The hottest Models Substack posts right now

And their main takeaways
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Top Business Topics

Language Models and Friends: Gorilla, HuggingGPT, TaskMatrix, and More

Deep (Learning) Focus • 176 implied HN points • 05 Jun 23
🕹 Technology 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 Models
  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 Models
  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.

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 Models
  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 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.
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The edge of reason

Logging the World • 219 implied HN points • 28 Dec 22
🔬 Science Models
  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 Sequence Knowledge #756: The Simplest Approach to Synthetic Data Generation

TheSequence • 21 implied HN points • 18 Nov 25
🕹 Technology Models
  1. Generative synthesis creates new data by understanding the patterns in existing datasets. It's like learning how a recipe works and then creating a dish that tastes similar.
  2. This method is used to build realistic examples of data, making it helpful for expanding small datasets and reducing bias. It can help create balanced data where some important types might be missing.
  3. Generative synthesis is also important for privacy since it can produce data that looks like real sensitive information without revealing any actual details.

The Reasoning Race: Can Small Models Reason?

TheSequence • 182 implied HN points • 05 Jan 25
🕹 Technology Models
  1. The Sequence newsletter is evolving to offer more focused content, catering to both AI scientists and engineers. This means you'll get richer discussions on research and practical applications.
  2. There will be new editions each week that cover a variety of topics like education, engineering, interviews, and insights. This change aims to make the content shorter and easier to digest.
  3. The discussions around reasoning in AI are expanding to include smaller models, challenging the idea that only large models are capable of complex reasoning. It's an exciting area of exploration.

Weekly Top Picks #59

The Algorithmic Bridge • 477 implied HN points • 22 Jan 24
🕹 Technology Models
  1. Artificial Intelligence may outsmart humans, depending on perspectives.
  2. Scientists from Google DeepMind may leave to start their own AI company.
  3. Different views on Transformer models and diffusion models in AI.

📝 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 Models
  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.

RLHF learning resources in 2024

Democratizing Automation • 435 implied HN points • 12 Jan 24
🕹 Technology Models
  1. The post shares a categorized list of resources for learning about Reinforcement Learning from Human Feedback (RLHF) in 2024.
  2. The resources include videos, research talks, code, models, datasets, evaluations, blog posts, and other related materials.
  3. The aim is to provide a variety of learning tools for individuals with different learning styles interested in going deeper into RLHF.

Looking for your keys under the light

Trevor Klee’s Newsletter • 671 implied HN points • 13 Jun 23
🔬 Science Models
  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.

Good Enough AI

Teaching computers how to talk • 131 implied HN points • 05 Feb 25
🕹 Technology Models
  1. A new AI model called DeepSeek shows that we can create powerful tools without spending too much money. This could change how we think about making AI.
  2. The average person might not notice a big difference between high-end and cheaper AI models. Many consumers just want something that works well and is affordable.
  3. The AI industry might become more competitive and focused on meeting everyday needs instead of creating super advanced technology. This means consumers may benefit more while companies earn less.

The Sequence Opinion #476: The DeepSeek Effect: The Remarkable Innovations and Controversies Surrounding the New Challenger in Open-Source AI

TheSequence • 133 implied HN points • 24 Jan 25
🕹 Technology Models
  1. DeepSeek is a new player in open-source AI, quickly gaining attention for its innovative models. They have released powerful AI tools that can think and reason well, challenging the idea that only big models can do this.
  2. The company was founded in May 2023 and has shown rapid progress by continually improving its technology. This quick success highlights their commitment to pushing the limits of AI performance and efficiency.
  3. However, the fast advancements by DeepSeek have raised some controversies. People are discussing the implications of their rapid growth in the AI space, suggesting that it might impact the future of AI development.

A few charts on where AI adoption is going

Tanay’s Newsletter • 113 implied HN points • 19 Feb 25
🕹 Technology Models
  1. The cost of using advanced AI models has dropped dramatically, making it easier for businesses to experiment and integrate AI into their products. This change opens up new possibilities for reaching millions of users.
  2. Reinforcement learning is proving effective for tasks with clear outcomes, which could lead to better performance of AI models over time. As these models improve, we can expect more widespread use of AI.
  3. The journey to adopting AI takes time, but it's happening faster than past innovations like electricity or telephones. Today, a significant portion of people are regularly using AI tools.

Pinterest improves their Closeup Recommendation System through foundational changes

MLOps Newsletter • 98 implied HN points • 07 Oct 23
🕹 Technology Models
  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.

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

Mythical AI • 98 implied HN points • 24 Mar 23
🕹 Technology 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.

Fallacies in Computational Neuroscience

NeuroLogos • 98 implied HN points • 25 Apr 23
🔬 Science Models
  1. Garbage in, garbage out - common issue in computational models
  2. Unity Gain Simulation - building intricate models of basic concepts without gaining insights
  3. The Prayer Wheel - emphasizing model complexity and need for powerful computers as a form of validation

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 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 Models
  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.

DeepSeek, o3, and AI applications

Generating Conversation • 116 implied HN points • 06 Feb 25
🕹 Technology Models
  1. DeepSeek R1 is a strong AI model that has impressed the industry, but life goes on, and the world hasn't changed drastically because of it. More good models out there mean better choices for those building AI applications.
  2. Competition is heating up in the AI space. Other companies, like OpenAI, are responding by releasing new models quickly to keep up with emerging players like DeepSeek.
  3. The trend of making AI models more affordable is continuing. This can help more people and businesses use AI, solving new problems that weren’t possible before.

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

Rod’s Blog • 39 implied HN points • 28 Feb 24
🕹 Technology Models
  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.

State-space LLMs: Do we need Attention? 

Democratizing Automation • 395 implied HN points • 20 Dec 23
🕹 Technology Models
  1. Non-attention architectures for language modeling are gaining traction in the AI community, signaling the importance of considering different model architectures.
  2. Different language model architectures will be crucial based on the specific tasks they aim to solve.
  3. Challenges remain for non-attention technologies, highlighting that it is still early days for these advancements.

AI predictions for 2025

Artificial Ignorance • 126 implied HN points • 08 Jan 25
🕹 Technology Models
  1. In 2025, AI will focus more on improving reasoning abilities rather than just building larger models. This means smarter, more capable AI that can think through problems better.
  2. Expect personalized AI experiences to get better, with chatbots that can truly remember and learn about you. This could change how we interact with AI in our daily lives.
  3. There will likely be more AI 'agents' in workplaces, especially for customer service and sales, but many won't live up to the hype. We may see both benefits and gaps in their performance.

Large Language Models vs Small Language Models: A Comparison

Rod’s Blog • 39 implied HN points • 20 Feb 24
🕹 Technology Models
  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.

A safe harbor for AI evaluation and red teaming

AI Snake Oil • 307 implied HN points • 05 Mar 24
🕹 Technology Models
  1. Independent evaluation of AI models is crucial for uncovering vulnerabilities and ensuring safety, security, and trust
  2. Terms of service can discourage community-led evaluations of AI models, hindering essential research
  3. A legal and technical safe harbor is proposed to protect and encourage public interest research into AI safety, removing barriers and improving ecosystem norms

The Sequence Radar #559 : Two Remarkable Papers This Week: Self-Improving Agents and the Limits of LLM Memorization

TheSequence • 56 implied HN points • 08 Jun 25
🕹 Technology Models
  1. The Darwin Gödel Machine is a new AI system that can improve itself by changing its own code, leading to better performance in coding tasks. This approach mimics evolution by letting different versions of the AI compete and innovate.
  2. A recent study found that large language models have a limited capacity for memorizing information, roughly 3.6 bits per parameter. This helps us understand how these models learn and remember data.
  3. Both papers highlight how AI can evolve and learn, with one focusing on self-improvement and the other on what models can and cannot remember. Together, they show the potential and limits of AI development.

Graph Neural Networks in Tensorflow

MLOps Newsletter • 39 implied HN points • 10 Feb 24
🕹 Technology 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 Models
  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 Models
  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.

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

Mindful Modeler • 159 implied HN points • 29 Nov 22
🔬 Science Models
  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.

Mistral AI is stacking experts

John’s Contemplations • 39 implied HN points • 19 Jan 24
🕹 Technology Models
  1. Mistral AI introduced new MoE models like Mixtral 8x7B surpassing GPT-3.5.
  2. MoE architectures have evolved over time with advancements like Switch Transformer and GLaM.
  3. Mistral AI might be planning Mistral-Large with the same architecture but more and bigger experts.

Nine Big Predictions for AI in 2024

Future History • 270 implied HN points • 31 Dec 23
🕹 Technology Models
  1. Major proprietary AI models like GPT 4 may get hacked, leading to security concerns.
  2. Open weights models could surpass GPT-4, showcasing the power of open source AI.
  3. New techniques will be needed to see significant improvements in AI models beyond GPT-5.

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

The Algorithmic Bridge • 233 implied HN points • 06 Mar 24
🕹 Technology Models
  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.