Avsnitt
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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. -
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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. -
Summary of https://www.kaggle.com/whitepaper-agent-companion
This technical document, the Agents Companion, explores the advancements in generative AI agents, highlighting their architecture composed of models, tools, and an orchestration layer, moving beyond traditional language models.
It emphasizes Agent Ops as crucial for operationalizing these agents, drawing parallels with DevOps and MLOps while addressing agent-specific needs like tool management.
The paper thoroughly examines agent evaluation methodologies, covering capability assessment, trajectory analysis, final response evaluation, and the importance of human-in-the-loop feedback alongside automated metrics. Furthermore, it discusses the benefits and challenges of multi-agent systems, outlining various design patterns and their application, particularly within automotive AI.
Finally, the Companion introduces Agentic RAG as an evolution in knowledge retrieval and presents Google Agentspace as a platform for developing and managing enterprise-level AI agents, even proposing the concept of "Contract adhering agents" for more robust task execution.
Agent Ops is Essential: Building successful agents requires more than just a proof-of-concept; it necessitates embracing Agent Ops principles, which integrate best practices from DevOps and MLOps, while also focusing on agent-specific elements such as tool management, orchestration, memory, and task decomposition.Metrics Drive Improvement: To build, monitor, and compare agent revisions, it is critical to start with business-level Key Performance Indicators (KPIs) and then instrument agents to track granular metrics related to critical tasks, user interactions, and agent actions (traces). Human feedback is also invaluable for understanding where agents excel and need improvement.Automated Evaluation is Key: Relying solely on manual testing is insufficient. Implementing automated evaluation frameworks is crucial to assess an agent's core capabilities, its trajectory (the steps taken to reach a solution, including tool use), and the quality of its final response. Techniques like exact match, in-order match, and precision/recall are useful for trajectory evaluation, while autoraters (LLMs acting as judges) can assess final response quality.Human-in-the-Loop is Crucial: While automated metrics are powerful, human evaluation provides essential context, particularly for subjective aspects like creativity, common sense, and nuance. Human feedback should be used to calibrate and validate automated evaluation methods, ensuring alignment with desired outcomes and preventing the outsourcing of domain knowledge.Multi-Agent Systems Offer Advantages: For complex tasks, consider leveraging multi-agent architectures. These systems can enhance accuracy through cross-checking, improve efficiency through parallel processing, better handle intricate problems by breaking them down, increase scalability by adding specialized agents, and improve fault tolerance. Understanding different design patterns like sequential, hierarchical, collaborative, and competitive is important for choosing the right architecture for a given application. -
Summary of https://arxiv.org/pdf/2503.23674
Researchers Cameron R. Jones and Benjamin K. Bergen investigated whether advanced large language models (LLMs) can pass the standard three-party Turing test. Their study involved human interrogators conversing with both a human and an AI, then judging which was human.
The findings indicate that GPT-4.5, when prompted to adopt a persona, was identified as human significantly more often than the actual human participant, marking the first empirical evidence of an AI passing this rigorous version of the test.
While other models like LLaMa-3.1 showed some human-like qualities, only the persona-prompted models consistently fooled human judges. The study also explored the strategies used by interrogators and the implications of these results for our understanding of AI capabilities and their societal impact.
This study provides the first empirical evidence that a Large Language Model (LLM), specifically GPT-4.5 when prompted to adopt a humanlike persona, can pass a standard three-party Turing test. In the study, GPT-4.5-PERSONA was judged as human 73% of the time, significantly more often than the actual human participants.Prompting with a humanlike persona was crucial for success. While GPT-4.5-PERSONA and LLAMA-PERSONA were not reliably distinguished from humans, the same models without the persona prompt (GPT-4.5-NO-PERSONA and LLAMA-NO-PERSONA) performed significantly worse, often being identified as AI at rates significantly below chance in the undergraduate study.The study compared the performance of several AI systems, including GPT-4.5, LLaMa-3.1-405B, GPT-4o, and ELIZA. The baseline models, GPT-4o-NO-PERSONA and ELIZA, had significantly lower win rates, indicating that interrogators could generally distinguish them from humans. This suggests the interrogators were not simply guessing randomly.The research indicates that interrogators often relied on social, emotional, and linguistic cues rather than traditional measures of knowledge and reasoning when trying to distinguish between humans and AI. Interestingly, providing strange prompts or using "jailbreaks" was the most effective strategy for interrogators, while asking about the weather or human experiences was least effective.The findings have significant social and economic implications, suggesting that contemporary LLMs could potentially substitute for humans in short conversations, raising concerns about deception, misinformation, and the potential undermining of real human interaction. The study also found that general knowledge about LLMs and frequent chatbot interaction did not consistently improve participants' ability to distinguish AI from humans. -
Summary of https://imaginingthedigitalfuture.org/wp-content/uploads/2025/03/Being-Human-in-2035-ITDF-report.pdf
This Elon University Imagining the Digital Future Center report compiles insights from a non-scientific canvassing of technology pioneers, builders, and analysts regarding the potential shifts in human capacities and behaviors by 2035 due to advanced AI. Experts anticipate blurred boundaries between reality and fiction, human and artificial intelligence, and human and synthetic creations, alongside concerns about eroding individual identity, autonomy, and critical thinking skills.
The report explores both optimistic visions of AI augmenting human potential and creativity and pessimistic scenarios involving increased dependence, social division, and the erosion of essential human qualities like empathy and moral judgment. Ultimately, it highlights the critical need for ethical development, regulation, and education to navigate the profound societal changes anticipated in the coming decade.
A significant majority of experts anticipate deep and meaningful or even fundamental and revolutionary change in people’s native operating systems and operations as humans broadly adapt to and use advanced AI by 2035.
Experts predict mostly negative changes in several core human traits and behaviors by 2035, including social and emotional intelligence, the capacity for deep thinking, trust in shared values, empathy, mental well-being, sense of agency, and sense of identity and purpose.
Conversely, pluralities of experts expect mostly positive changes in human curiosity and capacity to learn, decision-making and problem-solving abilities, and innovative thinking and creativity due to interactions with AI.
Many experts express concern about the potential for AI to be used in ways that de-augment humanity, serving the interests of tool builders and those in power, potentially leading to a global sociotechnical dystopia. However, they also see the potential for AI to augment human intelligence and bring about universal enlightenment if the direction of development changes.
The experts underscore the critical importance of how humans choose to integrate AI into their lives and societies. They emphasize the need for ethical considerations, human-centered design, the establishment of human values in AI development and policy, and the preservation of human agency to ensure AI serves humanity's flourishing rather than diminishing essential human capacities.
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Summary of https://www.bain.com/globalassets/noindex/2025/bain_article_nvidia_gtc_2025_ai_matures_into_enterprise_infrastructure.pdf
Nvidia's GTC 2025 highlighted a significant shift in AI, moving from experimental phases to becoming core enterprise infrastructure. The event showcased how data remains crucial, but AI itself is now a data generator, leading to new insights and efficiencies.
Furthermore, smaller, specialized AI models are gaining prominence, offering cost advantages and improved control. While fully autonomous AI agents are still rare, structured semi-autonomous systems with human oversight are becoming standard.
Finally, the conference underscored the growing importance of digital twins, video analytics, and accessible off-the-shelf tools in democratizing enterprise AI adoption and fostering cross-functional collaboration through simulation.
AI has matured beyond pilot projects and is now being deployed at scale within the core operations of enterprises. Companies are re-architecting how they compete by moving AI from innovation teams into the business core.Data remains both a critical challenge and a significant opportunity for AI success. Successful AI deployments rely on clean, connected, and accessible data. Furthermore, AI is now generating a new layer of data through insights and generative applications.The trend is shifting towards smaller, specialized AI models that are more cost-effective and offer better control, latency, and privacy. Techniques like quantization, pruning, and RAG are facilitating this shift, although deploying and managing these custom models presents new operational complexities.Agentic AI is gaining traction, but its successful implementation hinges on structure, transparency, and human oversight. While fully autonomous agents are rare, semiautonomous systems with built-in safeguards and orchestration platforms are becoming the near-term standard.Digital twins and simulation have moved from innovation showcases to everyday enterprise tools, enabling faster rollout cycles, lower risk, and more informed decision-making. Simulation is also evolving into a collaboration platform for cross-functional teams. -
Summary of https://transformer-circuits.pub/2025/attribution-graphs/methods.html
Introduces a novel methodology called "circuit tracing" to understand the inner workings of language models. The authors developed a technique using "replacement models" with interpretable components to map the computational steps of a language model as "attribution graphs." These graphs visually represent how different computational units, or "features," interact to process information and generate output for specific prompts.
The research details the construction, visualization, and validation of these graphs using an 18-layer model and offers a preview of their application to a more advanced model, Claude 3.5 Haiku. The study explores the interpretability and sufficiency of this method through various evaluations, including case studies on acronym generation and addition.
While acknowledging limitations like missing attention circuits and reconstruction errors, the authors propose circuit tracing as a significant step towards achieving mechanistic interpretability in large language models.
This paper introduces a methodology for revealing computational graphs in language models using Cross-Layer Transcoders (CLTs) to extract interpretable features and construct attribution graphs that depict how these features interact to produce model outputs for specific prompts. This approach aims to bridge the gap between raw neurons and high-level model behaviors by identifying meaningful building blocks and their interactions.
The methodology involves several key steps: training CLTs to reconstruct MLP outputs, building attribution graphs with nodes representing active features, tokens, errors, and logits, and edges representing linear effects between these nodes. A crucial aspect is achieving linearity in feature interactions by freezing attention patterns and normalization denominators. Attribution graphs allow for the study of how information flows from the input prompt through intermediate features to the final output token.
The paper demonstrates the application of this methodology through several case studies, including acronym generation, factual recall, and small number addition. These case studies illustrate how attribution graphs can reveal the specific features and pathways involved in different cognitive tasks performed by language models. For instance, in the addition case study, the method uncovers a hierarchy of heuristic features that collaboratively solve the task.
Despite the advancements, the methodology has several significant limitations. A key limitation is the missing explanation of how attention patterns are formed and how they mediate feature interactions (QK-circuits), as the analysis is conducted with fixed attention patterns. Other limitations include reconstruction errors (unexplained model computation), the role of inactive features and inhibitory circuits, the complexity of the resulting graphs, and the difficulty of understanding global circuits that generalize across many prompts.
The paper also explores the concept of global weights between features, which are prompt-independent and aim to capture general algorithms used by the replacement model. However, interpreting these global weights is challenging due to issues like interference (spurious connections) and the lack of accounting for attention-mediated interactions. While attribution graphs provide insights on specific prompts, future work aims to enhance the understanding of global mechanisms and address current limitations, potentially through advancements in dictionary learning and handling of attention mechanisms.
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Summary of https://www.rand.org/content/dam/rand/pubs/research_reports/RRA100/RRA134-25/RAND_RRA134-25.pdf
A RAND Corporation report, utilizing surveys from the 2023-2024 school year, investigates the adoption and use of artificial intelligence tools by K-12 public school teachers and principals. The research highlights that roughly one-quarter of teachers reported using AI for instructional planning or teaching, with higher usage among ELA and science teachers and those in lower-poverty schools.
Simultaneously, nearly 60 percent of principals indicated using AI in their jobs, primarily for administrative tasks like drafting communications. The study also found that guidance and support for AI use were less prevalent in higher-poverty schools for both educators, suggesting potential inequities in AI integration. Ultimately, the report underscores the emerging role of AI in education and recommends developing strategies and further research to ensure its effective and equitable implementation.
A significant portion of educators are using AI tools, but there's considerable variation. Approximately one-quarter of teachers reported using AI tools for instructional planning or teaching, with higher rates among ELA and science teachers, as well as secondary teachers. Notably, nearly 60 percent of principals reported using AI tools in their jobs. However, usage differed by subject taught and school characteristics, with teachers and principals in higher-poverty schools being less likely to report using AI tools.Teachers primarily use AI for instructional planning, while principals focus on administrative tasks. Teachers most commonly reported using AI to generate lesson materials, assess students, and differentiate instruction. Principals primarily used AI to draft communications, support other school administrative tasks, and assist with teacher hiring, evaluation, or professional learning.Disparities exist in AI adoption and support based on school poverty levels. Teachers and principals in lower-poverty schools were more likely to use AI and reported receiving more guidance on its use compared to their counterparts in higher-poverty schools. Furthermore, schools in higher-poverty areas were less likely to be developing AI usage policies. This suggests a widening gap in AI integration and the potential for unequal access to its benefits.Educators have several concerns regarding AI use, including a lack of professional learning and data privacy. Principals identified a lack of professional development, concerns about data privacy, and uncertainty about how to use AI as major influences on their AI adoption. Teachers also expressed mixed perceptions about AI's helpfulness, noting the need to assess the quality of AI output and potential for errors.The report highlights the need for intentional strategies and further research to effectively integrate AI in education. The authors recommend that districts and schools develop strategies to support AI use in ways that improve instruction and learning, focusing on AI's potential for differentiated instruction, practice opportunities, and student engagement. They also emphasize the importance of research to identify effective AI applications and address disparities in access and guidance, particularly for higher-poverty schools. -
Summary of https://hai-production.s3.amazonaws.com/files/hai-issue-brief-expanding-academia-role-public-sector.pdf
Stanford HAI highlights a growing disparity between academia and industry in frontier AI research. Industry's access to vast resources like data and computing power allows them to outpace universities in developing advanced AI systems.
The authors argue that this imbalance risks hindering public-interest AI innovation and weakening the talent pipeline. To address this, the brief proposes increased public investment in academic AI, the adoption of collaborative research models, and the creation of new government-backed academic institutions. Ultimately, the aim is to ensure academia plays a vital role in shaping the future of AI in a way that benefits society.
Academia is currently lagging behind industry in frontier AI research because no university possesses the resources to build AI systems comparable to those in the private sector. This is largely due to industry's access to massive datasets and significantly greater computational power.Industry's dominance in AI development is driven by its unprecedented computational resources, vast datasets, and top-tier talent, leading to AI models that are considerably larger than those produced by academia. This resource disparity has become a substantial barrier to entry for academic researchers.For AI to be developed responsibly and in the public interest, it is crucial for governments to increase investment in public sector AI, with academia at the forefront of training future innovators and advancing cutting-edge scientific research. Historically, academia has been the source of foundational AI technologies and prioritizes public benefit over commercial gain.The significant cost of developing advanced AI models has created a major divide between industry and academia. The expense of computational resources required for state-of-the-art models has grown exponentially, making it challenging for academics to meaningfully contribute to their development.The growing resource gap in funding, computational power, and talent between academia and industry is concerning because it restricts independent, public-interest AI research, weakens the future talent pipeline by incentivizing students to join industry, and can skew AI policy discussions in favor of well-funded private sector interests. -
Summary of https://arxiv.org/pdf/2502.12447
Explores the rapidly evolving influence of Generative AI on human cognition, examining its effects on how we think, learn, reason, and engage with information. Synthesizing existing research, the authors analyze these impacts through the lens of educational frameworks like Bloom's Taxonomy and Dewey's reflective thought theory.
The work identifies potential benefits and significant concerns, particularly regarding critical thinking and knowledge retention among novices. Ultimately, it proposes implications for educators and test designers and suggests future research directions to understand the long-term cognitive consequences of AI.
Generative AI (GenAI) is rapidly reshaping human cognition, influencing how we engage with information, think, reason, and learn. This adoption is happening at a much faster rate compared to previous technological advancements like the internet.While GenAI offers potential benefits such as increased productivity, enhanced creativity, and improved learning experiences, there are significant concerns about its potential long-term detrimental effects on essential cognitive abilities, particularly critical thinking and reasoning. The paper primarily focuses on these negative impacts, especially on novices like students.GenAI's impact on cognition can be understood through frameworks like Krathwohl’s revised Bloom’s Taxonomy and Dewey’s conceptualization of reflective thought. GenAI can accelerate access to knowledge but may bypass the cognitive processes necessary for deeper understanding and the development of metacognitive skills. It can also disrupt the prerequisites for reflective thought by diminishing cognitive dissonance, reinforcing existing beliefs, and creating an illusion of comprehensive understanding.Over-reliance on GenAI can lead to 'cognitive offloading' and 'metacognitive laziness', where individuals delegate cognitive tasks to AI, reducing their own cognitive engagement and hindering the development of critical thinking and self-regulation. This is particularly concerning for novice learners who have less experience with diverse cognitive strategies.To support thinking and learning in the AI era, there is a need to rethink educational experiences and design 'tools for thought' that foster critical and evaluative skills. This includes minimizing AI use in the early stages of learning to encourage productive struggle, emphasizing critical evaluation of AI outputs in curricula and tests, and promoting active engagement with GenAI tools through methods like integrating cognitive schemas and using metacognitive prompts. The paper also highlights the need for long-term research on the sustained cognitive effects of AI use. - Visa fler
