ibl.ai
ibl.ai is a generative AI education platform based in NYC. This podcast, curated by its CTO, Miguel Amigot, focuses on high-impact trends and reports about AI.
Episodes
This toolkit from Microsoft provides a comprehensive guide for education institutions to embark on their AI journey. It outlines a five-step implementation process that covers exploration, planning, data preparation, governance, and policy development, emphasizin...
Summary of https://www.nature.com/articles/s44271-025-00258-x
Explores the emotional intelligence capabilities of Large Language Models (LLMs), specifically their ability to solve and create emotional intelligence tests. It highlights that several LLMs, including ChatGPT-4, consistently outperformed human averages on various established emotional intelligence assessments.
The research also investigated LLMs' capacity to generate ne...
This guide from OpenAI introduces a multi-agent collaboration system built using the OpenAI Agents SDK, specifically designed for complex tasks like investment research. It demonstrates a "hub-and-spoke" architecture where a central Portfolio Manager agent orchestrates specialized agents (Macro, Fundame...
This Boston Consulting Group (BCG) Executive Perspectives document, from June 2025, addresses how companies can become "AI-first" to achieve future success. It explains that the democratization of AI, shifting business economics, and the ability of AI-native firms to scale rapidly with lean teams necessit...
McKinsey & Company report, "Seizing the Agentic AI Advantage," examines the current "gen AI paradox," where widespread adoption of generative AI has led to minimal organizational impact.
The authors explain that AI agents, which are autonomous and goa...
Presents the findings of a research project on the impacts of generative AI on children, combining both quantitative survey data from children, parents, and teachers with qualitative insights gathered from school workshops.
The research, guided by a framework focusing on children's wellb...
Report detailing OpenAI's efforts to identify and counter various abusive activities leveraging their AI models. It presents ten distinct case studies of disrupted operations, including deceptive employment schemes, covert influence operations, cyberattacks, and scams.
The report highlight...
Summary of https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5250447
Argues that human memory remains crucial even in the age of AI. It explores the neuroscience behind learning, detailing how the brain utilizes declarative and procedural memory systems and organizes knowledge into schemata and neural manifolds.
The authors propose that cognitive offloading to digital tools, while seemingly efficient, can undermine these internal...
Summary of https://plc.pearson.com/sites/pearson-corp/files/asking-to-learn.pdf
Analyzing student queries to an AI-powered study tool reveals that while many questions focus on basic factual and conceptual knowledge, a significant portion demonstrates higher-order thinking skills, suggesting the tool can support deeper learning.
Insights from this study are being used to develop features that encourage students to ask more complex ...
Summary of https://ml-site.cdn-apple.com/papers/the-illusion-of-thinking.pdf
Explores the capabilities and limitations of Large Reasoning Models (LRMs), which generate detailed thinking processes, compared to standard Large Language Models (LLMs). The authors use controllable puzzle environments like Tower of Hanoi and River Crossing to systematically evaluate performance as complexity increases.
Findings indicate that LRMs outperf...
Summary of https://cdn.openai.com/business-guides-and-resources/a-practical-guide-to-building-agents.pdf
Practical guide explains that agents are advanced systems utilizing large language models (LLMs) to independently perform multi-step workflows by leveraging tools. It identifies suitable applications for agents in scenarios involving complex decisions, unstructured data, or unwieldy rule-based systems, emphasizing that simpler L...
Summary of https://www.gaiin.org/the-ai-labor-playbook
Advocates a fundamental shift in how organizations view and utilize generative AI, proposing it be treated as a new form of labor rather than simply a tool.
The author argues that success hinges on a conceptual change: recognizing AI as a workforce to be led and scaled, emphasizing the importance of strategic labor planning over mere technology procurement.
A core concept intro...
Summary of https://cdn.openai.com/business-guides-and-resources/ai-in-the-enterprise.pdf
Outlines OpenAI's approach to enterprise AI adoption, focusing on practical lessons learned from working with seven "frontier" companies. It highlights three key areas where AI delivers measurable improvements: enhancing workforce performance, automating routine tasks, and powering products with more relevant customer experiences.
The text emph...
Summary of https://arxiv.org/pdf/2504.11436
Details a large-scale randomized experiment involving over 7,000 knowledge workers across multiple industries to study the impact of a generative AI tool integrated into their workflow. The researchers measured changes in work patterns over six months by comparing workers who received access to the AI tool with a control group.
Key findings indicate that the AI tool primarily influenced i...
Summary of https://link.springer.com/article/10.1007/s13347-025-00883-8
This academic paper argues from a Deweyan perspective that artificial intelligence (AI), particularly in its current commercial Intelligent Tutoring System form, is unlikely to democratize education.
The author posits that while proponents focus on AI's potential to increase access to quality education, a truly democratic education, as defined by John Dewey, re...
Based on a survey of technology leaders and senior developers, the document explores the increasing adoption of open source solutions within AI technology stacks across various industries and geographies.
It highlights that over ...
Summary of https://www.scribd.com/document/855023851/BCG-AI-Agent-Report-1745757269
Outlines the evolution of AI Agents from simple applications to increasingly autonomous systems. It highlights the growing adoption of Anthropic's open-source Model Context Protocol (MCP) by major technology companies as a key factor in enhancing AI Agent reliability and safety.
The document underscores the need for continued progress in AI's reason...
Summary of https://arxiv.org/pdf/2504.16902
Explores the critical need for secure communication protocols as AI systems evolve into complex networks of interacting agents. It focuses on Google's Agent-to-Agent (A2A) protocol, designed to enable secure and structured communication between autonomous agents.
The authors analyze A2A's security through the MAESTRO threat modeling framework, identifying potential vulnerabilities like ag...
Summary of https://arxiv.org/pdf/2412.15473
Investigates whether student log data from educational technology, specifically from the first few hours of use, can predict long-term student outcomes like end-of-year external assessments.
Using data from a literacy game in Uganda and two math tutoring systems in the US, the researchers explore if machine learning models trained on this short-term data can effectively predict performanc...
This is a Policy Research Working Paper from the World Bank's Education Global Department, published in May 2025. Titled "From Chalkboards to Chatbots: Evaluating the Impact of Generative AI on Learning Outcomes in Nigeria," it details a study on the effectiveness of using large language models, specifically Mi...
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