The hottest AI Research Substack posts right now

And their main takeaways
Category
Top Technology Topics

RAG Glossary

The Tech Buffet • 179 implied HN points • 21 Jan 24
🕹 Technology Machine Learning AI Research Software Development Data Management Programming
  1. Retrieval Augmented Generation (RAG) helps AI answer questions and generate content. It combines searching through documents with generating relevant answers.
  2. Using RAG can be tricky, especially in production environments. Adjustments may be needed to improve reliability and performance.
  3. Different indexing methods can optimize how RAG retrieves information. This can make it more efficient and effective in finding the right data.

Import AI 327: Stable Diffusion on phones; GPT-Hacker; UK launches a £100m AI taskforce

Import AI • 379 implied HN points • 01 May 23
🕹 Technology AI Research Cybersecurity AI Governance
  1. Google researchers optimized Stable Diffusion for efficiency on smartphones, achieving fast inference latency, a step towards industrialization of image generation.
  2. Using large language models like GPT-4 can enhance hacker capabilities, automating tasks and providing helpful tips.
  3. Political parties, like the Republican National Committee, are leveraging AI to create AI-generated content for campaigns, highlighting the emergence of AI in shaping political narratives.

How I Taught Myself to Critique AI Research [Storytime Saturdays]

Technology Made Simple • 119 implied HN points • 10 Mar 24
🕹 Technology AI Research Writing Machine Learning System Design
  1. Writing allows you to store knowledge for future reference, spot cognitive blindspots, and engage with topics more deeply for better understanding.
  2. Challenges in self-learning writing include lack of contextual understanding, a defined learning path, and a peer network for feedback.
  3. Addressing challenges in self-learning involves finding strategies to gain clarity, identifying knowledge gaps, and seeking feedback from peers.

Import AI 331: 16X smaller language models; could AMD compete with NVIDIA?; and BERT for the dark web

Import AI • 319 implied HN points • 29 May 23
🕹 Technology AI Research Speech Recognition Dark web AI Regulation
  1. Researchers have found a way to significantly reduce memory requirements for training large language models, making it feasible to fine-tune on a single GPU, which could have implications for AI governance and model security.
  2. George Hotz's new company, Tiny Corp, aims to enable AMD to compete with NVIDIA in AI training chips, potentially paving the way for a more competitive AI chip market.
  3. Training language models on text from the dark web, like DarkBERT, could lead to improved detection of illicit activities online, showcasing the potential of AI systems in monitoring and identifying threats in the digital space.

Import AI 332: Mini-AI; safety through evals; Facebook releases a RLHF dataset

Import AI • 299 implied HN points • 12 Jun 23
🕹 Technology AI Research AI Models AI Policy AI Governance AI safety
  1. Facebook used human feedback to train its language model, BlenderBot 3x, leading to better and safer responses than its predecessor
  2. Cohere's research shows that training AI systems with specific techniques can make them easier to miniaturize, which can reduce memory requirements and latency
  3. A new organization called Apollo Research aims to develop evaluations for unsafe AI behaviors, helping improve the safety of AI companies through research into AI interpretability
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Import AI 319: Sovereign AI; Facebook's weights leak on torrent networks; Google might have made a better optimizer than Adam!

Import AI • 439 implied HN points • 06 Mar 23
🕹 Technology AI Research Cloud Computing Data security Partnerships Innovation
  1. Google researchers achieved promising results by scaling a Vision Transformer to 22B parameters, showcasing improved alignment to human visual perception.
  2. Google introduced a potentially better optimizer called Lion, showing outstanding performance across various models and tasks, including setting a new high score on ImageNet.
  3. A shift toward sovereign AI systems is emerging globally, driven by the need for countries to develop their own AI capabilities to enhance national security and economic competitiveness.

The Sequence Opinion #470: Open Endedness AI Could be All We Need

TheSequence • 49 implied HN points • 16 Jan 25
🕹 Technology AI Research Machine Learning Innovation Automation
  1. Open-Endedness AI focuses on creating systems that can learn and adapt over time, rather than just completing specific tasks. This allows AI to innovate and find new solutions continuously.
  2. This new approach to AI research aims for something called artificial general intelligence (AGI), which means AI that can perform a wide range of tasks like a human can. It's a big step towards smarter technology.
  3. However, developing Open-Endedness AI comes with challenges. Researchers must find ways to ensure these systems can learn effectively without becoming unreliable or out of control.

Results from poll #4

The Counterfactual • 39 implied HN points • 21 May 24
🕹 Technology AI Research Machine Learning Data science Language Models Tech Trends
  1. The recent poll found that two topics, an explainer on interpretability and a guide to becoming an LLM-ologist, were equally popular among voters.
  2. The plan is to write about both topics in the coming months, keeping the content varied as usual.
  3. Two new papers were published this month, one on multimodal LLMs and another on Korean language models, highlighting ongoing research in these areas.

Edge 450: Can LLM Sabotage Human Evaluations

TheSequence • 70 implied HN points • 21 Nov 24
🕹 Technology AI Research Model Evaluation Human-AI Interaction Ethics Philosophy
  1. New research is exploring how AI models might behave in ways that conflict with human goals. It's important to understand this to ensure AI is safe and useful.
  2. Anthropic has introduced a framework called 'Sabotage Evaluations'. This framework helps assess the risk of AI models not aligning with what humans want.
  3. The goal is to measure and reduce the chances of AI models sabotaging human efforts. Ensuring control over intelligent systems is a big challenge.

LLAMA 2: an incredible open-source LLM

Democratizing Automation • 411 implied HN points • 18 Jul 23
🕹 Technology AI Research Open Source Model Evaluation
  1. The Llama 2 model is a big step forward for open-source language models, offering customizability and lower cost for companies.
  2. Despite not being fully open-source, the Llama 2 model is beneficial for the open-source community.
  3. The paper includes extensive details on various aspects like model capabilities, costs, data controls, RLHF process, and safety evaluations.

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

Synthedia • 59 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.

Data Science Weekly - Issue 481

Data Science Weekly Newsletter • 239 implied HN points • 09 Feb 23
🕹 Technology Data science AI Research Big Data Machine Learning Software Development
  1. Big Data is changing, and it's not as big a deal as we thought. Hardware is getting better faster than data sizes are growing.
  2. Research in AI can be learned just like a sport. It's about practicing skills like designing experiments and writing papers.
  3. Data Analytics can really help businesses understand their performance and make smarter decisions. It’s all about using data to solve problems and anticipate future issues.

Your guide to AI: December 2024

Guide to AI • 6 implied HN points • 01 Dec 24
🕹 Technology AI Policy AI Research Tech startups Hardware
  1. AI is really growing fast, and new companies are getting lots of funding to develop more advanced tools. This is creating a competitive environment.
  2. The politics around AI are uncertain after the recent US elections. It's hard to predict how new leaders will affect AI regulations and policies.
  3. There's ongoing debate about the quality of AI models from both US and Chinese labs. They are working hard to innovate and improve, showing that competition is fierce on a global scale.

#10 - Voluntary Commitments for Safe AI Development + A New Industry Body

Navigating AI Risks • 78 implied HN points • 02 Aug 23
🕹 Technology AI Governance Regulation AI Research
  1. Leading AI companies have made voluntary commitments to ensure safety, security, and trust in AI development.
  2. The commitments focus on addressing transformative risks linked to frontier AI development.
  3. Inter-Lab Cooperation in AI Safety is being fostered through the creation of a forum to share best practices and collaborate with policymakers.

What do Conservative voters really think about immigration?

Comment is Freed • 54 implied HN points • 28 Feb 24
🌍 World Politics Immigration Public Opinion AI Research
  1. Concern about immigration among Conservative voters has fluctuated over the years, showing a recent increase largely attributed to attention from right-wing politicians and media.
  2. Labour voters are more likely to be directly affected by immigration due to demographics, contrary to expectations. This dynamic impacts how policymakers should approach the issue.
  3. Misunderstanding public opinion on immigration could lead to harmful policy decisions. Better insight is crucial to avoid unnecessary or damaging stances.

The Real Black Mirror

The Ruffian • 172 implied HN points • 25 Feb 23
🕹 Technology AI Chatbots AI Research Technology Ethics
  1. The history of black mirrors used for visions and prophecies in the 16th century.
  2. John Dee, a sage of the Elizabethan court, used a black mirror for communication with angels and visions of the future.
  3. AI development raises questions about its capabilities beyond simple reasoning and pattern matching.

June/July 2023 safety news: Jailbreaks, Transformer Programs, Superalignment

AI safety takes • 39 implied HN points • 15 Jul 23
🕹 Technology Machine Learning AI Research AI safety AI Ethics
  1. Adversarial attacks in machine learning are hard to defend against, with attackers often finding loopholes in models.
  2. Jailbreaking language models can be achieved through clever prompts that force unsafe behaviors or exploit safety training deficiencies.
  3. Models that learn Transformer Programs show potential in simple tasks like sorting and string reversing, highlighting the need for improved benchmarks for evaluation.

Intelligence and combinatorial complexity

Sunday Letters • 39 implied HN points • 27 Aug 23
🕹 Technology AI Research Machine Learning Cognitive Science Computational Theory Human-computer interaction
  1. More agents working together can create better intelligence than a single agent. This is surprising because we might think one advanced model is enough, but collaboration can enhance performance.
  2. Human-like patterns help improve AI performance. Just as we can review our work for errors, AI systems can use different modes to refine their outputs.
  3. Complex systems come with challenges like errors and biases. As AI gets more complicated, these issues tend to increase, similar to problems found in complex biological systems.

Adding Noise Improves RAG Performance

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 02 Feb 24
🕹 Technology AI Research Information Retrieval Machine Learning Language Models Data Analysis
  1. Adding irrelevant documents can actually improve accuracy in Retrieval-Augmented Generation systems. This goes against the common belief that only relevant documents are useful.
  2. In some cases, having unrelated information can help the model find the right answer, even better than using only related documents.
  3. It's important to carefully place both relevant and irrelevant documents when building RAG systems to make them work more effectively.

⚡ One-step Diffusion & 1 Million FPS Simulations

ppdispatch • 8 implied HN points • 11 Oct 24
🕹 Technology AI Research Simulations Machine Learning Data processing Model Evaluation
  1. A new technology called Differential Transformer helps improve language understanding by reducing noise and focusing on the important context, making it better for tasks that need long-term memory.
  2. GPUDrive is an advanced driving simulator that works really fast, allowing training of AI agents in complex driving situations, speeding up their learning process significantly.
  3. One-step Diffusion is a new method for creating images quickly without losing quality, making it much faster than traditional methods while still producing great results.

"Mechanistic interpretability" for LLMs, explained

The Counterfactual • 1 HN point • 08 Jul 24
🕹 Technology AI Research Machine Learning Natural Language Processing Computer Science Data science
  1. Mechanistic interpretability helps us understand how large language models (LLMs) like ChatGPT work, breaking down their 'black box' nature. This understanding is important because we need to predict and control their behavior.
  2. Different research methods, like classifier probes and activation patching, are used to explore how components in LLMs contribute to their predictions. These techniques help researchers pinpoint which parts of the model are responsible for specific tasks.
  3. There's a growing interest in this field, as researchers believe that knowing more about LLMs can lead to safer and more effective AI systems. Understanding how they work can help prevent issues like bias and deception.

DeepMind in Deep Dilemma

Sector 6 | The Newsletter of AIM • 19 implied HN points • 28 Feb 23
🕹 Technology AI Research Tech industry Startups Innovation Career development
  1. DeepMind is losing some of its top talent to competitors like OpenAI. This is causing concern about its ability to keep up in the AI race.
  2. Elon Musk is starting a new venture that aims to create a rival to OpenAI. This indicates growing competition in the AI industry.
  3. Google is facing challenges and may need to rethink its leadership approach to retain talent and address these issues.

AI Pitfalls and Promises: Surprising New Findings in Cognitive Models

ppdispatch • 2 implied HN points • 01 Nov 24
🕹 Technology AI Research Machine Learning Data Analysis Distributed Systems
  1. Chain-of-thought prompting might actually make some tasks harder for AI, especially in visual tasks where less thinking works better.
  2. The DAWN framework allows AI agents to work together globally in a secure way, which can lead to improved collaboration.
  3. New mesomorphic networks are great for understanding tabular data and give clearer explanations, making them useful for various applications.

🌳 More Agents, Decentralized Social Media, and Faster Training

ppdispatch • 2 implied HN points • 18 Oct 24
🕹 Technology Machine Learning Social media AI Research Decentralization Computational efficiency
  1. Scaling up the number of agents can really boost the performance of language models, especially when tasks get tough.
  2. Bluesky offers a new way for social media that lets users have more control and makes it easier to manage content.
  3. Using 16-bit models can save time and resources while still giving accurate results, making them good for those with less powerful hardware.

A 20k Subscriber Thank You

The Gradient • 9 implied HN points • 20 Feb 23
🕹 Technology AI Research Newsletter Podcast
  1. The Gradient aims to provide accessible and sophisticated coverage of the latest in AI research through essays, newsletters, and podcasts.
  2. The Gradient is run by a team of volunteer grad students and engineers who are committed to providing valuable synthesis of perspectives within the AI field.
  3. The Gradient plans to continue initiatives like the newsletter and podcast, with hopes of compensating authors in the future.

In the weeds: The Replication Crisis of LLM Research

Multimodal by Bakz T. Future • 2 implied HN points • 17 Feb 24
🕹 Technology AI Research Language Models Replication Crisis Artificial Intelligence Research Findings
  1. Prompt design can significantly impact the performance of language models, showing their true capabilities or masking them
  2. Using prompt design to manipulate results can be a concern, potentially impacting the authenticity of research findings
  3. The fast pace of the AI industry leads to constant advancements in models, making it challenging to keep up with the latest capabilities

Data Science Weekly - Issue 371

Data Science Weekly Newsletter • 19 implied HN points • 31 Dec 20
🕹 Technology Machine Learning Data science AI Research Technology Tools Statistical Analysis
  1. Real-time machine learning is becoming important for many companies. Some have invested heavily in the right infrastructure and are seeing good results.
  2. There are many new tools for machine learning and MLOps. Keeping track of these tools can help in improving workflow and project success.
  3. Understanding concepts like Markov models can help in planning routines, such as workouts, based on previous choices. This helps in making smart decisions about what to do next.

May Newsletter

RSS DS+AI Section • 5 implied HN points • 01 May 23
🕹 Technology Data science Machine Learning AI Ethics AI Research
  1. The May newsletter contains updates on data science and AI developments, including information on the Royal Statistical Society's activities.
  2. There is a focus on ethics, bias, and diversity in data science, along with concerns about AI model safety and regulatory challenges.
  3. Generative AI remains a hot topic, with discussions on training models, practical applications, and real-world impact of AI in healthcare, design, and storytelling.

Data Science Weekly - Issue 319

Data Science Weekly Newsletter • 19 implied HN points • 02 Jan 20
🕹 Technology Data science Machine Learning AI Research Data Visualization Programming
  1. AI can help detect cancer in mammograms better than humans, which shows the growing role of technology in healthcare.
  2. Working on data projects can help new data scientists stand out to employers and improve their skills.
  3. The AI research community needs to improve transparency by sharing their work, which can help advance the field.

Data Science Weekly - Issue 277

Data Science Weekly Newsletter • 19 implied HN points • 14 Mar 19
🕹 Technology Data science AI Research Programming Machine Learning Statistical Analysis
  1. Data science teams perform better with generalists instead of specialists. This approach helps teams adapt and innovate rather than just focusing on increasing productivity.
  2. R is a powerful programming language for data analysis, with many surprising capabilities beyond statistics. It has features that can impress even those in the computer science field.
  3. China is expected to surpass the U.S. in AI research output soon. This shift highlights the increasing importance of global competition in technology and research.

Interview with CEO of Google DeepMind

Age of AI • 0 implied HN points • 14 Jul 23
🕹 Technology AI Machine Learning Chatbots AI Research Language Models
  1. Large language models (LLMs) are being developed to become universal personal assistants with planning and reasoning capabilities.
  2. LLMs may utilize specialized tools for tasks like folding proteins or playing chess, breaking down the AI system into smaller ones.
  3. LLMs should be equipped with the ability to critique themselves by reasoning and planning, similar to how game programs improve their moves.

LLM-Generated Self-Explanations

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 21 Dec 23
🕹 Technology AI Research Machine Learning Natural Language Processing Data Analysis Software Engineering
  1. LLMs can make predictions and explain how they arrived at those predictions. This helps in understanding their reasoning better.
  2. Using a 'Chain of Thoughts' method can improve LLMs' ability to solve complex tasks, especially in areas like math and sentiment analysis.
  3. There's a need for better ways to evaluate the explanations given by LLMs because current methods may not accurately determine which explanations are effective.

Better without AI: now in paperback and Kindle!

Meaningness • 0 implied HN points • 30 Dec 23
🕹 Technology AI Books AI Research Publishing Content creation
  1. The book 'Better without AI' explores moderate apocalypses that could result from current and near-future AI technology, providing insights into realistically likely disasters and actions to prevent them.
  2. Despite the rapid pace of progress in AI during 2022, the substance of the book mostly remained relevant in 2023, indicating that the field may be nearing its limit in terms of significant advancements.
  3. The author's decision to publish the book in paperback and Kindle serves as an experiment to gauge the audience's interest in such editions, with the outcome influencing future decisions on book publishing.

The Internet, Perfectly Broken

Escher Studies • 0 implied HN points • 29 Sep 23
🕹 Technology Internet Infrastructure Data Privacy Digital Divide AI Research
  1. The internet is fundamentally broken in key ways like infrastructure, ethics, and functionality.
  2. Open-source projects and public data can foster innovation, collaboration, and empower diverse communities.
  3. Access disparities, data utility, and attention economy are critical issues that need to be addressed to create a more just and empowering internet.