Ivancast Podcast

En este nuevo episodio, SHIFTERLABS se sumerge en el paper “A Collectivist, Economic Perspective on AI”, donde Michael I. Jordan —una de las voces más influyentes en el campo del aprendizaje automático— propone una forma radicalmente distinta de entender la inteligencia artificial. A diferencia de la narrativa dominante, Jordan argumenta que los modelos de lenguaje no son “mentes individuales”, si...

In this episode of our AI-focused season, SHIFTERLABS uses Google LM to unravel the groundbreaking research “How AI and Human Behaviors Shape Psychosocial Effects of Chatbot Use: A Longitudinal Randomized Controlled Study”conducted by researchers from the MIT Media Lab and OpenAI.   Over a span of 28 days and 300,000+ messages exchanged, 981 participants were immersed in conversations with ChatGPT...

In this compelling episode of our research-driven season, SHIFTERLABS once again harnesses Google LM to decode the latest frontiers of human-AI interaction. Today, we explore “Investigating Affective Use and Emotional Well-being on ChatGPT,” a collaborative study by Jason Phang, Michael Lampe, Lama Ahmad, Sandhini Agarwal (OpenAI) and Cathy Fang, Auren Liu, Valdemar Danry, Samantha Chan, Pattie Ma...

In this episode of our special season, SHIFTERLABS leverages Google LM to demystify cutting-edge research, translating complex insights into actionable knowledge. Today, we explore “AI Agents and Education: Simulated Practice at Scale”, a groundbreaking study by Ethan Mollick, Lilach Mollick, Natalie Bach, LJ Ciccarelli, Ben Przystanski, and Daniel Ravipinto from the Generative AI Lab at the Whart...

In this episode of our special season, SHIFTERLABS leverages Google LM to demystify cutting-edge research, translating complex insights into actionable knowledge. Today, we dive into the “International Scientific Report on the Safety of Advanced AI (2025)”, chaired by Prof. Yoshua Bengio and developed with contributions from 96 AI experts representing 30 countries, the UN, the EU, and the OECD.  ...

In this episode of our special season, SHIFTERLABS leverages Google LM to demystify cutting-edge research, translating complex insights into actionable knowledge. Today, we dive into “Superagency in the Workplace: Empowering People to Unlock AI’s Full Potential”, a compelling study by Hannah Mayer, Lareina Yee, Michael Chui, and Roger Roberts, published in January 2025.   Drawing from extensive re...

In this episode of our special season, SHIFTERLABS leverages Google LM to demystify cutting-edge research, translating complex insights into actionable knowledge. Today, we explore “Emergent Misalignment: Narrow Finetuning Can Produce Broadly Misaligned LLMs”, a striking study by researchers from Truthful AI, University College London, the Center on Long-Term Risk, Warsaw University of Technology,...

In this episode of our special season, SHIFTERLABS leverages Google LM to demystify cutting-edge research, translating complex insights into actionable knowledge. Today, we dive into “Utility Engineering: Analyzing and Controlling Emergent Value Systems in AIs”, a pivotal study by researchers from the Center for AI Safety, the University of Pennsylvania, and the University of California, Berkeley....

In this episode of our special season, SHIFTERLABS leverages Google LM to demystify cutting-edge research, translating complex insights into actionable knowledge. Today, we explore the study “LLM Post-Training: A Deep Dive into Reasoning Large Language Models”, conducted by researchers from Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), the University of Oxford, the University o...

En este episodio de nuestra temporada especial, SHIFTERLABS aprovecha Google LM para desmitificar la investigación de vanguardia, traduciendo conocimientos complejos en ideas accionables. Hoy exploramos el estudio “LLM Post-Training: A Deep Dive into Reasoning Large Language Models”, elaborado por investigadores de la Universidad Mohamed bin Zayed de Inteligencia Artificial (MBZUAI), la Universida...

In this episode of our ongoing season, SHIFTERLABS leverages Google LM to demystify cutting-edge research, translating complex insights into actionable knowledge. We dive into “The Widespread Adoption of Large Language Model-Assisted Writing Across Society”, a groundbreaking study by researchers from Stanford University, the University of Washington, and Emory University.   This research examines...

🚀 In this episode, we dive deep into the book “The Education Revolution Through Artificial Intelligence: Enhancing Skills, Safeguarding Rights, and Facilitating Human-Machine Collaboration” by Carlos Hervás-Gómez, M. Dolores Díaz-Noguera, and Fulgencio Sánchez-Vera. Published by Horizontes Universidad, this work critically examines how AI is transforming education, offering insights on its role i...

In this episode, we dive into the paper “Scaling Evidence-Based Instructional Design Expertise through Large Language Models” by Gautam Yadav from Carnegie Mellon University. This research explores how AI, particularly GPT-4, can transform instructional design by bridging the gap between educational theory and real-world application. Through two detailed case studies, we examine AI-driven content...

How can AI models improve their reasoning at test time without extensive retraining? In this episode, we explore the groundbreaking paper “Simple Test-Time Scaling” by researchers from Stanford University, University of Washington, and the Allen Institute for AI. This research introduces a novel approach to scaling reasoning performance by using extra compute at inference time, surpassing even Ope...

In this episode, we explore OpenAI’s latest advancements in large reasoning models (LRMs) and their impact on competitive programming and software development. Based on the research paper “Competitive Programming with Large Reasoning Models” by OpenAI researchers, this discussion highlights how reinforcement learning is revolutionizing AI’s problem-solving capabilities.   From outperforming human...

In this episode, we dive deep into “Research on Project-Based Teaching Methods in the Introduction to Artificial Intelligence” by Zhiqiang Hu, Zhongjin Guo, Shan Jiang, Xiaodong Zhao, and Xiaoqian Li from Taishan University, China. This study explores the effectiveness of project-based teaching methods in AI education and their impact on students’ academic performance, practical skills, and learni...

In this episode, we analyze the research paper “The Impact of Large Language Models on Programming Education and Student Learning Outcomes” by Gregor Jošt, Viktor Taneski, and Sašo Karakatič from the University of Maribor. Published in Applied Sciences (2024), this study explores how the use of AI models like ChatGPT and Copilot affects programming students’ performance, focusing on critical think...

How is Generative AI reshaping the labor market? In this episode, we break down the key findings from the paper “The Labor Market Effects of Generative Artificial Intelligence” by Jonathan S. Hartley (Stanford University), Filip Jolevski (World Bank), Vitor Melo (Clemson University), and Brendan Moore (Stanford University).   This Stanford & World Bank-backed study explores how Generative AI t...

In this episode of SHIFTERLABS: The Future of Education, we explore the paper “Generative AI in Higher Education: A Global Perspective of Institutional Adoption Policies and Guidelines”, authored by Yueqiao Jin, Lixiang Yan, Vanessa Echeverria, Dragan Gašević, and Roberto Martinez-Maldonado from Monash University and Escuela Superior Politécnica del Litoral (Ecuador).   This study, published in Co...

How is Generative AI revolutionizing English language education? In this episode, we dive into the latest research from Lucas Kohnke, Di Zou, and Fan Su (2025), published in Computers & Education: Artificial Intelligence. This study, conducted across universities in Hong Kong, China, explores how first-year university students use GenAI tools like ChatGPT, Grammarly, and Quillbot to enhance th...

In this episode, we dive deep into “Utility Engineering: Analyzing and Controlling Emergent Value Systems in AIs”, a research paper from the Center for AI Safety, University of Pennsylvania, and University of California, Berkeley. As AI models become more agentic, their values and goals might not align with human priorities. Researchers found that Large Language Models (LLMs) exhibit coherent, str...

In this episode, we dive deep into a groundbreaking study published in Computers & Education: Artificial Intelligence by researchers Yuk Mui Elly Heung and Thomas K.F. Chiu from The Chinese University of Hong Kong. This systematic review and meta-analysis explore the effects of ChatGPT on student behavioral, cognitive, and emotional engagement.   The study analyzed 766 articles, selecting 17 e...

In this episode, we dive into “AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking”by Michael Gerlich from the Center for Strategic Corporate Foresight and Sustainability, SBS Swiss Business School, Zurich. This study explores the growing concern that frequent use of AI tools may lead to cognitive offloading—reducing deep thinking and independent problem-solvin...

Which industries are adopting AI the fastest? In this episode, we explore the study “Which Economic Tasks are Performed with AI?”, conducted by Anthropic, analyzing millions of Claude conversations to understand how AI is transforming the job market.   We discuss key findings, including the dominance of AI in software development and writing and its emerging role in other fields. We also tackle th...

🎙️ In this episode, we dive into the fascinating world of AI-driven education, analyzing the latest study, “Effects of LLM Use and Note-Taking on Reading Comprehension and Memory: A Randomised Experiment in Secondary Schools.” This groundbreaking research, conducted by a team from Cambridge University Press and Microsoft Research, sheds light on how students interact with Large Language Models (LL...