GPU compute hardware - basic units, grouped units (Streaming Multiprocessor or Compute Unit), and final GPU architecture - reveals a high level of computational power compared to CPUs.

Chip Letter Links No. 18: NexGen, Nvidia, Die Topology, FastGPT in Fortran and more

The Chip Letter • 2055 implied HN points • 04 Jun 23
🕹 Technology Semiconductors Machine Learning Tech Companies
  1. Nvidia briefly joined the trillion dollar market cap club, surpassing Intel, AMD, and TSMC combined.
  2. Jensen Huang, CEO of Nvidia, gave a commencement speech while unveiling the Grace Hopper 'superchip'.
  3. Explanation on why Rosetta 2 runs so fast on Apple Silicon Macs, highlighting the engineering tradeoffs made.

Edge 376: The Creators of Vicuna and Chatbot Arena Built SGLang for Super Fast LLM Inference

TheSequence • 98 implied HN points • 07 Mar 24
🕹 Technology Artificial Intelligence Programming Open Source Machine Learning
  1. SGLang is a new open source project from Berkeley University designed to enhance interactions with Large Language Models (LLMs), making them faster and more manageable.
  2. SGLang integrates backend runtime systems with frontend languages to provide better control over LLMs, aiming to optimize the processes involved in working with these models.
  3. The framework created by LMSys offers significant optimizations that can boost the inference times in LLMs by up to 5 times, showcasing advancements in processing vast amounts of data at incredible speeds.

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.

Edge 378: Meet TimesFM: Google's New Foundation Model for Time-Series Forecasting

TheSequence • 70 implied HN points • 14 Mar 24
🕹 Technology Machine Learning AI
  1. Time series forecasting is crucial in various fields like retail, finance, manufacturing, healthcare, and more, despite lagging behind other areas in AI development.
  2. Google has introduced TimeFM, a pretrain model with 200M parameters trained on over 100 billion time series data points, aiming to advance forecasting accuracy.
  3. The new TimeFM model from Google will soon be accessible in Vertex AI, showcasing a shift towards leveraging pretrained models for time series forecasting.

Google ships it: Gemma open LLMs and Gemini backlash

Democratizing Automation • 118 implied HN points • 22 Feb 24
🕹 Technology AI Open Source Machine Learning Research
  1. Google released Gemma, an open-weight model, which introduces new standards with 7 billion parameters and has unique architecture choices.
  2. The Gemma model addresses training issues with a unique pretraining annealing method, REINFORCE for fine-tuning, and a high capacity model.
  3. Google faced backlash for image generations from its Gemini series, highlighting the complexity in ensuring multimodal RLHF and safety fine-tuning in AI models.

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.

From 0 to 2M Users in Generative AI

Startup Pirate by Alex Alexakis • 235 implied HN points • 12 Jan 24
🕹 Technology AI Product Design Machine Learning Artificial Intelligence Tech Innovation
  1. Uizard has over 2 million users and enables fast product design creation with AI and an intuitive editor.
  2. Their technology includes deep learning, computer vision, and natural language processing to power their platform.
  3. Product market fit for Uizard was achieved by shifting focus to non-experts and iterating based on user feedback.

Creating Individualised Chess Engines

Chess Engine Lab • 39 implied HN points • 26 Mar 24
🕹 Technology Artificial Intelligence Data Analysis Programming Chess Machine Learning
  1. An engine called Maia focused on predicting human moves accurately instead of just being the strongest in chess, resulting in a more meaningful impact, especially for club-level players.
  2. By individualizing chess engines to predict moves of specific players, accuracy can be increased by 4-5% and players can be identified with 98% accuracy from a pool of 400, based on their game patterns.
  3. Identifying players through their mistakes is a crucial aspect - as mistakes are unique to individual players, understanding and fixing them can greatly aid in chess improvement.

RLHF is the Problem and the Solution

From the New World • 86 implied HN points • 28 Feb 24
🕹 Technology AI Machine Learning Ethics Policy Innovation
  1. The goal of AI Pluralism is to ensure that machine models are not manipulated by third parties to conform to specific ideologies.
  2. Machine learning typically involves two stages: developing the model's capabilities and fine-tuning, which can influence the model's ideology and style.
  3. Requiring the release of both stages of the model can help curb extremist influence, but it may not completely eliminate ideological contamination in AI development.

Edge 372: Learn About CALM, Google DeepMind's Method to Augment LLMs with Other LLMs

TheSequence • 98 implied HN points • 22 Feb 24
🕹 Technology Artificial Intelligence Machine Learning Data science Research
  1. Knowledge augmentation is crucial in LLM-based applications with new techniques constantly evolving to enhance LLMs by providing access to external tools or data.
  2. Exploring the concept of augmenting LLMs with other LLMs involves merging general-purpose anchor models with specialized ones to unlock new capabilities, such as combining code understanding with language generation.
  3. The process of combining different LLMs might require additional training or fine-tuning of the models, but can be hindered by computational costs and data privacy concerns.

Monosemanticity at Home: My Attempt at Replicating Anthropic's Interpretability Research from Scratch

Jake Ward's Blog • 2 HN points • 30 Apr 24
🕹 Technology AI Data science Machine Learning Research Interpretability
  1. Large language models like ChatGPT have complex, learned logic that is difficult to interpret due to 'superposition' - where single neurons correspond to multiple functions.
  2. Techniques like sparse dictionary learning can decompose artificial neurons into 'features' that exhibit 'monosemanticity', making the models more interpretable.
  3. Reproducing research on model interpretability shows promise for breakthroughs and indicates a shift towards engineering challenges over scientific barriers.

Google announces AI system for diagnostic medical reasoning and conversation

MLOps Newsletter • 176 implied HN points • 20 Jan 24
🕹 Technology AI Machine Learning Data science Software Development
  1. Google announced an AI system for medical diagnosis and conversation called AMIE.
  2. AMIE's architecture includes multi-turn dialogue management, hierarchical reasoning model, and modular design.
  3. The AI system AMIE showed promising performance in simulated diagnostic conversations, outperforming PCPs and matching specialist physicians.

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

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

Why reward models are key for alignment

Democratizing Automation • 110 implied HN points • 14 Feb 24
🕹 Technology AI Machine Learning Models Datasets Tools
  1. Reward models provide a unique way to assess language models without relying on traditional prompting and computation limits.
  2. Constructing comparisons with reward models helps identify biases and viewpoints, aiding in understanding language model representations.
  3. Generative reward models offer a simple way to classify preferences in tasks like LLM evaluation, providing clarity and performance benefits in the RL setting.