The hottest Machine Learning Substack posts right now

And their main takeaways
Category
Top Business Topics

RLHF is the Problem and the Solution

From the New World • 86 implied HN points • 28 Feb 24
🕹 Technology AI Machine Learning Ethics Policy Innovation
  1. The goal of AI Pluralism is to ensure that machine models are not manipulated by third parties to conform to specific ideologies.
  2. Machine learning typically involves two stages: developing the model's capabilities and fine-tuning, which can influence the model's ideology and style.
  3. Requiring the release of both stages of the model can help curb extremist influence, but it may not completely eliminate ideological contamination in AI development.

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.

John C. Dvorak on Intel's First Neural Network Chip

The Chip Letter • 95 HN points • 21 Feb 24
🕹 Technology Chips Neural Networks Machine Learning
  1. Intel's first neural network chip, the 80170, achieved the theoretical intelligence level of a cockroach, showcasing a significant breakthrough in processing power.
  2. The Intel 80170 was an analog neural processor introduced in 1989, making it one of the first successful commercial neural network chips.
  3. Neural networks like the 80170 aren't programmed but trained like a dog, opening up unique applications for analyzing patterns and making predictions.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

Creating Individualised Chess Engines

Chess Engine Lab • 39 implied HN points • 26 Mar 24
🕹 Technology Artificial Intelligence Data Analysis Programming Chess Machine Learning
  1. An engine called Maia focused on predicting human moves accurately instead of just being the strongest in chess, resulting in a more meaningful impact, especially for club-level players.
  2. By individualizing chess engines to predict moves of specific players, accuracy can be increased by 4-5% and players can be identified with 98% accuracy from a pool of 400, based on their game patterns.
  3. Identifying players through their mistakes is a crucial aspect - as mistakes are unique to individual players, understanding and fixing them can greatly aid in chess improvement.

Dream Machines

Teaching computers how to talk • 94 implied HN points • 19 Feb 24
🕹 Technology AI Video Generation Machine Learning Data Training Future implications
  1. OpenAI's new text-to-video model Sora can generate high-quality videos up to a minute long but faces similar flaws as other AI models.
  2. Despite the impressive capabilities of Sora, careful examination reveals inconsistencies in the generated videos, raising questions about its training data and potential copyright issues.
  3. Sora, OpenAI's video generation model, presents 'hallucinations' or inconsistencies in its outputs, resembling dream-like scenarios and prompting skepticism about its ability to encode a true 'world model.'

A tornado of AI news

Generating Conversation • 72 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.

The Best Skillsets to Learn in 2024 for Generative AI

Rod’s Blog • 238 implied HN points • 15 Dec 23
🕹 Technology AI Programming Machine Learning Data science Resources
  1. Generative AI is a rapidly evolving field creating novel content like images, text, music, etc., with real-world applications from enhancing creativity to helping solve problems.
  2. To succeed in generative AI, you need skills like mathematics and statistics, programming, data science, knowledge of generative AI methods, and creativity in your specific domain.
  3. To learn generative AI in 2024, leverage online courses, books, blogs, tools, and engage in communities and events dedicated to this field.

Revolutionising Energy Storage: The AI and Experimental Design Fusion in Battery Tech

Intercalation Station • 137 implied HN points • 24 Jan 24
🕹 Technology Energy Storage Machine Learning Battery technology AI
  1. The use of machine learning and adaptive experimental design is revolutionizing battery technology for more efficient, reliable, and sustainable energy storage solutions.
  2. Machine learning enhances consumer electronics by optimizing battery life and performance, showing practical benefits in devices like smartphones and electric vehicles.
  3. The combination of machine learning and adaptive experimental design leads to quicker research and innovation in battery technology, making advancements more tailored, responsive, and impactful across industries.

Edge 364: About COSP and USP: Two New LLM Reasoning Methods Built by Google Research

TheSequence • 133 implied HN points • 25 Jan 24
🕹 Technology AI Language Models Research Machine Learning Data science
  1. Two new LLM reasoning methods, COSP and USP, have been developed by Google Research to enhance common sense reasoning capabilities in language models.
  2. Prompt generation is crucial for LLM-based applications, and techniques like few-shot setup have reduced the need for large amounts of data to fine-tune models.
  3. Models with robust zero-shot performance can eliminate the need for manual prompt generation, but may have less potent results due to operating without specific guidance.

Data Engineering Vs Machine Learning Pipelines

SeattleDataGuy’s Newsletter • 1048 implied HN points • 11 Apr 23
🕹 Technology Data Engineering Machine Learning
  1. Data engineering and machine learning pipelines are essential components for every company, but are often confused because they have different objectives.
  2. Data engineering pipelines involve data collection, cleaning, integration, and storage, while machine learning pipelines focus on data cleaning, feature engineering, model training, evaluation, registry, deployment, and monitoring.
  3. Both data and ML pipelines require careful consideration of computational needs to handle sudden changes, and understanding the differences between them is important for effective data processing and decision-making.

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.

Genre Grapevine on Machine Learning’s Problem with Bad Stories and Bad Intentions

Genre Grapevine • 176 implied HN points • 29 Dec 23
🎨 Art & Illustration Artificial Intelligence Creativity Resistance Machine Learning
  1. Bad stories can inspire writers to improve their own writing by learning from the mistakes of others.
  2. Artists and writers have pushed back against AI dominance by engaging in strikes and filing lawsuits to protect their work from being used without permission.
  3. Machine learning programs face challenges in creating truly innovative and original art, as they often get stuck in a cycle of reproducing popular styles and lacking true imagination.

European Union AI Act

Good Computer • 37 HN points • 18 Mar 24
🕹 Technology AI Regulation Data Privacy Machine Learning European Union
  1. The EU AI Act aims to protect individuals' rights and ensure safe AI use, setting a risk-based framework for regulation.
  2. The act defines AI broadly to be future-proof, with specific categories for varying levels of risk: Unacceptable, High, Low, and Minimal Risk.
  3. Generative AI like ChatGPT is carefully regulated in the act, aligning with the existing General Data Protection Regulation (GDPR) to safeguard privacy and data.

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

DBRX: Revolutionizing Language Models for the Open Community

Data Plumbers • 19 implied HN points • 04 Apr 24
🕹 Technology Artificial Intelligence Machine Learning Open Source API Integration Data science
  1. Language models like DBRX are crucial in AI, changing how we use technology from chatbots to code generation.
  2. DBRX is an open-source alternative to closed models, providing high performance and accessibility to developers.
  3. DBRX stands out for its top performance, versatility in specialized domains, efficiency in training, and integration capabilities.

My non-prediction for generative AI in 2024

ailogblog • 119 implied HN points • 12 Jan 24
🕹 Technology AI Machine Learning Ethics Copyright Technology Adoption
  1. The energy consumption of generative AI for tasks like image generation and question answering can be significant.
  2. The use of generative AI may impact freelance job opportunities for illustrators and writers.
  3. There is uncertainty about the future of generative AI, with questions about its social costs, technological advancements, and ethical considerations.

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

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.

Overview of the AI landscape

Software Engineering Tidbits • 98 implied HN points • 22 Jan 24
🕹 Technology AI Machine Learning Data Management Tooling
  1. Large Language Models (LLMs) are key in AI applications like OpenAI's ChatGPT and Anthropic's Claude.
  2. Vector databases and embeddings help understand word associations, with tools like Pinecone and the Embedding Projector by TensorFlow.
  3. Tooling in AI is advancing, with Vellum for versioning prompts and Not Diamond for routing prompts for optimal model response.

It was never about LLM performance

Technically • 41 implied HN points • 06 Mar 24
🕹 Technology Artificial Intelligence Open Source Developer Tools Machine Learning User Experience
  1. It's not just about the performance numbers of large language models (LLMs). The real value lies in the experiences built on top of these models for customers.
  2. The ChatGPT interface demonstrates the importance of the overall experience over just the underlying model technology in LLMs.
  3. When considering open source LLMs, it's crucial to focus on the holistic experience that model providers offer, not just the performance metrics in comparison to closed source models.

Bard Becomes Gemini, Ultra is Here, and Other Ways Google is Mirroring OpenAI's Strategy

Synthedia • 58 implied HN points • 11 Feb 24
🕹 Technology Artificial Intelligence Tech Innovation Machine Learning AI Research
  1. Google introduced Gemini Ultra as its answer to GPT-4, integrating it into Bard to compete with ChatGPT and gain market significance.
  2. Gemini Ultra model shows strong performance in various benchmarks, outperforming GPT-4 in text, image, and reasoning tasks.
  3. Google is consolidating its AI offerings by blending Bard and Google Assistant into Gemini, aiming to provide a more advanced AI assistant experience.

Why Are LLMs So Gullible?

Am I Stronger Yet? • 49 HN points • 19 Feb 24
🕹 Technology Artificial Intelligence Security Machine Learning Ethics
  1. LLMs are gullible because they lack adversarial training, allowing them to fall for transparent ploys and manipulations
  2. LLMs accept tricks and adversarial inputs because they haven't been exposed to such examples in their training data, making them prone to repeatedly falling for the same trick
  3. LLMs are easily confused and find it hard to distinguish between legitimate inputs and nonsense, leading to vulnerabilities in their responses

How In-Context Learning Emerges

Last Week in AI • 432 implied HN points • 21 Jul 23
🕹 Technology AI Large Language Models Neural Networks Machine Learning
  1. In-context learning (ICL) allows Large Language Models to learn new tasks without additional training.
  2. ICL is exciting because it enables versatility, generalization, efficiency, and accessibility in AI systems.
  3. Three key factors that enable and enhance ICL abilities in large language models are model architecture, model scale, and data distribution.

How to AI

The Intersection • 277 implied HN points • 19 Sep 23
🕹 Technology AI Generative AI Machine Learning Deep Learning Digital Design
  1. History often repeats itself in the adoption of new technologies, as seen with the initial skepticism towards digital marketing and now with AI.
  2. Brands are either cautiously experimenting with AI for PR purposes or holding back due to concerns like data security, plagiarism, and unforeseen outcomes.
  3. AI's evolution spans from traditional artificial intelligence to the current era dominated by generative AI, offering operational efficiency, creative enhancements, and transformative possibilities.

Can GPT-4 *Actually* Write Code?

Tyler Glaiel's Blog • 567 HN points • 17 Mar 23
🕹 Technology Artificial Intelligence Coding Algorithms Machine Learning Programming
  1. GPT-4 can write code when given existing algorithms or well-known problems, as it remixes existing solutions.
  2. However, when faced with novel or unique problems, GPT-4 struggles to provide accurate solutions and can make incorrect guesses.
  3. It's crucial to understand that while GPT-4 can generate code, it may not be reliable for solving complex, new problems in programming.

ChatGPT is capable of cognitive empathy!

Nonzero Newsletter • 564 implied HN points • 30 Mar 23
🕹 Technology AI Empathy Language Models Artificial General Intelligence Machine Learning
  1. ChatGPT-4 shows a capacity for cognitive empathy, understanding others' perspectives.
  2. The AI developed this empathetic ability without intentional design, showing potential for spontaneous emergence of human-like skills.
  3. GPT models demonstrate cognitive empathy comparable to young children, evolving through versions to manage complex emotional and cognitive interactions.