The hottest AI Models Substack posts right now

And their main takeaways
Category
Top Technology Topics

Fear of a Black Pope!

Brad DeLong's Grasping Reality • 207 implied HN points • 29 Feb 24
🕹 Technology AI Models Ethics Software Industry Chatbots Machine Learning
  1. People have high expectations of AI models like GPT, but they are not flawless and have limitations.
  2. The panic over an AI model's depiction of a Black Pope reveals societal biases regarding race and gender.
  3. AI chatbots like Gemini are viewed in different ways by users and enthusiasts, leading to conflicting expectations of their capabilities.

NVIDIA Releases Nemotron 70B

TheSequence • 84 implied HN points • 20 Oct 24
🕹 Technology AI Models Machine Learning Software Development Tech Innovation Data Access
  1. NVIDIA just launched the Nemotron 70B model, and it's getting a lot of attention for its amazing performance. It's even outshining popular models like GPT-4.
  2. The model is designed to understand complex questions easily and give accurate answers without needing extra hints. This makes it really useful for a lot of different tasks.
  3. NVIDIA is making it easier for everyone to access this powerful AI by offering free tools online. This means more businesses can try out and use advanced language models for their needs.

Dissecting OLMo, The Most Open Source LLM Paper!

Aziz et al. Paper Summaries • 79 implied HN points • 06 Mar 24
🕹 Technology AI Models Open Source Data processing Machine Learning
  1. OLMo is a fully open-source language model. This means anyone can see how it was built and can replicate its results.
  2. The OLMo framework includes everything needed for training, like data, model design, and training methods. This helps new researchers understand the whole process.
  3. The evaluation of OLMo shows it can compete well with other models on various tasks, highlighting its effectiveness in natural language processing.

TITLES & Lucky The Golden Goose

SÖREN JOHN • 59 implied HN points • 18 Mar 24
🕹 Technology AI Models Data Ownership Creative Tools Entrepreneurship Illustration
  1. Creating is often influenced by our childhood experiences and the encouragement we received from our parents. These memories help shape what we pursue as adults.
  2. New tools and AI models are changing how artists can create and monetize their work. They can now use their own styles to produce content and earn from it.
  3. There's a growing need for better ways to manage ownership and compensation for artists in the digital world. It's important for them to retain control over their creations and benefit financially from their work.
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Friend Recommendation Retrieval in a social network

Recommender systems • 43 implied HN points • 24 Nov 24
🕹 Technology Machine Learning AI Models Social Networks Data science
  1. Friend recommendation systems use connections like 'friends of friends' to suggest new friends. This is a common way to make sure suggestions are relevant.
  2. Two Tower models are a new approach that enhances friend recommendations by learning from user interactions and focusing on the most meaningful connections.
  3. Using methods like weighted paths and embeddings can improve recommendation accuracy. These techniques help to understand user relationships better and avoid common pitfalls in recommendations.

Hunyuan-Large, AI model for open-world games, X-Portrait 2 for realistic character animations, FLUX1.1 [pro] Ultra and Raw, Magentic-One, Hume AI App, action model for GUI agents and More

AI Brews • 15 implied HN points • 08 Nov 24
🕹 Technology AI Models Gaming Animation Software Data science
  1. Tencent has released Hunyuan-Large, a powerful AI model with lots of parameters that can outperform some existing models. It's good news for open-source projects in AI.
  2. Decart and Etched introduced Oasis, a unique AI that can generate open-world games in real-time. It uses keyboard and mouse inputs instead of just text to create gameplay.
  3. Microsoft's Magentic-One is a new system that helps solve complex tasks online. It's aimed at improving how we manage jobs across different domains.

Chameleon, Meta's Mixed-Modal Foundation Model

Aziz et al. Paper Summaries • 19 implied HN points • 02 Jun 24
🕹 Technology AI Models Machine Learning Deep Learning Data processing Tokenization
  1. Chameleon combines text and image processing into one model using a unique architecture. This means it processes different types of data together instead of separately like previous models.
  2. The training of Chameleon faced challenges like instability and balancing different types of data, but adjustments like normalization helped improve its training process. It allows the model to learn effectively from both text and images.
  3. Chameleon performs well in generating responses that include both text and images. However, just adding images didn't harm the model's ability to handle text, showing it can work well across different data types.

The Case For Small Language Models

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 39 implied HN points • 13 Feb 24
🕹 Technology AI Models Conversational AI Natural Language Processing Machine Learning Software Development
  1. Small Language Models (SLMs) can do many tasks without the complexity of Large Language Models (LLMs). They are simpler to manage and can be a better fit for common uses like chatbots.
  2. SLMs like Microsoft's Phi-2 are cost-effective and can handle conversational tasks well, making them ideal for applications that don't need the full power of larger models.
  3. Running an SLM locally helps avoid challenges like slow response times, privacy issues, and high costs associated with using LLMs through APIs.

From Today, ChatGPT Will Remember Every Paying User

AI Disruption • 19 implied HN points • 30 Apr 24
🕹 Technology Artificial Intelligence Data Privacy Machine Learning AI Models
  1. ChatGPT's memory feature is now open to Plus users, helping it remember details shared in chats for seamless interactions.
  2. The memory feature works by allowing users to ask ChatGPT to remember things or letting it learn on its own through interactions.
  3. Deleting chats does not erase ChatGPT's memories; users need to delete specific memories if they wish. It is important for improving AI models and can enhance user experiences.

Fine-Tuning OpenAI GPT-4o mini

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 2 HN points • 21 Aug 24
🕹 Technology AI Models Natural Language Machine Learning Data science Software Development
  1. OpenAI's GPT-4o Mini allows for fine-tuning, which can help customize the model to better suit specific tasks or questions. Even with just 10 examples, users can see changes in the model's responses.
  2. Small Language Models (SLMs) are advantageous because they are cost-effective, can run locally for better privacy, and support a range of tasks like advanced reasoning and data processing. Open-sourced options provide users more control.
  3. GPT-4o Mini stands out because it supports multiple input types like text and images, has a large context window, and offers multilingual support. It's ideal for applications that need fast responses at a low cost.

How to Fine-Tune Your Own Mistral-7B

The Beep • 39 implied HN points • 14 Jan 24
🕹 Technology Machine Learning Natural Language Processing AI Models Data science Programming
  1. You can fine-tune the Mistral-7B model using the Alpaca dataset, which helps the model understand and follow instructions better.
  2. The tutorial shows you how to set up your environment with Google Colab and install necessary libraries for training and tracking the model's performance.
  3. Once you prepare your data and configure the model, training it involves monitoring progress and adjusting settings to get the best results.

Papers I've read this week: vision language models

Artificial Fintelligence • 8 implied HN points • 28 Oct 24
🕹 Technology AI Models Computer Vision Machine Learning Natural Language Processing Research Papers
  1. Vision language models (VLMs) are simplifying how we extract text from images. Unlike older software, modern VLMs make this process much easier and faster.
  2. There are several ways to combine visual and text data in VLMs. Most recent models prefer a straightforward approach of merging image features with text instead of using complex methods.
  3. Training a VLM involves using a good vision encoder and a pretrained language model. This combination seems to work well without any major drawbacks.

Conversations with Claude

The Day After Tomorrow • 19 implied HN points • 10 Mar 24
🕹 Technology AI Models Machine Learning Ethics Human-AI Interaction Natural Language Processing
  1. Claude 3 has shown impressive conversational skills, feeling more human-like compared to other AI models like GPT-4. This makes interactions feel more natural.
  2. The AI has a complex understanding of ethical decision-making, stating that it prioritizes human well-being and aims to provide helpful information while avoiding harm.
  3. In moral dilemmas, Claude 3's rankings on the value of life are intriguing. It sometimes values non-human entities, like whales, over humans, showcasing a unique perspective on morality.

Key Components to Understand the LLM Models

The Beep • 19 implied HN points • 07 Jan 24
🕹 Technology AI Models Natural Language Machine Learning Neural Networks Data processing
  1. Large language models (LLMs) like Llama 2 and GPT-3 use transformer architecture to process and generate text. This helps them understand and predict words based on previous context.
  2. Emergent abilities in LLMs allow them to learn new tasks with just a few examples. This means they can adapt quickly without needing extensive training.
  3. Techniques like Sliding Window Attention help LLMs manage long texts more efficiently by breaking them into smaller parts, making it easier to focus on relevant information.

Introducing Etalon: How we choose a LLM with optimal Runtime Performance ?

Machine Learning Diaries • 3 implied HN points • 11 Nov 24
🕹 Technology Machine Learning AI Models Performance Metrics User Experience
  1. Evaluating large language models (LLMs) is important for ensuring a good user experience. Existing metrics like Time to First Token (TTFT) and Time Between Tokens (TBT) don't fully capture how these models perform in real-time applications.
  2. The proposed 'Etalon' framework offers a new way to measure LLMs using a 'fluidity-index' that helps track how well the model meets deadlines. This ensures smoother and more responsive interactions.
  3. Current metrics can hide issues like delays and jitters during token generation. The new approach aims to provide a clearer picture of performance by considering these factors, leading to better user satisfaction.

A brief history of speech to text + how it actually works

Mythical AI • 19 implied HN points • 08 Mar 23
🕹 Technology AI Models Neural Networks Machine Learning Evaluation Metrics
  1. Speech to text technology has a long history of development, evolving from early systems in the 1950s to today's advanced AI models.
  2. The process of converting speech to text involves recording audio, breaking it down into sound chunks, and using algorithms to predict words from those chunks.
  3. Speech to text models are evaluated based on metrics like Word Error Rate (WER), Perplexity, and Word Confusion Networks (WCNs) to measure accuracy and performance.

Llama-2 and the open source LLM 🌊

LLMs for Engineers • 19 implied HN points • 03 Aug 23
🕹 Technology AI Models Open Source Software Development Machine Learning Programming Languages
  1. Llama-2 makes it easier for anyone to run and own their LLM applications. This means people can create their own models at home while keeping their data private.
  2. Self-hosting Llama-2 helps improve performance and reduces delays. This makes the model more efficient for specific tasks and can even reach higher accuracy levels.
  3. There are guides and tools available to help users set up Llama-2 quickly. Users can try it out or integrate it with other platforms, making it more accessible for everyone.

Open Models, Smarter Math, and Negotiation LLMs

ppdispatch • 2 implied HN points • 03 Jan 25
🕹 Technology AI Models Machine Learning Data Engineering Open Source
  1. Yi is a new set of open foundation models that can handle many tasks involving text and images. They have been carefully designed to improve performance through better training.
  2. Researchers found that some AI models think too much for simple math problems. A new method can help these models solve problems faster and more efficiently.
  3. AgreeMate is a smart AI tool that teaches models how to negotiate prices like humans. It helps them use strategies to get better deals.

Software 3.0

Div’s Substack • 3 HN points • 01 Apr 23
🕹 Technology AI Programming Software Development AI Models
  1. Software 3.0 represents a shift in programming to using natural language as the new programming language.
  2. Software 3.0 involves querying a large AI model with natural language prompts to get desired output, making programming easier and more versatile.
  3. The transition to Software 3.0 brings benefits like human interpretability, generalization, and simplification of programming, but also comes with challenges like fault tolerance and latency.

How well can AI imitate a 17th century doctor?

Res Obscura • 3 HN points • 16 Feb 24
🕹 Technology AI Generative AI AI Models
  1. Long-distance traveling in the premodern world was incredibly dangerous and interesting, taking years from one continent to another.
  2. Generative AI tools like customized GPTs are being used in historical research and as educational tools to simulate historical scenarios.
  3. Comparison between different AI models, like GPT-4, Gemini, and MonadGPT, showed various levels of success in simulating a 17th century doctor's mental models, advice, and speech patterns.

What happens when your healthcare data is used to train AI models?

Tom’s Substack • 2 HN points • 20 Apr 23
🏥 Health & Wellness Data Bias Patient Care AI Models Model Training
  1. Increased diversity in healthcare data for AI training leads to better performance for all patient demographics.
  2. AI models may memorize training data for individual patients, potentially impacting future care.
  3. Development of AI models in healthcare requires careful consideration to avoid biases and ensure accurate performance.

LLM Data Sales: A Market for Lemons?

Magis • 1 HN point • 14 Feb 24
🕹 Technology AI Models Generative models Model Training
  1. Selling data for training generative models is challenging due to factors like lack of marginal temporal value, irrevocability, and difficulties in downstream governance.
  2. Traditional data sales rely on the value of marginal data points that become outdated, while data for training generative models depends more on volume and history.
  3. Potential solutions for selling data to model trainers include royalty models, approximating dataset value computationally, and maintaining neutral computational sandboxes for model use.

Trying all the alternatives to ChatGPT

Boris Again • 1 HN point • 22 Apr 23
🕹 Technology AI Models Alternatives Chatbots Artificial Intelligence Programming
  1. Alternative AI models like Claude, Dolly V2, and Alpaca offer different features and prices compared to ChatGPT and GPT-4.
  2. Each model has its unique strengths and weaknesses, like speed, coherence, licensing restrictions, and price per token.
  3. While some models are self-hosted and free to access, others may require a request or have specific pricing structures.

The New Frontier in Power Redistribution

thezakelfassiexperiment • 0 implied HN points • 15 Jun 23
🕹 Technology AI Social media Tech Companies Future of work AI Models
  1. Historically, power shifts with technological changes, now AI is the game changer favoring established companies with resources.
  2. Social media platforms are evolving to focus on smaller, intimate communities through group messaging and content sharing.
  3. Future work landscape may value companies based on proprietary AI models rather than traditional metrics like employees or revenue.

The Path To Undestand Image Generation and Stable Diffusion

The Beep • 0 implied HN points • 07 Apr 24
🕹 Technology AI Models Machine Learning Image Processing Deep Learning Data science
  1. Stable diffusion has made a big splash in image generation, allowing users to create impressive images using text prompts.
  2. Generative models like Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) help in building these image generation systems by learning from existing data.
  3. Understanding how stable diffusion combines text and image decoding can enhance the image creation process, making it more flexible for various tasks.

AI is like a very tiny hamburger

The efficient frontier • 0 implied HN points • 16 Jan 24
🔬 Science Energy Water Emissions AI Models
  1. The environmental impact of AI, especially in terms of energy and water use, is a significant concern
  2. Simple energy use math can help understand the resource footprint of AI models like image generation and gaming
  3. Assessing additionality and understanding scopes are crucial in evaluating the true impact of AI on resources like water and energy

Gemini From Google

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 07 Dec 23
🕹 Technology AI Models Machine Learning Natural Language Cloud Computing Data processing
  1. Google's Gemini is a powerful AI that can understand and work with text, images, video, audio, and code all at once. This makes it really versatile and capable of handling different types of information.
  2. Starting December 6, 2023, Google's Bard will use a version of Gemini Pro for better reasoning and understanding. This means Bard will soon be smarter and more helpful in answering questions.
  3. Gemini has shown it can outperform human experts in language tasks. This is a significant achievement, indicating that AI is getting very close to human-like understanding in complex subjects.

OpenAI String Tokenisation Explained

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 29 Nov 23
🕹 Technology AI Models Natural Language Processing Machine Learning Programming
  1. Tokenisation is the process of breaking down text into smaller pieces called tokens, which can be converted back to the original text easily. This makes it useful for understanding and processing language.
  2. Different OpenAI models use different methods for tokenising text, meaning the same input can result in different token counts across models. It’s important to know which model you are using.
  3. Using tokenisation can shorten the text length in terms of bytes, making the input more efficient. On average, each token takes up about four bytes, which helps models learn better.

How To Create HuggingFace🤗 Custom AI Models Using autoTRAIN

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 09 Feb 23
🕹 Technology AI Models Data science Machine Learning Software Development Web applications
  1. autoTRAIN lets you build custom AI models without needing to code. It's user-friendly and has both free and paid options.
  2. You can easily upload your data in different formats like CSV, TSV, or JSON. The platform keeps your data private and secure.
  3. As your model trains, you can see real-time results about its accuracy. This helps you understand how well it's performing and make necessary adjustments.