ibl.ai

University of Chicago: Agentic Systems – A Guide to Transforming Industries with Vertical AI Agents

Summary of https://arxiv.org/pdf/2501.00881
This paper introduces agentic systems, a new generation of AI solutions using Large Language Models (LLMs) to create adaptable, industry-specific software agents. These agents offer advantages over traditional systems by providing domain expertise, real-time adaptability, and end-to-end workflow automation.
The paper details the core components of these agents, including memory, a reasoning engine, cognitive skills modules, and tools, and explores different categories of agentic systems: task-specific, multi-agent, and human-augmented.
Finally, it discusses current industry and academic efforts in building these systems and outlines future research directions.

Google: Agents – Architecture, Tools, and Applications

Summary of https://www.kaggle.com/whitepaper-agents
This whitepaper explains Generative AI agents, programs extending the capabilities of language models. Agents achieve goals by using tools (Extensions, Functions, and Data Stores) to access external information and perform actions.
The paper details agent architecture, including the model, tools, and orchestration layer, and explores various reasoning frameworks like ReAct and Chain-of-Thought.
It also discusses methods for enhancing model performance through targeted learning and provides examples using LangChain and Vertex AI. Finally, it summarizes the key components and future directions of agent development.

Swiss Business School: AI's Impact on Critical Thinking

Summary of https://www.mdpi.com/2075-4698/15/1/6
This research study explores the effects of Artificial Intelligence (AI) tool usage on critical thinking skills.  The study employed a mixed-method approach, using surveys and interviews with 666 participants to investigate the relationship between AI use, cognitive offloading, and critical thinking abilities.
Quantitative analyses, including ANOVA and correlation analysis, revealed a significant negative correlation between frequent AI tool use and critical thinking scores. Qualitative data from interviews supported these findings, highlighting concerns about AI dependence and reduced cognitive engagement.
The research concludes that while AI tools offer benefits,  educational strategies are needed to promote critical thinking in an AI-driven world.

World Economic Forum: Navigating the AI Frontier – A Primer on the Evolution and Impact of AI Agents

Summary of https://reports.weforum.org/docs/WEF_Navigating_the_AI_Frontier_2024.pdf
This white paper from the World Economic Forum and Capgemini examines the rapid evolution of AI agents, defining them as autonomous systems that perceive and act within their environments. The paper traces their development from rule-based systems to sophisticated models capable of complex decision-making, highlighting key technological trends like large language models and various machine learning techniques.
It explores both the significant benefits of AI agents across numerous sectors and the substantial risks associated with their increasing autonomy, including malfunctions, malicious use, and socioeconomic disruptions.
Finally, the paper emphasizes the urgent need for robust governance frameworks, ethical guidelines, and cross-sectoral collaboration to ensure the responsible integration of AI agents into society.

UNESCO: Guidance for Generative AI in Education and Research

Summary of https://unesdoc.unesco.org/ark:/48223/pf0000386693
This UNESCO publication offers global guidance on the ethical and effective use of generative AI (GenAI) in education and research. It examines GenAI's capabilities and limitations, addressing controversies such as bias, copyright infringement, and the potential exacerbation of digital inequalities.
The document proposes regulatory steps for governments, AI providers, institutions, and individual users, emphasizing a human-centered approach that prioritizes human agency and inclusivity. Recommendations are provided for developing AI competencies, integrating GenAI responsibly into teaching and learning, and rethinking assessment methodologies.
Finally, it explores the long-term implications of GenAI for knowledge creation and the future of education.

Cambridge: How Educators Can Help Future Learners Outwit the Robots

Summary of https://www.cambridgeassessment.org.uk/insights/is-education-ready-ai-rose-luckin/
Professor Rose Luckin's keynote speech at the Cambridge Summit of Education discusses the implications of artificial intelligence (AI) in education. The speech emphasizes the need to cultivate uniquely human forms of intelligence—social intelligence and meta-intelligences—that AI cannot replicate, arguing that these skills are crucial for navigating the Fourth Industrial Revolution.
Luckin advocates for a collaborative approach, bringing together AI developers and educators to create effective AI-driven educational tools and to ensure ethical AI development and implementation. The provided text also includes information about Cambridge Assessment's online courses and resources related to assessment and research, demonstrating a focus on enhancing educational practices in the age of AI.
Ultimately, the sources highlight the transformative potential of AI in education while simultaneously emphasizing the essential role of human-centered learning and ethical considerations.

Deloitte: Powering Artificial Intelligence – A Study of AI's Environmental Footprint, Today and Tomorrow

Summary of https://www.deloitte.com/content/dam/assets-zone2/fr/no-index/docs/explore/powering-artificial-intelligence.pdf
This report from Deloitte examines the environmental impact of artificial intelligence (AI), focusing on the rapidly increasing energy consumption of data centers. It projects a near tripling of data center electricity use by 2030, driven primarily by AI applications, and explores various scenarios for future energy demand.
The report also proposes strategies to mitigate AI's carbon footprint, emphasizing renewable energy adoption, enhanced transparency, ecosystem collaboration, and improvements in energy efficiency.
These strategies aim to achieve "Green AI," minimizing AI's environmental impact while maximizing its potential benefits for climate change mitigation.
Finally, the report underscores the need for coordinated action from both industry and policymakers to ensure a sustainable future for AI.

Google: LearnLM – Improving Gemini for Learning

Summary of https://arxiv.org/pdf/2412.16429
This research paper details the development and evaluation of LearnLM, a Google AI model designed for educational applications. LearnLM improves upon existing models by incorporating pedagogical instruction following, allowing for greater control over the model's teaching style.
Through rigorous human evaluation, LearnLM demonstrated superior performance compared to other leading AI models in various learning scenarios. The researchers highlight the model's effectiveness in adhering to detailed instructions and its ability to promote active learning.
Future work focuses on refining evaluation methods and exploring broader educational applications.

University of Michigan: Artificial Intelligence Research Committee Recommendations Report

Summary of https://research.umich.edu/wp-content/uploads/2024/11/AI-Report-2024.pdf
A University of Michigan committee of experts examined key investments needed to advance U-M's AI capabilities, internal strategies for enhancing collaboration and competitiveness, and ethical guidelines for AI research.
The report proposes substantial investments in computing infrastructure and personnel, improved coordination among university entities, and clear ethical principles to guide responsible AI development and use.
It also recommends establishing an ongoing AI advisory committee and creating a centralized resource hub for AI information. Finally, the report suggests strategies for increasing U-M's influence in national AI initiatives and promoting collaborations with industry and other institutions.

Capgemini: Harnessing the Value of Generative AI - 2nd Edition: Top Use Cases Across Sectors

Summary of https://www.capgemini.com/wp-content/uploads/2024/11/Generative-AI-in-Organizations-Refresh_25112024.pdf
This Capgemini Research Institute report examines the rapidly expanding adoption of generative AI across various sectors. The report highlights a significant increase in organizational investment and implementation of generative AI, showcasing tangible benefits like improved productivity and customer satisfaction.
A key focus is the emergence of AI agents, their potential for enhanced automation, and the need for robust governance frameworks. The research is based on a global survey of 1,100 executives and provides recommendations for organizations to successfully integrate generative AI into their operations.
The report also addresses ethical considerations and environmental impacts associated with generative AI.