Afleveringen
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Summary of https://www.mckinsey.com/~/media/mckinsey/business%20functions/quantumblack/our%20insights/seizing%20the%20agentic%20ai%20advantage/seizing-the-agentic-ai-advantage.pdf
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 goal-driven, can overcome this paradox by transforming complex business processes beyond simple task automation. The report outlines a strategic shift required for CEOs to implement agentic AI effectively, emphasizing the need to move from scattered experiments to integrated, large-scale transformations.
This includes reimagining workflows around agents, establishing a new agentic AI mesh architecture, and addressing the human and governance challenges associated with deploying autonomous AI. Ultimately, the text argues that successful adoption of agentic AI will redefine how organizations operate, compete, and create value.
The Generative AI Paradox: Despite widespread adoption, nearly eight in ten companies using generative AI (gen AI) report no significant bottom-line impact. This "gen AI paradox" stems from an imbalance where easily scaled "horizontal" enterprise-wide tools (like copilots and chatbots) provide diffuse, hard-to-measure gains, while more transformative "vertical" (function-specific) use cases remain largely stuck in pilot mode.Agentic AI as the Catalyst: AI agents offer a way to overcome this paradox by automating complex business processes. Unlike reactive gen AI tools, agents combine autonomy, planning, memory, and integration to become proactive, goal-driven virtual collaborators, unlocking potential far beyond mere efficiency gains.Reinventing Workflows is Crucial: Realizing the full potential of agentic AI requires more than simply plugging agents into existing workflows; it necessitates reimagining and redesigning those workflows from the ground up, with agents at the core. This involves reordering steps, reallocating responsibilities between humans and agents, and leveraging agents' strengths like parallel execution and real-time adaptability for transformative impact.New Architecture and Enablers for Scale: To effectively scale agents, organizations need a new AI architecture paradigm called the "agentic AI mesh". This composable, distributed, and vendor-agnostic framework enables agents to collaborate securely across systems while managing risks like uncontrolled autonomy and sprawl. Additionally, scaling requires critical enablers such as upskilling the workforce, adapting technology infrastructure, accelerating data productization, and deploying agent-specific governance mechanisms.The CEO's Mandate and Human Challenge: The primary challenge in scaling agentic AI is not technical but human: earning trust, driving adoption, and establishing proper governance for autonomous systems. CEOs must lead this transformation by concluding the experimentation phase, realigning AI priorities with strategic programs, redesigning AI governance, and launching high-impact agent-driven projects to redefine how their organizations operate. -
Summary of https://www.turing.ac.uk/sites/default/files/2025-05/combined_briefing_-_understanding_the_impacts_of_generative_ai_use_on_children.pdf
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 wellbeing, explores how children use generative AI for activities like creativity and learning. Key findings indicate that nearly a quarter of children aged 8-12 have used generative AI, primarily ChatGPT, with usage varying by factors such as age, gender, and educational needs.
The document also highlights parent, carer, and teacher concerns regarding potential exposure to inappropriate content and the impact on critical thinking skills, while noting that teachers are generally more optimistic about their own use of the technology than its use by students.
The research concludes with recommendations for policymakers and industry to promote child-centered AI development, improve AI literacy, address bias, ensure equitable access, and mitigate environmental impacts.
Despite a general lack of research specifically focused on the impacts of generative AI on children, and the fact that these tools have often not been developed with children's interests, needs, or rights in mind, a significant number of children aged 8-12 are already using generative AI, with ChatGPT being the most frequently used tool.The patterns of generative AI use among children vary notably based on age, gender, and additional learning needs. Furthermore, there is a clear disparity in usage rates between children in private schools (52% usage) and those in state schools (18% usage), indicating a potential widening of the digital divide.There are several significant concerns shared by children, parents, carers, and teachers regarding generative AI, including the risk of children being exposed to inappropriate or inaccurate information (cited by 82% and 77% of parents, respectively), worries about the negative impact on children's critical thinking skills (shared by 76% of parents/carers and 72% of teachers), concerns about environmental impacts, potential bias in outputs, and teachers reporting students submitting AI-generated work as their own.Despite concerns, the research highlights potential benefits of generative AI, particularly its potential to support children with additional learning needs, an area children and teachers both support for future development. Teachers who use generative AI also report positive impacts on their own work, including increased productivity and improved performance on teaching tasks.To address the risks and realize the benefits, the sources emphasize the critical need for child-centred AI design, meaningful participation of children and young people in decision-making processes, improving AI literacy for children, parents, and teachers, and ensuring equitable access to both the tools and educational resources about them. -
Zijn er afleveringen die ontbreken?
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Summary of https://cdn.openai.com/threat-intelligence-reports/5f73af09-a3a3-4a55-992e-069237681620/disrupting-malicious-uses-of-ai-june-2025.pdf
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 highlights how threat actors, often originating from China, Russia, Iran, Cambodia, and the Philippines, utilized AI for tasks ranging from generating social media content and deceptive resumes to developing malware and social engineering tactics.
OpenAI emphasizes that their use of AI to detect these activities has paradoxically increased visibility into malicious workflows, allowing for quicker disruption and sharing of insights with industry partners.
OpenAI's mission is to ensure that artificial general intelligence (AGI) benefits all of humanity by deploying AI tools to solve difficult problems and defend against various abuses. This includes preventing AI use by authoritarian regimes, and combating covert influence operations (IO), child exploitation, scams, spam, and malicious cyber activity.OpenAI has successfully detected, disrupted, and exposed a range of abusive activities by leveraging AI as a force multiplier for their expert investigative teams. These malicious uses of AI include social engineering, cyber espionage, deceptive employment schemes (like the "IT Workers" case), covert influence operations (such as "Sneer Review," "High Five," "VAGue Focus," "Helgoland Bite," "Uncle Spam," and "STORM-2035"), cyber operations ("ScopeCreep," "Vixen," and "Keyhole Panda"), and scams (like "Wrong Number").These malicious operations originated from various global locations, demonstrating a widespread threatscape. Four of the ten cases in the report likely originated from China, spanning social engineering, covert influence operations, and cyber threats. Other disruptions involved activities from Cambodia (task scam), the Philippines (comment spamming), and covert influence attempts potentially linked with Russia and Iran. Additionally, deceptive employment schemes showed behaviors consistent with North Korea (DPRK)-linked activity.Threat actors utilized AI to evolve and scale their operations, yet this reliance also increased their exposure and aided in their disruption. For example, AI was used for automating resume creation, generating social media content, translating messages for social engineering, and developing malware. Paradoxically, this integration of AI into their workflows provided OpenAI with insights, enabling quicker identification and disruption of these threats.AI investigations are an evolving discipline, and ongoing disruptions help refine defenses and contribute to a broader understanding of the AI threatscape. OpenAI emphasizes that each disrupted operation improves their understanding of how threat actors abuse their models, allowing them to refine their defenses and share findings with industry peers and authorities to strengthen collective defenses across the internet. -
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 cognitive processes, potentially contributing to phenomena like the reversal of the Flynn Effect.
They advocate for educational approaches that balance technology use with the active internalization of knowledge, suggesting that understanding the brain's natural learning mechanisms is key to designing effective education in the digital age.
The central "Memory Paradox" is that in the age of generative AI and ubiquitous digital tools, increasing reliance on external aids to store or handle information can weaken human cognitive capacities by reducing the exercise of internal memory systems.Neuroscience explains that developing deep understanding, fluency, and intuition requires internalizing knowledge through repeated practice, allowing information to transition from the declarative memory system (facts and concepts) to the procedural memory system (skills and routines); excessive reliance on external tools prevents this crucial "proceduralization".Building robust internal mental frameworks, known as schemata, which are supported by optimized neural patterns called neural manifolds, is essential for organizing knowledge, enabling efficient thinking, detecting errors, and supporting critical thinking and creativity; constantly looking information up hinders the formation of these internal structures.Shifts in educational practices away from emphasizing memorization and explicit content instruction, coinciding with the rise of digital tools and cognitive offloading, are linked to the recent reversal of the Flynn Effect—the decline in IQ scores observed in developed countries—suggesting societal-level consequences for cognitive performance when internal memory is devalued.Effective learning in the digital age requires balancing the use of external technology to support internal cognitive work rather than replacing it. Strategies should promote active engagement, structured practice, memorization of foundational knowledge, and utilizing tools that encourage the brain's natural learning mechanisms like prediction error detection and schema formation. -
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 questions. The authors emphasize that meaningfully integrating AI tools into learning can foster a richer, more active educational experience.
A large-scale study analyzed 128,725 student queries from 8,681 unique users interacting with the "Explain" feature of an AI-powered study tool embedded in an eTextbook. The analysis focused on the open-ended nature of the Explain feature queries as insights into student thought processes.Using Bloom's Taxonomy, the analysis found that 80% of student inputs related to basic Factual or Conceptual knowledge, such as definitions or understanding connections. This aligns with the introductory biology course context.However, the data also showed that about one-third of inputs reflected more advanced cognitive complexity, and 20% were at levels suggesting higher-order thinking skills (Analyze and above), indicating potential for deeper learning beyond basic recall.The presence of higher-level queries suggests that many students are actively framing their inquiries rather than passively seeking information, pointing to the tool's potential to foster more advanced cognitive skills when thoughtfully integrated.Insights from the analysis have directly informed the development of a new "Go Deeper" feature which suggests follow-up questions targeting higher cognitive levels to encourage deeper engagement. -
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 outperform LLMs on medium-complexity tasks but both struggle and eventually fail at high complexities. Surprisingly, LRMs show a decrease in reasoning effort (measured by tokens) as problems become extremely difficult, and they exhibit limitations in executing precise algorithmic steps.
Current Large Reasoning Models (LRMs) face a complete accuracy collapse beyond certain complexity levelswhen evaluated using controllable puzzle environments. This study found three distinct performance regimesbased on problem complexity: standard LLMs perform better at low complexity, LRMs show an advantage at medium complexity, and both types of models fail at high complexity.LRMs exhibit a counter-intuitive scaling limit in their reasoning effort (measured by inference thinking tokens) relative to problem complexity. While reasoning effort initially increases with complexity, it declines as problems approach the complexity threshold where accuracy collapses, even when ample token budget is available.Analysis of the intermediate reasoning traces ("thoughts") reveals complexity-dependent reasoning patterns. For simple problems, LRMs often find correct solutions early but continue exploring incorrect alternatives, a phenomenon termed "overthinking". At moderate complexity, correct solutions tend to emerge later in the thinking process, after exploring incorrect paths. Beyond a certain high complexity threshold, models fail to generate any correct solutions within their thought process.The research questions the reliance on established mathematical and coding benchmarks for evaluating LRMs, noting issues like data contamination and lack of insight into reasoning traces. Controllable puzzle environments were adopted to allow for systematic variation of complexity while maintaining consistent logical structures and enabling detailed analysis of solutions and internal reasoning.Surprising limitations were uncovered in LRMs' ability to perform exact computation and follow explicit algorithms. For instance, providing the solution algorithm for the Tower of Hanoi puzzle did not improve performance or prevent the accuracy collapse. Models also demonstrated inconsistent reasoning, succeeding on some puzzles with higher move counts (like Tower of Hanoi with N=5 requiring 31 moves) but failing much earlier in others with lower required move counts (like River Crossing with N=3 having an 11-move solution). -
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 LLM applications are not considered agents.
The document outlines the fundamental components of an agent as an LLM model, external tools for interaction, and explicit instructions. It also explores orchestration patterns, from single-agent systems to more complex multi-agent architectures, and stresses the importance of robust guardrails and planning for human intervention to ensure safe and reliable agent operation.
Agents are LLM-powered systems capable of independently accomplishing complex, multi-step tasks by managing workflow execution and leveraging tools to interact with external systems.Agents are particularly well-suited for workflows involving complex decision-making, difficult-to-maintain rules, or heavy reliance on unstructured data, where traditional automation methods encounter friction.The foundational components of an agent include the Model (the LLM for reasoning), Tools (external functions/APIs to take action), and Instructions (explicit guidelines for behavior).Agent orchestration can follow Single-agent systems (using tools within a loop) or Multi-agent systems(coordinating specialized agents via a manager or peer-to-peer handoffs), often starting with a single agent and scaling up as complexity requires.Implementing Guardrails (such as relevance/safety classifiers and tool safeguards) and planning for Human Intervention (for failures or high-risk actions) are critical to ensure agents operate safely, predictably, and reliably. -
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 introduced is the "labor-to-token exchange," where prompts represent tasks delegated to AI and tokens are the units of work and cost. The paper stresses the need to train all employees to effectively lead AI labor through natural language chat interfaces, which are presented as the primary marketplace for this new workforce.
Finally, it highlights that organizational architecture and strategy should prioritize modular, open systems to ensure access to the best AI labor at competitive costs, ultimately aiming to amplify human capability and drive innovation rather than focusing solely on cost reduction.
AI is labor, not software. Organizations should shift from thinking about AI as a tool or product to procure, and instead treat it as a workforce or labor to be led, developed, and scaled. Prompts are tasks assigned to this AI labor market, and AI models are programmable workers that require oversight, guidance, and leadership.Labor-to-token exchanges are fundamental. These exchanges convert traditionally human tasks into interactions with generative AI systems, measured and priced in tokens. This transforms labor into a fluid, scalable, and programmable form, enabling tasks previously not possible for computers, especially cognitive ones, to be delegated through natural language. The cost of an exchange is measured by the input and output tokens.AI labor amplifies human potential, rather than replacing it. The primary strategic shift is recognizing that this transformation is about doing more, doing new things, and unlocking latent capacity for innovation, not just cutting costs or headcount. Humans remain essential as orchestrators, supervisors, and integrators of AI labor, providing the creativity, ethical reasoning, and context that AI cannot replicate. The goal is to empower humans to amplify their thinking and enhance the enjoyment of their work.Effective deployment requires strategic architectural and cultural changes. A major barrier is that directing AI labor is a new skill requiring training in communication, problem-solving, and system design. Organizations must avoid vendor lock-in and siloed AI within tools; instead, they should build open, modular systems, decoupling the AI labor interface (enterprise chat), the reasoning engine, the system integration (APIs), and the supervisory layer. Enterprise chat emerges as a crucial interface for accessing and assigning tasks to AI labor using natural language.AI labor strategy must focus on empowering the workforce. The greatest returns come from distributing AI widely and training everyone to lead it effectively. Success requires overcoming fear and misunderstanding, creating champions, building learning into daily work, normalizing exploration, and emphasizing conversation and persistence. Teaching how to collaborate with AI labor, including prompt engineering and problem decomposition, is the new digital literacy essential for unlocking scale, creativity, and agility. -
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 emphasizes an iterative development process and an experimental mindset for successful AI integration, detailing seven essential strategies such as starting with rigorous evaluations, embedding AI into products, investing early, customizing models, empowering experts, unblocking developers, and setting ambitious automation goals, all while ensuring data security and privacy are paramount.
Embrace an iterative and experimental approach: Successful companies treat AI as a new paradigm, adopting an iterative development approach to learn quickly, improve performance and safety, and get to value faster with greater buy-in. An open, experimental mindset is key, supported by rigorous evaluations and safety guardrails.Start early and invest for compounding benefits: Begin AI adoption now and invest early because the value compounds through continuous testing, refinement, and iterative improvements. Encouraging organization-wide familiarity and broad adoption helps companies move faster and launch initiatives more efficiently.Prioritize strategic implementation with evaluations: Instead of broadly injecting AI, start with systematic evaluations to measure how models perform against specific use cases, ensuring quality and safety. Align implementation around high-return opportunities such as improving workforce performance, automating routine operations, or powering products.Customize models and empower experts: Investing in customizing and fine-tuning AI models to specific data and needs can dramatically increase value, improve accuracy, relevance, and consistency. Getting AI into the hands of employees who are closest to the processes and problems is often the most powerful way to find AI-driven solutions.Set bold automation goals and unblock developers: Aim high by setting bold automation goals to free people from repetitive tasks so they can focus on high-impact work. Unblock developer resources, which are often a bottleneck, by accelerating AI application builds through platforms or automating aspects of the software development lifecycle. -
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 individual behaviors, significantly reducing time spent on email and moderately speeding up document completion, while showing no significant effect on collaborative activities like meeting time.
The study highlights that while AI adoption can lead to noticeable shifts in personal work habits, broader changes in job responsibilities and coordinated tasks may require more systemic organizational adjustments and widespread tool adoption.
A 6-month, cross-industry randomized field experiment involving 7,137 knowledge workers from 66 large firms studied the impact of access to Microsoft 365 Copilot, a generative AI tool integrated into commonly used applications like email, document creation, and meetings.Workers who used the AI tool regularly spent 3.6 fewer hours per week on email, a 31% reduction from their pre-period average. Intent-to-treat estimates showed a 1.3 hour reduction per week. This time saving condensed email work, opening up almost 4 hours per week of concentration time and reducing out-of-hours email activity for regular users.While there was suggestive evidence that users completed documents moderately faster (5-25% faster for regular users), especially collaborative documents, there was no significant change in time spent in meetings or the types of meetings attended. There was also no change in the number of documents authored by the primary editor.The observed changes primarily impacted behaviors workers could change independently, such as managing their own email inbox. Behaviors requiring coordination with colleagues or significant organizational changes, like meeting duration or reassigning document responsibilities, did not change significantly. This suggests that in the early adoption phase, individual exploration and time savings on solitary tasks were more common than large-scale workflow transformations.Copilot usage intensity varied widely across workers and firms, but firm-specific differences were the strongest predictor of usage, explaining more variation than industry differences, pre-experiment individual behavior, or the share of coworkers with access to Copilot. -
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, requires cultivating skills for democratic living, providing experience in communication and cooperation, and allowing for student participation in shaping their education.
The paper suggests that the emphasis on individualization, mastery of curriculum, and automation of teacher tasks in current educational AI tools hinders the development of these crucial democratic aspects, advocating instead for public development of AI that augments teachers' capabilities and fosters collaborative learning experiences.
The paper argues that current commercial AI, especially Intelligent Tutoring Systems (ITS), is likely to negatively impact democratic education based on John Dewey's philosophy.A Deweyan understanding of democratic education involves preparing students for democratic living, incorporating democratic practices, democratic governance, and ensuring equal access. The paper contrasts this with a narrow view often used by AI proponents, which primarily focuses on increasing access to quality education.Current commercial educational AI tools are characterized by an emphasis on the individualization of learning, a narrow focus on the mastery of the curriculum, and the automation of teachers' tasks.These characteristics are seen as obstacles to democratic education because they can deprive children of experiences in democratic living, hinder the acquisition of communicative and collaborative skills, habituate them to environments with little control, and reduce opportunities for intersubjective deliberation and experiencing social differences.Increased reliance on AI from private companies also poses a threat by reducing public influence and democratic governance over education and creating environments where students have little say. While current AI poses challenges, the author suggests alternative approaches like using AI to augment teachers or for simulations could better serve democratic goals. -
Summary of https://www.mckinsey.com/~/media/mckinsey/business%20functions/quantumblack/our%20insights/open%20source%20technology%20in%20the%20age%20of%20ai/open-source-technology-in-the-age-of-ai_final.pdf
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 half of respondents utilize open source AI in data, models, and tools, driven by benefits like performance, ease of use, and lower costs compared to proprietary alternatives. However, the report also acknowledges perceived risks associated with open source AI, including cybersecurity, regulatory compliance, and intellectual property concerns, and discusses the safeguards organizations are implementing to mitigate these issues.
Ultimately, the survey indicates a strong expectation for continued growth in the use of open source AI technologies, often in conjunction with proprietary solutions.
Open source AI is widely adopted and its use is expected to grow, with over 50 percent of respondents using it in data, models, and tools areas of the tech stack. Seventy-five percent of respondents anticipate increasing their use of open source AI technologies in the next few years.Key benefits driving the adoption of open source AI include lower implementation costs (60 percent of respondents) and lower maintenance costs (46 percent) compared to proprietary tools. Performance and ease of use are also top reasons for satisfaction. Developers value experience with open source tools for their careers and job satisfaction.Despite the benefits, organizations perceive higher risks with open source AI, particularly regarding cybersecurity (62 percent of respondents), regulatory compliance (54 percent), and intellectual property (50 percent). Organizations are implementing safeguards like guardrails and third-party evaluations to manage these risks.Organizations show a preference for partially open models (models with open weights but potentially non-OSI-approved licenses or limited data), which may be influenced by the performance of such models and the ability to self-host them for better data privacy and control.The AI technology landscape is evolving towards a hybrid approach, with most organizations open to using a mixture of open source and proprietary solutions across their tech stack. Popular open source tools are often developed by large technology companies like Meta (Llama) and Google (Gemma). -
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 reasoning, integration, and social understanding capabilities to achieve full autonomy. Furthermore, it discusses the emergence of product-market fit for agents in various sectors, while also addressing the critical importance of measuring and improving their effectiveness.
Finally, the report examines the role of MCP in enabling agentic workflows and the associated security considerations.
The open-source Model Context Protocol (MCP), launched by Anthropic, is rapidly gaining traction among major tech companies like OpenAI, Microsoft, Google, and Amazon, marking a shift in how AI Agents observe, plan, and act with their environments, thereby enhancing reliability and safety.AI Agents are significantly evolving, moving beyond simple workflow systems and chatbots towards autonomous and multi-agent systems capable of planning, reasoning, using tools, observing, and acting. This maturity is driving a shift from predefined workflows to self-directed agents.Agents are demonstrating growing product-market fit, particularly coding agents, and organizations are gaining significant value from agentic workflows through benefits such as reduced time-to-decision, reclaiming developer time, accelerated execution, and increased productivity.While AI Agents can currently reliably complete tasks taking human experts up to a few minutes, measuring their reliability and effectiveness is an ongoing focus, with benchmarks evolving to assess tool use and multi-turn tasks, and full autonomy dependent on advancements in areas like reasoning, integration, and social understanding.Building and scaling agents involves implementing Agent Orchestration platforms and leveraging MCP to access data and systems; however, this expanded access introduces new security risks, such as malicious tools and tool poisoning, requiring robust security measures like OAuth + RBAC and isolating trust domains. -
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 agent card spoofing, task replay, and authentication issues, and propose mitigation strategies and best practices for secure implementation.
The paper also discusses how A2A synergizes with the Model Context Protocol (MCP) to create robust agentic systems and emphasizes the importance of continuous security measures in the evolving landscape of multi-agent AI.
Agentic AI and A2A Protocol Foundation: The emergence of intelligent, autonomous agents interacting across boundaries necessitates secure and interoperable communication. Google's Agent-to-Agent (A2A) protocol provides a foundational, declarative, identity-aware framework for structured, secure communication between agents, enabling them to discover capabilities via standardized Agent-Cards, authenticate, and exchange tasks.A2A Core Concepts: The A2A protocol defines key elements including the AgentCard (a public JSON metadata file describing agent capabilities), A2A Server and Client (for sending/receiving requests), the Task (the fundamental unit of work with a lifecycle), Message (a communication turn), Part (basic content unit like text or files), and Artifact (generated outputs). Communication flows involve discovery, initiation (using tasks.send or tasks.sendSubscribe), processing, input handling, and completion, potentially with push notifications.MAESTRO Threat Modeling: Traditional threat modeling falls short for agentic AI systems. The MAESTROframework (Multi-Agent Environment, Security, Threat, Risk, and Outcome), a seven-layer approach specifically for agentic AI, identifies threats relevant to A2A, including Agent Card spoofing, A2A Task replay, A2A Server impersonation, Cross-Agent Task Escalation, Artifact Tampering, Authentication & Identity Threats, and Poisoned AgentCard (embedding malicious instructions).Key Mitigation Strategies: Addressing A2A security threats requires specific controls and best practices. Crucial mitigations include using digital signatures and validation for Agent Cards, implementing replay protection (nonce, timestamp, MACs), enforcing strict message schema validation, employing Mutual TLS (mTLS) and DNSSEC for server identity, applying strict authentication/authorization (RBAC, least privilege), securing artifacts (signatures, encryption), implementing audit logging, using dependency scanning, and applying strong JWT validation and secure token storage.A2A and MCP Synergy: A2A and the Model Context Protocol (MCP) are complementary, operating at different layers of the AI stack. A2A enables horizontal agent-to-agent collaboration and task delegation, while MCP facilitates vertical integration by connecting agents to external tools and data sources. Their combined use enables complex hierarchical workflows but introduces security considerations at the integration points, requiring a comprehensive strategy. -
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 performance.
They examine the accuracy of different machine learning algorithms and identify some common predictive features across the diverse datasets. Additionally, the study analyzes the prediction quality for different student performance levels and the impact of including pre-assessment scores in the models.
Short-term log data (2-5 hours) can effectively predict long-term outcomes. The study found that machine learning models using data from a student's first few hours of usage with educational technology provided a useful predictor of end-of-school year external assessments, with performance similar to models using data from the entire usage period (multi-month). This finding was consistent across three diverse datasets from different educational contexts and tools. Interestingly, performance did not always improve monotonically with longer horizon data; in some cases, accuracy estimates were higher using a shorter horizon.Certain log data features are consistently important predictors across different tools. Features like the percentage of success problems and the average number of attempts per problem were frequently selected as important features by the random forest model across all three datasets and both short and full horizons. This suggests that these basic counting features, which are generally obtainable from log data across many educational platforms, are valuable signals for predicting long-term performance.While not perfectly accurate for individual students, the models show good precision at predicting performance extremes. The models struggled to accurately predict students in the middle performance quintiles but showed relatively high precision when predicting students in the lowest (likely to struggle) or highest (likely to thrive) performance groups. For instance, the best model for CWTLReading was accurate 77% of the time when predicting someone would be in the lowest performance quintile (Q1) and 72% accurate for predicting the highest (Q5). This suggests potential for using these predictions to identify students who might benefit from additional support or challenges.Using a set of features generally outperforms using a single feature. While single features like percentage success or average attempts per problem still perform better than a baseline, machine learning models trained on the full set of extracted log features generally outperformed models using only a single feature. This indicates that considering multiple aspects of student interaction captured in the log data provides additional predictive power.Pre-assessment scores are powerful indicators and can be combined with log data for enhanced prediction.Pre-test or pre-assessment scores alone were found to be strong predictors for long-term outcomes, often outperforming using log data features alone. When available, combining pre-test scores with log data features generally resulted in improved prediction performance (higher R2 values) compared to using either source of data alone. However, the study notes that short-horizon log data can be a useful tool for prediction when pre-tests are not available or take time away from instruction. -
Summary of https://documents1.worldbank.org/curated/en/099548105192529324/pdf/IDU-c09f40d8-9ff8-42dc-b315-591157499be7.pdf
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 Microsoft Copilot powered by GPT-4, as virtual tutors for secondary school students in Nigeria.
The research, conducted through a randomized controlled trial over six weeks, found that the intervention led to significant improvements in English, digital, and AI skills among participating students, particularly female students and those with higher initial academic performance.
The paper emphasizes the cost-effectiveness and scalability of this AI-powered tutoring approach in low-resource settings, although it also highlights the need to address potential inequities in access and digital literacy for broader implementation.
Significant Positive Impact on Learning Outcomes: The program utilizing Microsoft Copilot (powered by GPT-4) as a virtual tutor in secondary education in Nigeria resulted in a significant improvement of 0.31 standard deviation on an assessment covering English language, artificial intelligence (AI), and digital skills for first-year senior secondary students over six weeks. The effect on English skills, which was the main outcome of interest, was 0.23 standard deviations. These effect sizes are notably high when compared to other randomized controlled trials (RCTs) in low- and middle-income countries.High Cost-Effectiveness: The intervention demonstrated substantial learning gains, estimated to be equivalent to 1.5 to 2 years of 'business-as-usual' schooling. A cost-effectiveness analysis revealed that the program ranks among some of the most cost-effective interventions for improving learning outcomes, achieving 3.2 equivalent years of schooling (EYOS) per $100 invested per participant. When considering long-term wage effects, the benefit-cost ratio was estimated to be very high, ranging from 161 to 260.Heterogeneous Effects Identified: While the program yielded positive and statistically significant treatment effects across all levels of baseline performance, the effects were found to be stronger among students with better prior academic performance and those from higher socioeconomic backgrounds. Treatment effects were also stronger among female students, which the authors note appeared to compensate for a deficit in their baseline performance.Attendance Linked to Greater Gains: A strong linear association was found between the number of days a student attended the intervention sessions and improved learning outcomes. Based on attendance data, the estimated effect size was approximately 0.031 standard deviation per additional day of attendance. Further analysis predicts substantial gains (1.2 to 2.2 standard deviations) for students participating for a full academic year, depending on attendance rates.Key Policy Implications for Low-Resource Settings: The findings suggest that AI-powered tutoring using LLMs has transformative potential in the education sector in low-resource settings. Such programs can complement traditional teaching, enhance teacher productivity, and deliver personalized learning, particularly when designed and used properly with guided prompts, teacher oversight, and curriculum alignment. The use of free tools and local staff contributes to scalability, but policymakers must address potential inequities stemming from disparities in digital literacy and technology access through investments in infrastructure, teacher training, and inclusive digital education. -
Summary of https://cookbook.openai.com/examples/agents_sdk/multi-agent-portfolio-collaboration/multi_agent_portfolio_collaboration
Introduces a multi-agent system built using the OpenAI Agents SDK for complex investment research. It outlines an "agent as a tool" pattern where a central Portfolio Manager agent orchestrates specialized agents (Fundamental, Macro, Quantitative) and various tools to analyze market data and generate investment reports.
The text highlights the modularity, parallelism, and transparency offered by this architecture for building robust and scalable agent workflows. It details the different tool types supported by the SDK and provides an example output of the system in action, emphasizing the importance of structured prompts and tracing for building effective agent systems.
Complex tasks can be broken down and delegated to multiple specialist agents for deeper, higher-quality results. Instead of using a single agent for everything, multi-agent collaboration allows different autonomous agents to handle specific subtasks or expertise areas. In the investment research example, specialists like Macro, Fundamental, and Quantitative agents contribute their expertise, leading to a more nuanced and robust answer synthesized by a Portfolio Manager agent.
The "Agent as a Tool" pattern is a powerful approach for transparent and scalable multi-agent systems. This model involves a central agent (like the Portfolio Manager) calling other agents as tools for specific subtasks, maintaining a single thread of control and simplifying coordination. This approach is used in the provided example and allows for parallel execution of sub-tasks, making the overall reasoning transparent and auditable.
The OpenAI Agents SDK supports a variety of tool types, offering flexibility in extending agent capabilities.Agents can leverage built-in managed tools like Code Interpreter and WebSearch, connect to external services via MCP servers (like for Yahoo Finance data), and use custom Python functions (like for FRED economic data or file operations) defined with the function_tool decorator. This broad tool support allows agents to perform advanced actions and access domain-specific data.
Structured prompts and careful orchestration are crucial for building robust and consistent multi-agent workflows. The Head Portfolio Manager agent's system prompt encodes the firm's philosophy, tool usage rules, and a step-by-step workflow, ensuring consistency and auditability across runs. Modularity, parallel execution (enabled by features like parallel_tool_calls=True), and clear tool definitions are highlighted as best practices enabled by the SDK.
The system design emphasizes modularity, extensibility, and observability. By wrapping specialist agents as callable tools and structuring the workflow with a central coordinator, it's easier to update, test, or add new agents or tools. OpenAI Traces provide detailed visibility into every agent and tool call, making the workflow fully transparent and easier to debug.
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Summary of https://www.bondcap.com/report/pdf/Trends_Artificial_Intelligence.pdf
Extensively examines the rapid evolution of Artificial Intelligence, highlighting its unprecedented growth in user adoption, usage, and capital expenditure.
It details the competitive landscape, noting the rise of open-source models and the significant presence of China alongside the USA in AI development.
The text also explores AI's increasing integration into the physical world, its impact on workforces, and the ongoing investment in infrastructure like data centers and chips necessary to support this technological advancement.
The pace of change catalyzed by AI is unprecedented, ramping materially faster than the Internet's early growth. This is demonstrated by record-breaking user and usage growth for AI products like ChatGPT, which reached 800 million weekly active users in just 17 months, and significantly faster user adoption compared to previous technologies. Capital expenditure (CapEx) by major technology companies is also growing rapidly, increasingly directed towards building AI infrastructure like data centers and specialized hardware.A key economic dynamic in AI is the tension between high and rising model training costs and rapidly falling inference costs per token. While training a frontier AI model can cost hundreds of millions or potentially billions of dollars, the cost to run these models (inference) has plummeted, with energy required per token falling drastically due to hardware and algorithmic advancements. This cost reduction is increasing accessibility and driving rising developer usage and new product creation, but also raises questions about the monetization and profitability of general-purpose LLMs.The AI landscape is marked by rising competition among tech incumbents, emerging attackers, and global powers. Key threats to monetization include this intense competition, the growing capabilities and accessibility of open-source models which are closing the performance gap with closed models, and the rapid advancement and relevance of China's AI capabilities, which are catching up to USA models, increasingly powered by local semiconductors, and dominating domestic usage.AI adoption and evolution are happening across diverse sectors and applications at a rapid pace. Beyond digital applications, AI is increasingly integrating into the physical world, enabling autonomous systems in areas like transportation, defense, agriculture, and robotics. It is also fundamentally transforming work, driving productivity improvements for employees and leading to significant growth in AI-related job postings and the adoption of AI tools by firms.AI is poised to fundamentally reshape the internet experience for the next wave of global users, who may come online through AI-native interfaces (like conversational agents) powered by expanding satellite connectivity, potentially bypassing traditional app ecosystems. This technological shift is intertwined with increasing geopolitical competition, particularly between the United States and China, where leadership in AI is viewed as a critical component of national resilience and geopolitical influence, creating an AI "space race" with significant international implications. -
Summary of https://cdn.prod.website-files.com/65af2088cac9fb1fb621091f/682f96d6b3bd5a3e1852a16a_AI_Agents_Report.pdf
Presents an overview of AI agents, defined as autonomous systems capable of complex tasks without constant human supervision, highlighting their rapid progression from research to real-world application.
It identifies three major risks: catastrophic misuse through malicious applications, gradual human disempowerment as decision-making shifts to algorithms, and significant workforce displacement due to automation of cognitive tasks.
The report proposes four policy recommendations for Congress, including an Autonomy Passport for registration and oversight, mandatory continuous monitoring and recall authority, requiring human oversight for high-consequence decisions, and implementing workforce impact research to address potential job losses. These measures aim to mitigate the risks while allowing the beneficial aspects of AI agent development to continue.
AI agents represent a significant shift in AI capabilities, moving from research to widespread deployment. Unlike chatbots, these systems are autonomous and goal-directed, capable of taking a broad objective, planning their own steps, using external tools, and iterating without continuous human prompting. They can operate across multiple digital environments and automate decisions, not just steps. Agent autonomy exists on a spectrum, categorized into five levels ranging from shift-length assistants to frontier super-capable systems.The widespread adoption of autonomous AI agents presents three primary risks: catastrophic misuse, where agents could enable dangerous attacks or cyber-intrusions; gradual human disempowerment, as decision-making power shifts to opaque algorithms across economic, cultural, and governmental systems; and workforce displacement, with projections indicating that tasks equivalent to roughly 300 million full-time global positions could be automated, affecting mid-skill and cognitive roles more rapidly than previous automation waves.To mitigate these risks, the report proposes four key policy recommendations for Congress. These include creating a federal Autonomy Passport system for registering high-capability agents before deployment, mandating continuous oversight and recall authority (including containment and provenance tracking) to quickly suspend problematic deployments, requiring human oversight by qualified professionals for high-consequence decisions in domains like healthcare, finance, and critical infrastructure, and directing federal agencies to monitor workforce impacts annually.The proposed policy measures are designed to be proportional to the level of agent autonomy and the domain of deployment, focusing rigorous oversight on where autonomy creates the highest risk while allowing lower-risk innovation to proceed. For instance, the Autonomy Passport requirement and continuous oversight mechanisms target agents classified at Level 2 or higher on the five-level autonomy scale.Early deployments demonstrate significant productivity gains, and experts project agents could tackle projects equivalent to a full human work-month by 2029. However, the pace of AI agent development is accelerating faster than the governance frameworks designed to contain its risks, creating a critical mismatch and highlighting the need for proactive policy intervention before the next generation of agents is widely deployed. -
Summary of https://conference.pixel-online.net/files/foe/ed0015/FP/8250-ESOC7276-FP-FOE15.pdf
This conceptual paper explores the potential of AI-driven conversations, such as those from ChatGPT, to function as dynamic Open Educational Resources (OER) that support self-directed learning (SDL).
Unlike traditional, static resources, AI-powered dialogues offer personalized, interactive, and adaptive experiences that align with learners' needs. The paper argues that these tools can nurture key SDL competencies while acknowledging ethical, pedagogical, and technical considerations.
Ultimately, the authors propose that thoughtfully designed AI-driven OER can empower learners and teachers and contribute to a more inclusive and responsive future for open education.
AI-driven conversations can act as dynamic OER to support SDL. AI-driven conversations, such as those facilitated by ChatGPT, have the potential to function as dynamic Open Educational Resources (OER). Unlike traditional static resources, these dialogues offer personalised, interactive, and adaptive experiences that align with learners' unique needs and goals. This dynamic capability contrasts with static OER.AI supports core principles and competencies of Self-Directed Learning (SDL). AI-driven conversations and generative AI tools can nurture key SDL competencies such as goal setting, self-monitoring, and reflective practice. They support learner autonomy, responsibility, self-motivation, and empower students to take initiative, plan, and manage their learning processes. AI also enhances online collaboration, creativity, problem-solving, and communication skills, which align with SDL characteristics.AI integration can enhance Open Educational Practices (OEP) and improve access and inclusivity.Integrating AI into OEP holds the potential to address long-standing challenges in open education, such as learner engagement, the wider reach and adaptability of resources, and inclusive access. AI supports the creation of diverse and inclusive learning resources, facilitating multilingual and culturally relevant content generation. This integration aligns with the values of access, equity, and transparency that underpin open education.Significant challenges exist in integrating AI into open education. Key challenges include legal and ethical concerns related to copyright, data privacy, and potential biases in AI outputs. There are also technical limitationsdue to fragmented OER infrastructure and a critical need for teacher preparedness and AI literacy, as many educators lack the foundational knowledge and confidence to use AI technologies effectively.Successful integration requires thoughtful planning, policy, and professional development. To effectively realise the potential of AI-driven OER for SDL within OEP, it requires thoughtful design, robust infrastructure, inclusive policies, and sustained professional development for teachers. Recommendations include developing ethical guidelines, investing in compatible OER infrastructure, promoting inclusive AI design, providing professional development focused on both AI literacy and SDL skills for teachers, and encouraging ongoing research. - Laat meer zien
