The hottest Models Substack posts right now

And their main takeaways
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On creativity and language models

Philosophy bear • 48 implied HN points • 15 Feb 24
🔬 Science AI Creativity Language Models Philosophy
  1. Creativity involves putting things together in a new way, whether it's useful, thoughtful, beautiful, or admirable. It's all about recombining existing elements.
  2. The level of creativity depends on how new and good something is. Any new sentence can be seen as somewhat creative, but the degree varies.
  3. There doesn't seem to be a definite line between different levels of creativity; they all involve rearrangements of existing elements. It's a spectrum of newness and usefulness.

Why Doesn’t My Model Work?

The Gradient • 36 implied HN points • 24 Feb 24
🔬 Science Machine Learning Data Analysis Research Models Errors
  1. Machine learning models can sometimes seem good but fail when applied to real-world data due to complexities that cause overfitting without being obvious
  2. Issues with machine learning models are increasingly reported in scientific and popular media, impacting tasks like pandemic response or water quality assessments
  3. Preventing mistakes in machine learning involves using tools like the REFORMS checklist for ML-based science to ensure reproducibility and accuracy

Large Language Models vs Small Language Models: A Comparison

Rod’s Blog • 39 implied HN points • 20 Feb 24
🕹 Technology AI NLP Models Comparison
  1. Language models come in different sizes, architectures, training data, and capabilities.
  2. Large language models have billions or trillions of parameters, enabling them to be more complex and expressive.
  3. Small language models have less parameters, making them more efficient and easier to deploy, though they might be less versatile than large language models.

I have access to Claude-3 Opus, a (seemingly) considerably more advanced model than GPT-4, ask it anything

Philosophy bear • 27 implied HN points • 05 Mar 24
🕹 Technology AI Models Artificial Intelligence Data Machine Learning
  1. Claude-3 Opus is a highly advanced model compared to GPT-4, especially in reasoning capabilities, scoring impressively on GPQA and other tests.
  2. The model's knowledge base is top-notch, performing as well as or better than a graduate student with Google access in specific sciences.
  3. Questions posed to Claude-3 Opus should be challenging, aiming for queries that most people would answer correctly but the model might get wrong, to reveal its strengths and weaknesses.
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OpenAI Dev Day

Bojan’s Newsletter • 157 implied HN points • 15 Nov 23
🕹 Technology AI API Models Development Future Tech
  1. Key announcements at OpenAI Dev Day included GPT4 Turbo, GPT Store launch, ChatGPT API introduction, new Text-to-speech API, DALL-E 3 API, Whisper 3 unveil, and Copyright Shield.
  2. Developers can create and customize GPTs for specific use cases easily.
  3. OpenAI emphasized gradual AI model advancements and the transformative impact AI will have on various industries in the near future.

Graph Neural Networks in Tensorflow

MLOps Newsletter • 39 implied HN points • 10 Feb 24
🕹 Technology LLMs Chatbots Frameworks Models
  1. Graph Neural Networks in TensorFlow address data complexity, limited resources, and generalizability in learning from graph-structured data.
  2. RadixAttention and Domain-Specific Language (DSL) are key solutions for efficiently controlling Large Language Models (LLMs), reducing memory usage, and providing a user-friendly interface.
  3. VideoPoet demonstrates hierarchical LLM architecture for zero-shot learning, handling multimodal input, and generating various output formats in video generation tasks.

Edge 379: A Summary Of Our Series About LLM Reasoning

TheSequence • 14 implied HN points • 19 Mar 24
🕹 Technology AI Research Tech Models Applications
  1. The series explored different methods and technologies related to reasoning in Large Language Models (LLMs).
  2. Reasoning in LLMs involves working through problems logically to reach conclusions, emerging at a certain scale and not applicable to small models.
  3. The series covered topics like Chain-of-Thought (CoT), System 2 Attention (S2A), tree-of-thoughts, and graph-of-thoughts as techniques for LLM reasoning.

AI Roundup 050: Synthetic Geometry

Artificial Ignorance • 54 implied HN points • 19 Jan 24
🕹 Technology AI Hardware Elections Models Research
  1. A new Google Deepmind model named AlphaGeometry can solve International Math Olympiad problems at a near-gold medalist level.
  2. OpenAI is addressing concerns about AI in worldwide elections by focusing on preventing abuse, transparency of AI content, and improving access to voting information.
  3. Samsung's Galaxy Unpacked event introduced new AI features for Samsung phones, including live translation and AI-powered note organization.

So... what is multi-modal AI? And why is the internet losing their mind about it? [Math Mondays]

Technology Made Simple • 159 implied HN points • 10 Oct 23
🕹 Technology AI Machine Learning Data Models Embeddings
  1. Multi-modal AI integrates multiple types of data in the same training process, allowing models to represent data in a common n-dimensional space.
  2. Multi-modality adds an extra dimension to data, expanding the search space exponentially, enabling more diverse and powerful AI applications.
  3. While multi-modality enhances model performance, it does not solve fundamental issues with AI models like GPT, and simpler technologies may be more effective for certain use-cases.

Ensemble Weather Forecast API

Open-Meteo • 351 implied HN points • 05 Jun 23
🕹 Technology Weather API Forecasting Models Documentation
  1. Ensemble weather forecasts show a range of possibilities, helping to understand the uncertainty in predictions.
  2. Weather forecasts differ in reliability based on location and weather patterns, affecting the level of uncertainty in predictions.
  3. The Ensemble API combines various weather models, providing access to different weather variables for various purposes.

AI Safety: A Technical & Ethnographic Overview

jonstokes.com • 391 implied HN points • 30 Mar 23
🕹 Technology AI Ethics Research Models Intelligence
  1. The AI safety debate involves technical details about AI systems like GPT-4 and cultural dynamics around the issue.
  2. The discussion includes concerns about regulating and measuring AI capabilities, as well as the divisions and allegiances within different groups.
  3. Some groups, like the Intelligence Deniers, have strong beliefs about AI being a scam and hold firm against AI progress, leading to potential divisions among AI safety proponents.

Democratizing AI: MosaicML's Impact on the Open-Source LLM Movement

Deep (Learning) Focus • 255 implied HN points • 03 Jul 23
🕹 Technology AI Open Source Software Models Training
  1. Creating a more powerful base model is crucial for improving downstream applications of Large Language Models (LLMs).
  2. MosaicML's release of MPT-7B and MPT-30B has revolutionized the open-source LLM community by offering high-performing, commercially-usable models for practitioners in AI.
  3. MPT-7B and MPT-30B showcase innovations like ALiBi, FlashAttention, and low precision layer norm, leading to faster training, better performance, and support for longer context lengths.

A model of models of probability

Bram’s Thoughts • 78 implied HN points • 23 Nov 23
🔬 Science Probability Models Human behavior
  1. People generally have a simplified internal model of probability with five main categories.
  2. People tend to struggle with accurately gauging differences in expected values within the 40-60% range.
  3. Individuals often display overconfidence in their predictions for probable events and can become overly upset when these predictions fail.

Calude 3 Opus, Train a 70b language model at home, Firewall for AI, Fast 3D Object Generation from Single Images, multimodal foundation model for any-to-any search tasks, and more

AI Brews • 12 implied HN points • 08 Mar 24
🕹 Technology AI Models Open Source Tools Partnerships
  1. New advanced AI models like Claude 3 are being introduced with enhanced features and capabilities, outperforming previous models on various benchmarks.
  2. Innovations in AI technology include tools like a fast 3D object generation model from a single image and a multimodal foundation model for diverse search tasks.
  3. Developments in AI also focus on enabling training large language models at home, creating AI firewalls for protection, and making AI tools more accessible and efficient.

Prompt Ensembles Make LLMs More Reliable

Deep (Learning) Focus • 275 implied HN points • 15 May 23
🔬 Science Research Machine Learning AI Techniques Models
  1. Reliability is crucial when working with large language models, and prompt ensembles offer a straightforward way to make them more accurate and consistent.
  2. Prompt ensembles show generalization across different language models, reducing sensitivity to changing underlying models and prompts.
  3. Aggregation of multiple outputs from prompt ensembles is complex but crucial for improving model performance, requiring sophisticated strategies beyond simple majority voting.

Models

Sriram Krishnan’s Newsletter • 216 implied HN points • 20 Jun 23
🕹 Technology AI Data Models
  1. Large-language models are open-sourced and ranked based on benchmarks like ChatGPT and Google Bard.
  2. Model performance improves with each iteration, leading to better models rising and lesser ones fading out.
  3. Different types of data sources contribute to the creation of unique models, with more gated data leading to more variety.

Catechizing the Bots, Part 1: Foundation Models and Fine-Tuning

jonstokes.com • 237 implied HN points • 28 May 23
🕹 Technology AI Text Training Models Ethics
  1. Foundation models for large language models go through fine-tuning phases to make them more user-friendly.
  2. Humans play a critical role in shaping the values and behaviors of these models during the fine-tuning process.
  3. Supervised fine-tuning involves exposing the model to smaller sets of carefully selected examples to anchor its output and establish dominant language structures.

Leviathan wakes

Gradient Ascendant • 16 implied HN points • 21 Feb 24
🕹 Technology AI Forecasting Generative AI Models Artificial Intelligence
  1. The author quit their job to work on a new AI-related project motivated by the transformative potential of modern AI technology.
  2. Google's Gemini 1.5 model is a significant advancement in AI capabilities, able to handle an impressive 10 million tokens for input, marking a major leap forward in AI development.
  3. Despite its imperfections, Gemini 1.5 and other advanced AI models are drastically reducing limitations and opening up new possibilities for future technological innovations.

Pinterest improves their Closeup Recommendation System through foundational changes

MLOps Newsletter • 98 implied HN points • 07 Oct 23
🕹 Technology AI Data ML Models Libraries
  1. Pinterest improved their Closeup Recommendation System with foundational changes like hybrid data logging and sampling.
  2. Pinterest uses a model refreshing framework to keep their Closeup Recommendation model up-to-date and adaptable.
  3. Distilling step-by-step can help train smaller, more efficient, and interpretable language models like LLMs.

TikTok's Recommendation Engine Explained: Monolith

MLOps Newsletter • 157 implied HN points • 30 Jul 23
🕹 Technology AI Machine Learning Libraries Models
  1. TikTok's recommendation system is designed to give real-time suggestions by using sparsity-aware factorization machines, online learning, and caching.
  2. Multimodal deep learning focuses on text-image modeling due to lack of large annotated datasets for other modalities like video and audio.
  3. A new framework called Parsel enables automatic implementation of complex algorithms with code language models, leading to better problem-solving results in competitions.

How to train ChatGPT on your own text (train a text AI to generate content about your docs, book, website, etc)

Mythical AI • 235 implied HN points • 19 Feb 23
🕹 Technology AI Chatbots Models Training Applications
  1. Large language models like ChatGPT can summarize articles, write stories, and engage in conversations.
  2. To train ChatGPT on your own text, you can use methods like giving the AI data in the prompt, fine-tuning a GPT3 model, using a paid service, or using an embedding database.
  3. Interesting use cases for training GPT3 on your own data include personalized email generators, chatting in the style of famous authors, creating blog posts, chatting with an author or book, and customer service applications.

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.

Musk’s Legal Drama, OpenAI’s Sassy Comeback, Talk-to-ChatGPT's Special & More

HackerPulse Dispatch • 8 implied HN points • 08 Mar 24
🕹 Technology AI Legal Partnerships Models Voice technology
  1. Elon Musk sues OpenAI over claims of prioritizing profit over public interest in developing AGI tech.
  2. OpenAI responds to Musk's legal action, highlighting their commitment to building widely-available AI tools for various sectors like healthcare and language preservation.
  3. Significant advancements in AI technology include Anthropic's introduction of the Claude 3 Model Family and OpenAI's new feature allowing ChatGPT responses to be read aloud.

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.

📝 Guest Post: An introduction to Similarity Search*

TheSequence • 182 implied HN points • 03 Apr 23
🕹 Technology Embeddings Models Algorithms
  1. Vector similarity search is essential for recommendation systems, image search, and natural language processing.
  2. Vector search involves finding similar vectors to a query vector using distance metrics like L1, L2, and cosine similarity.
  3. Common vector search strategies include linear search, space partitioning, quantization, and hierarchical navigable small worlds.

The edge of reason

Logging the World • 219 implied HN points • 28 Dec 22
🔬 Science Mathematics Models Analogies Limitations Uncertainty
  1. When adding numbers, there are basic properties like getting another number, having a special zero that doesn't change sums, and having partners that return to zero when added.
  2. Mathematicians use abstraction to find essential properties, like in groups, to study various systems efficiently and effectively.
  3. Seeking historical analogies in current events can be misleading; it's important to understand the limitations of models and not be overconfident in applying mathematical rules to real-world situations.

Global Summitry for AI Safety + Will Responsible Scaling Policies Secure the Future?

Navigating AI Risks • 58 implied HN points • 03 Oct 23
🕹 Technology AI Governance Legislation Models Ethics
  1. Anthropic released a Responsible Scaling Policy for safe AI development, defining AI safety levels and associated risks.
  2. The upcoming UK AI Safety Summit will address misuse and loss of control risks associated with advanced AI models.
  3. The UK invited China to the summit, sparking debates on the global governance of AI and the role of different countries.