The hottest Models Substack posts right now

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Where is Midjourney for video? Text to video is coming (but not here yet)

Mythical AI • 98 implied HN points • 24 Mar 23
🕹 Technology AI Video Tools Research Papers Models
  1. Creating videos from text prompts is challenging because it involves understanding and replicating movement besides images.
  2. Existing text to image systems are amazing but doing text to video requires additional capabilities.
  3. While there are research papers and tools for text to video, there's no high-quality solution yet, but advancements are expected in the future.

ChatGPT vs Google's Bard: A Side-by-Side Comparison and Why It Matters | Join Me for an AI Happy Hour in SF!

The A.I. Analyst by Ben Parr • 98 implied HN points • 23 Mar 23
🕹 Technology AI Comparison Programming Generative AI Models
  1. Google's Bard falls short compared to Open AI's ChatGPT in various tasks like essay writing and problem-solving.
  2. Open AI's ChatGPT outperformed Google's Bard in a side-by-side comparison in tasks like math problem-solving and coding.
  3. The quality of AI technology, like ChatGPT, influences public opinion about tech giants and their future.

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.

The Double-Edged Sword of GPT Models: Navigating Developer Activism and Bias

Rod’s Blog • 39 implied HN points • 28 Feb 24
🕹 Technology AI Ethics Development Models Bias
  1. GPT models have revolutionized natural language processing, opening new opportunities in technology and communication.
  2. Developer activists have been exploiting GPT models for various reasons, like gaining unauthorized access to APIs, which raises ethical questions.
  3. The power of GPT models comes with significant responsibility to ensure appropriate use and prevent potential misuse.
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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.

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.

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.

XGen, a 7B LLM trained on up to 8K sequence length from SalesForce

MLOps Newsletter • 78 implied HN points • 05 Aug 23
🕹 Technology AI/ML Weather Climate Models Libraries
  1. ClimaX is a deep learning model designed for weather and climate tasks like forecasting temperature and predicting extreme weather events.
  2. XGen is a 7B LLM trained on up to 8K sequence length, achieving state-of-the-art results in tasks like MMLU, QA, and HumanEval.
  3. GPT-4 API from OpenAI provides easy access to a powerful language model capable of generating text, translating languages, and answering questions.

Should You Use AI to Diagnose Patients: Foresight and ChatGPT

AI for Healthcare • 78 implied HN points • 20 Mar 23
🏥 Health & Wellness AI Diagnosis Healthcare Models Patient Care
  1. Using AI for diagnosing patients is not recommended yet due to lack of real-world healthcare testing.
  2. Foresight and ChatGPT are two AI models explored for patient diagnosis, with Foresight showing slightly superior relevancy performance.
  3. AI models like Foresight can be valuable in healthcare for decision support, patient monitoring, digital twins, education, and matching patients to clinical trials.

Tutorial: How to narrate video with Sora, GPT-Vision, and ElevenLabs

Artificial Ignorance • 79 implied HN points • 28 Feb 24
🕹 Technology AI Video Tutorial Narration Models
  1. The emergence of tools like Sora from OpenAI is revolutionizing video production with realistic outputs and seamless object interactions.
  2. Creating nature documentaries and other narrative videos through automated processes involving Sora, GPT-Vision, and ElevenLabs is becoming increasingly feasible.
  3. The future of entertainment and media is set to be transformed by AI-driven technologies, enabling faster video generation and real-time content creation for indie filmmakers and creators.

Unlock Causal Inference: 3 Obstacles, 8 Insights & 1 Resource

Mindful Modeler • 159 implied HN points • 29 Nov 22
🔬 Science Causal Inference Statistics Models Education Resources
  1. Causal inference can be challenging to start due to various obstacles like diverse approaches and neglected education on the topic.
  2. Understanding causal inference involves adjusting your modeling mindset to view it as a unique approach rather than just adding a new model.
  3. Key insights for causal inference include the importance of directed acyclic graphs, starting from a causal model, and the challenges of estimating causal effects from observational data.

A tornado of AI news

Generating Conversation • 70 implied HN points • 01 Mar 24
🕹 Technology Machine Learning Models Ethics Open Source
  1. OpenAI, Google, Meta AI, and others have been making significant advancements in AI with new models like Sora, Gemini 1.5 Pro, and Gemma.
  2. Issues with model alignment and fast-paced shipping practices can lead to controversies and challenges in the AI landscape.
  3. Exploration of long-context capabilities in AI models like Gemini and considerations for multi-modality and open-source development are shaping the future of AI research.

Thinking through Linear With Threshold

Gordian Knot News • 65 implied HN points • 02 Mar 24
🔬 Science Radiation Models Repair
  1. Linear No Threshold (LNT) is criticized for over-predicting harm in low dose rate situations like nuclear power plant releases.
  2. Linear With Threshold (LWT) models have variations where the threshold is on dose or dose rate.
  3. LWT models, although an improvement, still have flaws in considering the repair period after radiation exposure.

More Agents Is All You Need

Gonzo ML • 63 implied HN points • 18 Feb 24
🕹 Technology Artificial Intelligence Machine Learning Models Improvements
  1. Having more agents and aggregating their results through voting can improve outcome quality, as demonstrated by a team from Tencent
  2. The approach of generating multiple samples from the same model and conducting a majority vote shows promise for enhancing various tasks like Arithmetic Reasoning, General Reasoning, and Code Generation
  3. Ensembling methods showed quality improvement with the ensemble size but plateaued after around 10 agents, with benefits being stable across different hyperparameter values

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.

LLM links, 2/27

In My Tribe • 91 implied HN points • 27 Feb 24
🕹 Technology AI Models Systems Predictions
  1. Compound AI systems are proving more effective than individual AI models, showing that combining different components can lead to better results.
  2. Providing extensive context can enhance AI capabilities, enabling new use cases and more effective training through models like Sora.
  3. The emergence of an AI computer virus is predicted to become a major concern, potentially causing widespread panic and technological shutdowns.

AI Roundup 054: Ten million tokens

Artificial Ignorance • 58 implied HN points • 16 Feb 24
🕹 Technology AI Regulation Models Tools Research
  1. Google introduces Gemini 1.5, a powerful model with a context window of up to 10 million tokens, promising significant improvements in AI capabilities.
  2. OpenAI releases Sora, a text-to-video model that can create photorealistic videos and simulate the real world, showcasing advancements in video generation technology.
  3. US Patent and Trademark Office states that AI cannot be named as a patent inventor, aligning AI with being a tool and not a creative entity, impacting patent regulations and inventorship.

AI Roundup 055: Race conditions

Artificial Ignorance • 54 implied HN points • 23 Feb 24
🕹 Technology AI Models Deepfakes Partnerships Regulation
  1. Google faced criticism for its Gemini AI not depicting images of white people, prompting the company to pause that capability.
  2. Reddit made a $60 million content licensing deal with Google as part of its IPO plans, reflecting a trend in publishing deals for AI training purposes.
  3. Tech companies signed agreements to prevent deepfakes from impacting elections, with a focus on political deepfakes and the need for more regulations.

The Most Generated Barn in America

Cybernetic Forests • 79 implied HN points • 08 Jan 23
🕹 Technology AI Ontology Images Data Models
  1. Different names proposed before settling on 'photograph' offer unique perspectives on how people made sense of images.
  2. AI images are not photographs, as they use light differently and inscribe ontologies onto noise using data and categories.
  3. Ontolography, a proposed term for AI-generated images, emphasizes the domain-specific knowledge influencing their production and underlines how they are shaped by the category assignments and labels given to them.

Big Post About Big Context

Gonzo ML • 49 HN points • 29 Feb 24
🕹 Technology AI Machine Learning Models Programming AGI
  1. The context size in modern LLMs keeps increasing significantly, from 4k to 200k tokens, leading to improved model capabilities.
  2. The ability of models to handle 1M tokens allows for new possibilities like analyzing legal documents or generating code from videos, enhancing productivity.
  3. As AI models advance, the nature of work for entry positions may change, challenging the need for juniors and suggesting a shift towards content validation tools.

On creativity and language models

Philosophy bear • 50 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.

📝 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.

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.

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

GPT-4 invalidates the Turing test

Gray Mirror • 110 implied HN points • 13 Apr 23
🕹 Technology AI Computing Intelligence Models Algorithms
  1. Large language models like GPT-4 are not AI, but they are powerful tools that connect patterns and rely on intuition.
  2. The Turing test is not a valid test for AGI, as machines like LLMs can invalidate it by excelling in certain tasks while lacking in others.
  3. Understanding the difference between general and special intelligence is key to not overestimating the capabilities of tools like GPT-4.

Understanding Google's GPT-Killer- The Revolutionary Pathways Architecture [Storytime Saturdays]

Technology Made Simple • 39 implied HN points • 19 Feb 23
🕹 Technology AI Machine Learning Models Training
  1. Google's Bard is designed to be more versatile than ChatGPT, with a unique model architecture called Pathways.
  2. Google's approach includes training a single model for multiple tasks, working with different modalities like images and text, and using sparse activation to specialize network parts.
  3. The Pathways architecture sets Google apart by enabling their AI models to handle a wide range of tasks, making them cost-effective and versatile.

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

Philosophy bear • 28 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.