The hottest Models Substack posts right now

And their main takeaways
Category
Top Business Topics

Google's reasoning model, New open-source physics AI engine, Odyssey's Explorer, OmniAudio, AI phone calling, FACTS Leaderboard, Meta Apollo, Global Talent Network, Falcon 3, Veo 2,DeepSeek-VL2 & more

AI Brews • 17 implied HN points • 20 Dec 24
🕹 Technology AI Models Software Development Innovation
  1. Google has launched a new reasoning model called Gemini Flash Thinking that shows its thoughts, making it better at reasoning. It has top scores on the Chatbot Arena leaderboard.
  2. There is a new open-source physics simulation platform called Genesis that can help with robotics and AI applications by creating detailed, dynamic worlds.
  3. Meta has introduced a family of models called Apollo that can efficiently process long videos, and other companies are also launching new AI tools for audio and video generation.

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.

Looking for your keys under the light

Trevor Klee’s Newsletter • 671 implied HN points • 13 Jun 23
🔬 Science Research Biology Models Healthcare
  1. When searching for something, we tend to look where it is easiest to see, even if it might not be the best place to find it.
  2. This behavior can lead to wasting time and effort on ineffective or inefficient search strategies.
  3. It is important to be mindful of not getting stuck looking in familiar or visible places, but to explore all possibilities.

📝 Guest Post: LoRA Land: 25 Fine-Tuned Mistral-7b LLMs that Rival or Outperform GPT-4

TheSequence • 413 implied HN points • 23 Feb 24
🕹 Technology AI Fine-tuning Models Efficiency Infrastructure
  1. Efficient fine-tuning with specialized models like Mistral-7b LLMs can outperform leading commercial models like GPT-4 while being cost-effective.
  2. Incorporating techniques like Parameter Efficient Fine-Tuning and serving models via platforms like LoRAX can significantly reduce GPU costs and make deployment scalable.
  3. Using smaller, task-specific fine-tuned models is a practical alternative to expensive, large-scale models, making AI deployment accessible and efficient for organizations with limited resources.

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.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

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.

The GPT-4 Generation: Why Are the Best AI Models Equally Intelligent?

The Algorithmic Bridge • 233 implied HN points • 06 Mar 24
🕹 Technology AI Models Intelligence Behavior Training
  1. Top AI models like GPT-4, Gemini Ultra, and Claude 3 Opus are at a similar level of intelligence, despite differences in personality and behavior.
  2. Different AI models can display unique behaviors due to factors like prompts, prompting techniques, and system prompts set by AI companies.
  3. Deeper layers of AI models, such as variations in training, architecture, and data, contribute to the differences in behavior and performance among models.

Import AI 320: Facebook's AI Lab Leak; open source ChatGPT clone; Google makes a universal translator.

Import AI • 339 implied HN points • 13 Mar 23
🕹 Technology AI Data Robots Decentralization Models
  1. Google is making strides with a universal translator by training models on diverse unlabeled data from multiple languages.
  2. The FTC is calling out companies for lying about AI capabilities, emphasizing the importance of truthful representation in the AI industry.
  3. OpenChatKit, an open-source ChatGPT clone, is released with a focus on decentralized training and customization for chatbot creation.

Synthetic data: Anthropic’s CAI, scaling, OpenAI’s Superalignment, tips, and open-source examples

Democratizing Automation • 332 implied HN points • 29 Nov 23
🕹 Technology AI Data Models Methods Applications
  1. Synthetic data is becoming more important in AI, with a focus on removing human involvement.
  2. Proponents believe that using vast amounts of synthetic data can lead to breakthroughs in AI models.
  3. Open and closed communities are both utilizing synthetic data for different end goals.

Edge 438: Meet DataGemma: Google DeepMind's Effort to Ground LLMs in Factual Knowledge

TheSequence • 112 implied HN points • 10 Oct 24
🕹 Technology AI Data Research Models Applications
  1. DataGemma is a new model developed by Google DeepMind that helps large language models (LLMs) use factual information.
  2. It aims to reduce errors, known as hallucinations, and make LLMs more reliable for important tasks.
  3. The model uses a large data source called DataCommons to verify the information it provides.

OpenAI’s Sora for video, Gemini 1.5's infinite context, and a secret Mistral model

Democratizing Automation • 221 implied HN points • 16 Feb 24
🕹 Technology AI Models ML Video Innovation
  1. OpenAI introduced Sora, an impressive video generation model blending Vision Transformer and diffusion model techniques
  2. Google unveiled Gemini 1.5 Pro with nearly infinite context length, advancing the performance and efficiency using the Mixture of Expert as the base architecture
  3. The emergence of Mistral-Next model in the ChatBot Arena hints at an upcoming release, showing promising test results and setting expectations as a potential competitor to GPT4

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.

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.

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.

Foundations of Physical Theory: Function of Models

Fields & Energy • 3 HN points • 02 Sep 24
🔬 Science Physics Mathematics Models Theory Electricity Magnetism
  1. Models in physics help us understand complex ideas by simplifying them into more relatable forms. They allow us to reason about things we can't observe directly.
  2. It's important to consider the medium through which forces act, rather than just thinking of actions at a distance. This helps explain phenomena like electricity and magnetism more clearly.
  3. Using analogies can be helpful in learning new concepts, but we must be careful not to confuse them with the actual properties of the things we are studying.

Mixtral: The best open model, MoE trade-offs, release lessons, Mistral raises $400mil, Google's loss, vibes vs marketing

Democratizing Automation • 237 implied HN points • 11 Dec 23
🕹 Technology AI Models Community Machine Learning Ethics
  1. Mixtral model is a powerful open model with impressive performance in handling different languages and tasks.
  2. Mixture of Expert (MoE) models are popular due to their better performance and scalability for large-scale inference.
  3. Mistral's swift releases and strategies like instruction-tuning show promise in the open ML community, challenging traditional players like Google.

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.

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.

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.

The koan of an open-source LLM

Democratizing Automation • 142 implied HN points • 06 Mar 24
🕹 Technology Artificial Intelligence Copyright Regulation Models
  1. The definition and principles of open-source software, such as the lack of usage-based restrictions, have evolved over time to adapt to modern technologies like AI.
  2. There is a need for clarity in identifying different types of open language models, such as distinguishing between models with open training data and those with limited information available.
  3. Open ML faces challenges related to transparency, safety concerns, and complexities around licensing and copyright, but narratives about the benefits of openness are crucial for political momentum and support.

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.

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.

Why Claude 3 is a big upgrade

Artificial Ignorance • 130 implied HN points • 06 Mar 24
🕹 Technology Artificial Intelligence Models AI safety Competition Benchmarking
  1. Claude 3 introduces three new model sizes; Opus, Sonnet, and Haiku, with enhanced capabilities and multi-modal features.
  2. Claude 3 boasts impressive benchmarks with strengths like vision capabilities, multi-lingual support, and operational speed improvements.
  3. Safety and helpfulness were major focus areas for Claude 3, addressing concerns like reducing refusals while balancing between answering most harmless requests and refusing genuinely harmful prompts.

Do we need RL for RLHF?

Democratizing Automation • 182 implied HN points • 06 Dec 23
🕹 Technology AI Research Algorithms Data Models
  1. The debate around integrating human preferences into large language models using RL methods like DPO is ongoing.
  2. There is a need for high-quality datasets and tools to definitively answer questions about the alignment of language models with RLHF.
  3. DPO can be a strong optimizer, but the key challenge lies in limitations with data, tooling, and evaluation rather than the choice of optimizer.

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.

It's 2024 and they just want to learn

Democratizing Automation • 150 implied HN points • 03 Jan 24
🕹 Technology Machine Learning Artificial Intelligence Models Communities Open Source
  1. 2024 will be a year of rapid progress in ML communities with advancements in large language models expected
  2. Energy and motivation are high in the machine learning field, driving people to tap into excitement and work towards their goals
  3. Builders are encouraged to focus on building value-aware systems and pursuing ML goals with clear principles and values

Weekly Top Picks #64

The Algorithmic Bridge • 116 implied HN points • 26 Feb 24
🕹 Technology AI Models Ethics Research
  1. New AI models like Google Gemma and Mistral Large are making waves in the tech world.
  2. Google Genie is an AI focused on game creation, showcasing the versatility of artificial intelligence applications.
  3. Ethical considerations, such as the Gemini anti-whiteness problem, are gaining attention within the AI community.

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.

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.

Watch now:"AI under the hood and off to the races" exploring ChatGPT, NeRF, Stable Diffusion and more

Ubiquitous Thoughts • 98 implied HN points • 19 Jul 23
🕹 Technology AI Machine Learning Entrepreneurs Models Virtual Events
  1. The virtual event covered the basics of AI models like ChatGPT, NeRF, and Stable Diffusion.
  2. Entrepreneurs can integrate AI into their startup products at different levels of depth.
  3. The event emphasized the importance of understanding how AI works, even without prior technical experience.