Está no ar, o Data Hackers News !! Os assuntos mais quentes da semana, com as principais notícias da área de Dados, IA e Tecnologia, que você também encontra na nossa Newsletter semanal, agora no Podcast do Data Hackers !! Aperte o play e ouça agora, o Data Hackers News dessa semana ! Para saber tudo sobre o que está acontecendo na área de dados, se inscreva na Newsletter semanal: https://www.datahackers.news/ Conheça nossos comentaristas do Data Hackers News: Monique Femme Paulo Vasconcellos Matérias/assuntos comentados: Inscreva-se no evento do ifood. Meta demite quase 4 mil funcionários. Dona do TikTok lança tecnologia que gera vídeos ultrarrealistas. Demais canais do Data Hackers: Site Linkedin Instagram Tik Tok You Tube
talk-data.com
Topic
AI/ML
Artificial Intelligence/Machine Learning
9014
tagged
Activity Trend
Top Events
David Jayatillake joins me to chat about semantic layers, assessing value in data work, AI, and much more.
David's LinkedIn: https://www.linkedin.com/in/david-jayatillake/
Create LLM-powered autonomous agents and intelligent assistants tailored to your business and personal needs. From script-free customer service chatbots to fully independent agents operating seamlessly in the background, AI-powered assistants represent a breakthrough in machine intelligence. In AI Agents in Action, you'll master a proven framework for developing practical agents that handle real-world business and personal tasks. Author Micheal Lanham combines cutting-edge academic research with hands-on experience to help you: Understand and implement AI agent behavior patterns Design and deploy production-ready intelligent agents Leverage the OpenAI Assistants API and complementary tools Implement robust knowledge management and memory systems Create self-improving agents with feedback loops Orchestrate collaborative multi-agent systems Enhance agents with speech and vision capabilities You won't find toy examples or fragile assistants that require constant supervision. AI Agents in Action teaches you to build trustworthy AI capable of handling high-stakes negotiations. You'll master prompt engineering to create agents with distinct personas and profiles, and develop multi-agent collaborations that thrive in unpredictable environments. Beyond just learning a new technology, you'll discover a transformative approach to problem-solving. About the Technology Most production AI systems require many orchestrated interactions between the user, AI models, and a wide variety of data sources. AI agents capture and organize these interactions into autonomous components that can process information, make decisions, and learn from interactions behind the scenes. This book will show you how to create AI agents and connect them together into powerful multi-agent systems. About the Book In AI Agents in Action, you’ll learn how to build production-ready assistants, multi-agent systems, and behavioral agents. You’ll master the essential parts of an agent, including retrieval-augmented knowledge and memory, while you create multi-agent applications that can use software tools, plan tasks autonomously, and learn from experience. As you explore the many interesting examples, you’ll work with state-of-the-art tools like OpenAI Assistants API, GPT Nexus, LangChain, Prompt Flow, AutoGen, and CrewAI. What's Inside Knowledge management and memory systems Feedback loops for continuous agent learning Collaborative multi-agent systems Speech and computer vision About the Reader For intermediate Python programmers. About the Author Micheal Lanham is a software and technology innovator with over 20 years of industry experience. He has authored books on deep learning, including Manning’s Evolutionary Deep Learning. Quotes This is about to become the hottest area of applied AI. Get a head start with this book! - Richard Davies, author of Prompt Engineering in Practice Couldn’t put this book down! It’s so comprehensive and clear that I felt like I was learning from a master teacher. - Radhika Kanubaddhi, Amazon An enlightening journey! This book transformed my questions into answers. - Jose San Leandro, ACM-SL Expertly guides through creating agent profiles, using tools, memory, planning, and multi-agent systems. Couldn’t be more timely! - Grigory Sapunov author of JAX in Action
Business runs on tabular data in databases, spreadsheets, and logs. Crunch that data using deep learning, gradient boosting, and other machine learning techniques. Machine Learning for Tabular Data teaches you to train insightful machine learning models on common tabular business data sources such as spreadsheets, databases, and logs. You’ll discover how to use XGBoost and LightGBM on tabular data, optimize deep learning libraries like TensorFlow and PyTorch for tabular data, and use cloud tools like Vertex AI to create an automated MLOps pipeline. Machine Learning for Tabular Data will teach you how to: Pick the right machine learning approach for your data Apply deep learning to tabular data Deploy tabular machine learning locally and in the cloud Pipelines to automatically train and maintain a model Machine Learning for Tabular Data covers classic machine learning techniques like gradient boosting, and more contemporary deep learning approaches. By the time you’re finished, you’ll be equipped with the skills to apply machine learning to the kinds of data you work with every day. About the Technology Machine learning can accelerate everyday business chores like account reconciliation, demand forecasting, and customer service automation—not to mention more exotic challenges like fraud detection, predictive maintenance, and personalized marketing. This book shows you how to unlock the vital information stored in spreadsheets, ledgers, databases and other tabular data sources using gradient boosting, deep learning, and generative AI. About the Book Machine Learning for Tabular Data delivers practical ML techniques to upgrade every stage of the business data analysis pipeline. In it, you’ll explore examples like using XGBoost and Keras to predict short-term rental prices, deploying a local ML model with Python and Flask, and streamlining workflows using large language models (LLMs). Along the way, you’ll learn to make your models both more powerful and more explainable. What's Inside Master XGBoost Apply deep learning to tabular data Deploy models locally and in the cloud Build pipelines to train and maintain models About the Reader For readers experienced with Python and the basics of machine learning. About the Authors Mark Ryan is the AI Lead of the Developer Knowledge Platform at Google. A three-time Kaggle Grandmaster, Luca Massaron is a Google Developer Expert (GDE) in machine learning and AI. He has published 17 other books. Quotes
In this episode, I chat with Daliana Liu of The Data Scientist Show! She talks about her career journey, including her tenure at Amazon, and offers practical advice on making data science impactful in business. Tune in to discover what truly makes a great data scientist and check out Daliana's Data Science Career Accelerator course, designed to help data scientists advance their careers: https://maven.com/dalianaliu/ds-career 💌 Join 10k+ aspiring data analysts & get my tips in your inbox weekly 👉 https://www.datacareerjumpstart.com/newsletter 🆘 Feeling stuck in your data journey? Come to my next free "How to Land Your First Data Job" training 👉 https://www.datacareerjumpstart.com/training 👩💻 Want to land a data job in less than 90 days? 👉 https://www.datacareerjumpstart.com/daa 👔 Ace The Interview with Confidence 👉 https://www.datacareerjumpstart.com/interviewsimulator ⌚ TIMESTAMPS 00:00 - Introduction 13:55 - Focusing on non-technical skills 18:07 - The importance of communication skills 23:11 - How to have positive visibility in your company 28:25 - Data Science & ML Career Accelerators 🔗 CONNECT WITH DALIANA 🎥 YouTube Channel: https://www.youtube.com/@UCa0RTSXWyZdh7IciV9r-3ow 🤝 LinkedIn: https://www.linkedin.com/in/dalianaliu/ 📸 Instagram: https://www.instagram.com/dalianaliu/ Website: https://www.dalianaliu.blog/ 🔗 CONNECT WITH AVERY 🎥 YouTube Channel: https://www.youtube.com/@averysmith 🤝 LinkedIn: https://www.linkedin.com/in/averyjsmith/ 📸 Instagram: https://instagram.com/datacareerjumpstart 🎵 TikTok: https://www.tiktok.com/@verydata 💻 Website: https://www.datacareerjumpstart.com/ Mentioned in this episode: Join the last cohort of 2025! The LAST cohort of The Data Analytics Accelerator for 2025 kicks off on Monday, December 8th and enrollment is officially open!
To celebrate the end of the year, we’re running a special End-of-Year Sale, where you’ll get: ✅ A discount on your enrollment 🎁 6 bonus gifts, including job listings, interview prep, AI tools + more
If your goal is to land a data job in 2026, this is your chance to get ahead of the competition and start strong.
👉 Join the December Cohort & Claim Your Bonuses: https://DataCareerJumpstart.com/daa https://www.datacareerjumpstart.com/daa
The book explores the fundamental principles and transformative advancements in cutting-edge algorithmic technologies, detailing their application and impact on revolutionizing healthcare. This book provides an in-depth account of how technologies such as artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) are reshaping healthcare, transitioning from traditional diagnostic and treatment approaches to data-driven solutions that improve predictive accuracy and patient outcomes. The text also addresses the challenges and considerations associated with adopting these technologies, including ethical implications, data security concerns, and the need for human-centered approaches in algorithmic medicine. After introducing digital twin technology and its potential to enhance healthcare delivery, the book examines the broader effects of digital technology on the healthcare system. Subsequent chapters explore topics such as innovations in medical imaging, predictive analytics for improved patient outcomes, and deep learning algorithms for brain tumor detection. Other topics include generative adversarial networks (GANs), convolutional neural networks (CNNs), smart wearables for remote patient monitoring, effective IoT solutions, telemedicine advancements, and blockchain security for healthcare systems. The integration of biometric systems driven by AI, securing cyber-physical systems in healthcare, and digitizing wellness through electronic health records (EHRs) and electronic medical records (EMRs) are also discussed. The book concludes with an extensive case study comparing the impacts of various healthcare applications, offering insights and encouraging further research and innovation in this dynamic field. Audience This book is suitable for academicians and professionals in health informatics, bioinformatics, biomedical science and engineering, artificial intelligence, as well as clinicians, IT specialists, and policymakers in healthcare.
As the software landscape becomes more fragmented, the importance of product integrations continues to rise. For those working in data and engineering roles, this presents both challenges and opportunities. How do you efficiently manage and scale integrations across diverse systems? What tools and strategies can help you maintain data integrity and streamline workflows? And how can you ensure that your integration strategy aligns with broader business goals and customer expectations? Gil Feig is the Co-Founder and CTO of Merge, the leading unified API platform. Previously, Gil was the Head of Engineering at Untapped and worked as a software engineer at Wealthfront and LinkedIn. A graduate of Columbia University, he lives and works in New York City. In the episode, Richie and Gil explore the complexities of product integrations, the evolution of software ecosystems, the challenges of scaling integrations, the role of data engineers, the impact on business operations, and the future of integration technology, and much more. Links Mentioned in the Show: MergeConnect with GilCourse: Implementing AI Solutions in BusinessRelated Episode: Building Multi-Modal AI Applications with Russ d'Sa, CEO & Co-founder of LiveKitSign up to RADAR: Skills Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business
Deepti Srivastava, Founder of Snow Leopard AI and former Spanner Product Lead at Google Cloud, joined Yuliia to chat what's wrong with current approaches to AI integration. Deepti introduces a paradigm shift away from ETL pipelines towards federated, real-time data access for AI applications. She explains how Snow Leopard's intelligent data retrieval platform enables enterprises to connect AI systems directly to operational data sources without compromising security or freshness. Through practical examples Deepti explains why conventional RAG approaches with vector stores are not good enough for business-critical AI applications, and how a systems thinking approach to AI infrastructure can unlock greater value while reducing unnecessary data movement.Deepti's linkedin - https://www.linkedin.com/in/thedeepti/Snowleopard.ai - http://snowleopard.ai/
Bruce Kasman is joined by Joe Lupton to discuss how the global expansion remains on solid footing for now. According to this week’s January surveys, the firming in global industry late last year looks to have continued into 2025. However, US policy churn—including an unexpected trade war on its closest trading partners—has increased uncertainties that are likely to be a new headwind, particularly on business spending. US fiscal policies are limited by already elevated deficits, adding to concerns of sustainability.
This podcast was recorded on February 7, 2025.
This communication is provided for information purposes only. Institutional clients please visit www.jpmm.com/research/disclosures for important disclosures. © 2025 JPMorgan Chase & Co. All rights reserved. This material or any portion hereof may not be reprinted, sold or redistributed without the written consent of J.P. Morgan. It is strictly prohibited to use or share without prior written consent from J.P. Morgan any research material received from J.P. Morgan or an authorized third-party (“J.P. Morgan Data”) in any third-party artificial intelligence (“AI”) systems or models when such J.P. Morgan Data is accessible by a third-party. It is permissible to use J.P. Morgan Data for internal business purposes only in an AI system or model that protects the confidentiality of J.P. Morgan Data so as to prevent any and all access to or use of such J.P. Morgan Data by any third-party.
Send us a text Welcome to the cozy corner of the tech world where ones and zeros mingle with casual chit-chat. Data Topics Unpluggedis your go-to spot for relaxed discussions on tech, news, data, and society. This week, we’re joined by returning guest Tim Leers, who helps us navigate the ever-evolving landscape of AI regulation, open-source controversies, and the battle for the future of large language models. Expect deep dives, hot takes, and a sprinkle of existential dread as we discuss: The EU AI Act and its ripple effects – What does it actually change? And is Meta pulling back on AI development because of it?Meta’s “Frontier AI” framework – A strategic move or just regulatory camouflage?OpenAI vs. the world – From copyright drama to OpenAI accusing competitors of using its models, is this just karma in action?DeepSeek and global AI competition – Why are government agencies banning it, and is it really a game-changer?The EU’s AI investment plans – Can Europe ever catch up, or is 1.5 billion euros just a drop in the compute ocean?OpenAI’s sudden love for open source – Sam Altman says they were on the "wrong side of history." Are they really changing, or is this just another strategic pivot?OpenAI’s latest tech update – we discuss Tim’s experience with o3 and show it liveAll that, plus some existential musings on AI’s role in society, competitive dynamics between the US, EU, and China, and whether we’re all just picking our preferred bias in a world of competing LLMs. Got thoughts? Drop us a comment or question—we might even read it on the next episode!
Presentation by Daniel Saad about BundesFlow and its AI-driven access to German parliamentary data.
As AI continues to dominate industry conversations, the notion of AI readiness becomes a focal point for organizations. It's a multifaceted challenge that goes beyond technology, encompassing business processes and cultural shifts. For professionals, this means grappling with questions like: How do you choose the right AI projects that align with business goals? What skills and team structures are necessary to support AI initiatives? And how do you manage the change that comes with integrating AI into your operations? Venky Veeraraghavan is the Chief Product Officer at DataRobot. As CPO, Venky drives the definition and delivery of the DataRobot Enterprise AI Suite. Venky has twenty-five years of experience focusing on big data and AI as a product leader and technical consultant at top technology companies (Microsoft) and early-stage startups (Trilogy). In the episode, Richie and Venky Veeraraghavan explore AI readiness in organizations, the importance of aligning AI with business processes, the roles and skills needed for AI integration, the balance between building and buying AI solutions, the challenges of implementing AI-driven changes, and much more. Links Mentioned in the Show: DatarobotConnect with VenkySkill Track: Artificial Intelligence (AI) LeadershipRelated Episode: Aligning AI with Enterprise Strategy with Leon Gordon, CEO at Onyx DataAttend RADAR Skills Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business
In this episode, host Jason Foster is joined by Amanda Bickerstaff, CEO & Founder, AI for Education.
Together they explore the impact of AI on young people, particularly in the context of education. They discuss AI literacy, the need for AI literacy among students, teachers, and policymakers and the need for understanding AI's capabilities, limitations, and ethical considerations.
They also discuss social media & AI integration and challenges in the education system. The conversation touches on how AI is embedded in platforms like Snapchat, TikTok, and video games, influencing young people's perceptions and behaviours, sometimes without their awareness. *****
Cynozure is a leading data, analytics and AI company that helps organisations to reach their data potential. It works with clients on data and AI strategy, data management, data architecture and engineering, analytics and AI, data culture and literacy, and data leadership. The company was named one of The Sunday Times' fastest-growing private companies in both 2022 and 2023, and recognised as The Best Place to Work in Data by DataIQ in 2023 and 2024. Cynozure is a certified B Corporation.
Change is inevitable, but adoption is key. From AI/ML tools to leadership shifts, success depends on aligning people, not just technology. Published at: https://www.eckerson.com/articles/managing-change-in-the-age-of-ai-the-head-heart-and-herd-framework
Supported by Our Partners • Swarmia — The engineering intelligence platform for modern software organizations. • Graphite — The AI developer productivity platform. • Vanta — Automate compliance and simplify security with Vanta. — On today’s episode of The Pragmatic Engineer, I’m joined by Chip Huyen, a computer scientist, author of the freshly published O’Reilly book AI Engineering, and an expert in applied machine learning. Chip has worked as a researcher at Netflix, was a core developer at NVIDIA (building NeMo, NVIDIA’s GenAI framework), and co-founded Claypot AI. She also taught Machine Learning at Stanford University. In this conversation, we dive into the evolving field of AI Engineering and explore key insights from Chip’s book, including: • How AI Engineering differs from Machine Learning Engineering • Why fine-tuning is usually not a tactic you’ll want (or need) to use • The spectrum of solutions to customer support problems – some not even involving AI! • The challenges of LLM evals (evaluations) • Why project-based learning is valuable—but even better when paired with structured learning • Exciting potential use cases for AI in education and entertainment • And more! — Timestamps (00:00) Intro (01:31) A quick overview of AI Engineering (05:00) How Chip ensured her book stays current amidst the rapid advancements in AI (09:50) A definition of AI Engineering and how it differs from Machine Learning Engineering (16:30) Simple first steps in building AI applications (22:53) An explanation of BM25 (retrieval system) (23:43) The problems associated with fine-tuning (27:55) Simple customer support solutions for rolling out AI thoughtfully (33:44) Chip’s thoughts on staying focused on the problem (35:19) The challenge in evaluating AI systems (38:18) Use cases in evaluating AI (41:24) The importance of prioritizing users’ needs and experience (46:24) Common mistakes made with Gen AI (52:12) A case for systematic problem solving (53:13) Project-based learning vs. structured learning (58:32) Why AI is not the end of engineering (1:03:11) How AI is helping education and the future use cases we might see (1:07:13) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: • Applied AI Software Engineering: RAG https://newsletter.pragmaticengineer.com/p/rag • How do AI software engineering agents work? https://newsletter.pragmaticengineer.com/p/ai-coding-agents • AI Tooling for Software Engineers in 2024: Reality Check https://newsletter.pragmaticengineer.com/p/ai-tooling-2024 • IDEs with GenAI features that Software Engineers love https://newsletter.pragmaticengineer.com/p/ide-that-software-engineers-love — See the transcript and other references from the episode at https://newsletter.pragmaticengineer.com/podcast — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe
Tariffs could lift global inflation this year, but business cycle dynamics are likely to play an important role in driving core inflation performance. Consistent with our forecast that the global economy turns into the new year generating above-trend growth, we anticipate that global core CPI (ex China and Türkiye) will rise at a 3%ar in 1H25, in line with its 2024 outcome. Alongside a firming in core goods inflation, services inflation looks set to continue but should be limited and divergent across countries. Our bias is for a larger Euro area inflation slide vis a vis the US and UK, while EM is likely to remain differentiated. Tariffs add to upside risk and will provide a further test of persistent inflation and psychology.
Speakers Nora Szentivanyi, Global Economist
Bruce Kasman, Chief Economist
This podcast was recorded on February 05, 2025.
This communication is provided for information purposes only. Institutional clients can view the related reports at
https://www.jpmm.com/research/content/GPS-4795397-0
https://www.jpmm.com/research/content/GPS-4895168-0
for more information; please visit www.jpmm.com/research/disclosures for important disclosures.
© 2025 JPMorgan Chase & Co. All rights reserved. This material or any portion hereof may not be reprinted, sold or redistributed without the written consent of J.P. Morgan. It is strictly prohibited to use or share without prior written consent from J.P. Morgan any research material received from J.P. Morgan or an authorized third-party (“J.P. Morgan Data”) in any third-party artificial intelligence (“AI”) systems or models when such J.P. Morgan Data is accessible by a third-party. It is permissible to use J.P. Morgan Data for internal business purposes only in an AI system or model that protects the confidentiality of J.P. Morgan Data so as to prevent any and all access to or use of such J.P. Morgan Data by any third-party.
S1 Ep#30: From Engineering to Data Strategy: Driving AI and Decision-Making The Data Product Management In Action podcast, season 1, is brought to you by Soda and executive producer Scott Hirleman, is a platform for data product management practitioners to share insights and experiences. Our guest this week is Theo Bell, a data product manager. She chats with host Nick Zerviudis and shares her transition from mechanical engineering to roles at Goldman Sachs and Palantir, emphasizing the importance of data integration in strategic decision-making. She discusses how Palantir helped a manufacturer prioritize client orders during raw material shortages and explores the challenges of convincing stakeholders to adopt new data models, advocating for production-ready pilots over proof-of-concepts. Theo also offers insights on fostering AI adoption within organizations, using a news summarization tool for a CEO as an example. She recommends the GTD framework and Surrounded by Idiots for enhancing productivity and communication. About our Host Nick Zervoudis: Nick is Head of Product at CKDelta, an AI software business within the CK Hutchison Holdings group. Nick oversees a portfolio of data products and works with sister companies to uncover new opportunities to innovate using data, analytics, and machine learning. Nick's career has revolved around data and advanced analytics from day one, having worked as an analyst, consultant, product manager, and instructor for startups, SMEs, and enterprises including PepsiCo, Sainsbury's, Lloyds Banking Group, IKEA, Capgemini Invent, BrainStation, QuantSpark, and Hg Capital. Nick is also the co-host of London's Data Product Management meetup, and speaks & writes regularly about data & AI product management. Connect with Nick on LinkedIn and through his newsletter, Value from Data & AI. About our Guest Theo Bell: Theo is the Head of AI Product at Rimes, where she leads the company’s efforts to leverage AI technology in order to provide cutting-edge data management solutions to clients. Previously, Theo held key roles at Palantir Technologies and Goldman Sachs, where she enabled various industries to leverage data through AI/ML-driven software, notably Airbus' Skywise platform, the NHS, and the UK Ministry of Defense. Theo is dedicated to using AI and technology for global challenges, particularly in improving health, enhancing society, and fostering sustainable businesses. She holds a PhD in Engineering from the University of Cambridge. Connect with Theo in LinkedIn. All views and opinions expressed are those of the individuals and do not necessarily reflect their employers or anyone else. Join the conversation on LinkedIn. Apply to be a guest or nominate someone that you know. Do you love what you're listening to? Please rate and review the podcast, and share it with fellow practitioners you know. Your support helps us reach more listeners and continue providing valuable insights! .
Está no ar, o Data Hackers News !! Os assuntos mais quentes da semana, com as principais notícias da área de Dados, IA e Tecnologia, que você também encontra na nossa Newsletter semanal, agora no Podcast do Data Hackers !!
Aperte o play e ouça agora, o Data Hackers News dessa semana !
Para saber tudo sobre o que está acontecendo na área de dados, se inscreva na Newsletter semanal:
https://www.datahackers.news/
Conheça nossos comentaristas do Data Hackers News:
Monique Femme
Paulo Vasconcellos
Matérias/assuntos comentados:
OpenAI anuncia o3-mini de graça;
DeepSeek se torna o app mais baixado na App Store;
OpenAI lança Deep Research
Demais canais do Data Hackers:
Site
Linkedin
Instagram
Tik Tok
You Tube