Shows
Redwood Research Blog“Four places where you can put LLM monitoring” by Fabien Roger, Buck Shlegeris Subtitle: To wit: LLM APIs, agent scaffolds, code review, and detection-and-response systems. To prevent potentially misaligned LLM agents from taking actions with catastrophic consequences, you can try to monitor LLM actions - that is, try to detect dangerous or malicious actions, and do something about it when you do (like blocking the action, starting an investigation, …).1 But where in your infrastructure should your LLM monitor be implemented, and what team in your organization should own it? Historically, researchers have mostly focused on implementing monitoring inside the agent scaffold, which would mean that it would be ow...2025-08-0915 min
PaperLedgeComputer Vision - BEV-LLM Leveraging Multimodal BEV Maps for Scene Captioning in Autonomous DrivingHey PaperLedge crew, Ernis here, ready to dive into some seriously cool autonomous driving tech! Today, we're looking at a paper that's trying to make self-driving cars a whole lot smarter and easier to understand.
Think about it: right now, a self-driving car is basically a black box. It sees the world through its sensors, crunches a bunch of numbers, and then... decides to turn left. But why did it turn left? That's the question this research tackles.
This paper introduces a new system called BEV-LLM (try saying that three times fast!). The core idea...2025-07-2805 min
一位小米前高管的AI学习日报AIOS: LLM Agent Operating SystemThe source introduces AIOS, an innovative Large Language Model (LLM) agent operating system designed to optimize the performance and deployment of LLM-based intelligent agents. It addresses key challenges like suboptimal scheduling, resource allocation, and context management by integrating LLMs as the "brain" of the operating system. AIOS features a layered architecture including application, kernel (OS and LLM kernels), and hardware layers, with the LLM kernel providing crucial modules for agent scheduling, context management, memory/storage management, and tool/access control. The system aims to facilitate concurrent execution of multiple agents and enhance their ability to solve complex, real-world tasks...2025-07-2629 min
Inside Algomatic#116 AI/LLM学術ニュース Weekly #14: LLMのカスタマイズを数秒で Drag-and-Drop LLMs(DnD)「AI/LLM学術ニュースWeekly」では最新のAI/LLM学術ニュースについてWeeklyで語ります。ファシリテーターはAI Transformation(AX) カンパニー AIエンジニア 岩城、語り手はAIエンジニア 渋谷でお送りします。第14回は、「Drag-and-Drop LLMs(DnD)」について紹介します。Drag-and-Drop LLMs(DnD)はプロンプトを与えるだけで、従来は数時間かかっていたLLMのファインチューニングとほぼ同等の効果を発揮する重みの更新を数秒で完了させます。これにより、モデルを特定のタスクへ高速に適応させることが可能になり、リアルタイムでのモデル変更を実施できます。詳しくはPodcastをお聞きください。出演者AI Transformation(AX) AIエンジニア 岩城（@yukl_dev）AI Transformation(AX) AIエンジニア 渋谷（@sergicalsix)note・https://note.com/algomatic_oa/n/n3dd05738f8b1技術紹介のリンク・https://arxiv.org/abs/2506.16406Algomaticグループでは一緒に働く仲間を募集中です！「AI/LLM学術ニュース Weekly」でご紹介しているような生成AI/LLMの技術に興味がある方々、Algomaticに興味がある方々、まずはカジュアル面談でお気軽に連絡いただければと思います。採用情報はこちら：https://jobs.algomatic.jp/2025-07-1010 minPaperLedgeComputation and Language - Efficiency-Effectiveness Reranking FLOPs for LLM-based RerankersHey PaperLedge crew, Ernis here, ready to dive into some seriously cool research! Today, we're tackling a paper that's all about making those super-smart Large Language Models, or LLMs, work smarter, not just harder, when it comes to finding you the info you need.
Now, you've probably heard of LLMs like ChatGPT. They're amazing at understanding and generating text, and researchers have been using them to improve search results – it's like having a super-powered librarian that knows exactly what you're looking for. This is done by reranking search results; taking the initial list from a search engine an...2025-07-0906 min
Pi TechNews: етичні проблеми LLM, ШІ в фантастиці та в житті, чи всі мають програмувати?ШІ вже генерує код, створює контент і навіть сам себе перевіряє — але чи може він мислити?🔹 локальна LLM-модель від Microsoft🔹 Apple зберігає мовчанку щодо власної AI-стратегії🔹 сучасні моделі мають суттєві обмеження у міркуванні та розумінніТакож говоримо про те, як ШІ змінює створення контенту, ревʼю коду та навіть поведінку в ігрових симуляціях. У межах експерименту LLM-моделі зіграли у гру Дипломатія — і результати виявилися напрочуд «людськими»: одні брехали, інші вели переговори, дехто — діяв агресивно.Етика, стабільність ринку праці, майбутнє людської креативності — про все це говоримо в новому випуску.00:32 — огляд локальної LLM-моделі від Microsoft02:00 — розвиток ШІ в Apple та пов’язані виклики04:22 — можливості й обмеження LLM09:30 — етичні питання у сфері ШІ та LLM15:46 — LLM у стратегічних іграх23:08 — генерація відео за допомогою ШІ та майбутнє створення контенту33:43 — небезпека вічних хімікатів38:43 — автономні транспортні засоби та роль ШІ в доставленні43:10 — захист авторських прав 49:38 — вплив ШІ на технологічну індустрію
2025-07-0156 min
The Platform PlaybookLLM Security Exposed! Breaking Down the Zero-Trust Blueprint for AI WorkloadsIn this episode, we break down our recent YouTube video : “LLM Security Exposed!”, where we explore the rising security risks in Large Language Model (LLM) deployments — and how Zero-Trust principles can help mitigate them.🔍 We dive deeper into:The top LLM threats you can’t afford to ignore — from prompt injection to data leakage and malicious packagesWhy LLM applications need the same level of protection as any production workloadWhat a Zero-Trust Architecture looks like in the AI spaceHow tools like LLM Guard, Rebuff, Vigil, Guardrail AI, and Kubernete...2025-06-2725 min
Macro Lab 總經實驗室EP28 | AI 自由還是枷鎖？開源 VS 閉源 LLM，哪條路才是未來？— SWOT 深度解析（AI 語音）Macro Lab | 總經實驗室
EP28 | AI 自由還是枷鎖？開源 VS 閉源 LLM，哪條路才是未來？— SWOT 深度解析（AI 語音）
本集《Macro Lab 總經實驗室》帶你從概念到策略，一次看懂開源與閉源大型語言模型（LLM）的核心差異──為何Meta 選擇開放，Google、OpenAI 與 Anthropic 則走商業化路線？我們用最實務的案例與最新SWOT分析，助你快速掌握各自優劣與風險，並在複雜市場中做出最佳選擇！
Chin 帶你進入 LLM 抉擇的核心對話：當「成本、隱私、性能、可定制化」彼此競合，企業和開發者該如何權衡？
1️⃣ 開源LLM詳解 [0:49]
Meta Llama、Hugging Face 社群生態自由度高
高透明度＋社群協作：可檢視偏見、快速迭代
免授權費×可重現研究，但需承擔資安與維護成本
2️⃣ 開源LLM SWOT分析 [1:14]
Strengths：技術透明、成本靈活、社群驅動
Weaknesses：濫用風險、技術門檻、可持續性挑戰
Opportunities：監管合規優勢、邊緣運算應用
Threats：生態碎片化、商業變現限制
3️⃣ 閉源LLM詳解 [3:00]
GPT 系列、Google Gemini、Claude 等商業模型
精選訓練資料＋企業級安全控管
API 即用體驗佳，但客製化彈性低、需信任供應商
4️⃣ 閉源LLM SWOT分析 [3:06]
Strengths：專有技術壁壘、商業化效率、責任明確
Weaknesses：黑箱風險、供應商鎖定、透明度不足
Opportunities：多模態整合、垂直領域專用化
Threats：開源追趕、監管壓力上升
5️⃣ 未來趨勢與策略對比 [3:11]
混合模式崛起：開源基礎＋閉源增值服務
監管驅動開放：企業需保持透明與合規
選型關鍵：依場景、數據敏感度、團隊能力決策
🎯 為何一定要聽？
全方位決策依據：結合 SWOT 與實務選型指標，助你從策略高度與落地層面雙管齊下 unisys.combusinessinsider.com
風險與對策掌握：學會如何利用 Reflection Prompts、Cognitive Scaffold 等設計強化 AI 應用中的安全與可靠性
🔔 立即收聽 → 訂閱 Macro Lab，解鎖更多 科技 × 經濟 × 社會 深度剖析！
👍 按讚＋分享，加入 IG @amateur_economists ｜Medium｜每天早晨 10 分鐘，「通勤咖啡」帶你洞悉世界動能。
Macro Lab: Macroeconomics decoded for builders and doers—because the big picture drives better business.
（對於本集討論的 SWОT 分析與研究報告，歡迎讀者深入原始文獻並提出交流！）
Reference
Mohammad et al. (2024) 《Exploring LLMs: A systematic review with SWOT analysis》
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Hosting provided by SoundOn 2025-06-2613 minPaperLedgeArtificial Intelligence - The Effect of State Representation on LLM Agent Behavior in Dynamic Routing GamesHey PaperLedge crew, Ernis here, ready to dive into some fascinating research! Today, we're talking about Large Language Models, or LLMs – think of them as super-smart chatbots – and how we can use them to make decisions in complex situations, like playing games.
Now, LLMs have a bit of a memory problem. They don't naturally remember what happened in the past, which is kind of a big deal when you're trying to, say, play a game that unfolds over multiple rounds. Imagine playing chess, but forgetting all the moves that came before your turn! That's where this paper come...2025-06-2106 min
Study for the Bar in Your CarEvidence - Hearsay - Part 1Ready to decode the courtroom? Dive into "Evidence - Hearsay - Part 1," brought to you by "Study for the Bar in Your Car." Your hosts, Ma and Claude, unpack the often-confusing world of hearsay, using Angela’s incredibly detailed notes—the product of an LLM law student and former judicial law clerk. This episode is your essential guide to understanding the foundational definition of hearsay and, crucially, why it's generally inadmissible due to reliability concerns, the lack of oath, and the absence of cross-examination.We illuminate key categories of statements that are NOT hearsay at all, meaning you...2025-06-1834 minPaperLedgeComputation and Language - Steering LLM Thinking with Budget GuidanceAlright learning crew, Ernis here, ready to dive into some fascinating research that's all about making our AI overlords... I mean, helpful assistants... think smarter, not necessarily longer.
We're talking about Large Language Models, or LLMs – those powerful AIs that can write essays, answer questions, and even code. Think of them as super-smart students, but sometimes, they get a little too caught up in their own thought processes. Imagine giving a student a simple math problem, and they fill up pages and pages with calculations, even though a shorter, more direct approach would have worked just as we...2025-06-1707 min
Study for the Bar in Your CarEvidence - Relevancy and the Exclusion of EvidenceReady to unravel the intricacies of evidence law? Tune into "Study for the Bar in Your Car" episode three, "Relevancy and the Exclusion of Evidence", where hosts Ma and Claude, powered by the incredibly detailed notes of LLM law student and former judicial law clerk Angela, guide you through the fundamental principles that determine what information gets heard in court.This deep dive is your essential resource for understanding how the legal system filters information. We dissect the absolute baseline of relevancy—when evidence has any tendency to make a fact more or less probable and that fa...2025-06-1538 min
Study for the Bar in Your CarEvidence - Presentation of EvidenceReady to decode the courtroom? Dive into "Evidence - Presentation of Evidence," episode two of "Study for the Bar in Your Car," where hosts Ma and Claude (powered by Angela’s incredibly detailed notes as an LLM law student and former judicial law clerk) break down the crucial rules governing how information actually makes it before a judge and jury.This essential deep dive illuminates the foundational concepts that dictate witness testimony and evidence handling, offering you a shortcut to understanding the practical mechanics of a trial.You’ll uncover insights into:Witness Competency: The...2025-06-1416 min
Study for the Bar in Your CarEvidence - IntroductionAre you ready to master evidence law? Dive deep into the absolutely fundamental rules that dictate what information makes it into court with "Study for the Bar in Your Car." Your hosts, Ma and Claude, unpack the complexities of evidence using incredibly detailed notes generously provided by Angela, an LLM law student and former judicial law clerk. Angela, a real-life human being and former judicial law clerk who dealt with evidence issues constantly, contributed significantly to the material.This essential podcast acts as your shortcut to understanding the fundamental rules that shape how facts are determined in...2025-06-1336 min
Elixir WizardsLangChain: LLM Integration for Elixir Apps with Mark EricksenMark Ericksen, creator of the Elixir LangChain framework, joins the Elixir Wizards to talk about LLM integration in Elixir apps. He explains how LangChain abstracts away the quirks of different AI providers (OpenAI, Anthropic’s Claude, Google’s Gemini) so you can work with any LLM in one more consistent API. We dig into core features like conversation chaining, tool execution, automatic retries, and production-grade fallback strategies.
Mark shares his experiences maintaining LangChain in a fast-moving AI world: how it shields developers from API drift, manages token budgets, and handles rate limits and outages. He also reveals test...2025-06-1238 min
Today's tech newsKindle読書×LLMの新体験アプリ『cleo』を徹底解説！ | AI Podcast🎙️ AIが生成したポッドキャスト📰 元記事情報タイトル: jpa (@josephpalbanese): "new project: cleo (kindle + llm) 📚cleo is an ios app that pairs whatever book you're reading on kindle with an llm (o3) — ask questions, get recaps/summaries, or listen/discuss (think audiobooks but interactive). the llm has context on exactly where you are + the book contents.best part? no complex setup needed. just link your kindle account and that's it. it just works.reply / rt for testflight invite 👇" | nitterURL: https://nitter.net/josephpalbanese/status/1927444663776002302📝 エピソード概要iOS向けの革新的な読書アプリ『cleo』について、Kindleと大規模言語モデル（LLM）を組み合わせた新しい読書体験の魅力を2人のキャスターが深掘りします。設定不要で手軽に使える点や、インタラクティブな機能の可能性、そして今後の展望を多角的に議論する内容です。🔑 主要ポイント1. 『cleo』はKindleで読んでいる本と連携するiOSアプリで、LLM（大規模言語モデル）を利用して質問応答、要約、朗読、ディスカッションが可能。2. 設定が非常に簡単で、Kindleアカウントをリンクするだけで使い始められる。3. LLMが読書中の正確な位置と本の内容を把握しているため、文脈に沿った回答が可能。4. インタラクティブな読書体験を提供し、従来のオーディオブックとは異なる双方向性が特徴。5. プライバシーの取り扱いやAndroid版への展開など、今後の課題や期待も存在。📚 参考記事についてhttps://nitter.net/josephpalbanese/status/1927444663776002302🗣️ 会話スクリプト（抜粋）Speaker1: こんにちは！今日は、ちょっとワクワクする新しいアプリの話題をお届けしますよ。Kindleで読んでいる本と連携して、質問したり要約を聞いたりできる、そんな夢みたいなiOSアプリ『cleo』についてご紹介します！Speaker2: はい、こんにちは。『cleo』、名前からして可愛いですね。Kindleと大規模言語モデル、つまりLLMを組み合わせて、読書をもっとインタラクティブにしてしまうという新プロジェクトなんですよね。すごく興味深いです。Speaker1: そうなんです。この記事によると、『cleo』はKindleで読んでいる本の内容を把握して、その場所の文脈も理解した上で質問に答えたり、要約を提供したり、あるいはオーディオブックのように朗読やディスカッションもできるというんです。まさに読書の新体験。Speaker2: なるほど。普通のオーディオブックは一方通行ですが、これは対話的に本の内容を深掘りできるわけですね。例えば、わからない箇所を聞いたり、内容のまとめを求めたりできるのは、読書の助けになるし学習効果も高まりそうです。Speaker1: ええ、そして一番のポイントは、設定が超簡単ってところ。Kindleアカウントを連携するだけで使えるので、技術的なハードルが低いんですよ。これってすごく大事ですよね。せっかく便利なツールでも設定が難しいと使わなくなっちゃうし。Speaker2: そうですよね。ユーザーフレンドリーな設計は普及の鍵です。ちなみに、このLLMはO3というモデルが使われているそうですが、大規模言語モデルの進化のおかげで、こうした自然言語での読書体験が実現できるんですね。Speaker1: そうそう。...(続きは音声でお楽しみください)🤖 このポッドキャストについてこのエピソードは、AI（人工知能）によって記事から自動生成されたポッドキャストです。2人の話者による自然な会話形式で、記事の内容を分かりやすく解説しています。生成日時: 2025/5/30 14:30:122025-05-3003 min
Verbos: AI og Softwareudvikling# 89 - LLM'er Lokalt I Browseren m. Rasmus Aagaard og Jakob Hoeg MørkI denne episode af Verbos Podcast diskuterer vært Kasper Junge de nyeste fremskridt inden for Large Language Models (LLM'er) og deres anvendelse i browseren med Rasmus Aagaard og Jakob Hoeg Mørk. De dækker emner som use cases, teknologier, udfordringer og fremtiden for LLM'er i browseren, samt hvordan udviklere kan komme i gang med at implementere disse modeller.Læs Rasmus Aagaards transformers.js tutorial her: https://rasgaard.com/posts/getting-started-transformersjs/Kapitler00:00 Introduktion til LLM'er i Browseren03:01 Erfaringer med LLM'er og Open...2025-05-2957 min
AI+Crypto FM【保存版】今さら聞けない！AIの成り立ち、LLM、RAG、AIエージェントの仕組みからその歴史まで 前編*ラスト音声が入っていなかったのでアップしなおしました！YouTubeはこちら：https://youtu.be/6YTX2ofptEQkinjo https://x.com/illshin|AKINDO : https://x.com/akindo_ioKanazawa: https://x.com/k_another_waAIエージェント時代を制する鍵：企業の最先端MCP活用事例LT【東京AI祭プレイベント】単なるメモから知的資産へー松濤Vimmer流 Obsidian in Cursorの知的生産システムChapters00:00 AIとクリプトの最新トレンド03:03 AIエージェントの仕組みと歴史06:05 AIイベントの紹介と参加方法09:00 AIの定義とその広がり12:04 AIブームの歴史と進化12:26 AIの歴史と進化14:50 ディープラーニングの登場17:04 データの重要性とAIの影響18:24 ヒントン教授とディープラーニングの発展20:16 機械学習とディープラーニングの違い25:41 大規模言語モデルの進化27:55 トランスフォーマーの仕組み30:06 トランスフォーマーのデータ処理31:59 パラメータと計算リソースの関係35:00 GPTの進化と実用性38:06 AIの限界と未来の可能性38:42 AIの限界と未来41:30 人間とAIの知性の違い44:49 オープンソースとクローズドモデルの違い49:57 オープンソースの意義と企業の戦略50:35 ブロックチェーンとトークンの価値51:38 日本語対応のAIモデルの課題52:59 日本のAI開発の現状54:45 オープンソースとAPIの選択肢56:00 GPUとAI企業の未来58:42 NVIDIAの技術的優位性01:00:45 AIモデルの性能比較01:02:48 次回のテーマとまとめ1. AIの基礎：そもそもAIって何？-AIの歴史-機械学習 vs. 深層学習の簡単な違い-LLM（大規模言語モデル）って何？ ChatGPTの裏側2.LLMの仕組みと進化Transformerとは？GPTシリーズの進化LLMのトレーニング方法と限界オープンソース/クローズドLLMLLMを作るには...2025-05-291h 01AI+Crypto FM【保存版】今さら聞けない！AIの成り立ち、LLM、RAG、AIエージェントの仕組みからその歴史まで 前編YouTubeはこちら：https://youtu.be/5V4A_6fdDQokinjo https://x.com/illshin|AKINDO : https://x.com/akindo_ioKanazawa: https://x.com/k_another_waAIエージェント時代を制する鍵：企業の最先端MCP活用事例LT【東京AI祭プレイベント】単なるメモから知的資産へー松濤Vimmer流 Obsidian in Cursorの知的生産システム1. AIの基礎：そもそもAIって何？-AIの歴史-機械学習 vs. 深層学習の簡単な違い-LLM（大規模言語モデル）って何？ ChatGPTの裏側2.LLMの仕組みと進化Transformerとは？GPTシリーズの進化LLMのトレーニング方法と限界オープンソース/クローズドLLMLLMを作るには？LLMを使った実装とは3.RAGってなに？ なぜ注目されてる？・Retrieval-Augmented Generation の仕組み・LLMだけではなぜダメなのか？・検索＋生成のメリットと課題4.AIエージェントとは？・単なるチャットボットとの違い・メモリ・ツール・プランニング：エージェントの中身・AutoGPTやOpenAIのAgentsの事例紹介・web3文脈でのAIエージェントとの違い5.今後の発展について・ブロックチェーンとの融合・AGI、ASIが来る未来をどうみてる・AIが浸透していく時代の課題感・今年のAIの発展に期待していること2025-05-281h 01
Daily Paper CastDeciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective
🤗 Upvotes: 33 | cs.CL, cs.AI
Authors:
Junnan Liu, Hongwei Liu, Linchen Xiao, Shudong Liu, Taolin Zhang, Zihan Ma, Songyang Zhang, Kai Chen
Title:
Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective
Arxiv:
http://arxiv.org/abs/2505.19815v1
Abstract:
We propose a novel framework for comprehending the reasoning capabilities of large language models (LLMs) through the perspective of meta-learning. By conceptualizing reasoning trajectories as pseudo-gradient descent updates to the LLM's parameters, we identify parallels between LLM reasoning and various meta-learning paradigms. We formalize the training process for reasoning tasks as...2025-05-2821 min
Study for the Bar in Your CarCivil Procedure - Bonus Episode - Essay QuestionsReady for a unique approach to bar exam practice? Dive into our bonus episode of Study for the Bar in Your Car! Angela, our LLM law student host, pushes the boundaries of bar prep by putting NCBE MEE Civil Procedure questions into AI and having it generate new, original questions based on the patterns.In this special episode, Angela shares and breaks down the first AI-generated essay question, focusing on the critical topic of Subject Matter Jurisdiction. Angela reads the challenging fact pattern involving complex issues of diversity and amount in controversy. Then, her AI...2025-05-2729 min
Study for the Bar in Your CarCivil Procedure - Wrap up and ReviewReady to conquer Civil Procedure? Join Ma and Claude on Study for the Bar in Your Car for a comprehensive wrap-up of this challenging but crucial subject! Drawing on Angela's detailed notes – informed by her LLM studies and experience as a judicial law clerk who saw these rules in action – this episode is your roadmap from the start of a lawsuit all the way through appeal. It focuses on what's critical for the bar exam and where pitfalls lie.We tackle the foundational building blocks you need to nail the bar and navigate litigation:Jurisdiction & Venue: Mast...2025-05-2632 min
AI報報報EP-64 一套LLM幻覺偵測工具：uqlm，給了多種評測方式來偵測AI輸出幻覺，可以根據不同的場景選擇適合的方法**一套LLM幻覺檢測工具：uqlm，給了多種評測方式來檢測AI輸出幻覺，可以根據不同的場景選擇合適的方法** 1.黑盒評分器，透過多次產生並比較相同提示的回復來評估一致性。就反覆問同一個問題，如果每次回答的核心意思不一致或差異很大，表示它可能在瞎編 2.白盒評分器，利用token機率估計不確定性。看看LLM對自己答案中每個部分的自信程度，如果它對某個字或句子很不確定，就表示這部分內容可能有問題 3.專家評審法 (LLM 作為評審評分器)，使用一個或多個 LLM評估原始LLM回應的可靠性，就是找其他LLM來評判答案的可靠性 4.綜合評估法 (整合評分器)，結合以上幾種方法進行綜合評估 github：https://github.com/cvs-health/uqlm 加入免費會員，更新資訊不漏接： https://open.firstory.me/join/cma3mukjr127j01w5h4m56giw 小額贊助支持本節目： https://open.firstory.me/user/cma3mukjr127j01w5h4m56giw 留言告訴我你對這一集的想法： https://open.firstory.me/user/cma3mukjr127j01w5h4m56giw/comments Powered by Firstory Hosting2025-05-2607 min
Best AI papers explainedAuto-Differentiating Any LLM Workflow: A Farewell to Manual PromptingThis document introduces LLM-AutoDiff, a novel framework for Automatic Prompt Engineering (APE) that aims to automate the challenging task of designing prompts for complex Large Language Model (LLM) workflows. By viewing these workflows as computation graphs where textual inputs are treated as trainable parameters, the system uses a "backward engine" LLM to generate textual gradients – feedback that guides the iterative improvement of prompts. Unlike previous methods that focus on single LLM calls, LLM-AutoDiff supports multi-component pipelines, including functional operations like retrieval, handles cycles in iterative processes, and separates different parts of prompts (like instructions and examples) into peer nodes fo...2025-05-2319 min
GitHub Daily TrendGitHub - llm-d/llm-d: llm-d is a Kubernetes-native high-performance distributed LLM inference fra...https://github.com/llm-d/llm-d llm-d is a Kubernetes-native high-performance distributed LLM inference framework - llm-d/llm-d Powered by VoiceFeed. https://voicefeed.web.app/lp/podcast?utm_source=githubtrenddaily&utm_medium=podcast Developer:https://twitter.com/_horotter2025-05-2102 min
Study for the Bar in Your Car Civil Procedure - IntroductionLooking for a smart way to maximize your bar exam study time, especially on the go? Welcome to Study for the Bar in Your Car!Created by Angela, an LLM law student who feels like she's been studying forever, this podcast is designed to provide actual bar study content in an audio format. Tired of not finding resources for studying while driving or travelingIn this deep dive episode, your study companion focuses squarely on a foundational area critical for bar success: Civil Procedure. Think of this episode like a roadmap, covering the journey from...2025-05-1937 min
GenAI Level UPRAG-MCP: Mitigating Prompt Bloat and Enhancing Tool Selection for LLMLarge Language Models (LLMs) face significant challenges in effectively using a growing number of external tools, such as those defined by the Model Context Protocol (MCP). These challenges include prompt bloat and selection complexity. As the number of available tools increases, providing definitions for every tool in the LLM's context consumes an enormous number of tokens, risking overwhelming and confusing the model, which can lead to errors like selecting suboptimal tools or hallucinating non-existent ones.To address these issues, the RAG-MCP framework is introduced. This approach leverages Retrieval-Augmented Generation (RAG) principles applied to tool selection. Instead of...2025-05-1313 min
Experiencing Data w/ Brian T. O’Neill (UX for AI Data Products, SAAS Analytics, Data Product Management)169 - AI Product Management and UX: What’s New (If Anything) About Making Valuable LLM-Powered Products with Stuart Winter-TearToday, I'm chatting with Stuart Winter-Tear about AI product management. We're getting into the nitty-gritty of what it takes to build and launch LLM-powered products for the commercial market that actually produce value. Among other things in this rich conversation, Stuart surprised me with the level of importance he believes UX has in making LLM-powered products successful, even for technical audiences.
After spending significant time on the forefront of AI’s breakthroughs, Stuart believes many of the products we’re seeing today are the result of FOMO above all else. He shares a belief...2025-05-131h 01
Innovation on ArmEP07 I 從雲端到邊緣：為何眾多開發者選擇在 Arm 架構上運行 LLM？隨著大型語言模型（LLM）的技術演進，開發者不再只能依賴雲端，越來越多人開始將 LLM 推論部署在高效能的 Arm 架構上。這一轉變的背後，是推論框架優化、模型壓縮與量化技術的進步。
本集節目將由來自 Arm 的 Principal Solutions Architect 深入淺出介紹什麼是 LLM? 各家基於 Arm Neoverse 架構導入 LLM 應用的雲端平台，包括 AWS Graviton、Google Cloud、Microsoft Azure等，同時也介紹了推動 LLM 生態發展的關鍵開源社群，例如 Hugging Face、ModelScope 等平台。此外，講者也分享了有趣的 use case。Arm 架構的優勢包括：高能效比、低總體擁有成本、跨平台一致性、以及提升資料隱私的本地運算能力，LLM on Arm 不再是遙遠的構想，已經正在發生，Arm 將是您雲端與邊緣部署的理想選擇!
此外，Arm 將在 COMPUTEX 2025 期間舉辦一系列活動，包括 Arm 前瞻技術高峰演講以及 Arm Developer Experience 開發者大會。
1. Arm 前瞻技術高峰演講 (地點：台北漢來大飯店三樓)：
2025 年 5 月 19 日下午 3 點至 4 點，Arm 資深副總裁暨終端產品事業部總經理 Chris Bergey 將親臨現場以「雲端至邊緣：共築在 Arm 架構上的人工智慧發展」為題，分享橫跨晶片技術、軟體開發、雲端與邊緣平台的最新趨勢與創新成果，精彩可期，座位有限，提早報到者還有機會獲得 Arm 與 Aston Martin Aramco F1 車隊的精美聯名限量贈品，歡迎立即報名！
Arm 前瞻技術高峰演講報名網址：https://reurl.cc/2KrR2X
2. Arm Developer Experience 開發者系列活動 (地點：台北漢來大飯店六樓；5/20 Arm Cloud AI Day 13:00-17:00、5/21 Arm Mobile AI Day 9:00-13:00)：
Arm 首次將於2025 年 5/20-5/22 COMPUTEX 期間，為開發人員舉辦連續三天的系列活動，包括以下四大活動：
A. 5/20 下午 13:00-17:00 以 Cloud AI 為主題的技術演講與工作坊：
部分議程包括；
Accelerating development with Arm GitHub Copilot
Seamless cloud migration to Arm
Deploying a RAG-based chatbot with llama-cpp-python using Arm KleidiAI on Google Axion processors, plus live Q&A
Ubuntu: Unlocking the Power of Edge AI
B. 5/21 早上 9:00-13:00 以 Mobile AI 為主題的技術演講：
部分議程包括；
Build next-generation mobile gaming with Arm Accuracy Super Resolution (ASR)
Vision LLM inference on Android with KleidiAI and MNN
Introduction to Arm Frame Advisor/Arm Performance Studio
C. Arm 開發者小酌輕食見面會 ( 5/20 晚上 17:30-19:30)
我們將於 5/20舉辦開發者見面會！誠摯邀請您與 Arm 開發者專家當面交流！
D. 5/20-5/22 Arm Developer Chill Out Lounge ( 5/20-5/22 9:00- 17:00)：
為期三天，我們布置了舒適的休憩空間，讓您可以在輕鬆的環境與 Arm 開發者專家交流、觀看 Arm 產品展示、為您的裝置充電、進行桌遊以及休憩等。
無需報名，歡迎隨時來參觀。
席位有限，誠摯邀請您立即報名上述活動，報名者還有機會於現場抽中包括 Keychron K5 Max 超薄無線客製機械鍵盤，以及 AirPods 4 耳機等大獎喔！
讓 Arm 協助您擴展雲端應用、提升行動裝置效能、優化遊戲等，助力您開發下一代 AI 解決方案！
Arm Developer Experience 開發者系列活動報名網址: https://reurl.cc/bWNo56
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Hosting provided by SoundOn 2025-04-2331 min
Kabir's Tech DivesThe Next Token and Beyond: Unraveling the LLM EnigmaYes, I can certainly provide a long and detailed elaboration on the topics covered in the sources, particularly focusing on LLM-generated text detection and the nature of LLMs themselves.The emergence of powerful Large Language Models (LLMs) has led to a significant increase in text generation capabilities, making it challenging to distinguish between human-written and machine-generated content. This has consequently created a pressing need for effective LLM-generated text detection. The necessity for this detection arises from several critical concerns, as outlined in the survey. These include the potential for misuse of LLMs in spreading disinformation...2025-04-1619 min
LessWrong (Curated & Popular)“Reducing LLM deception at scale with self-other overlap fine-tuning” by Marc Carauleanu, Diogo de Lucena, Gunnar_Zarncke, Judd Rosenblatt, Mike Vaiana, Cameron BergThis research was conducted at AE Studio and supported by the AI Safety Grants programme administered by Foresight Institute with additional support from AE Studio. SummaryIn this post, we summarise the main experimental results from our new paper, "Towards Safe and Honest AI Agents with Neural Self-Other Overlap", which we presented orally at the Safe Generative AI Workshop at NeurIPS 2024. This is a follow-up to our post Self-Other Overlap: A Neglected Approach to AI Alignment, which introduced the method last July.Our results show that the Self-Other Overlap (SOO) fine-tuning drastically[1] reduces deceptive...2025-03-1712 min
AI Portfolio PodcastMaxime Labonne: LLM Scientist Roadmap, AI Scientist, LLM Course & Open Source - AI Portfolio PodcastMaxime Labonne, Co-author of the LLM Engineers Handbook, creator of the LLM course on github with over 40k stars, and author of Hands on Graph Neural Networks.Follow/Connect:Maxime LinkedIn:https://www.linkedin.com/in/maxime-labonne/Mark Linkedin: https://www.linkedin.com/in/markmoyou/Chapters:📌 00:00 – Intro📚 01:51 – Maxime: Books & Courses🤖 07:30 – AI Scientist vs. AI Engineer🚀 09:05 – Path to Becoming an AI Expert🎓 11:13 – Do You Need a Degree?⏳ 13:01 – How Long Does It Take to Become an AI Scientist?👨🔬 15:58 – Individual Contributor Role as an LLM Scientist🧠 26:04 – Understanding LLM Personality🎯 30:07 – Objective Func...2025-03-121h 27Daily Paper CastAutellix: An Efficient Serving Engine for LLM Agents as General Programs
🤗 Upvotes: 15 | cs.LG, cs.AI, cs.DC
Authors:
Michael Luo, Xiaoxiang Shi, Colin Cai, Tianjun Zhang, Justin Wong, Yichuan Wang, Chi Wang, Yanping Huang, Zhifeng Chen, Joseph E. Gonzalez, Ion Stoica
Title:
Autellix: An Efficient Serving Engine for LLM Agents as General Programs
Arxiv:
http://arxiv.org/abs/2502.13965v1
Abstract:
Large language model (LLM) applications are evolving beyond simple chatbots into dynamic, general-purpose agentic programs, which scale LLM calls and output tokens to help AI agents reason, explore, and solve complex tasks. However, existing LLM serving systems ign...2025-02-2122 min
ibl.aiOWASP: LLM Applications Cybersecurity and Governance ChecklistSummary of https://genai.owasp.org/resource/llm-applications-cybersecurity-and-governance-checklist-english
Provides guidance on securing and governing Large Language Models (LLMs) in various organizational contexts. It emphasizes understanding AI risks, establishing comprehensive policies, and incorporating security measures into existing practices.
The document aims to assist leaders across multiple sectors in navigating the challenges and opportunities presented by LLMs while safeguarding against potential threats. The checklist helps organizations formulate strategies, improve accuracy, and reduce oversights in their AI adoption journey.
It also includes references to external resources like OWASP and MITRE to facilitate a robust cybersecurity plan...2025-02-1820 minDaily Paper CastCan 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time Scaling
🤗 Upvotes: 71 | cs.CL
Authors:
Runze Liu, Junqi Gao, Jian Zhao, Kaiyan Zhang, Xiu Li, Biqing Qi, Wanli Ouyang, Bowen Zhou
Title:
Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time Scaling
Arxiv:
http://arxiv.org/abs/2502.06703v1
Abstract:
Test-Time Scaling (TTS) is an important method for improving the performance of Large Language Models (LLMs) by using additional computation during the inference phase. However, current studies do not systematically analyze how policy models, Process Reward Models (PRMs), and problem difficulty influence TTS. This lack of analysis limits the und...2025-02-1222 min
Bliskie Spotkania z AI#11 GenAI i LLM - Wszystko, co musisz wiedzieć, zanim zaczniesz działać | Mariusz KorzekwaNie uczysz się AI? Spokojnie, AI już uczy się, jak Cię zastąpić!🔔 Subskrybuj, aby nie przegapić nowych odcinków!Tym razem moim gościem jest Mariusz Korzekwa, ekspert od #AI specjalizujący się w #promptEngineeringu i integracjach z #LLM-ami.W tym odcinku rozmawiamy o modelach językowych (LLM) i ich zastosowaniach w sztucznej inteligencji. Poruszamy temat generative AI, omawiając jego definicję oraz kluczowe różnice między LLM a klasyczną AI. Analizujemy znaczenie multimodalności, a także roli inputu i outputu w kontekście działania...2025-02-112h 40Daily Paper CastPreference Leakage: A Contamination Problem in LLM-as-a-judge
🤗 Upvotes: 25 | cs.LG, cs.AI, cs.CL
Authors:
Dawei Li, Renliang Sun, Yue Huang, Ming Zhong, Bohan Jiang, Jiawei Han, Xiangliang Zhang, Wei Wang, Huan Liu
Title:
Preference Leakage: A Contamination Problem in LLM-as-a-judge
Arxiv:
http://arxiv.org/abs/2502.01534v1
Abstract:
Large Language Models (LLMs) as judges and LLM-based data synthesis have emerged as two fundamental LLM-driven data annotation methods in model development. While their combination significantly enhances the efficiency of model training and evaluation, little attention has been given to the potential contamination brought by this new...2025-02-0521 min
AI Engineering PodcastOptimize Your AI Applications Automatically With The TensorZero LLM GatewaySummaryIn this episode of the AI Engineering podcast Viraj Mehta, CTO and co-founder of TensorZero, talks about the use of LLM gateways for managing interactions between client-side applications and various AI models. He highlights the benefits of using such a gateway, including standardized communication, credential management, and potential features like request-response caching and audit logging. The conversation also explores TensorZero's architecture and functionality in optimizing AI applications by managing structured data inputs and outputs, as well as the challenges and opportunities in automating prompt generation and maintaining interaction history for optimization purposes.AnnouncementsHello...2025-01-221h 03Daily Paper CastMulti-LLM Text Summarization
🤗 Upvotes: 3 | cs.CL
Authors:
Jiangnan Fang, Cheng-Tse Liu, Jieun Kim, Yash Bhedaru, Ethan Liu, Nikhil Singh, Nedim Lipka, Puneet Mathur, Nesreen K. Ahmed, Franck Dernoncourt, Ryan A. Rossi, Hanieh Deilamsalehy
Title:
Multi-LLM Text Summarization
Arxiv:
http://arxiv.org/abs/2412.15487v1
Abstract:
In this work, we propose a Multi-LLM summarization framework, and investigate two different multi-LLM strategies including centralized and decentralized. Our multi-LLM summarization framework has two fundamentally important steps at each round of conversation: generation and evaluation. These steps are different depending on whether our multi-LLM decentralized sum...2024-12-2423 min
Futuro informatico. Automazioni Iac, AI e Realtà Aumentata.LLM IA comprensione di cosa sono ed evoluzioneIl Futuro dell'Informatica: Evoluzione dei Modelli LLM e loro logiche di ragionamentoIntroduzioneIl futuro dell'informatica è un mondo di grandi possibilità e di progressi tecnologici che stanno cambiando il modo in cui viviamo e lavoriamo. Uno dei settori che sta ricevendo particolare attenzione è quello dei modelli di apprendimento automatico (LLM), che stanno diventando sempre più sofisticati e potenti.Evoluzione dei Modelli LLMI modelli di apprendimento automatico (LLM) sono stati un'innovazione significativa nella tecnologia dell'informatica, consentendo di creare sistemi che possono apprendere da dati e dati di esempio. I modelli LLM sono stati util...2024-12-1410 min
LLMAlpha Alpha's $500 Million Challenge
In this episode, Jaeden Schafer discusses the challenges faced by Alpha Alpha, a German LLM that raised $500 million but struggles to compete with giants like OpenAI and Anthropic. The conversation explores Alpha Alpha's innovative beginnings, their pivot towards enterprise-focused AI solutions, and the competitive landscape of the AI industry.
My Podcast Course: https://podcaststudio.com/courses/
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2024-12-1409 min
Louise Ai agent - David S. NishimotoThe next generation of llm for ai agent will need better understanding of context
To improve an LLM's ability to understand context more effectively, several key enhancements and advancements would be necessary.
1. Enhanced Memory and Attention Mechanisms: Implementing more sophisticated memory and attention mechanisms within the LLM could allow it to retain and recall contextual information more effectively. By giving the model the ability to focus on relevant details and remember them throughout the text generation process, it can better understand the context in which certain information is presented.
2. Multi-Modal Learning: Integrating multi-modal learning capabilities into the LLM would enable it...2024-12-0300 minDaily Paper CastFrom Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge
🤗 Paper Upvotes: 19 | cs.AI, cs.CL
Authors:
Dawei Li, Bohan Jiang, Liangjie Huang, Alimohammad Beigi, Chengshuai Zhao, Zhen Tan, Amrita Bhattacharjee, Yuxuan Jiang, Canyu Chen, Tianhao Wu, Kai Shu, Lu Cheng, Huan Liu
Title:
From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge
Arxiv:
http://arxiv.org/abs/2411.16594v1
Abstract:
Assessment and evaluation have long been critical challenges in artificial intelligence (AI) and natural language processing (NLP). However, traditional methods, whether matching-based or embedding-based, often fall short of judging subtle attributes and delivering satisfactory results. Recent advancements in...2024-11-2721 min
Biznes MyśliBM132: LLM i prawo, możliwości, wyzwania, narzędziaCzy duże modele językowe (LLM) to rewolucja, czy zagrożenie dla prawników? W tym odcinku przybliżam możliwości dużych modeli językowych (LLM) w automatyzacji procesów prawnych, tworzeniu dokumentów, tłumaczeniach prawniczych i compliance. To, co wydaje się przyszłością, dzieje się już teraz – ale czy to na pewno oznacza koniec klasycznego prawa?Partnerem podcastu jest DataWorkshop.🎯 W tym odcinku dowiesz się:- Jak LLM może wspierać pracę prawników- Jakie są praktyczne zastosowania AI w prawie- Dlaczego człowiek pozostanie kluczowym elementem procesu2024-11-0658 minBiznes MyśliBM131: Praktyczny LLMCzy cały szum wokół LLM to tylko marketingowa bańka? 🤔 Choć szum wokół LLM powoli cichnie, ich prawdziwy potencjał LLM dopiero się ujawnia. Kluczem do sukcesu nie jest ślepe podążanie za trendami, ale świadome i ustrukturyzowane podejście, oparte na zrozumieniu zarówno możliwości, jak i ograniczeń tych modeli. W tym odcinku podcastu Biznes Myśli kontynuję wątek o praktycznym zastosowania LLM w biznesie.Partnerem podcastu jest DataWorkshop.Dowiesz się:- Czym różni się myślenie specjalisty od ML od programisty i dlaczego to kluczowe w pracy z LLM?- Jakie są największe wyzw...2024-10-231h 03
Revise and Resubmit - The Mayukh ShowHow Companies Can Use LLM-Powered Search to Create Value (Gurdeniz et al., 2024)Welcome to Revise and Resubmit, where we explore the most innovative ideas shaping the future of business, management, and technology. Today, we dive into a fascinating new research article, "How Companies Can Use LLM-Powered Search to Create Value", authored by Ege Gürdeniz, Ilana Golbin Blumenfeld, and Jacob T. Wilson, and recently published in the prestigious Harvard Business Review—a journal recognized on the FT50 list, marking it as one of the world's top business publications.
Imagine a world where searching for information is no longer about combing through links but engaging in fluid conversations with advanced AI...2024-10-1416 min
What’s the BUZZ? — AI in BusinessRed-Teaming And Safeguards For LLM Apps (Guest: Steve Wilson)In this episode, Steve Wilson (Co-Lead OWASP Top 10 for LLM Apps & Author) and Andreas Welsch discuss red-teaming and safeguards for LLM applications. Steve shares his insights how Generative AI vulnerabilities have evolved from embarrassing to financially risky and provides valuable advice for listeners looking to improve the security of their Generative AI applications.Key topics:- One year after OWASP Top 10 for LLM apps, how have LLM security and vulnerabilities evolved?- How do you build Generative AI safeguards into your app? What’s the impact on cost for checking and regenerating output?2024-10-0727 min
MLOps.communityMaking Your Company LLM-native // Francisco Ingham // #266Francisco Ingham, LLM consultant, NLP developer, and founder of Pampa Labs.Making Your Company LLM-native // MLOps Podcast #266 with Francisco Ingham, Founder of Pampa Labs.// AbstractBeing an LLM-native is becoming one of the key differentiators among companies, in vastly different verticals. Everyone wants to use LLMs, and everyone wants to be on top of the current tech but - what does it really mean to be LLM-native?LLM-native involves two ends of a spectrum. On the one hand, we have the product or service that the company offers, which surely offers many automation opportunities. LLMs can be applied strategically to s...2024-10-0657 min
Mad Tech Talk#26 - Rethinking AI Evaluation: The Panel of LLM Evaluators (PoLL)In this episode of Mad Tech Talk, we explore an innovative method for evaluating the performance of large language models (LLMs) using a "Panel of LLM Evaluators" (PoLL). Based on a recent research paper, we discuss the advantages of this novel approach and how it compares to traditional single-model evaluations.
Key topics covered in this episode include:
Evaluating LLMs: Discuss the advantages and disadvantages of using large language models as judges for evaluating other LLMs. Understand the biases and costs associated with traditional single-model evaluation approaches.
Introduction to PoLL: Discover the "Panel of LLM...2024-10-0611 min
共识粉碎机讨论会03|LLM保险销售(对谈暖哇莫子皓）大家好，欢迎来到共识粉碎机AI颠覆软件讨论会系列的第三期播客！本篇播客讨论于9月初，距今只有1个月时间，但莫子皓老师仍然说有了很大的变化。共识粉碎机是AI圈的老社群了，从去年3月份开始我们就坚持举办AI颠覆软件讨论会系列，最近也开始同步播客信息！与过去大家听到的AI播客不同，我们同时会讨论场景、应用，也会直接进入到技术细节。讨论节奏会非常快速，信息量非常密集，相信每一期对于听众都是一次信息大爆炸。【本期嘉宾】莫子皓：互联网保险暖哇科技合伙人欢迎关注莫子皓老师的公众号《过程即奖励》，以及莫子皓老师的文章《Workflow Based 企业内部大模型落地 Roadmap》【共识粉碎机主持人】周默：共识粉碎机公众号主理人，前美元对冲基金投资人，前腾讯/微软战略与投资经理。共识粉碎机目前也承接投研咨询合作与AI战略转型/大模型技术落地项目，欢迎戳公众号了解。同时我们的新书《大模型启示录》也上架啦，欢迎戳这里购买【参考资料】本期的内容已经抽取成文字纪要，请查阅共识粉碎机公众号的文章：《EP20：非常好的LLM保险销售案例(对谈暖哇)》【本期内容】本次讨论主要围绕LLM保险销售，有特别多的落地细节，从企业微信落地到电销落地。以下是我们聊得：00:01:06 莫子皓与暖哇介绍00:03:47 Sales Agent与Copilot00:06:59 企业微信LLM销售：用户生命周期的产品设计00:09:29 企业微信LLM销售：群发前的服务Agent00:10:55 企业微信LLM销售：为什么拆生命周期00:12:38 企业微信LLM销售：完成闭环00:15:04 企业微信LLM销售：是否需要线索分层00:17:03 企业微信LLM销售：拆SOP00:19:38 企业微信LLM销售：SOP用不同大模型00:23:01 行业Knowhow的意义00:24:24 怎么做SFT数据标注00:31:02 数据标注的人工要求00:37:25 企业微信LLM销售：ROI00:43:16 行业Knowhow公司vs标准化SaaS公司00:46:05 电销vs企业微信00:48:38 电销LLM销售：延迟与座席反应速度00:50:08 电销LLM销售：实现情况00:53:00 电销LLM销售：关单率与时间节省00:57:57 电销vs客服00:59:12 电销LLM销售：提升方向01:01:14 电销LLM销售：延迟解决方法01:05:15 行业横向拓展：保险内与保险外01:14:04 LLM销售产品落地会有打磨期吗01:16:48 未来的预期01:20:20 QA环节：数据标注能否使用第三方01:22:00 QA环节：不同SOP模型会合并成一个模型吗2024-09-251h 23Mad Tech Talk#4 - Evaluating AI with AI: The LLM-as-a-Judge Framework
In this episode of Mad Tech Talk, we explore an innovative approach to AI evaluation with a focus on the feasibility of using large language models (LLMs) as judges to assess the quality of other LLMs, specifically chatbots. This groundbreaking framework, termed "LLM-as-a-judge," aims to automate and scale the evaluation process by aligning LLMs with human preferences.
Key topics covered in this episode include:
Introduction to LLM-as-a-Judge: Understand the rationale and design behind the LLM-as-a-judge framework, which leverages the sophisticated understanding of LLMs like GPT-4 to evaluate chatbot performance.
Benchmarks an...2024-09-2110 minBiznes MyśliBM128: Czy warto inwestować LLM? Czy w klasyczny ML?Czy sztuczna inteligencja zastąpi klasyczne uczenie maszynowe? Dowiedz się, jak skutecznie wykorzystać obie technologie w biznesie!✔ Subskrybuj kanał: / https://www.youtube.com/@DataWorkshop?sub_confirmation=1👍 Zostaw like!❗Obserwuj mnie na LinkedIn https://www.linkedin.com/in/vladimiralekseichenko📢 Poleć ten podcast znajomym zainteresowanym praktycznym wykorzystaniem AI w biznesie! Przedstawiam różne perspektywy, dzieli się osobistymi doświadczeniami i analizuję, jak te technologie mogą wspierać decyzje biznesowe. W tym odcinku dowiesz się:• Jakie są kluczowe różnice między LLM a klasycznym ML?• Kiedy warto inwestować w LLM, a kiedy lepiej stosować tradycyjne podejśc...2024-09-1159 min
Resilient CyberResilient Cyber w/ Steve Wilson - Securing the Adoption of GenAI & LLM'sIn this episode we sit down with GenAI and Security Leader Steve Wilson to discuss securing the explosive adoption of GenAI and LLM's. Steve is the leader of the OWASP Top 10 for LLM's and the upcoming book The Developer's Playbook for LLM Security: Building Secure AI Applications-- First off, for those not familiar with your background, can you tell us a bit about yourself and what brought you to focusing on AI Security as you have currently?- Many may not be familiar with the OWASP LLM Top 10, can you tell...2024-08-2828 min
Стартап-секреты с Дмитрием БеговатовымБизнес на ИИ #7: Нейросети для рекламы – Роман Нестер (Arteus LLM, Segmento)⚡️Ранний доступ к базе знаний и чату про ИИ-стартапы – https://startupsecrets.ru/ai/🔹Подписывайся на подкаст в Телеграм: https://t.me/podcaststartup🙌Спец-сезон создается при поддержке Yandex Cloud.***Роман Нестер – сооснователь проектов Segmento (продан Сберу в 2019 году) и Arteus LLM.Роман рассказал про свой опыт запуска ИТ-бизнеса в 2011 году, развитие проекта в российского Единорога, его продажу Сберу за несколько миллиардов рублей. Про рекламный рынок тогда и сейчас, про технологии тогда и сейчас, создание и развитие команды, продажи в стартапе и подводные камни бизнеса на нейросетях.***Тайм-коды:Знакомство с гостем. Роман Нестер продал Segmento за пару миллиардов и создал с партнерами Arteus LLM2:36 Рома, ты миллиардер? 6:47 Рынок рекламных технологий и стартапы13:22 Как стартапу конкурировать с Google и Яндекс18:17 Что помогло Роме продать свой стартап дороже на 1 миллиард20:16 Как Рома привлекал деньги от Сбера и МТС. Корпоративные менеджеры против стартаперов25:27 Рынок ИИ-технологий за последние 10, 20 лет33:50 Почему не надо делать SaaS, а B2B лучше, чем B2C49:39 Как бизнес внедряет ИИ. Рома про свой новый стартап Arteus LLM57:44 «Поезд ушел» – Рома про создание собственных LLM59:32 Какие специалисты нужны в команде ИИ-стартапа1:05:19 Как создавалась первая версия Arteus LLM1:07:36 Сколько денег потратили на стартап1:09:15 Рома про LLM first подход к команде стартапа 1:10:59 Почему Рома стартовал проект с российского рынка1:13:23 Чего Рома ждет от Arteus LLM как основатель1:16:21 Продуктивность команды – главная метрика стартапа1:19:17 Главный секрет успеха стартапа от Ромы2024-08-271h 21
Inteligencia Artificial para los Negocios#70 Cómo seleccionar el mejor LLM para tu proyecto de Inteligencia ArtificialChatGPT es el LLM más famoso, pero no es el único, y está lejos de ser perfecto.
En este episodio, Nolan Gaete y Sebastián Cisterna discuten sobre los LLM (large language models) o modelos de lenguaje enormes, discutiendo diferencias y similitudes, como también cuales son las variables principales a considerar a la hora de elegir el mejor modelo para tu proyecto.
Además, conversamos sobre las últimas actualizaciones y las tendencias de los LLM que se han visto últimamente.
Únete a nosotros en esta conversación.
#LLM #Claude3...2024-08-1855 minBiznes MyśliBM126: RAG w LLM: Dlaczego popularne rozwiązania to droga donikąd?RAG w LLM: Dlaczego popularne rozwiązania to droga donikąd?Problem: Tradycyjne podejście do RAG (Retrieval-Augmented Generation) w dużych modelach językowych (LLM), choć obiecujące w teorii, często zawodzi w praktyce. Sztuczne dzielenie danych na fragmenty (chunki) prowadzi do utraty kontekstu i generowania niespójnych, a nawet błędnych odpowiedzi.Przyczyny:-- Mechaniczne dzielenie tekstu: Tradycyjne metody dzielą dane na chunki na podstawie liczby znaków, ignorując semantykę i kontekst. To prowadzi do utraty sensu i generowania chaotycznych odpowiedzi.- Nadmierne poleganie na embeddingu: Choć embedding jest potężnym narzędziem, ni...2024-08-1458 min
MLOps.communityReliable LLM Products, Fueled by Feedback // Chinar Movsisyan // #251Chinar Movsisyan is the co-founder and CEO of Feedback Intelligence (formerly Manot), an MLOps startup based in San Francisco. She has been in the AI field for more than 7 years from research labs to venture-backed startups.Reliable LLM Products, Fueled by Feedback // MLOps Podcast #250 with Chinar Movsisyan, CEO of Feedback Intelligence.// AbstractWe live in a world driven by large language models (LLMs) and generative AI, but ensuring they are ready for real-world deployment is crucial. Despite the availability of numerous evaluation tools, many LLM products still struggle to make it to production.We propose a new perspective...2024-07-3049 min
ЧТНП: Просто об ITКИРИЛЛ ОВЧИННИКОВ – будущее с LLM: какое оно? | ЧТНПНа IT-конференции DUMP-2024 нам удалось записать подкаст с главным по LLM в Сбере. В спецвыпуске ЧТНП ML-разработчик Кирилл Овчинников ответил:- Возможно ли внедрить LLM во все сферы жизни?
- Как это скажется на нас?
- Как нейросети влияют на профессии?Таймкоды
00:00 Представление участников
03:52 Куда внедряют LLM
12:36 Изменится ли кардинально наша жизнь благодаря LLM в ближайшие годы
15:32 За какие рынки мы можем побороться?
19:54 LLM в медицине: почему так сложно внедрять
21:29 Как изменится рынок профессий в будущем?
26:26 Обязательно ли применять LLM, чтобы преуспеть в IT-бизнесе?Упомянутые материалы
Сервис от Doubletapp, который переводит юридический язык на русский: https://tagline.ru/doubletapp/cases/servis--kotoriy-perevodit-dokumenty-s-byurokraticheskogo-yazyka-na-chelovecheskiy/
Сайт: https://2tapp.cc/site39
YouTube: https://youtube.com/@doubletapp.studioVK: https://vk.com/doubletapp
Telegram: https://t.me/doubletapp
Instagram*: https://www.instagram.com/doubletapp.ai/
Facebook*: https://www.facebook.com/doubletapp.ai/
Dribble: https://dribbble.com/Doubletapp
*Запрещены на территории РФ
2024-07-1827 min
Machine Learning Tech Brief By HackerNoonIntroducing LLM Sandbox: Securely Execute LLM-Generated Code with Ease
This story was originally published on HackerNoon at: https://hackernoon.com/introducing-llm-sandbox-securely-execute-llm-generated-code-with-ease.
LLM Sandbox: a secure, isolated environment to run LLM-generated code using Docker. Ideal for AI researchers, developers, and hobbyists.
Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning.
You can also check exclusive content about #llm, #langchain, #llamaindex, #ai-agent, #llm-sandbox, #ai-development, #ai-tools, #code-interpreter, and more.
This story was written by: @vndee. Learn more about this writer by checking @vndee's about page,
and for more stories, please visit hackernoon.com.
LLM Sandbox is a lightweight and portable environment designed...2024-07-1303 min
Tech Stories Tech Brief By HackerNoonYaFSDP - An LLM Training Tool That Cuts GPU Usage by 20% - Is Out Now
This story was originally published on HackerNoon at: https://hackernoon.com/yafsdp-an-llm-training-tool-that-cuts-gpu-usage-by-20percent-is-out-now.
YaFSDP is an open-source tool that promises to revolutionize LLM training.
Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories.
You can also check exclusive content about #llm-fine-tuning, #llm-optimization, #llm-training, #gpu-utilization, #what-is-yafsdp, #open-source-tools, #good-company, #imporve-llm-training, and more.
This story was written by: @yandex. Learn more about this writer by checking @yandex's about page,
and for more stories, please visit hackernoon.com.
YaFSDP is an open-source tool that promises to revolutionize LLM training. In a pre-training scenario involving...2024-06-2306 min
ALL STAR SAAS PODCASTスタートアップにおけるPMMとAI×SaaSの可能性〜LayerX AI・LLM事業部 小林 誉幸〜今回はLayerXのLLM事業部の小林さんをゲストにお迎えし、PMとしてのキャリア形成に加え、昨今のAI・LLMの発展についてのポイントをお届けします。日本銀行・シンクタンクを経て、なぜSaaS企業に関心を持ったのか。弁護士ドットコムの執行役員に至るまでのキャリア形成について。そしてLayerXでのLLM事業部ではどのような戦略でプロダクト開発を行っているのかなど深掘りの内容が盛りだくさんとなっております。
ハイライト
日銀→シンクタンク→SaaS企業というキャリアの深掘り
日銀時代に感じた、マクロ視点での日本のスタートアップの課題
弁護士ドットコムでの執行役員へのプロモーションの背景
PMM組織・プロダクト開発に携わる上で重要視していたこと
LayerXとAI・LLM事業部の関係や期待されていること
AI・LLM事業部に力を入れている背景や技術的な革新性をどのように感じられているか
昨今のLLMを取り巻くトレンド、事業部でのホットトピックス
文系出身のAIプロダクトのPMキャリア
PMMというキャリアの広がりや可能性
プロフィール
小林 誉幸 LayerX AI・LLM事業部（@yuki_koba8）
東京大学法学部卒業後、日本銀行に入行し、経済調査や政府統計、決済制度の企画立案などに携わる。三菱UFJリサーチ&コンサルティングでの戦略コンサルタントを経て、2020年に弁護士ドットコム入社。クラウドサインを担当する執行役員として事業戦略やプロダクトマーケティングを管掌。 2023年12月にAI・LLM事業の立ち上げメンバーとしてLayerXに入社。2024-06-1946 min
Machine Learning Street Talk (MLST)What’s the Magic Word? A Control Theory of LLM Prompting.These two scientists have mapped out the insides or “reachable space” of a language model using control theory, what they discovered was extremely surprising.
Please support us on Patreon to get access to the private Discord server, bi-weekly calls, early access and ad-free listening.
https://patreon.com/mlst
YT version: https://youtu.be/Bpgloy1dDn0
Aman Bhargava from Caltech and Cameron Witkowski from the University of Toronto to discuss their groundbreaking paper, “What’s the Magic Word? A Control Theory of LLM Promptin...2024-06-051h 17
知識衝浪 Knowledge SurfingEP60.【知識衝浪｜中堅份子系列】 LLM 與 AI 研究員的養成手冊 ft.聯發創新基地 YC最近，Google 和 OpenAI 之間的 AI 大戰打得越來越激烈，而背後決定勝敗的關鍵技術，就是比誰的大語言模型（LLM）最聰明、最好聊、反應更快。只是，過去你我熟悉的 LLM，大多來自海外科技巨頭，事實上，台灣也有不少業者正在研發在地繁中 LLM，聯發科旗下的聯發創新基地（MediaTek Research）就是其中的代表。這一集，我們邀請到聯發創新基地資深機器學習研究員 —— 陳宜昌 YC，挑戰用最白話的方式分享 LLM 到底是什麼，以及作為一名 AI 研究員的日常。 本集踏浪指南 🌊 🚀 LLM 如何從文字接龍進化到能「推理」？ 🧠 打造繁中 LLM —— Breeze & BreeXe 📖 大語言模型 vs 多模態模型 📱 怎麼把 LLM 裝進手機裡？ 🤯 AI 研究員的讀 Paper 與推理日常 👍 訓練 LLM，何謂「好資料」、「壞資料」 👨💻 YC 個人職涯分享，從物理系到自學 AI ＊本集內容皆非投資建議，投資前應謹慎研究、評估風險 Powered by Firstory Hosting2024-05-2341 min
Machine Learning Tech Brief By HackerNoonEnhance Sentiment Analysis with Role-Flipping Multi-LLM Negotiation
This story was originally published on HackerNoon at: https://hackernoon.com/enhance-sentiment-analysis-with-role-flipping-multi-llm-negotiation.
Enhance sentiment analysis accuracy and interpretability with a role-flipping multi-LLM negotiation method.
Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning.
You can also check exclusive content about #sentiment-analysis, #multi-llm-framework, #ai-and-sentiment-analysis, #llm-negotiations, #in-context-learning, #collaborative-ai-frameworks, #sentiment-analysis-framework, #llm-performance-evaluation, and more.
This story was written by: @textmodels. Learn more about this writer by checking @textmodels's about page,
and for more stories, please visit hackernoon.com.
Our role-flipping multi-LLM negotiation method improves sentiment analysis accuracy and interpretability, outperforming traditional methods across various...2024-05-2131 min
Ürün Odaklı MühendisYerli ve Milli LLM Çalışmaları! Trendyol ve Commencis LLMÜlkemizdeki LLM çalışmalarıyla ilgili son durumu aktardım. İyi seyirler.
Video tercih edenler için: https://youtu.be/sLS2uzjDkuc
Yapay zeka ve teknoloji gelişmeleri için abone olmayı unutmayın!
Trendyol LLM: https://huggingface.co/Trendyol
Commencis LLM: https://huggingface.co/Commencis/Commencis-LLM
FinLLM'ler: https://arxiv.org/abs/2402.02315
Bölümler
00:00 Giriş
00:22 LLM nedir?
01:15 Ülkemizde LLM
01:24 Trendyol LLM
02:01 Commencis LLM
02:46 Yatırım tavsiyeleri
LLM’ler yani Büyük Dil Modelleri (Large Language Model), genellikle büyük miktarda metin verisi üzerinde eğitilmiş yapay zeka sistemlerini ifade eder. Bu modeller, doğal dil işleme (NLP) görevlerinde ku...2024-05-1305 min
VUX WorldChatbot and LLM Analytics with Eric GriffingFrom the granular details of measuring intent and LLM-based chatbot effectiveness to the strategic implementation of AI across business units, Eric provides an in-depth analysis on the importance of data-driven decisions in the era of AI transformation.Join us as we discuss how businesses can leverage analytics to optimise customer interactions, the common pitfalls in current AI strategies, and the future of AI in customer service.In this episode00:00 Introduction and UNPARSED conference - Find out more at https://unparsedconf.com4:32 Welcom Eric Griffing7:25 Best...2024-05-1057 min
The MLSecOps PodcastEvaluating RAG and the Future of LLM Security: Insights with LlamaIndexSend us a textIn this episode of the MLSecOps Podcast, host Neal Swaelens, along with co-host Oleksandr Yaremchuk, sit down with special guest Simon Suo, co-founder and CTO of LlamaIndex. Simon shares insights into the development of LlamaIndex, a leading data framework for orchestrating data in large language models (LLMs). Drawing from his background in the self-driving industry, Simon discusses the challenges and considerations of integrating LLMs into various applications, emphasizing the importance of contextualizing LLMs within specific environments.The conversation delves into the evolution of retrieval-augmented generation (RAG) techniques and the future trajectory...2024-04-2331 minLLMThe LLM Project: Apple's Vision for AI ToolsExplore Apple's visionary approach to AI-integrated tools through their innovative LLM project, offering a glimpse into the future of technology.
Get on the AI Box Waitlist: https://AIBox.ai/Join our ChatGPT Community: https://www.facebook.com/groups/739308654562189/Follow me on Twitter: https://twitter.com/jaeden_ai
2024-04-0511 min
Tech FrontierST-LLM: Large Language Models Are Effective Temporal LearnersLarge Language Models (LLMs) have showcased impressive capabilities in text comprehension and generation, prompting research efforts towards video LLMs to facilitate human-AI interaction at the video level. However, how to effectively encode and understand videos in video-based dialogue systems remains to be solved. In this paper, we investigate a straightforward yet unexplored question: Can we feed all spatial-temporal tokens into the LLM, thus delegating the task of video sequence modeling to the LLMs? Surprisingly, this simple approach yields significant improvements in video understanding. Based upon this, we propose ST-LLM, an effective video-LLM baseline with Spatial-Temporal sequence modeling inside LLM...2024-04-0303 min
The New Stack PodcastLLM Observability: The BreakdownLLM observability focuses on maximizing the utility of larger language models (LLMs) by monitoring key metrics and signals. Alex Williams, Founder and Publisher for The New Stack, and Janikiram MSV, Principal of Janikiram & Associates and an analyst and writer for The New Stack, discusses the emergence of the LLM stack, which encompasses various components like LLMs, vector databases, embedding models, retrieval systems, read anchor models, and more. The objective of LLM observability is to ensure that users can extract desired outcomes effectively from this complex ecosystem.Similar to infrastructure observability in DevOps and SRE practices, LLM observability...2024-03-2825 min
The Prompt DeskPrompt Injections to Coerce any LLM to do Anything You Want with Jonas GeipingPrompt Injections might be the single biggest risk to the deployment of LLM systems. In this episode of The Prompt Desk, your host Bradley Arsenault sits down with researcher Jonas Geiping to talk about his research on LLM security.Jonas and his colleagues have found optimization techniques that allow them to find prompts that they can put into any LLM model that can make it do anything. The open-ended nature of the AI security flaws they have found pose serious risks to pretty much any company adopting LLM technology.See the paper: "Coercing LLMs to...2024-03-2742 min
New Paradigm: AI Research SummariesA Summary of Salesforce AI Research 'AgentLite: A Lightweight Library for Building and Advancing Task-Oriented LLM Agent System'This is a summary of the AI research paper: AgentLite: A Lightweight Library for Building and Advancing Task-Oriented LLM Agent System Available at: https://arxiv.org/pdf/2402.15538.pdf This summary is AI generated, however the creators of the AI that produces this summary have made every effort to ensure that it is of high quality. As AI systems can be prone to hallucinations we always recommend readers seek out and read the original source material. Our intention is to help listeners save time and stay on top of trends and new discoveries. ...2024-03-2610 minNew Paradigm: AI Research SummariesA Summary of 'LLM Agent Operating System'This is a summary of the AI research paper: LLM Agent Operating System Available at: https://arxiv.org/abs/2403.16971 This summary is AI generated, however the creators of the AI that produces this summary have made every effort to ensure that it is of high quality. As AI systems can be prone to hallucinations we always recommend readers seek out and read the original source material. Our intention is to help listeners save time and stay on top of trends and new discoveries. You can find the introductory section of this recording provided below... This...2024-03-2613 min
Papers Read on AIFormal-LLM: Integrating Formal Language and Natural Language for Controllable LLM-based AgentsRecent advancements on Large Language Models (LLMs) enable AI Agents to automatically generate and execute multi-step plans to solve complex tasks. However, since LLM's content generation process is hardly controllable, current LLM-based agents frequently generate invalid or non-executable plans, which jeopardizes the performance of the generated plans and corrupts users' trust in LLM-based agents. In response, this paper proposes a novel ``Formal-LLM'' framework for LLM-based agents by integrating the expressiveness of natural language and the precision of formal language. Specifically, the framework allows human users to express their requirements or constraints for the planning process as an automaton. A stack-based...2024-03-1430 min
Austrian Artificial Intelligence Podcast52. Markus Keiblinger - Texterous - Building custom LLM Solutions# Summary
For the last two years AI has been flooded with news about LLMs and their successes, but how many companies are actually making use of them in their products and services?
Today on the show I am talking to Markus Keiblinger, Managing partner of Texterous. A startup that focus on building custom LLM Solutions to help companies automate their business.
Markus will tell us about his experience when talking and working with companies building such LLM focused solutions.
Telling us about the expectations companies have on the capabilities of LLMs...2024-02-1346 minLLMLeading Compliance Transformation: Patronus AI Debuts LLM Evaluation Tool for Regulated SectorsJoin the forefront of compliance transformation with Patronus AI's debut of its LLM evaluation tool designed for regulated sectors. Explore how this innovative solution revolutionizes compliance practices, offering organizations unprecedented efficiency and insight into regulatory compliance.
Get on the AI Box Waitlist: AIBox.aiJoin our ChatGPT Community: Facebook GroupFollow me on Twitter: Jaeden's Twitter
2024-02-1004 min
MLOps.communityLLM Evaluation with Arize AI's Aparna Dhinakaran // #210Large Language Models have taken the world by storm. But what are the real use cases? What are the challenges in productionizing them? In this event, you will hear from practitioners about how they are dealing with things such as cost optimization, latency requirements, trust of output, and debugging. You will also get the opportunity to join workshops that will teach you how to set up your use cases and skip over all the headaches.
Join the AI in Production Conference on February 15 and 22 here: https://home.mlops.community/home/events/ai-in-production-2024-02-15
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Aparna Dhinakaran is the C...2024-02-0955 minLLMShaping the Future: Patronus AI's LLM Evaluation Tool Transforms Compliance in Regulated IndustriesExplore how Patronus AI is shaping the future of compliance management in regulated industries with its innovative LLM Evaluation Tool. Learn how this tool empowers organizations to achieve regulatory compliance with efficiency and precision.
Get on the AI Box Waitlist: AIBox.aiJoin our ChatGPT Community: Facebook GroupFollow me on Twitter: Jaeden's Twitter
2024-02-0704 minLLMTech Reshaping: Unveiling the World's Largest Open-Source LLM Dataset - Navigating Language Model AdvancementsJoin the conversation on tech reshaping as the world's largest open-source LLM dataset, featuring 3 trillion tokens, is unveiled, offering insights into navigating advancements in language models. In this episode, explore the potential impact on industry standards, the technological advancements, and the ongoing dialogue between artificial intelligence and the future of language understanding. 🌐💡 #TechReshaping #OpenSourceLLM
Get on the AI Box Waitlist: https://AIBox.ai/
Join our ChatGPT Community: https://www.facebook.com/groups/739308654562189/
Follow me on Twitter: https://twitter.com/jaeden_ai
2024-02-0108 minLLMRevolutionizing Data Security: DynamoFL's $15.1M Funding Transforms LLM Data Leak DefenseJoin the revolution in data security as DynamoFL's $15.1 million funding transforms the landscape of defending against Large Language Model (LLM) data leaks. In this episode, explore the potential impact on safeguarding sensitive information, the technological advancements, and the ongoing dialogue between artificial intelligence and the future of data security. 🔄🔐 #DataSecurityRevolution #DynamoFLDataLeakDefenseTransformation
Get on the AI Box Waitlist: https://AIBox.ai/
Join our ChatGPT Community: https://www.facebook.com/groups/739308654562189/
Follow me on Twitter: https://twitter.com/jaeden_ai
2024-01-3108 minPapers Read on AIPersonal LLM Agents: Insights and Survey about the Capability, Efficiency and SecuritySince the advent of personal computing devices, intelligent personal assistants (IPAs) have been one of the key technologies that researchers and engineers have focused on, aiming to help users efficiently obtain information and execute tasks, and provide users with more intelligent, convenient, and rich interaction experiences. With the development of smartphones and IoT, computing and sensing devices have become ubiquitous, greatly expanding the boundaries of IPAs. However, due to the lack of capabilities such as user intent understanding, task planning, tool using, and personal data management etc., existing IPAs still have limited practicality and scalability. Recently, the emergence of foundation...2024-01-252h 22LLMGoogle's Bard & LLM PaLM 2 Explored: What's Fresh?Explore the freshness in Google's Bard and LLM PaLM 2, discovering the latest elements reshaping the landscape of AI language models.
Get on the AI Box Waitlist: https://AIBox.ai/
Join our ChatGPT Community: https://www.facebook.com/groups/739308654562189/
Follow me on Twitter: https://twitter.com/jaeden_ai
2024-01-0715 min
Machine Learning Tech Brief By HackerNoonHow to Effectively Evaluate Your RAG + LLM Applications
This story was originally published on HackerNoon at: https://hackernoon.com/how-to-effectively-evaluate-your-rag-llm-applications.
Ever wondered how some of today's applications seem almost magically smart? A big part of that magic comes from something called RAG and LLM.
Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning.
You can also check exclusive content about #rag-architecture, #rag-plus-llm-applications, #dual-role-of-master-llm, #llm-cycle-of-improvement, #human-in-loop-feedback, #prompt-tuning-by-master-llm, #automating-evaluation-pipeline, #hackernoon-top-story, and more.
This story was written by: @vndee. Learn more about this writer by checking @vndee's about page,
and for more stories, please visit hackernoon.com.
Ever wondered how some...2023-12-2813 min
AIA Podcast by EngXЧТО СЛУЧИЛОСЬ В OpenAI? / Уязвимость LLM’ок, Claude 2.1 и ПЕРВАЯ ИИ-РЕЛИГИЯ / AIA Podcast #22👉 Курс по ИИ от Anywhere Club: https://bit.ly/49Z05w5👉 Learning Week 2023: https://bit.ly/3GpbyaH👉 Промокод от AlgoHack на 5 недель: AIAPODCAST👉 AW.club (статья от Вити тут): https://bit.ly/3uNKVtl👉 Наш чатик в ТГ: https://t.me/aiapodcast Сегодня попытаемся разобраться, что же случилось в OpenAI, какие есть популярные теории, и кто тут злодей - Сэм Альтман или Илья Суцкевер. В остальной части выпуска говорим про новые LLM'ки и их уязвимости, про Cloude 2.1 с здоровенным контекстом, про прогресс в text-to-video, про научные AI-инструменты, о военном Репликаторе и о первой в мире ИИ-религии! Потому, всем приятного прослушивания и поехали. НАВИГАЦИЯ0:00 Разгон про второй мозг6:10 Новости Anywhere Club14:45 Что же случилось с OpenAI47:30 Новая уязвимость ChatGPT и LLM58:35 PartyRock1:01:42 Amazon Q1:05:50 Meta распустила команду Responsible AI1:08:31 КОНКУРС1:09:55 Emu Video и Emu Edit1:12:23 StabilityAI выпустила Stable Diffusion Video, но чувствует себя плохо1:15:30 Idea-to-video от pika.art: Pika 1.01:20:58 Claude 2.1 от Antropic и слухи про Клод 31:27:05 Inflection AI показал опенсорстную LLM Inflection-2, которая как GPT-3.51:32:52 Наука: белки и сетка Chroma, кристаллы и GNoME от DeepMind, первые испытания Neuralink и проект PRIME Study1:49:02 Grok от xAI скоро появится в твиттере (X)1:53:45 Copilot в собеседованиях от AlgoHack и ии-подкаст Expresso.today2:01:55 Новые LLM для ваших ноутов: Starling 7B и Yi-34B-Chat2:04:10 Новые видео: инструкция...2023-12-012h 39
Coffee Power: Tecnología, Desarrollo de Software y Liderazgo#139 - Modelos de Lenguaje Grandes (LLM) y AI Embeddings (feat. Lina Osorio)En el Episodio 139, conversamos sobre los Modelos de Lenguaje Largos (LLM) y AI Embeddings con Lina Osorio (Advanced analytics head at ADL Digital Lab) y discutimos acerca de machine learning, deep learning, redes neuronales y procesamiento de lenguaje natural. Examinamos cómo emplear LLMs con datos personalizados y su papel como AI generativa. Abordamos el papel de OpenAI y ChatGPT en la industria, cómo los científicos de datos pueden mantenerse actualizados y concluimos con detalles sobre cursos de embedding y prompting. Este episodio es una fuente de conocimiento vital para los interesados en la inteligencia artificial avanzada.
EPISODIO 139 FUL...2023-11-2745 min
The MAD Podcast with Matt TurckHumanloop: LLM Collaboration and Optimization with CEO Raza HabibToday, we have the pleasure of chatting with Raza Habib, CEO of Humanloop, the platform for LLM collaboration and evaluation. Matt and Raza cover how to understand and optimize model performance, lessons learned about model evaluation and feedback, and explore the future of model fine-tuning.twitter.com/RazRazclehumanloop.comData Driven NYC YouTube Channeltwitter.com/mattturcklinktr.ee/mattturckShownotes: [00:00:47] How Humanloop helps product and engineering teams build reliable applications on top of large...2023-11-2235 min
LayerX NOW!#74 AI・LLM事業部爆誕！プライバシーテックとの融合とその目的【事業部長nrryuya×y_matsuwitter】今回はCTO松本(@y_matsuwitter)とAI・LLM事業部長の中村（@nrryuya)のトーク回です。最近爆誕したAI・LLM事業部について、事業化の背景やこれまでのプライバシーテック事業とのつながりについて話しました。以下のnoteも合わせてご覧ください。
Labsから事業部へ、生成AIによるプロセスのリデザイン｜Matsumoto Yuki
LLM・生成AIという巨大トレンドにどう挑むか （LayerX創業以来の経験を踏まえて）｜中村 龍矢 | LayerX 事業部執行役員 AI・LLM事業部長
▼話のハイライト（AI作成）
01:17 AI・LLM事業部の背景とプライバシーテックとの融合
07:44 市場の波とお客さんの層の変化
12:41 ユースケースの理論上の可能性
15:11 LLMのテクノロジーの特異性とポジショニング
21:09 社内向けアプリケーションと業務プロセス
23:12 LLMが当たり前になった未来
30:50 オープンソースと開発の楽しさ
33:28 チームの特徴と採用について
▼LayerX Now!とは・・・ LayerXの日常を伝えるPodcast。 CTOの松本とHRのmaasaが（ほぼ）交代でホストを務め、社員がLayerXで働く様子を赤裸々にお伝えします
▼ メディア情報
LayerX採用情報：https://jobs.layerx.co.jp/
LayerX エンジニアブログ：https://tech.layerx.co.jp/
LayerX 公式note：https://note.layerx.co.jp/
CEO福島のnote：https://note.com/fukkyy2023-11-2236 min
MLOps.communityGuarding LLM and NLP APIs: A Trailblazing Odyssey for Enhanced Security // Ads Dawson // #190MLOps podcast #190 with Ads Dawson, Senior Security Engineer at Cohere, Guarding LLM and NLP APIs: A Trailblazing Odyssey for Enhanced Security.
// Abstract
Ads Dawson, a seasoned security engineer at Cohere, explores the challenges and solutions in securing large language models (LLMs) and natural language programming APIs. Drawing on his extensive experience, Ads discusses approaches to threat modeling LLM applications, preventing data breaches, defending against attacks, and bolstering the security of these critical technologies. The presentation also delves into the success of the "OWASP Top 10 for Large Language Model Applications" project, co-founded by Ads, which identifies key vulnerabilities in the...2023-11-1459 minInside Algomatic#5 シゴラクAIが目指すのは「LLMが話しかけてくる世界」CTOたちの技術トーク今回は、初の技術トーク回。横断CTOの南里（@neonankiti）とシゴラクAIカンパニーCTOの菊池（@_pochi）が話題のLLM技術について語ります！シゴラクAIが目指すのは「LLMが話しかけてくる世界」です。そんなシゴラクAIにおけるLLMエンジニアリングの面白さとは？
シゴラクAIにおけるLLMエンジニアリング
Dev Dayでも感じた外部環境の激しさ
LLMの不確実性を減らすための取り組み
性能を高めるための評価やインプットデータ
シゴラクAIの目指すLLMが話しかけてくる世界
Algomaticの採用に興味をお持ち頂いた方は、こちらからカジュアル面談のご応募をぜひお願いします！
https://jobs.algomatic.jp2023-11-1051 min
The UnionHow to Limit LLM HallucinationsTo effectively limit LLM hallucinations, you need to treat LLMs more like journalists instead of storytellers. Journalists weave stories using factual, real-time information. Similarly, you need to feed your LLMs with real-time information to ensure their generated content aligns more closely with reality. Storytellers on the other hand don't require real-time information, since they are creating tales in a fictional world. In our experience, LLMs are primarily used to distribute static content, but static content queries only cover about 20% of the answers users seek. The majority of queries require real-time information. For instance, asking for a current c...2023-09-1326 min
Dev and Doc: AI For Healthcare Podcast#02 A clinical introduction to Large language models (LLM), AI chatbots, Med-PaLMIn this episode, we introduce large language models in healthcare, their potentials and pitfalls. We put AI chatbots like ChatGPT to the test, discuss our thoughts on Google's Med-PaLM, and dabble in a bit of philosophy of artificial general intelligence.
Like what you're hearing? Support us by subscribing and reaching out to us. We want to encourage open discussion between clinicians and developers.
Dev&Doc is a podcast where doctors and developers deep dive into the potential of AI in healthcare.
👨🏻⚕️Doc - Dr. Joshua Au Yeung
🤖Dev - Zeljko Kraljevic
LinkedIn Newsletter
...2023-09-121h 41
MLOps.communityFrugalGPT: Better Quality and Lower Cost for LLM Applications // Lingjiao Chen // MLOps Podcast #172MLOps Coffee Sessions #172 with Lingjiao Chen, FrugalGPT: Better Quality and Lower Cost for LLM Applications.
This episode is sponsored by QuantumBlack.
We are now accepting talk proposals for our next LLM in Production virtual conference on October 3rd. Apply to speak here: https://go.mlops.community/NSAX1O
// Abstract
There is a rapidly growing number of large language models (LLMs) that users can query for a fee. We review the cost associated with querying popular LLM APIs, e.g. GPT-4, ChatGPT, J1-Jumbo, and find that these models have heterogeneous pricing structures, with fees that can differ...2023-08-221h 02
Scaling Tech - The Blueprint for Successful Tech TeamsLLM Transformation Is the New Digital Transformation with Dan MasonLLM fluency: what is it, why do tech teams need it, and why should we care about it? LLM transformation is the new digital transformation, and Dan Mason, a principal at Stride Consulting, is today's guest to help us understand all we need to know about Large Language Model transformation.Dan is a prolific product and engineering team leader who's led teams and products at ESPN, People Magazine, Viacom, and more! Dan has put together a course on LLM fluency and so is uniquely positioned to offer tech leaders out there valuable insight on how they can u...2023-06-1327 min
Cloud Security PodcastAI Security - Can LLM be Attacked?AI Security Podcast - ChatGPT and other Generative AI use Large Language Model (LLM) but can these AI systems be attacked? ☠ 🤔 . In this 3 part AI Security series from Cloud Security Podcast Original episode, we're going to talk about the importance of AI security and how to protect your Language Model aka llm program from attack. How can LLMs be attacked by malicious threat actors - beyond the phishing email that everyone has been talking about.
Who is this episode for?
If you work with LLMs used by AI system or working on securing of internal LLM being built; then you wo...2023-05-3014 minThe MLSecOps PodcastIndirect Prompt Injections and Threat Modeling of LLM Applications; With Guest: Kai GreshakeSend us a textThis talk makes it increasingly clear. The time for machine learning security operations - MLSecOps - is now. In “Indirect Prompt Injections and Threat Modeling of LLM Applications,” (transcript here -> https://bit.ly/45DYMAG) we dive deep into the world of large language model (LLM) attacks and security. Our conversation with esteemed cyber security engineer and researcher, Kai Greshake, centers around the concept of indirect prompt injections, a novel adversarial attack and vulnerability in LLM-integrated applications, which Kai has explored extensively. Our host, Daryan Dehghanpisheh, is joined by specia...2023-05-2436 min