ibl.ai

ibl.ai
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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.

  1. 4 DAYS AGO

    New York City: 2025 Artificial Intelligence Advantage – Driving Economic Growth and Technological Transformation

    Summary of https://edc.nyc/sites/default/files/2025-01/NYCEDC-NYC-AI-Advantage-2025-Report.pdf This report from the NYC Economic Development Corporation (NYCEDC) examines New York City's burgeoning artificial intelligence (AI) ecosystem. It highlights NYC's strengths, including a robust talent pool, substantial venture capital investment, and a thriving startup scene, positioning the city as a global leader in applied AI. The report also addresses challenges such as responsible AI development, workforce transitions, and regulatory considerations. Furthermore, it proposes initiatives to foster AI innovation, support businesses, and develop a skilled workforce, ensuring equitable access to AI's opportunities. Finally, the report includes an extensive appendix defining key terms and detailing NYC's AI training providers and relevant nonprofits.

    41 min

  2. JAN 27

    Northeastern University: Foundations of Large Language Models

    Summary of https://arxiv.org/pdf/2501.09223 Detail foundational concepts and advanced techniques in large language model (LLM) development. It covers pre-training methods, including masked language modeling and discriminative training, and explores generative model architectures like Transformers. The text also examines scaling LLMs for size and context length, along with alignment strategies such as reinforcement learning from human feedback (RLHF) and instruction fine-tuning. Finally, it discusses prompting techniques, including chain-of-thought prompting and prompt optimization methods to improve LLM performance and alignment with human preferences.

    23 min

  3. JAN 27

    Princeton University: Cognitive Architectures for Language Agents

    Summary of https://www.researchgate.net/publication/373715148_Cognitive_Architectures_for_Language_Agents This research paper proposes a framework called CoALA (Cognitive Architectures for Language Agents) for building more sophisticated language agents. CoALA draws parallels between Large Language Models (LLMs) and production systems from symbolic AI, suggesting that control flow mechanisms used in cognitive architectures can be applied to LLMs to improve reasoning, grounding, learning, and decision-making. The authors present CoALA as a blueprint for organizing existing methods and guiding future development of more capable language agents, highlighting key components like memory modules and various action types. The paper examines several existing language agents through the lens of CoALA and proposes actionable directions for future research. Finally, the authors address some conceptual questions regarding the boundaries of agents and their environments.

    19 min

  4. JAN 27

    Georgia Department of Education: Leveraging AI in the K-12 Setting

    Summary of https://lor2.gadoe.org/gadoe/file/b172b8fe-0ac8-46a7-bfdb-282df86b52ed/1/Leveraging%20AI%20in%20the%20K-12%20Setting.pdf This January 2025 Georgia Department of Education document provides guidance on the ethical and effective use of artificial intelligence (AI) in K-12 schools. It emphasizes responsible AI implementation, including data privacy protection and adherence to federal regulations like FERPA and COPPA. The document outlines procedures for adopting AI policies, vetting AI tools, and providing staff training. It stresses the importance of transparency, human oversight of AI-generated content, and avoiding high-stakes uses of AI. The guide also offers best practices for classroom AI integration and addressing AI attribution in student work.

    16 min

  5. JAN 27

    Peking University: Beware of Metacognitive Laziness – Effects of Generative AI on Learning Motivation, Processes, and Performance

    Summary of https://bera-journals.onlinelibrary.wiley.com/doi/10.1111/bjet.13544 Investigates the effects of using generative AI, specifically ChatGPT, on university students' learning. A randomized controlled trial compared students using ChatGPT to those using human expert support, writing analytics tools, or no support at all. The study examined intrinsic motivation, self-regulated learning processes, and learning performance across different groups. Results showed ChatGPT improved essay scores but did not significantly enhance motivation or knowledge transfer, raising concerns about "metacognitive laziness"—over-reliance on AI hindering deeper learning. The study concludes that AI should supplement, not replace, human interaction in education.

    16 min

  6. JAN 27

    MIT AI Risk Repository: Latest Update

    Summary of https://airisk.mit.edu This research paper and its accompanying materials create the AI Risk Repository, a comprehensive resource for understanding and addressing risks from artificial intelligence. The repository includes a database of over 3,000 real-world AI incidents, along with two taxonomies classifying AI risks: a causal taxonomy (by entity, intent, and timing) and a domain taxonomy (by seven broad domains and 23 subdomains). Based on the AI Risk Repository, here are the top 10 AI risks, presented in bullet points, and categorized by their domain, with emphasis on their frequency in the source documents: AI System Safety, Failures & Limitations: This domain is the most frequently discussed, and includes these top risks: AI pursuing its own goals in conflict with human goals or values: This risk is mentioned in 46% of the documents. Lack of capability or robustness: A frequently discussed risk, mentioned in 59% of the documents Socioeconomic & Environmental Harms: This domain is also frequently discussed and includes: Power centralization and unfair distribution of benefits, mentioned in 37% of the documents Increased inequality and decline in employment quality, mentioned in 34% of the documents Discrimination & Toxicity: A frequently discussed domain including: Unfair discrimination and misrepresentation: This risk is mentioned in 63% of the documents. Privacy & Security: This domain includes: Compromise of privacy by obtaining, leaking, or correctly inferring sensitive information: This risk is mentioned in 61% of the documents. Malicious Actors & Misuse: This domain includes: Cyberattacks, weapon development or use, and mass harm: This risk is mentioned in 54% of the documents. Misinformation: This domain includes: False or misleading information: Mentioned in 39% of the documents. Human-Computer Interaction: This domain includes: Overreliance and unsafe use: This risk is mentioned in 24% of documents. It is important to note that while these risks are frequently discussed in the source documents, other risks which are discussed less frequently, such as AI welfare and rights, and pollution of the information ecosystem and loss of consensus reality, may also be of significant importance.

    13 min

  7. JAN 24

    American Association of Colleges and Universities: Leading Through Disruption – Higher Education Executives Assess AI’s Impacts on Teaching and Learning

    Summary of https://iblnews.org/ai-will-generate-better-student-learning-outcomes-as-teaching-models-change-says-aacu This report summarizes a survey conducted by the American Association of Colleges and Universities (AAC&U) and Elon University's Imagining the Digital Future Center on the impact of generative AI on higher education. The survey of 337 college leaders reveals widespread student use of AI tools, but a significant lack of faculty preparedness and concerns about academic integrity. While many leaders anticipate positive impacts on learning and research, they also express worries about over-reliance, equity issues, and the need for ethical considerations in AI education. The report highlights the need for institutional change, including policy updates, faculty development, and curriculum adjustments to effectively integrate AI into teaching and learning. Overall, a cautiously optimistic outlook prevails, with most leaders expecting positive impacts despite significant challenges. Key findings and takeaways: Only 2% of higher ed leaders think most of their faculty are ready to use AI in teaching! 65% feel 2024 grads weren't ready for AI-driven workplaces. 54% say faculty can't spot AI-generated content. 🕵️‍♀️, while 59% say cheating has increased! Students are using AI much more than faculty. Faculty resistance, not students, is seen as the biggest barrier to adopting AI. Only 19% of schools have AI majors/minors. 🎓, while 69% have policies about AI use. While 91% believe AI enhances learning, 92% worry it undermines deep learning!

    18 min

  8. JAN 24

    Google: From Data to Discovery – AI's Role in Higher Education

    Summary of https://connect.chronicle.com/from-data-to-discovery.html Google outlines a roadmap for higher education institutions to leverage artificial intelligence. It emphasizes the crucial role of robust data management and a strong data culture, highlighting the challenges of "dark data" and siloed information. The document showcases successful examples of data-driven initiatives in universities, emphasizing the need for data literacy training and improved data architectures to unlock AI's potential. Finally, it promotes the use of partnerships and tools to achieve efficient and secure data integration, enabling institutions to make better strategic decisions and improve student outcomes.

    17 min
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About

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.

Information

  • Creator
    ibl.ai
  • Years Active
    2024 - 2025
  • Episodes
    84
  • Rating
    Clean
  • Copyright
    © Copyright 2024 All rights reserved.
  • Show Website
    ibl.ai

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