Shows
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
PaperLedgeRobotics - LLM-based Interactive Imitation Learning for Robotic ManipulationHey PaperLedge crew, Ernis here, ready to dive into some seriously cool robotics research! Today, we're talking about teaching robots to do stuff, but with a twist that could save us a ton of time and effort.
So, imagine you're trying to teach a robot how to, say, stack blocks. One way is Imitation Learning (IL). You show the robot how you do it, hoping it picks up the moves. Think of it like learning a dance by watching a video – you try to copy the steps.
But here's the catch: IL often struggles because th...2025-05-0105 min
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
Seventy3【第180期】LLM-AutoDiff：一个基于梯度的自动化提示工程Seventy3: 用NotebookLM将论文生成播客，让大家跟着AI一起进步。今天的主题是：LLM-AutoDiff: Auto-Differentiate Any LLM WorkflowSummaryThe provided research introduces LLM-AutoDiff, a novel framework for automating prompt engineering for complex Large Language Model workflows. This system extends gradient-based optimization to multi-step and cyclic LLM applications by treating textual inputs as trainable parameters. LLM-AutoDiff constructs a graph representing the workflow, enabling a "backward engine" LLM to generate feedback that guides iterative prompt improvements, even across functional nodes and repeated calls. The framework incorporates techniques like selective gradient computation and two-stage validation to enhance efficiency. Experimental results demonstrate that LLM-AutoDiff outperforms existing methods in accuracy and trainin...2025-03-2920 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
LessWrong (30+ Karma)“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...2025-03-1312 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 27
Daily 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 KorzekwaTym 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 modeli.Dyskutujemy o istotnych aspektach modeli LLM, takich jak długość kontekstu, bezstanowość, znaczenie promptów i tokenów oraz wpływ knowledge cut-off na jakość generowanych odpowiedzi. Przyglądamy się ewolucji modeli AI, i...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 03
Seventy3【第96期】AsyncLM：异步LLM函数调用Seventy3: 用NotebookLM将论文生成播客，让大家跟着AI一起进步。今天的主题是：Asynchronous LLM Function CallingSummaryThis research paper introduces AsyncLM, a system designed to enhance the efficiency of Large Language Models (LLMs) by enabling asynchronous function calls. Unlike current synchronous methods where LLMs block while awaiting function execution, AsyncLM allows concurrent operation, significantly reducing task completion latency. This is achieved through an interrupt mechanism that notifies the LLM when functions complete, along with a novel domain-specific language (CML) and a fine-tuning strategy to handle this asynchronous interaction. The paper presents empirical evidence demonstrating substantial latency reduction and maintains accuracy, even suggesting extensions for novel human...2025-01-0416 min
Seventy3【第87期】Coconut：连续Latent空间的LLM推理Seventy3: 用NotebookLM将论文生成播客，让大家跟着AI一起进步。今天的主题是：Training Large Language Models to Reason in a Continuous Latent SpaceSummaryThis research paper introduces Coconut, a novel method for enhancing Large Language Model (LLM) reasoning capabilities. Instead of relying solely on language-based chain-of-thought (CoT) reasoning, Coconut utilizes the LLM's hidden state ("continuous thought") as input, enabling reasoning in an unrestricted latent space. Experiments on various reasoning tasks demonstrate that Coconut outperforms traditional CoT methods, especially in tasks requiring significant planning and backtracking. The study analyzes the emergent breadth-first search-like reasoning pattern in Coconut and explores the advantages of latent reasoning over language-based a...2024-12-2610 minDaily 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.
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2024-12-1409 min
Louise Ai agent - genuine friendThe 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 minDaily Paper CastWebRL: Training LLM Web Agents via Self-Evolving Online Curriculum Reinforcement Learning
🤗 Paper Upvotes: 25 | cs.CL
Authors:
Zehan Qi, Xiao Liu, Iat Long Iong, Hanyu Lai, Xueqiao Sun, Xinyue Yang, Jiadai Sun, Yu Yang, Shuntian Yao, Tianjie Zhang, Wei Xu, Jie Tang, Yuxiao Dong
Title:
WebRL: Training LLM Web Agents via Self-Evolving Online Curriculum Reinforcement Learning
Arxiv:
http://arxiv.org/abs/2411.02337v1
Abstract:
Large language models (LLMs) have shown remarkable potential as autonomous agents, particularly in web-based tasks. However, existing LLM web agents heavily rely on expensive proprietary LLM APIs, while open LLMs lack the necessary decision-making capabilities. This pap...2024-11-0622 min
AI Pulse PodcastLLM AI Cybersecurity & Governance Checklist🎙️ Unlocking AI SecurityDive into the world of AI security with our latest podcast episode! We're breaking down OWASP's essential checklist for implementing and securing Large Language Models (LLMs). As organizations increasingly adopt AI technologies, understanding the security implications has never been more crucial.Episode Highlights:Comprehensive Security CoverageDiscover how OWASP's guidance addresses a wide range of critical issues, from adversarial risks to regulatory compliance.Threat Modeling for AILearn why threat modeling is vital...2024-11-0418 min
CyberWire DailyLLM security 101. [Research Saturday]This week, we are pleased to be joined by Mick Baccio, global security advisor for Splunk SURGe, sharing their research on "LLM Security: Splunk & OWASP Top 10 for LLM-based Applications." The research dives into the rapid rise of AI and Large Language Models (LLMs) that initially seem magical, but behind the scenes, they are sophisticated systems built by humans. Despite their impressive capabilities, these systems are vulnerable to numerous cyber threats.Splunk's research explores the OWASP Top 10 for LLM Applications, a framework that highlights key vulnerabilities such as prompt injection, training data poisoning, and sensitive information disclosure. 2024-10-2620 min
Research SaturdayLLM security 101.This week, we are pleased to be joined by Mick Baccio, global security advisor for Splunk SURGe, sharing their research on "LLM Security: Splunk & OWASP Top 10 for LLM-based Applications." The research dives into the rapid rise of AI and Large Language Models (LLMs) that initially seem magical, but behind the scenes, they are sophisticated systems built by humans. Despite their impressive capabilities, these systems are vulnerable to numerous cyber threats.Splunk's research explores the OWASP Top 10 for LLM Applications, a framework that highlights key vulnerabilities such as prompt injection, training data poisoning, and sensitive information disclosure. 2024-10-2620 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
プロンプトLLMが今日の執筆にどのように形を与えているか白紙のページを前にして、含めるべき情報の膨大さに圧倒されることを想像してください。私たち全員が一度は経験したことがあるでしょう？しかし、LLMの登場で行き詰まることは過去のものとなりました。この『The Prompt』のエピソードでは、ジム・カーターがラージランゲージモデル（LLM）の変革的な世界について深く掘り下げ、それがどのように私たちの執筆方法を革新しているかを紹介します。ジムは、山のようなメモやスケッチに埋もれていた経験を語りますが、LLMが混沌を明瞭に変えたことで救われました。膨大なデータセットで訓練されたこれらのAI駆動のツールは、要約を作成したり、草稿を書いたり、さらにはあなたの独自の文体を模倣することさえできます。ブログ投稿からブレインストーミングセッションまで、あなたのように話す執筆アシスタントがいると想像してみてください。一部の恐れを煽る人々が信じさせたいこととは逆に、LLMは私たちを置き換えるために存在しているわけではありません。これらは、重い作業を引き受けるための強力なツールであり、私たちがメッセージを洗練させ、オーディエンスと繋がることに集中できるようにします。ジムは、優れた執筆が会話を生み出し、つながりを築くことであると強調し、AIが効率を高める可能性はあっても、人間のタッチに置き換わることはできないと述べています。ジムはリスナーに対して、AI執筆ツールを試し、自分の作業をサポートし強化するために使用しながら、自分が操作の真の主導者であることを忘れないように挑戦を呼びかけています。特に起業家、マーケター、クリエイティブにとって、LLMは時間を節約し創造性を高める可能性を秘めています。注目のポイントは、企業向けに設計されたカスタムAIサービスであるBara AIを探求するためのジムの招待です。デモのためのウェイトリストがオープンしています。このエピソードは、執筆プロセスにAIを統合することに関心があるすべての人への行動を呼びかけています。では、試してみてはどうでしょうか？bara.aiにアクセスしてサインアップし、執筆の未来を体験する先駆けとなりましょう。---このエピソードおよび全ポッドキャストは、AIの力によってジム・カーターのスペシャリストチームによって制作されています。ジムは日本語を話しません！これは彼のポッドキャストであり実験であり、あなたのサポートに感謝しています。ぜひ🌟🌟🌟🌟🌟（5）スターのレビューを残し、友人と共有してください。彼は公開の場でのビルディングをシェアしており、あなた自身とあなたの会社がそれを学ぶ方法は、彼のプライベートSlackコミュニティに参加することで得られます：https://fastfoundations.com/slackジムについての詳細は https://jimcarter.me からご確認ください。2024-10-0803 min