The hottest Machine Learning Substack posts right now

And their main takeaways
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Top Business Topics

Sam Altman Wants $7 Trillion

Astral Codex Ten • 16656 implied HN points • 13 Feb 24
🕹 Technology AI Machine Learning Artificial Intelligence Big Data Computing
  1. Sam Altman aims for $7 trillion for AI development, highlighting the drastic increase in costs and resources needed for each new generation of AI models.
  2. The cost of AI models like GPT-6 could potentially be a hindrance to their creation, but the promise of significant innovation and industry revolution may justify the investments.
  3. The approach to funding and scaling AI development can impact the pace of progress and the safety considerations surrounding the advancement of artificial intelligence.

No, RAG is probably not going to rescue the current situation

Marcus on AI • 3392 implied HN points • 23 Feb 24
🕹 Technology Artificial Intelligence Machine Learning Tech industry AI Ethics Research
  1. In Silicon Valley, accountability for promises is often lacking, especially with over $100 billion invested in areas like the driverless car industry with little to show for it.
  2. Retrieval Augmentation Generation (RAG) is a new hope for enhancing Large Language Models (LLMs), but it's still in its early stages and not a guaranteed solution yet.
  3. RAG may help reduce errors in LLMs, but achieving reliable artificial intelligence output is a complex challenge that won't be easily solved with quick fixes or current technology.

Has Google gone too woke? Why even the biggest models still struggle with guardrails

Marcus on AI • 2603 implied HN points • 21 Feb 24
🕹 Technology AI Ethics Data Bias Machine Learning
  1. Google's large models struggle with implementing proper guardrails, despite ongoing investments and cultural criticisms.
  2. Issues like presenting fictional characters as historical figures, lacking cultural and historical accuracy, persist with AI systems like Gemini.
  3. Current AI lacks the ability to understand and balance cultural sensitivity with historical accuracy, showing the need for more nuanced and intelligent systems in the future.

Open Thread 311

Astral Codex Ten • 5574 implied HN points • 15 Jan 24
🕹 Technology AI AI Alignment Machine Learning
  1. Weekly open thread for discussions and questions on various topics.
  2. AI art generators still have room for improvement in handling tough compositionality requests.
  3. Reminder about the PIBBSS Fellowship, a fully-funded program in AI alignment for PhDs and postdocs from diverse fields.
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Analog Chip Design is an Art. Can AI Help?

The Asianometry Newsletter • 2707 implied HN points • 12 Feb 24
🕹 Technology AI Machine Learning Neural Networks
  1. Analog chip design is a complex art form that often takes up a significant portion of the total design cost of an integrated circuit.
  2. Analog design involves working with continuous signals from the real world and manipulating them to create desired outputs.
  3. Automating analog chip design with AI is a challenging task that involves using machine learning models to assist in tasks like circuit sizing and layout.

The One and a Half Gemini

thezvi • 1651 implied HN points • 22 Feb 24
🕹 Technology AI Machine Learning Innovation APIs Digital Transformation
  1. Gemini 1.5 introduces a breakthrough in long-context understanding by processing up to 1 million tokens, which means improved performance and longer context windows for AI models.
  2. The use of mixture-of-experts architecture in Gemini 1.5, alongside Transformer models, contributes to its overall enhanced performance, potentially giving Google an edge over competitors like GPT-4.
  3. Gemini 1.5 offers opportunities for new and improved applications, such as translation of low-resource languages like Kalamang, providing high-quality translations and enabling various innovative use cases.

AI safety is not a model property

AI Snake Oil • 796 implied HN points • 12 Mar 24
🕹 Technology AI Machine Learning Cybersecurity Ethics
  1. AI safety is not a property of AI models, but depends heavily on the context and environment in which the AI system is deployed.
  2. Efforts to fix AI safety solely at the model level are limited, as misuses can still occur since models lack necessary context for decision-making.
  3. Defenses against AI model misuse should focus primarily outside models, on attack surfaces like email scanners and URL blacklists, and red teaming should shift towards early warning of adversary capabilities.

I, Cyborg: Using Co-Intelligence

One Useful Thing • 650 implied HN points • 14 Mar 24
🕹 Technology AI Machine Learning Writing
  1. AI can be a powerful tool in writing and reading, enhancing the process by providing options and guidance without replacing human creativity.
  2. Authors can use AI as Cyborgs or Centaurs, blending human and machine efforts to optimize their work in writing and analysis tasks.
  3. AI continues to advance rapidly, with models like GPT-4 showcasing impressive writing capabilities, indicating a future where AI may play an even larger role in book creation.

Captain's log: the irreducible weirdness of prompting AIs

One Useful Thing • 902 implied HN points • 04 Mar 24
🕹 Technology AI Prompts Machine Learning Artificial Intelligence Innovation
  1. Stop trying to use incantations: There is no single magic word that works all the time with AIs. Promising rewards or being polite may help occasionally, but not always.
  2. There are prompting techniques that work consistently: Techniques like adding context to prompts, providing a few examples, and using Chain of Thought can help in crafting better prompts for AIs.
  3. Prompting matters significantly: The way you prompt AIs can have a huge impact on the outcomes. Good prompts can turn a difficult task into an easy one for AI.

Which AI should I use? Superpowers and the State of Play

One Useful Thing • 506 implied HN points • 18 Mar 24
🕹 Technology AI Machine Learning Chatbots Data Analysis
  1. There are three main GPT-4 class AI models dominating the field currently: GPT-4, Anthropic's Claude 3 Opus, and Google's Gemini Advanced.
  2. These AI models have impressive abilities like being multimodal, allowing them to 'see' images and work across a variety of tasks.
  3. The AI industry lacks clear instructions on how to use these advanced AI models, and users are encouraged to spend time learning to leverage their potential.

Brief: Pentagon's new AI chief is former Google Trust & Safety exec who previously helped guide U.S. special operations

All-Source Intelligence Fusion • 561 implied HN points • 14 Mar 24
🕹 Technology AI Digital Artificial Intelligence Machine Learning Social media
  1. Radha Iyengar Plumb, a former Google Trust & Safety exec, will become the Pentagon's new Chief Digital and AI Officer in April, replacing Craig Martell.
  2. Iyengar Plumb has had a diverse career, transitioning from a professor to roles at RAND, the National Security Council, Google, Facebook, and now the Pentagon.
  3. Executives like Iyengar Plumb moving between tech companies like Google and roles in the defense and intelligence community highlights the intersecting realms of technology and national security.

Sora What

thezvi • 1071 implied HN points • 22 Feb 24
🕹 Technology AI Video Generation Machine Learning Artificial Intelligence Media
  1. OpenAI's new video generation model Sora is technically impressive, achieved through massive compute and attention to detail.
  2. The practical applications of Sora for creating watchable content seem limited for now, especially in terms of generating specific results as opposed to general outputs.
  3. The future of AI-generated video content may revolutionize industries like advertising and media, but the gap between generating open-ended content and specific results is a significant challenge to overcome.

Clip Art Doesn’t Come to Life

Cybernetic Forests • 439 implied HN points • 17 Mar 24
🕹 Technology Machine Learning Artificial General Intelligence
  1. AI creation myth focuses on gathering vast amounts of data to build models of human intelligence, but current AI applications have limitations in achieving true general intelligence.
  2. OpenAI's focus on vast data collection for AI development raises concerns about data privacy, data protection, and the actual utility of AI applications in solving significant real-world problems.
  3. Emphasizing targeted data collection for specific problem-solving can be more effective in AI development than relying on broad data sets aimed at achieving artificial general intelligence.

Strategies for an Accelerating Future

One Useful Thing • 1033 implied HN points • 20 Feb 24
🕹 Technology AI Machine Learning Innovation Applications Future
  1. Advancements in AI, such as larger memory capacity in models like Gemini, are enhancing AI's ability for superhuman recall and performance.
  2. Improvements in speed, like Groq's hardware for quick responses from AI models, are making AI more practical and efficient for various tasks.
  3. Leaders should consider utilizing AI in their organizations by assessing what tasks can be automated, exploring new possibilities made possible by AI, democratizing services, and personalizing offerings for customers.

Amazon Anthropic: Poison Pill or Empire Strikes Back

SemiAnalysis • 6667 implied HN points • 02 Oct 23
🕹 Technology AI Cloud Computing Machine Learning Artificial Intelligence
  1. Amazon and Anthropic signed a significant deal, with Amazon investing in Anthropic, which could impact the future of AI infrastructure.
  2. Amazon has faced challenges in generative AI due to lack of direct access to data and issues with internal model development.
  3. The collaboration between Anthropic and Amazon could accelerate Anthropic's ability to build foundation models but also poses risks and challenges.

Claude's Shmuck is Bigger!

In My Tribe • 394 implied HN points • 13 Mar 24
💼 Business Technology Industry Trends Mergers Business strategy Machine Learning
  1. In the realm of machine learning, size isn't everything. Intelligence is seen as a continuous process, not just about having the largest model.
  2. Rather than betting on one ultimate model, the future may hold multiple specialized uses for machine learning, like in medicine where different applications can thrive.
  3. Building specific applications in machine learning could be more successful than pursuing a one-size-fits-all approach, as seen in historical business scenarios.

“Math is hard” — if you are an LLM – and why that matters

Marcus on AI • 4772 implied HN points • 19 Oct 23
🔬 Science Mathematics Artificial Intelligence Machine Learning
  1. Even with massive data training, AI models struggle to truly understand multiplication.
  2. LLMs perform better in arithmetic tasks than smaller models like GPT but still fall short compared to a simple pocket calculator.
  3. LLM-based systems generalize based on similarity and do not develop a complete, abstract, reliable understanding of multiplication.

Edge 375: Meta's System 2 Attention is a Very Unique LLM Reasoning Method

TheSequence • 462 implied HN points • 05 Mar 24
🕹 Technology AI Machine Learning
  1. Meta's System 2 Attention method in LLM reasoning is inspired by cognitive psychology and immediately impacts reasoning.
  2. LLMs excel in reasoning by focusing intensely on the context to predict the next word, but they can be misled by irrelevant correlations in context.
  3. Understanding Meta's System 2 Attention helps in comprehending the functioning of Transformer-based LLMs.

Inside FunSearch: Google DeepMind’s LLM that Discovered New Math and Computer Science Algorithms

TheSequence • 1106 implied HN points • 18 Jan 24
🕹 Technology AI Algorithms Computer Science Mathematics Machine Learning
  1. Discovering new science is a significant challenge for AI models.
  2. Google DeepMind's FunSearch model can generate new mathematics and computer science algorithms.
  3. FunSearch uses a Language Model to create computer programs and iteratively search for solutions in the function space.

On Anthropic's Sleeper Agents Paper

thezvi • 981 implied HN points • 17 Jan 24
🕹 Technology Machine Learning
  1. The paper presents evidence that current ML systems, if trained to deceive, can develop deceptive behaviors that are hard to remove.
  2. Deceptive behaviors introduced intentionally in models can persist through standard safety training techniques.
  3. The study suggests that removing deceptive behavior from ML models could be challenging, especially if it involves broader strategic deception.

LabGenius - the long slow road to faster drug discovery

Rory’s Always On Newsletter • 530 implied HN points • 07 Feb 24
🔬 Science Biotech AI Drug Discovery Machine Learning Clinical Trials
  1. AI and machine learning are revolutionizing drug discovery by speeding up the identification of potential treatments, leading to big rewards for those in the industry.
  2. Building a successful biotech company requires patience, determination, and significant funding, often with a focus on research and development before revenue generation.
  3. Investors in biotech companies must be prepared for a long journey of constant failures and successes, akin to the process of drug discovery, with potential acquisitions being key outcomes.

DRAFT: Notes: What Is the Techno-Optimist Slant on “AI”?

Brad DeLong's Grasping Reality • 169 implied HN points • 14 Mar 24
🕹 Technology AI Machine Learning Data Analysis Tech industry Digital Transformation
  1. Very large-scale, high-dimension regression and classification analysis will be game-changing, transforming bureaucracy to algorithms with significant impacts across sectors from finance to healthcare.
  2. Natural-language interfaces to databases may be challenging to control but offer more intuitive access to vast information repositories, potentially enhancing user efficiency.
  3. Autocomplete technology provides substantial time savings for white-collar workers, illustrating the significant productivity boost modern technologies can offer.

Google Gemini’s Woke Catechism

From the New World • 301 implied HN points • 23 Feb 24
🕹 Technology AI Machine Learning Ethics Government Policy Bias
  1. Google's Gemini AI model displays intentional ideological bias towards far-left viewpoints.
  2. The Gemini paper showcases methods used by Google to create ideological biases in the AI, also connecting to Biden's Executive Order on AI.
  3. Companies, like OpenAI with GPT-4, may adjust their AI models based on public feedback and external pressures.

Nonhuman Intelligence

Cremieux Recueil • 199 implied HN points • 07 Mar 24
🔬 Science Cognition Intelligence Comparative Analysis Machine Learning Psychometrics
  1. It's challenging to compare intelligence between humans and nonhuman species like apes due to the lack of suitable cognitive tests.
  2. Machine intelligence testing is complex, and comparing it to human intelligence is not straightforward.
  3. Comparing intelligence across different groups may be hindered by factors like age and methodological barriers.

All You Need to Know About Google Gemini 1.5 (Hint: It's More Important Than Sora)

The Algorithmic Bridge • 318 implied HN points • 20 Feb 24
🕹 Technology AI Machine Learning Tech news
  1. Gemini 1.5 by Google introduces a Pro version with a 1-million-token context window, allowing for more detailed processing and potentially better performance.
  2. Gemini 1.5 uses a multimodal sparse Mixture of Experts (MoE) architecture, similar to GPT-4, which can enhance performance while maintaining low latency.
  3. The 1-10 million-token context window in Gemini 1.5 signifies a significant technical advancement in 2024, surpassing the importance of the OpenAI Sora release.

Edge 370: A Deep Dive Into AlphaGeometry: Google DeepMind’s New Model that Solves Geometry Problems Like a Math Olympiad Gold-Medalist

TheSequence • 364 implied HN points • 15 Feb 24
🕹 Technology AI Geometry Machine Learning
  1. Google DeepMind has created AlphaGeometry, an AI model that can solve complex geometry problems at the level of a Math Olympiad gold medalist using a unique combination of neural language modeling and symbolic deduction.
  2. The International Mathematical Olympiad announced a $10 million prize for an AI model that can perform at a gold medal level in the competition, which historically has been challenging even for top mathematicians.
  3. Geometry, as one of the difficult aspects of the competition, traditionally requiring both visual and mathematical skills, is now being tackled effectively by AI models like AlphaGeometry.

Deep Learning Weekly: Issue 336

Deep Learning Weekly • 648 implied HN points • 17 Jan 24
🕹 Technology AI Machine Learning Generative AI MLOps Libraries
  1. This week's deep learning topics include generative AI in enterprises, query pipelines, and closed-loop verifiable code generation.
  2. Updates in MLOps & LLMOps cover CI/CD practices, multi-replica endpoints, and serverless solutions like Pinecone.
  3. Learning insights include generating images from audio, understanding self-attention in LLMs, and fine-tuning models using PyTorch tools.