LLM Interpretability and Sparse Autoencoders: Research from OpenAI and Anthropic

Deep Papers

Player FM - Internet Radio Done Right

23 subscribers

اضافه شده در two سال پیش

محتوای ارائه شده توسط Arize AI. تمام محتوای پادکست شامل قسمت‌ها، گرافیک‌ها و توضیحات پادکست مستقیماً توسط Arize AI یا شریک پلتفرم پادکست آن‌ها آپلود و ارائه می‌شوند. اگر فکر می‌کنید شخصی بدون اجازه شما از اثر دارای حق نسخه‌برداری شما استفاده می‌کند، می‌توانید روندی که در اینجا شرح داده شده است را دنبال کنید.https://fa.player.fm/legal

Curated Questions: Conversations Celebrating the Power of Questions!

1
Kevin Kelly 50:30

10 روز پیش50:30

پخش در آینده

لیست ها

پسندیدن

دوست داشته شد

50:30

Episode Notes [00:00] The Importance of Questioning [01:21] Introduction to Curated Questions [02:20] Meet Kevin Kelly [03:56] Kevin Kelly's Mentor: Stewart Brand [05:33] The Role of Questions in Intellectual Traditions [06:47] Disequilibrium and Growth [10:21] Embodied Questions and Exploration [11:11] Balancing Exploration and Exploitation [11:50] The Inefficiency of Questioning [15:53] The Abundance Mindset [18:39] The Inevitable and Quality Questions [19:26] Hill Climbing vs. Hill Making [22:28] The Challenge of Innovation [24:13] The Beauty of Engineering and Innovation [24:34] Navigating the Frontier of New Technologies [25:33] The Role of AI in Question Formulation [26:43] Challenges in Advancing AI Capabilities [29:11] The Long Now Foundation and the 10,000 Year Clock [29:56] Transmitting Values Over Time [31:03] Ethics in AI and Self-Driving Cars [33:26] The Art of Questioning [34:04] Photography: Capturing vs. Creating [36:12] The Inefficiency of Exploration [38:36] Daily Practice and Long-Term Success [40:17] The Importance of Quantity for Quality [43:22] Final Thoughts and Encouragement on Questioning [46:24] Summary Takeaways Resources Mentioned Wired Magazine Whole Earth Review WELL Hackers Conference What Technology Wants by Kevin Kelly The Inevitable by Kevin Kelly Cool Tools Project Long Now Foundation Stewart Brand Socratic Method Koan René Descartes Conde Nast Vouge Olivetti Typewriter Trolley Problem Terry Gross Lex Friedman Tim Ferriss KK.org Kevin2Kelly on Instagram Recomendo Newsletter Excellent Advice for Living Beauty Pill Producer Ben Ford Questions Asked When did you first understand the power of questions? Can I do that? Can that be something that you can learn? How did questions function differently between Eastern versus Western intellectual traditions? What role do you think embodied questions those we explore through doing rather than thinking play in developing wisdom? What's on the other side of the hill? What happens if you go to the end? What's the origin of this? How should one think about the exploratory in one's life? Is there anything that you would add to your list of 15 statements that define what makes a quality question? Is there a qualitative difference between the questions humans are asking and the questions our AI systems are beginning to formulate? What do you think would help them get there? Any idea on a forcing function on how we get them [LLMs] to ask the better questions so that they might improve in that direction? What were some novel questions that broke your brain at the time in thinking about this 10,000-year clock or beyond? What's it good for? What would you use it for? What else could you do over the long term for 10,000 years? How do you transmit values over time? How do you evolve values that need to change, and how do you make a difference? How do even know what you don't want to change? What do you want to continue? What's the most essential aspects of our civilization that we don't want to go away? What are the rules? What is the system? How do you pass things along in time and not change the ones you don't wanna change, and make sure you change the ones that are more adaptable so they can adapt? What do you think about questioning itself as an art form? How has being a photographer influenced the way you question reality, visually compared to verbally? Are you a photographer that takes photos or makes photos? What will happen? What will happen next? What are your right now questions that you are wrestling with or working with in your life? Can someone else do what I'm trying to do here? Am I more me in doing my art or more me in doing the writing? Do you have any other thoughts or encouragement about questions that we haven't explored? What makes a good question? How do you ask a good question? What questions do you dwell on to be in purposeful imbalance? What is your practice in embracing the inefficient nature of questions to achieve breakthroughs? What are the new hills you can build and frontiers you can explore? How can you use your curiosity and humanity to pursue questions that trend toward the fringes?…

حدود یک سال پیش 44:00

MP3•خانه قسمت

It’s been an exciting couple weeks for GenAI! Join us as we discuss the latest research from OpenAI and Anthropic. We’re excited to chat about this significant step forward in understanding how LLMs work and the implications it has for deeper understanding of the neural activity of language models. We take a closer look at some recent research from both OpenAI and Anthropic. These two recent papers both focus on the sparse autoencoder--an unsupervised approach for extracting interpretable features from an LLM. In "Extracting Concepts from GPT-4," OpenAI researchers propose using k-sparse autoencoders to directly control sparsity, simplifying tuning and improving the reconstruction-sparsity frontier. In "Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet," researchers at Anthropic show that scaling laws can be used to guide the training of sparse autoencoders, among other findings.

Learn more about AI observability and evaluation, join the Arize AI Slack community or get the latest on LinkedIn and X.

49 قسمت

#Science #Tech #Math #Business #Arize AI

LLM Interpretability and Sparse Autoencoders: Research from OpenAI and Anthropic

Deep Papers

23 subscribers

published حدود یک سال پیش

اشتراک گذاری

MP3•خانه قسمت

Learn more about AI observability and evaluation, join the Arize AI Slack community or get the latest on LinkedIn and X.

49 قسمت

#Science #Tech #Math #Business #Arize AI

همه قسمت ها

Deep Papers

1
Scalable Chain of Thoughts via Elastic Reasoning 28:54

23 hours پیش28:54

28:54

In this week's episode, we talk about Elastic Reasoning, a novel framework designed to enhance the efficiency and scalability of large reasoning models by explicitly separating the reasoning process into two distinct phases: thinking and solution . This separation allows for independent allocation of computational budgets, addressing challenges related to uncontrolled output lengths in real-world deployments with strict resource constraints. Our discussion explores how Elastic Reasoning contributes to more concise and efficient reasoning, even in unconstrained settings, and its implications for deploying LRMs in resource-limited environments. Read the paper here: https://arxiv.org/pdf/2505.05315 Sign up for the next discussion & see more AI research: arize.com/ai-research-papers Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

Deep Papers

1
Sleep-time Compute: Beyond Inference Scaling at Test-time 30:24

15 روز پیش30:24

30:24

What if your LLM could think ahead —preparing answers before questions are even asked? In this week's paper read, we dive into a groundbreaking new paper from researchers at Letta, introducing sleep-time compute: a novel technique that lets models do their heavy lifting offline , well before the user query arrives. By predicting likely questions and precomputing key reasoning steps, sleep-time compute dramatically reduces test-time latency and cost—without sacrificing performance. We explore new benchmarks—Stateful GSM-Symbolic, Stateful AIME, and the multi-query extension of GSM—that show up to 5x lower compute at inference, 2.5x lower cost per query, and up to 18% higher accuracy when scaled. You’ll also see how this method applies to realistic agent use cases and what makes it most effective.If you care about LLM efficiency, scalability, or cutting-edge research. Explore more AI research, or sign up to hear the next session live: arize.com/ai-research-papers Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

Deep Papers

1
LibreEval: The Largest Open Source Benchmark for RAG Hallucination Detection 27:19

29 روز پیش27:19

27:19

For this week's paper read, we actually dive into our own research. We wanted to create a replicable, evolving dataset that can keep pace with model training so that you always know you're testing with data your model has never seen before. We also saw the prohibitively high cost of running LLM evals at scale, and have used our data to fine-tune a series of SLMs that perform just as well as their base LLM counterparts, but at 1/10 the cost. So, over the past few weeks, the Arize team generated the largest public dataset of hallucinations, as well as a series of fine-tuned evaluation models. We talk about what we built, the process we took, and the bottom line results. 📃 Read the paper: https://arize.com/llm-hallucination-dataset/ Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

Deep Papers

1
AI Benchmark Deep Dive: Gemini 2.5 and Humanity's Last Exam 26:11

۶ weeks پیش26:11

26:11

This week we talk about modern AI benchmarks, taking a close look at Google's recent Gemini 2.5 release and its performance on key evaluations, notably Humanity's Last Exam (HLE). In the session we covered Gemini 2.5's architecture, its advancements in reasoning and multimodality, and its impressive context window. We also talked about how benchmarks like HLE and ARC AGI 2 help us understand the current state and future direction of AI. Read it on the blog: https://arize.com/blog/ai-benchmark-deep-dive-gemini-humanitys-last-exam/ Sign up to watch the next live recording: https://arize.com/resource/community-papers-reading/ Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

Deep Papers

1
Model Context Protocol (MCP) 15:03

۸ weeks پیش15:03

15:03

We cover Anthropic’s groundbreaking Model Context Protocol (MCP) . Though it was released in November 2024, we've been seeing a lot of hype around it lately, and thought it was well worth digging into. Learn how this open standard is revolutionizing AI by enabling seamless integration between LLMs and external data sources, fundamentally transforming them into capable, context-aware agents. We explore the key benefits of MCP, including enhanced context retention across interactions, improved interoperability for agentic workflows, and the development of more capable AI agents that can execute complex tasks in real-world environments. Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

Deep Papers

1
AI Roundup: DeepSeek’s Big Moves, Claude 3.7, and the Latest Breakthroughs 30:23

11 weeks پیش30:23

30:23

This week, we're mixing things up a little bit. Instead of diving deep into a single research paper, we cover the biggest AI developments from the past few weeks. We break down key announcements, including: DeepSeek’s Big Launch Week: A look at FlashMLA (DeepSeek’s new approach to efficient inference) and DeepEP (their enhanced pretraining method). Claude 3.7 & Claude Code: What’s new with Anthropic’s latest model, and what Claude Code brings to the AI coding assistant space. Stay ahead of the curve with this fast-paced recap of the most important AI updates. We'll be back next time with our regularly scheduled programming. Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

Deep Papers

1
How DeepSeek is Pushing the Boundaries of AI Development 29:54

12 weeks پیش29:54

29:54

This week, we dive into DeepSeek. SallyAnn DeLucia, Product Manager at Arize, and Nick Luzio, a Solutions Engineer, break down key insights on a model that have dominating headlines for its significant breakthrough in inference speed over other models. What’s next for AI (and open source)? From training strategies to real-world performance, here’s what you need to know. Read a summary: https://arize.com/blog/how-deepseek-is-pushing-the-boundaries-of-ai-development/ Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

Deep Papers

1
Multiagent Finetuning: A Conversation with Researcher Yilun Du 30:03

15 weeks پیش30:03

30:03

We talk to Google DeepMind Senior Research Scientist (and incoming Assistant Professor at Harvard), Yilun Du, about his latest paper "Multiagent Finetuning: Self Improvement with Diverse Reasoning Chains." This paper introduces a multiagent finetuning framework that enhances the performance and diversity of language models by employing a society of agents with distinct roles, improving feedback mechanisms and overall output quality. The method enables autonomous self-improvement through iterative finetuning, achieving significant performance gains across various reasoning tasks. It's versatile, applicable to both open-source and proprietary LLMs, and can integrate with human-feedback-based methods like RLHF or DPO, paving the way for future advancements in language model development. Read an overview on the blog Watch the full discussion Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

Deep Papers

1
Training Large Language Models to Reason in Continuous Latent Space 24:58

18 weeks پیش24:58

24:58

LLMs have typically been restricted to reason in the "language space," where chain-of-thought (CoT) is used to solve complex reasoning problems. But a new paper argues that language space may not always be the best for reasoning. In this paper read, we cover an exciting new technique from a team at Meta called Chain of Continuous Thought—also known as "Coconut." In the paper, "Training Large Language Models to Reason in a Continuous Latent Space" explores the potential of allowing LLMs to reason in an unrestricted latent space instead of being constrained by natural language tokens. Read a full breakdown of Coconut on our blog Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

Deep Papers

1
LLMs as Judges: A Comprehensive Survey on LLM-Based Evaluation Methods 28:57

21 weeks پیش28:57

28:57

We discuss a major survey of work and research on LLM-as-Judge from the last few years. "LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods" systematically examines the LLMs-as-Judge framework across five dimensions: functionality, methodology, applications, meta-evaluation, and limitations. This survey gives us a birds eye view of the advantages, limitations and methods for evaluating its effectiveness. Read a breakdown on our blog: https://arize.com/blog/llm-as-judge-survey-paper/ Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

Deep Papers

1
Merge, Ensemble, and Cooperate! A Survey on Collaborative LLM Strategies 28:47

23 weeks پیش28:47

28:47

LLMs have revolutionized natural language processing, showcasing remarkable versatility and capabilities. But individual LLMs often exhibit distinct strengths and weaknesses, influenced by differences in their training corpora. This diversity poses a challenge: how can we maximize the efficiency and utility of LLMs? A new paper, "Merge, Ensemble, and Cooperate: A Survey on Collaborative Strategies in the Era of Large Language Models," highlights collaborative strategies to address this challenge. In this week's episode, we summarize key insights from this paper and discuss practical implications of LLM collaboration strategies across three main approaches: merging, ensemble, and cooperation. We also review some new open source models we're excited about. Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

Deep Papers

1
Agent-as-a-Judge: Evaluate Agents with Agents 24:54

25 weeks پیش24:54

24:54

This week, we break down the “Agent-as-a-Judge” framework—a new agent evaluation paradigm that’s kind of like getting robots to grade each other’s homework. Where typical evaluation methods focus solely on outcomes or demand extensive manual work, this approach uses agent systems to evaluate agent systems, offering intermediate feedback throughout the task-solving process. With the power to unlock scalable self-improvement, Agent-as-a-Judge could redefine how we measure and enhance agent performance. Let's get into it! Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

Deep Papers

1
Introduction to OpenAI's Realtime API 29:56

27 weeks پیش29:56

29:56

We break down OpenAI’s realtime API. Learn how to seamlessly integrate powerful language models into your applications for instant, context-aware responses that drive user engagement. Whether you’re building chatbots, dynamic content tools, or enhancing real-time collaboration, we walk through the API’s capabilities, potential use cases, and best practices for implementation. Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

Deep Papers

1
Swarm: OpenAI's Experimental Approach to Multi-Agent Systems 46:46

29 weeks پیش46:46

46:46

As multi-agent systems grow in importance for fields ranging from customer support to autonomous decision-making, OpenAI has introduced Swarm, an experimental framework that simplifies the process of building and managing these systems. Swarm, a lightweight Python library, is designed for educational purposes, stripping away complex abstractions to reveal the foundational concepts of multi-agent architectures. In this podcast, we explore Swarm’s design, its practical applications, and how it stacks up against other frameworks. Whether you’re new to multi-agent systems or looking to deepen your understanding, Swarm offers a straightforward, hands-on way to get started. Read a Summary on the Blog Watch on YouTube Sign up for Upcoming Paper Readings Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

Deep Papers

1
KV Cache Explained 4:19

29 weeks پیش4:19

4:19

In this episode, we dive into the intriguing mechanics behind why chat experiences with models like GPT often start slow but then rapidly pick up speed. The key? The KV cache. This essential but under-discussed component enables the seamless and snappy interactions we expect from modern AI systems. Harrison Chu breaks down how the KV cache works, how it relates to the transformer architecture, and why it's crucial for efficient AI responses. By the end of the episode, you'll have a clearer understanding of how top AI products leverage this technology to deliver fast, high-quality user experiences. Tune in for a simplified explanation of attention heads, KQV matrices, and the computational complexities they present. Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

به Player FM خوش آمدید!

Player FM در سراسر وب را برای یافتن پادکست های با کیفیت اسکن می کند تا همین الان لذت ببرید. این بهترین برنامه ی پادکست است که در اندروید، آیفون و وب کار می کند. ثبت نام کنید تا اشتراک های شما در بین دستگاه های مختلف همگام سازی شود.