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Together, we're redefining what it means, looks and feels like, to be doing "woman's work" in the world today. With confidence and the occasional rant. From boardrooms to studios, kitchens to coding dens, we explore the multifaceted experiences of today's woman, confirming that the new definition of "woman's work" is whatever feels authentic, true, and right for you. We're shedding expectations, setting aside the "shoulds", giving our finger to the "supposed tos". We're torching the old playbook and writing our own rules. Who runs the world? You decide. Learn more at nicolekalil.com

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Agent-as-a-Judge: Evaluate Agents with Agents

for ca. et år siden 24:54

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Indhold leveret af Arize AI. Alt podcastindhold inklusive episoder, grafik og podcastbeskrivelser uploades og leveres direkte af Arize AI eller deres podcastplatformspartner. Hvis du mener, at nogen bruger dit ophavsretligt beskyttede værk uden din tilladelse, kan du følge processen beskrevet her https://da.player.fm/legal.

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.

… continue reading

46 episoder

#Science #Tech #Math #Business #Arize AI

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Agent-as-a-Judge: Evaluate Agents with Agents

22 subscribers

published for ca. et år siden

Del

MP3•Episode hjem

Indhold leveret af Arize AI. Alt podcastindhold inklusive episoder, grafik og podcastbeskrivelser uploades og leveres direkte af Arize AI eller deres podcastplatformspartner. Hvis du mener, at nogen bruger dit ophavsretligt beskyttede værk uden din tilladelse, kan du følge processen beskrevet her https://da.player.fm/legal.

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.

… continue reading

46 episoder

#Science #Tech #Math #Business #Arize AI

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

for 11 dage siden26:11

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

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Model Context Protocol (MCP) 15:03

for 21 dage siden15:03

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 .…

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

for 6 weeks siden30:23

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 .…

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

for 8 weeks siden29:54

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 .…

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

for 10 weeks siden30:03

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 .…

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

for 13 weeks siden24:58

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 .…

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

for 16 weeks siden28:57

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 .…

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

for 18 weeks siden28:47

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 .…

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

for 20 weeks siden24:54

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 .…

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1
Introduction to OpenAI's Realtime API 29:56

for 22 weeks siden29:56

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 .…

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

for 24 weeks siden46:46

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 .…

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KV Cache Explained 4:19

for 25 weeks siden4:19

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 .…

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The Shrek Sampler: How Entropy-Based Sampling is Revolutionizing LLMs 3:31

for 26 weeks siden3:31

3:31

In this byte-sized podcast, Harrison Chu, Director of Engineering at Arize, breaks down the Shrek Sampler. This innovative Entropy-Based Sampling technique--nicknamed the 'Shrek Sampler--is transforming LLMs. Harrison talks about how this method improves upon traditional sampling strategies by leveraging entropy and varentropy to produce more dynamic and intelligent responses. Explore its potential to enhance open-source AI models and enable human-like reasoning in smaller language models. Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

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Google's NotebookLM and the Future of AI-Generated Audio 43:28

for 26 weeks siden43:28

43:28

This week, Aman Khan and Harrison Chu explore NotebookLM’s unique features, including its ability to generate realistic-sounding podcast episodes from text (but this podcast is very real!). They dive into some technical underpinnings of the product, specifically the SoundStorm model used for generating high-quality audio, and how it leverages a hierarchical vector quantization approach (RVQ) to maintain consistency in speaker voice and tone throughout long audio durations. The discussion also touches on ethical implications of such technology, particularly the potential for hallucinations and the need to balance creative freedom with factual accuracy. We close out with a few hot takes, and speculate on the future of AI-generated audio. Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

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Exploring OpenAI's o1-preview and o1-mini 42:02

for 29 weeks siden42:02

42:02

OpenAI recently released its o1-preview, which they claim outperforms GPT-4o on a number of benchmarks. These models are designed to think more before answering and handle complex tasks better than their other models, especially science and math questions. We take a closer look at their latest crop of o1 models, and we also highlight some research our team did to see how they stack up against Claude Sonnet 3.5--using a real world use case. Read it on our blog: https://arize.com/blog/exploring-openai-o1-preview-and-o1-mini Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X .…

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