AI Edge Issue 3 – Open Models, Superintelligence & AI’s Next Leap

Alo. Everything about Al by °y GeskyAnts JULY 2025 —_—_ a: Huy i \ | Als Next Leap: Explores open models, agents,and timodal Al advances Meta’s | lind: How Metais pushing toward superintelligent Al systems | Global Al Governance: Urgency and fragmentation in Al policy around the world PREFACE Intelligence moves quietly through the systems we build, shaping patterns that guide how the world now operates. It settles into processes, decisions, and routines, altering their logic in ways that feel both seamless and irreversible. This issue of Al Edge follows that movement with close attention and deliberate care. It begins in research spaces where reasoning is carefully engineered, then extends to the expanding realities of medicine, governance, and human work. The chapters examine open models spreading across networks, agents designed to act with autonomy, and architectures that bring language, vision, and interaction into the same evolving frame of function and design. The purpose is to document this transformation as it steadily unfolds and deepens. These essays trace its progression through infrastructure and design, observing how intelligence enters systems, shapes intention, and gradually expands its reach across industries, embedding itself deeper into frameworks that define the rhythm of contemporary life. The Minds Behind the Magazine This Issue Wouldn't Exist Without The Dedication And Support Of The Incredible Individuals Who Helped Shape It. We're Grateful For Their Contributions And Belief In Our Mission To Inform, Inspire, And Connect. Saurabh Sahu CTO (Delivery) Gaurav Gupta Software Engineer III Sk Hapijul Hossen Al/ML Engineer |! Takasi Sandeep Tech Lead | Vidish Sirdesai Al/ML Engineer| © 2025 GeekyAnts. All rights reserved. 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For permissions or inquiries, please contact magazine@geekyants.com TABLE OF CONTENTS Editor's Notes Al shifts from support role to system backbone Al Tool of the Month ChatGPT Agents: Smart, autonomous Al helpers Al's Next Leap Explores open models, agents, and multimodal Al advances Meta's New Mind How Meta is pushing toward superintelligent Al systems How machines get personal From content-based to deep learning and generative Al Global Al Governance Urgency and fragmentation in Al policy around the world Data-Driven Healing Al's role in transforming healthcare and treatment delivery Cache-Augmented Generation How CAG improves Al inference speed vs traditional RAG End Tokens Al's soft side — humor, hype, and hot takes O1 02 03 09 11 17 21 25 29 O1 Editor’s Notes There are moments in technology that feel less like progress and more like a shift in gravity. This issue of Al Edge explores such a moment. Intelligence has moved from the margins to become the foundation of what we create. It is no longer an accessory to human thought; it operates as an active layer in decisions, economies, and design. The opening feature examines Meta’s creation of its Superintelligence Lab, a move that reflects an ambition to build systems capable of reasoning on scales beyond human ability. This development invites questions about control, responsibility, and the frameworks that define cognition when algorithms hold agency. Beyond this story lies a wider transformation. Open-source models spread the capability to every corner of the technology ecosystem. Autonomous agents execute tasks once thought to require human judgment. Multimodal systems combine sight, sound, and language into seamless intelligence. Innovation now moves in intervals measured not by years but by weeks. Healthcare becomes a second lens. Al- enabled precision medicine is already shaping treatment strategies and improving clinical outcomes through data-driven insight. Alongside these advances stands the unresolved question of governance. Regulatory structures remain fragmented, while the systems they seek to guide grow more sophisticated. This issue also studies the mechanics behind progress. Cache-augmented generation reduces latency in knowledge retrieval, and agentic systems like ChatGPT Agents extend automation into areas once closed to machines. Together they signal a future defined by context, continuity, and adaptive capability. These pages are written for those who need clarity in an accelerating world. Read closely. The next chapter of intelligence is already here. Al Tool of the Month GOpenAl The newest breakthrough from OpenAl, ChatGPT Agents, is redefining what it means to have a personal Al assistant. More than Just a conversational model, these agents are autonomous, highly customizable, and capable of executing multi-step workflows across a range of applications. They mark a shift from static Al tools to intelligent, task- oriented collaborators. Key Features *« Autonomous Task Execution: Agents can perform multi-step actions without constant human prompts, making them ideal for complex workflows. ¢ Customizable Behaviours: Tailor agents with specific skills, instructions, and integrated tools to align perfectly with personal or business needs. * Tool and API Integration: Seamlessly connect agents with external APIs, databases, or productivity platforms for end-to-end process automation. * Memory & Context Awareness: Agents remember context across sessions, enabling continuity and personalised interaction for recurring tasks. Agents What Makes ChatGPT Agents Stand Out ChatGPT Agents are built for autonomy, adaptability, and scale. Unlike traditional chatbots, they do more than answer questions —they execute tasks intelligently. From managing schedules and drafting reports to interacting with third-party APls, these agents act like digital team members. Their flexibility allows businesses to create agents specialised for areas such as customer service, research, or operations. When combined with OpenAl’s advanced reasoning models, they enable real-world automation at a level previously unimaginable. Real-world Use Cases ¢ Business Automation: Handle repetitive workflows such as invoice generation, CRM updates, and email campaigns without human intervention. * Customer Support: Deploy agents as first- line responders, resolving queries and escalating only when necessary. * Research & Analysis: Extract insights from documents, summarise reports, and generate actionable recommendations. * Personal Productivity: Schedule meetings, manage reminders, and even draft documents tailored to your tone and style. O02 03 AI'SNEXTLEAP ° The Accelerating Al Landscape: Open Models, Intelligent Agents, and the Future of Human-Computer Interaction Gaurav Gupta (Software Engineers II!) rtificial intelligence development has reached a point where the pace of innovation threatens to outstrip our ability to comprehend its implications. Daily model releases, breakthrough research papers, and unexpected capabilities emerge from laboratories worldwide, creating a landscape that shifts faster than traditional technology cycles. This acceleration presents both unprecedented opportunities and significant challenges for researchers, developers, and companies attempting to harness Al's potential. The current Al ecosystem reflects a fundamental transformation in how we approach machine intelligence. Where previous technological revolutions unfolded over decades, Al capabilities now evolve within months or weeks. This compressed timeline has forced industry participants to reconsider their strategies, from research methodologies to product development cycles. The traditional approach of careful, incremental progress has given way to rapid experimentation and deployment. Understanding this velocity requires examining the key forces driving change: the democratisation of Al through open-source models, the emergence of autonomous agents, widespread integration into existing tools, and the expansion into multimodal capabilities. These developments collectively represent a shift from Al as a niche tool to Al as a foundational technology layer that will reshape virtually every aspect of human-computer interaction. * Al’S NEXT LEAP The Open Source Revolution in Large Language Models The artificial intelligence landscape has witnessed a dramatic shift toward open-source development, fundamentally altering the competitive dynamics that once favoured proprietary systems. Meta's LLaMA 3 model exemplifies this transformation, delivering performance levels that rival OpenAl's GPT-4 while maintaining accessibility for researchers and developers worldwide. This achievement represents more than technical progress; it signals a broader movement toward accessibility of advanced Al capabilities. The emergence of models like Falcon, Mistral, and Mixtral has created a robust ecosystem of alternatives to closed-source systems. These models offer distinct advantages for enterprises that require local deployment or highly tailored solutions. Their performance characteristics strike a balance between computational efficiency and capability, making them practical choices for resource- constrained environments. This equilibrium has been instrumental in enabling widespread adoption across diverse and demanding use cases. Emad Mostaque, founder of Stability Al, has compared the rise of open models in Al to the impact Linux had on operating systems. This perspective underscores the potential of open- source Al to challenge established hierarchies, lower barriers to entry, and accelerate innovation through collaborative development. The implications extend beyond technical considerations to include economic and strategic factors that will shape the global distribution of Al capabilities. AI'SNEXTLEAP ° The Evolution of Autonomous Al Agents The progression from conversational Al to autonomous agents represents a qualitative leap in machine intelligence capabilities. These systems transcend simple question-answering to engage in complex reasoning, planning, and environmental interaction. OpenAl's Auto-GPT and Google's Agents-to-Agents (A2A) framework demonstrate how Al can autonomously navigate multi-step tasks, make decisions based on contextual information, and adapt to changing circumstances. Real-world applications of Al agents have already begun transforming business operations. Customer support represents a particularly compelling use case, where Al-powered agents now resolve over 60% of Tier 1 tickets without human intervention, according to recent Zendesk data. () Context * Addons This capability extends beyond simple troubleshooting to include complex problem-solving that requires understanding context, accessing multiple information sources, and providing tailored solutions. The implications of autonomous agents extend far beyond efficiency gains. These systems introduce new possibilities for how humans interact with technology, potentially eliminating the need for traditional interfaces in favour of natural language instruction and delegation. As agents become more sophisticated, they may fundamentally alter the relationship between human intent and technological execution, moving from explicit command-based interaction to collaborative problem-solving partnerships. vx Variables 6) Integration and Ubiquity in Everyday Tools The integration of Al capabilities into established software platforms has accelerated dramatically, transforming familiar tools into intelligent assistants. Microsoft Copilot's integration with Word, Notion's Al-powered workspace features, and similar implementations across the software ecosystem reflect a broader trend toward embedding intelligence directly into existing workflows. This approach reduces friction for users while maximising the practical impact of Al Capabilities. McKinsey research indicates that 79% of knowledge workers have utilised some form of Al tool within the past year, representing a significant increase from 54% in the previous year. This adoption pattern suggests that Al integration has moved beyond experimental phases into mainstream productivity applications. The speed of this adoption reflects both the maturity of underlying technologies and the effectiveness of seamless integration approaches. Current developments in this space include Claude-powered tools through Amazon Bedrock, ChatGPT's memory feature for tailored assistance, and Zapier's Al agents that automate complex workflows through natural language prompts. These implementations demonstrate how Al can enhance human capabilities without requiring extensive technical expertise or significant workflow modifications. The success of these integrations depends on their ability to augment rather than replace human judgment and creativity. Amazon Bedrock * Al'S NEXT LEAP Multimodal Capabilities and Embodied Intelligence The expansion of Al beyond text processing into visual, auditory, and physical domains marks a crucial evolution in machine intelligence. OpenAl's GPT-40 (omni) model introduced comprehensive multimodal capabilities, combining real-time vision, speech processing, and reasoning within a single system. This integration enables more natural interactions and opens possibilities for applications that require understanding across multiple sensory modalities. The convergence of language models with robotics has created new opportunities for embodied Al systems. Companies like Tesla and Boston Dynamics have begun integrating large language models into physical robots, enabling dynamic task execution based on natural language instructions. This development represents a significant step toward robots that can understand complex instructions, adapt to changing environments, and perform tasks that require both physical capability and cognitive reasoning. These multimodal developments extend beyond robotics to encompass applications in autonomous vehicles, smart home systems, and industrial automation. The ability to process visual information, understand spoken commands, and generate appropriate responses across multiple modalities creates possibilities for more intuitive and flexible Al systems. As these capabilities mature, they will likely enable new forms of human- Al collaboration that leverage the strengths of both biological and artificial intelligence. AI'SNEXTLEAP ° Emerging Developments and Future Directions The Al development pipeline continues to generate innovations that will shape the technology's trajectory. Anthropic's anticipated Claude 3.5 Vision release promises enhanced image reasoning and summarisation capabilities, while Hugging Face's testing of Inference Endpoints for Agents could significantly impact how developers deploy intelligent workflows. These developments reflect ongoing efforts to make Al capabilities more accessible and practical for real-world applications. The surge in Al-generated video capabilities, demonstrated by platforms like Sora from OpenAl and Kling from Alibaba, represents another significant frontier. These systems can create sophisticated visual content from text descriptions, ~. Hugging Face NEW) Al Tools are now available in HuggingChat The Al community building the future. opening new possibilities for content creation, education, and entertainment. The implications extend beyond creative applications to include training simulations, educational materials, and tailored media experiences. Practical implementation of these advances requires tools and frameworks that enable widespread adoption. The combination of LangChain with ChromaDB for building Al-powered research assistants, or Zapier with Claude for automated content curation, demonstrates how existing tools can be enhanced with Al capabilities. These implementations allow businesses to leverage advanced Al without requiring extensive technical infrastructure or deep expertise. Models Datasets Spaces Posts Docs Solutions Pricing = Log In Sign Up * Al’S NEXT LEAP Strategic Implications and Technological Maturation The rapid evolution of Al capabilities has created both opportunities and challenges for companies attempting to incorporate these technologies into their operations. The pace of change requires adaptive strategies that can accommodate continuous technological advancement while maintaining operational stability. This balance proves particularly challenging given the compressed timeline of Al develooment compared to traditional technology adoption cycles. The growing availability of open-source Al models has fundamentally altered competitive dynamics across industries. Organisations can no longer depend solely on proprietary systems for differentiation, as advanced alternatives are now widely accessible and continually improving. In this environment, success depends less on owning the most powerful model and more on the ability to implement effectively, adapt solutions to specific business contexts, and ensure seamless integration across existing technology stacks. The emergence of autonomous agents and multimodal capabilities positions Al as a foundational layer within modern technology ecosystems. This evolution compels businesses to reevaluate their architectural strategies and redefine patterns of human-Al interaction. Successful integration will rely on building systems that enhance human capability while preserving strong oversight and control. O09 METASNEWMIND ° Meta’s Superintelligence: The Cost of Building a New Mind Boudhayan Ghosh Technical Content Writer rtificial intelligence is approaching a threshold where the artificial becomes indistinguishable from the authentic. A model so advanced it appears indistinguishable from human reasoning. Meta’s launch of the Superintelligence lab is an ambition of that kind. Superintelligence refers to systems capable of autonomous thought, shaped not by imitation but by accumulation, association, and inference. It implies a shift is. et from tools that serve tasks © . *), to architectures that generat BS _ understanding. Intelligence, *_ - inthis frame, is layered, é recursive, and difficult to delimit. What emerges is not a mind in the human sense, but something that holds direction, memory, and decision within a - single evolving system. Ask Meta Al Anything Meta’'s New Mind In a memo to employees, Mark Zuckerberg announced the creation of Meta Superintelligence Labs (MSL), a new division that will consolidate all of Meta’s Al initiatives. The group will be led by Alexandr Wang, former CEO of Al training data company Scale Al, who will assume the newly established role of Chief Al Officer at Meta. Creating a rudimentary Al system that can be your shopping or writing assistant is not the pursuit; the focus is on building models capable of reasoning, planning, and decision-making at levels exceeding human capabilities. The lab also aims to embed advanced Al into Meta's products, including social media platforms, and Al-powered devices like smart glasses. The urgency of this grand vision is palpable. Meta’s aggressive hiring gives a clear picture that the superintelligence dream is not a distant ambition but an inevitability they want to make inevitable. As of now, Meta has pulled senior researchers from OpenAl, Google DeepMind, and Anthropic— individuals behind foundational models like GPT-40 and Gemini. Their expertise defines the frontier Meta now intends to command. To support its superintelligence ambitions, Meta has invested $14.3 billion in Scale Al, acquiring a 49% stake in the startup. This investment not only brought Wang into the fold but also provided Meta with critical data-labelling infrastructure essential for training advanced Al models. * METAS NEW MIND Toward the Summit: AGI A Reputation Under Review The question now is not whether Al will accelerate, The pessimism might seem too poetic to be true, but how it will reshape the competitive landscape but Meta’s books invite scrutiny for a reason. The —and with it, the world. The Al race seems to have company has faced multiple regulatory fines and a clear summit as of now, “Artificial General legal actions in recent years, including a record Intelligence”, a system capable of self-awareness €1.2 billion penalty from the EU for violating and meta-cognition, the ability to interpret GDPR, a $725 million settlement over the abstractions, more autonomy, and adaptability, Cambridge Analytica scandal, and VAT evasion basically a new kind of mind made of algorithms, investigations in Italy amounting to nearly €887.6 not neurons. million. Some significant connotations come with the In 2024, Meta drew further criticism for a $2.9 discovery of this brave new mind. They lead to two billion accounting adjustment tied to Al roads: one that is positive, a new world of infrastructure, a move seen by analysts as profit- cognitive evolution, a foundation and formulation boosting without corresponding revenue growth. of non-biological intelligence, and the plethora of Coupled with ongoing lawsuits and warnings from possible knowledge and opportunity it offers, a European and U.S. regulatory bodies, these metamorphosis that defines the zeitgeist of our patterns raise serious questions about financial times. transparency and ethical accountability. The other road leads to bleakness and doubts. A The Mind Yet to Speak highly intelligent power concentrated in the wrong hands. A society of subtle dystopia. Of confusion Meta has made bold promises earlier, like and contrasts, superimposing coherence and Zuckerberg’s ambitious metaverse project, that clarity. From privacy to truth, all repudiated. quietly receded into the background ever since Al took the spotlight. With their focus now on Al, from Llama models to superintelligent systems, the intricacies ahead remain to be seen. Meta has = pledged to open-source its innovation in the name Talk or type... cp) @ | 1 Stop of transparency and shared virtuosity, but whether that spirit holds as the stakes rise is a question still unanswered. Ask Meta Al Anything Where should we start? + Ask Meta AL... I! Let's get to know eech other 10 Learh how to use Al HOW MACHINES GETPERSONAL ° “You Might Also Like This”: The Evolution of Recommender Systems Vidish Sirdesai : (Al/ML Engineer 1) ver scrolled through Netflix and wondered how it knew you would enjoy that obscure thriller from 2013? Or stumbled upon the perfect product on Amazon before you even searched for it? That subtle nudge—the one that feels eerily accurate Aeroready Training T-shirt —is powered by what we now know as recommender systems. These are not just We've also got these for you complex algorithms; they are the automated — tools that efficiently guide your choices within a huge set of available options. Recommender systems have become essential tools for navigating the endless digital options that surround us. They work by filtering vast volumes of information to deliver just a handful of highly personalised suggestions, tailored to what you are most likely to click, watch, buy, or enjoy. The result is an experience that often feels effortless—and sometimes, almost telepathic. But how did these systems evolve from basic item-sorting utilities to the predictive powerhouses integrated into nearly every platform we use? The story spans decades of innovation, adaptation, and a growing understanding of how humans make decisions in digital spaces. You Might Also Like This | $49.99 edocs: $39.99 « totototk $24.99 »totototot: $19.99 0 ttt $34.99 etotictook $29.99 « "Perfect for summer!” "Great fit and style.” “Keeps the sun out.” "Stylish and effective." "Comfortable andchic.” "Ideal for * HOW MACHINES GET PERSONAL The First Wave: Content-Based Filtering In the early days of the internet, from the 1990s through the early 2000s, digital platforms struggled with discoverability. There was simply too much content and not enough intelligence guiding users through it. To address this, engineers developed the earliest form of recommender %, " systems: content-based filtering. we? ’ * eo, The logic was simple. If you watched science ul fiction films, the system would recommend more | science fiction. If you liked reading about artificial Movie 1 Movie 2 Movies Mevie 4 intelligence, it would point you toward similar articles. These systems worked by matching the characteristics of items you had already enjoyed with others that shared similar features. Similar Movies This approach was efficient and especially good at serving users with specific, consistent tastes. But it had blind spots. If your preferences changed or if there was something entirely new you might enjoy —something not related to your past behaviour— the system struggled to keep up. Many users found themselves stuck in feedback loops, circling the same genres and topics without you . 12 13 onitem features The Social Shift: Collaborative Filtering As platforms grew and user bases expanded, a more powerful method emerged: collaborative filtering. Unlike its content-based predecessor, collaborative filtering focused less on the items themselves and more on the behaviours of users. Its premise was elegantly simple: if two users shared similar preferences, then one user's new favourite could become the other's next discovery. If you and another user both loved three particular films, and that user also loved a fourth you had not seen, chances were you would enjoy it too. Collaborative Filtering a Oe Elete) } ive ecommendation Recommends items ~ ‘liked by similar users Recommends based on user behavior This method revolutionised online recommendation. Suddenly, systems were not just telling you what was similar—they were showing you what was popular among people with tastes like yours. It introduced spontaneity and surprise, creating space for the serendipitous discovery of things you might never have searched for on your own. Still, collaborative filtering came with challenges. The cold start problem made it difficult to serve new users or recommend brand-new items. The sparsity problem—too little data across a massive set of user-item pairs—also proved a hurdle. Yet despite these flaws, collaborative filtering quickly became the engine behind early successes at Amazon and Netflix, changing how people shopped, watched, and listened online. * HOW MACHINES GET PERSONAL Scaling Up: Matrix Factorisation and the Big Data Boom Between the mid-2000s and early 2010s, the internet exploded with activity. Suddenly, platforms were processing data from millions of users across billions of interactions. Traditional collaborative filtering methods were no longer scalable. This ushered in a new era built around matrix factorisation. Imagine a massive spreadsheet with users as rows, items as columns, and each cell representing a rating or interaction. Matrix factorisation techniques broke this giant matrix down into smaller, latent representations— essentially uncovering hidden “taste profiles” of users and the “styles” or “themes” of items. These techniques could infer patterns even when explicit connections were missing. Perhaps you and another user shared a subtle appreciation for quirky indie films with complex female leads, without either of you ever stating it outright. Matrix factorisation helped uncover those invisible affinities, improving the accuracy and depth of recommendations. This approach was central to the famous Netflix Prize, a competition that challenged researchers to improve the platform's recommendation accuracy. It helped solidify matrix factorisation as a core technique in large-scale recommender systems. HOW MACHINES GETPERSONAL ° The Intelligence Era: Deep Learning Takes Over Since the 2010s, deep learning has dramatically expanded what recommender systems can do. These models moved beyond surface-level data, learning from vast arrays of signals, many of which users never consciously provide. Modern systems now track implicit behaviours: how long you watch a video, which scenes you rewatch, what time of day you browse, your device type, your location, even the tone of your comments. This context-rich input helps build a multidimensional understanding of who you are and what you might want next. Deep learning architectures can model complex, non-linear relationships between users and content. They can understand the plot of a movie, the mood of a song, or the texture of a jacket, and match it to your preferences with eerie precision. Models like neural collaborative filtering and reinforcement learning-based recommenders continuously refine their suggestions based on engagement and feedback, often in real time. These systems have unlocked new capabilities: * Understanding nuanced patterns in user behaviour + Adapting recommendations within a single session « Offering diverse, not just similar, content + Handling cold-start issues using auxiliary data sources For the user, it means a system that responds not just to what you have liked in the past, but to what you might like in the moment, as your context evolves. 1 What Comes Next: Transparency, Causality, and Generative Intelligence The frontier of recommender systems is shifting once again. Today's researchers are focused not only on accuracy but also on fairness, explainability, and robustness. There is growing interest in causal recommendation models, which aim to understand the “why” behind a user's preferences, not just the “what.” Instead of merely correlating your behaviour with others, these systems seek to learn the underlying reasons that lead to a positive engagement. This could lead to more meaningful, less biased suggestions. Meanwhile, generative Al is beginning to reshape the user experience. Imagine a system that not only recommendsa film but also writes a short plot synopsis tailored to your unique interests. Or one that generates a playlist introduction in a tone and style that resonates with your mood. These are not hypotheticals—they are already being tested. There is also increasing investment in multimodal recommendation systems—models capable of seamlessly handling text, images, audio, and video. A future recommendation engine might suggest a video, an article, and a product, all tied to the same underlying interest or emotional tone. * HOW MACHINES GET PERSONAL Where Discovery Is Engineered From the early days of “people who bought this also bought that” to the neural engines of today, recommender systems have profoundly shaped how we discover the world online. They make our experiences smoother, more intuitive, and often more enjoyable, though not without ethical questions about influence and autonomy. The next generation of these systems will not just predict your preferences. They may help shape them, mediate them, or even create entirely new ones. As they continue to grow smarter, more context-aware, and more generative, the boundary between browsing and being guided will continue to blur. What once felt like a helpful suggestion is fast becoming a conversation between you and a system that learns more with every click, every scroll, every decision. Whether we notice it or not, these systems are becoming part of the rhythm of how we live. And chances are, they already know what we might like next. GLOBAL AlGOVERNANCE ° The Global Al Governance Crisis: Why We Need Rules Before It's Too Late he world is building its most powerful technology without a rulebook. While artificial intelligence systems influence elections, guide military decisions, and reshape entire economies, governments remain divided on fundamental questions: What constitutes ethical Al? Who determines acceptable risk? How do we prevent technological authoritarianism? This regulatory vacuum poses a direct threat to democratic institutions and processes. Every day, without coordinated oversight, Al systems grow more sophisticated while accountability mechanisms lag further behind. The Price of Fragmentation The global Al governance landscape remains fragmented, with each region taking a different path. The United States continues to prioritise innovation over regulation. China pursues rapid deployment under state control. The European Union has taken a more cautious, rights-based approach, anchored in its sweeping Al Act. That approach entered a new phase in July 2025, when the European Commission introduced a Voluntary Code of Practice for general-purpose Al systems. The code focuses on transparency, safety, security, and copyright compliance, offering developers early guidance ahead of the Act’s enforcement deadlines. But participation is optional, and several leading European companies, including Sie e' SAP, and Mistral, have publicly called for a delay. They argue that regulatory ambiguity a d hig ' compliance costs could harm European competitiveness, particularly for startups open-source developers. = Despite the pressure, the Commission has held firm. Enforcement of the Al Act will begin in August 2025 for general-purpose models and August 2026 for high-risk applications. Officials have acknowledged the concerns and committed to refining the implementation strategy, but they have not altered the timeline. Meanwhile, dozens of low- and middle-income countries lack the institutional capacity to shape these standards or enforce their own. This asymmetry reinforces the divide between rule- makers and rule-takers, and it risks locking large parts of the world out of meaningful influence over how Al is governed. This regulatory fragmentation creates dangerous incentives. Companies increasingly relocate Al development to jurisdictions with looser oversight. Nations compete to attract investment by relaxing ethical safeguards. The result is a race to the bottom—one that undermines trust, safety, and long-term stability across the ecosystem. * GLOBALAlGOVERNANCE The Ethics Maze Cultural differences complicate global coordination. Privacy expectations vary dramatically across societies. Surveillance tolerance differs between democracies and authoritarian states. Even basic concepts like fairness and accountability carry distinct meanings across legal traditions. Consider facial recognition technology: heavily restricted in Europe due to privacy concerns, widely deployed across Asia and Africa for surveillance purposes. These divergent approaches reflect deeper philosophical differences about the balance between security and liberty, collective benefit and individual rights. UNESCO's Al Ethics Framework represents progress toward shared principles, yet enforcement remains voluntary, and interpretation varies wildly. Over 60 countries have published national Al strategies, yet fewer than 15 include enforceable ethics requirements. eens oat Pe. / | ialbovenn al Hf tnetn.a UK Serkom ae E| Tas aE SEE —_: in § & Power Concentration Structural Obstacles A handful of actors dominate Al governance decisions, creating democratic deficits that undermine legitimacy. Major technology companies—OpenAl, Google DeepMind, Three fundamental tensions complicate global Al coordination: ¢ Speed versus deliberation: Al capabilities 19 Anthropic, Meta—often release powerful models before regulators understand their implications. These firms often outpace governments in Al expertise, affording them disproportionate influence over regulatory discourse. Government blocs like the EU and the United States shape international norms through market power and regulatory leadership. Multilateral forums, including the G7's Hiroshima Process and the Global Partnership on Al, facilitate dialogue among wealthy nations while marginalising developing countries. This concentration of influence creates governance by the few for the many—a dangerous precedent for technology that affects everyone. advance faster than democratic processes can respond. By the time legislation passes, technology has evolved beyond regulatory scope. This temporal mismatch favours rapid deployment over careful consideration. Sovereignty versus standardisation: Nations want domestic control over Al development while benefiting from international cooperation. Balancing national interests with global coordination requires a delicate compromise that proves difficult to achieve. Private versus public authority: Technology companies command resources and talent that exceed most government capacities. This imbalance shifts power from democratically accountable institutions to corporate entities with different incentives and responsibilities. A Framework for Progress Despite these challenges, meaningful global Al governance remains achievable through three strategic approaches: ¢ Establish minimum global standards: Create binding international agreements on Al safety, bias mitigation, and transparency. These digital human rights would provide baselines that nations can exceed while ensuring universal protection. ¢ Develop interoperable governance: Design frameworks allowing different countries to maintain sovereignty while adhering to shared protocols. International banking standards demonstrate how such systems can work across diverse legal and cultural contexts. ¢ Ensure inclusive participation: Expand decision-making beyond wealthy nations and major corporations. Low- and middle-income countries, Indigenous communities, and historically marginalised groups must have meaningful voices in shaping technologies that affect their lives. * GLOBALAlGOVERNANCE Learning from Success Several initiatives demonstrate effective approaches to Al governance coordination: The EU Al Act provides a risk-based regulatory framework that Brazil and Canada are adapting to their contexts. The U.S.-UK Joint Al Safety Institutes focus on aligning safety testing and sharing results transparently. The African Union's draft Al strategy prioritises inclusive development, local languages, and ethical applications tailored to regional needs. These examples show that coordination is possible when nations commit to shared principles while respecting local priorities. The Moment of Decision Global Al governance represents more than regulatory housekeeping—it determines whether artificial intelligence serves humanity or dominates it. The choices made today will echo for generations. Policymakers must act decisively. Balance innovation with responsibility. Pursue sovereignty within solidarity. Address local needs through global cooperation. The alternative is a world where the most transformative technology in history develops without democratic oversight or ethical constraint. The time for voluntary guidelines and aspirational frameworks has passed. The world needs binding agreements, enforceable standards, and inclusive institutions capable of governing Al in the public interest. Democracy depends on it. 20 DATA-DRIVEN HEALING ° Al-Enabled Precision In Healthcare Sk Hapijul Hossen (AI/ML Engineer !) patient walks into an oncology clinic. Within hours, artificial intelligence has analysed their tumour's genetic signature, cross-referenced thousands of similar cases, and identified the treatment most likely to succeed based on their unique biological profile. The days of standardised, one-size-fits-all medicine are rapidly ending, replaced by care that adapts to each individual's genetic makeup, lifestyle, and environment. The convergence of Al and precision medicine creates unprecedented opportunities to improve patient outcomes while fundamentally reshaping how healthcare organisations operate. As this technology matures from experimental applications to proven clinical tools, its impact extends far beyond individual patient care to encompass entire healthcare systems, research methodologies, and business models. Medicine: A Strategic Shift Understanding the Technology Precision medicine seeks to customise healthcare by considering the unique characteristics of each patient. When enhanced by Al, this approach becomes exponentially more powerful. Machine learning algorithms can analyse vast datasets—genomic sequences, electronic health records, imaging studies, and real-time biometric data—to identify patterns invisible to human observation. The scale of this analysis is staggering. A single genomic profile contains approximately 3 billion base pairs of DNA, while a typical patient generates thousands of data points throughout their healthcare journey. Al systems can process these enormous datasets in minutes, identifying correlations and predicting outcomes with remarkable accuracy. Consider mammography screening, where Al- assisted analysis has demonstrated the ability to reduce false positives by 5.7 percent while maintaining diagnostic accuracy (JAMA, 2023). This improvement translates directly to reduced patient anxiety, fewer unnecessary procedures, and more efficient use of healthcare resources. Clinical Applications and Outcomes The theoretical promise of Al-enabled precision medicine has evolved into measurable clinical benefits across multiple specialities. In oncology, Al algorithms analyse tumour DNA and biomarker patterns to guide treatment selection, resulting in up to 20 percent improvement in survival rates for certain cancer types (Nature, 2023). These systems can predict which patients will respond to specific therapies, eliminating the trial-and-error approach that has historically characterised cancer treatment. Cardiology has witnessed equally impressive advances. Predictive models incorporating Al analysis of electrocardiograms, imaging studies, and patient history achieve 87 percent accuracy in forecasting cardiac events (The Lancet, 2024). This capability enables preventive interventions that can avert heart attacks and strokes before they occur. Neurology presents perhaps the most compelling case for Al-enhanced precision medicine. Early detection of Alzheimer's disease, traditionally dependent on symptomatic presentation, now achieves 92 percent accuracy through Al analysis of brain imaging, cognitive assessments, and genetic markers (NIH, 2024). This early identification opens treatment windows that were previously impossible to access. The field of diabetes management exemplifies how Al can optimise treatment protocols. By analysing genetic variations that affect drug metabolism, a practice known as pharmacogenomics, Al systems guide medication selection and dosing with 33 percent greater effectiveness than standard approaches (Diabetes Care, 2023). * DATA-DRIVEN HEALING Market Dynamics and Growth The economic implications of this technological shift are substantial. The global precision medicine market is projected to expand from $102 billion in 2024 to $463 billion by 2034, with Al-driven segments experiencing compound annual growth rates of 36 percent (Statista, 2024). This expansion reflects both the clinical value of these technologies and their potential to create new revenue streams. Companies that establish early positions in this market stand to benefit from several competitive advantages. Al-powered drug discovery can reduce development timelines by up to 50 percent, significantly lowering the costs associated with bringing new treatments to market (McKinsey, 2023). Clinical trials enhanced by Al demonstrate improved success rates and faster regulatory approval processes. The emergence of new business models further amplifies these opportunities. Digital therapeutics platforms, Al-driven diagnostic services, and real-time monitoring systems represent entirely new categories of healthcare solutions. Companies like Tempus and Deep Genomics have built substantial valuations by developing Al-powered platforms that serve both clinical and research markets. Stats Character Usage ipti This shows usage for the selected Copilot fro... 1.06M 4.0m 2 o\( « o) @ a Total Copilots Total Users Text e T 12 Data Sources 14.27M Char 5 212 Ss) Document 2D. uces ——s—sés«S 32K ‘Char @ @ “ ® & © Notion Total Data Sources Total Threads W 4 124 240 o* OD Show Moré Copilot Activity @ 100 80 £ 60 é £ 40 20 0 12 Jun Last Syne 12 mins ago Hi, How can | help you? | can perform following actions for you What are trending outfits for boys? Men’s Jeans Show me cargo pants with more pockets 4 Jun 15 Jun 16 Jun Sat Sun Mon Tues13J 13Jun Type your message here.. Q. o +9 22 23 DATA-DRIVEN HEALING ° Transforming Healthcare Delivery For healthcare providers, Al-enabled precision medicine represents a fundamental shift in care delivery models. Rather than replacing clinical expertise, these systems augment human capabilities by providing decision support based on comprehensive data analysis. Clinicians gain access to evidence-based recommendations that consider the full spectrum of patient-specific factors. Emergency departments have demonstrated the practical benefits of this approach. Al-powered triage systems improve decision accuracy by 20 percent, enabling faster identification of critical cases and more efficient resource allocation (Health Affairs, 2023). These improvements translate directly to better patient outcomes and reduced healthcare costs. The administrative burden that consumes significant clinician time is also being addressed through Al automation. Documentation, coding, and scheduling tasks can be streamlined, freeing healthcare professionals to focus on direct patient care activities that require human judgment and interaction. uw Addressing Implementation Challenges The path to widespread adoption of Al-enabled precision medicine faces several significant obstacles that must be addressed strategically. Data security represents the most immediate concern, as healthcare organisations manage sensitive patient information that attracts cybercriminal attention. The average cost of a healthcare data breach ranges between $6 million and $15 million, making a robust cybersecurity infrastructure essential for any Al implementation. Algorithmic bias poses another critical challenge. Al systems trained on non-diverse datasets risk perpetuating healthcare disparities by delivering inequitable care to under-represented populations. Ongoing audits and inclusive training data collection are necessary to ensure these systems benefit all patients equally. Interoperability issues further complicate implementation. Over 60 percent of electronic health record systems cannot fully integrate Al tools, limiting the practical utility of these technologies (HIMSS, 2023). Healthcare systems must invest in infrastructure upgrades and standardisation efforts to realise the full potential of Al-enabled precision medicine. Strategic Imperatives The successful integration of Al into precision medicine requires coordinated action across multiple stakeholders. Healthcare institutions must prioritise pilot programs that demonstrate clinical value while building capabilities in data governance and Al literacy. These initiatives should focus on specific use cases where Al can deliver measurable improvements in patient outcomes or operational efficiency. Research institutions and clinicians need to develop expertise in both Al technologies and genomic medicine. This interdisciplinary knowledge base is essential for identifying appropriate applications and ensuring the responsible implementation of these powerful tools. Technology companies and investors must recognise the long-term nature of healthcare transformation while supporting innovations that address real clinical needs rather than pursuing technological novelty for its own sake. * DATA-DRIVEN HEALING Seizing the Precision Advantage The trajectory toward Al-enabled precision medicine represents an irreversible shift in healthcare delivery. As these technologies mature and demonstrate consistent clinical benefits, adoption will accelerate across all segments of the healthcare system. The institutions that begin this transition now will be positioned to lead the next phase of medical innovation. The transformation from reactive to proactive, personalised care is already underway. With Al and genomics working in concert, healthcare is moving toward continuous, predictive, and real-time care models that can intervene before conditions worsen. This evolution promises not only better patient outcomes but also more sustainable healthcare economics. The question facing healthcare leaders is no longer whether to embrace Al-enabled precision medicine, but how quickly they can adapt their institutions to capitalise on its potential. The window for strategic positioning is open, but it will not remain so indefinitely. 24 CACHE-AUGMENTED GENERATION ° From Hallucinations to Instant Vidish Sirdesai (AI/ML Engineer |) tandalone LLMs have a bit of a "Knowledge Problem’. Meaning, if a piece of information was not present in the data that was used to train them, they would not be able to recall it, which Is fine. Why? "What is not trained can never be learnt". This is the core principle that governs the entire domain of Artificial Intelligence (Al). A model not being able to recall something that it has never learnt is an acceptable explanation. But sometimes, a model may generate completely untrue results, a condition which is termed "model hallucination". The reasons for this may be, absence of the information that was being queried in the training data, or the information was not fetched in time. The second scenario is the foundation for why Cache may be the answer to address this problem of confabulation. You see, when an LLM encounters a query for which it does not have a clear, accurate answer in its parametric memory, it will “confidently’ fill in the gaps with plausible-sounding, but fabricated information. Recall: The Rise of Cache- Augmented Generation Enter RAG: Retrieval- Ausmented Generation Back in May 2020, Meta introduced RAG (Retrieval-Augmented Generation) in a paper titled "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" by Lewis et al. This was the very paper in which the term "RAG" was coined. Retrieval Augmented Generation (RAG) significantly enhances Large Language Models (LLMs) by providing them with external, up-to-date knowledge to prevent hallucination. When a user asks a question, a "retriever" first searches a curated Knowledge Base for the most relevant documents or passages. This retrieved information is then fed directly to the LLM as additional context alongside the original query. The LLM then generates its answer, relying on this precise external data rather than just its internal, potentially outdated training. This ts, in essence, how RAG works (Refer to Fig. 1 for the architecture). Search/ Embeddings Retrieve Relevant Vector Database/ Document Store Large Language Model Concatenate Query + Retrieved Context The Cost of Retrieval The problem with the above is that there is a latency that exists in the real-time retrieval process because the Knowledge Base that is used is stored externally. This "externally" could be a separate server, a database, a cloud storage, or the model may even access it via a web API. In short, the LLM queries a separate component, which is not a part of the component where the LLM is situated. This, of course, makes the entire process inefficient, bulky, and costly, and since there are so many components in the architecture, maintaining the system as a whole is not a very pleasant thing. * CACHE-AUGMENTED GENERATION Enter CAG: Cache-Augmented Generation This brings us to the current and relatively new reason for achieving the task of retrieval. Enter CAG. Cache-Augmented Generation or CAG was introduced in December 2024, in a paper titled, "Don't Do RAG: When Cache-Augmented Generation is All You Need for Knowledge Tasks" by Chan, B. J., Chen, C. -T., Cheng, J. -H., & Huang, H. -H. So, how does Cache Augmented Generation (CAG) work its magic? Well, it flips the script on traditional RAG. Instead of making your LLM frantically search for information every single time someone asks a question, CAG says, “Nope, we are doing things differently." Think of it like this: instead of cracking open a textbook mid-exam, a student has already memorised all the important bits beforehand. That is CAG in a nutshell. It preloads a stable, relevant Knowledge Base directly into the LLM's extended context window before any live queries even hit. Plus, it can precompute and store something called the "Key-Value (KV) cache" for this knowledge, which basically makes accessing that preloaded information lightning fast. Now, when you query about a thing, your LLM does not have to sit around waiting for some external retriever to retrieve facts. The information is already right there, "cached" and ready to go within its internal context and KV store. This is not just a small tweak; it slashes latency, because that time-consuming retrieval step is simply gone during inference. CAG shines brightest when you have a pretty stable Knowledge Base, fits comfortably into the LLM's context window, and when you need super-fast response times. It is a seriously streamlined way to make your LLM smarter, faster, and without the baggage of dynamic retrieval (Refer to Fig. 2 for the architecture). 26 CACHE-AUGMENTED GENERATION ° The Evolution: LLMs > RAG > CAG So, what does this journey from standalone LLMs to RAG, and now to the intriguing realm of Cache Augmented Generation, tell us? It is clear that the quest for smarter, more reliable, and ultimately faster Al is relentless. Standalone LLMs, with all their brilliance, will always grapple with the "knowledge problem’ and the occasional bout of confabulation. RAG stepped in as a powerful first responder, grounding these models in external truth, but not without introducing its own set of trade-offs in terms of latency and architectural complexity. Now, with the emergence of CAG, we are seeing another exciting evolution. By bringing that crucial external knowledge directly into the LLM's immediate grasp, we are not just reducing retrieval overhead; we are accelerating the very heartbeat of intelligent generation. It is about making the LLM not just able to find answers, but already possessing them, cached and ready to deploy at “blink-and-you-miss-it" speeds. While RAG will undoubtedly remain vital for vast, dynamic datasets, CAG offers a compelling vision for scenarios demanding instant, rock-solid accuracy from a more stable knowledge base. The landscape of Al is constantly shifting, and with innovations like CAG, it is becoming ever more efficient, ever more powerful, and undeniably, ever more intelligent. .. «te ea oe || y oe as sr. ° . _ a uN x -° oa? . tra: 84 Appendix: A Minimal Implementation of CAG in Python Setup and Dependencies To begin with, you will have to install the dependencies. To do so, run the following command in your terminal: pip install transformers torch Example: Notebook Code Next, use Jupyter Notebook (recommended) to run the following code and see the results: python from transformers import pipeline #1. Define your "Knowledge Base" knowledgebase text = """ The capital of France is Paris. The Eiffel Tower is located in Paris. The Louvre Museum is a famous art museum in Paris, France. Jupiter is the largest planet in our solar system. The speed of light in a vacuum is approximately 299,792,458 meters per second. #2. Initialize a powerful LLM print("Loading the LLM (this might take a moment)...") try: generator = pipeline( 'text-generation', model='HuggingFaceH4/ zephyr-7b-beta', torch dtype=torch.floati6, device=0 print("LLM loaded successfully!") except Exception as e: print(f"Error loading model, falling back to CPU or a smaller model: {e}") generator = pipeline('text- generation', model='distilgpt2') print("Fallback model (distilgpt2) loaded.") # 3. Formulate a query user_query = "What is the capital of France and where is the Eiffel Tower?" # 4. Simulate CAG by concatenating the knowledge base with the query cCag_input = f"Context: {knowledge_base_text}\n\nQuestion: {user_query}\n\nAnswer:" print("\n--- Generating response with preloaded knowledge (CAG-like) ---") print(f"Input to LLM: \n{cag_input}\n") response cag = generator( cag_input, max_new_tokens=50, num_return_sequences=1, do_sample=True, temperature=0.7, top p=0.9 ) print("CAG-like Response:") print(response_cag[0] [ 'generated_text']) * CACHE-AUGMENTED GENERATION # 5. Generate response without context print("\n--- Generating response without explicit context (Standalone LLM-like) ---") user_query_standalone = "What is the capital of France and where is the Eiffel Tower?" response standalone = generator( £"Question: {user _query_standalone}\n\nAnswer:" max_new_tokens=50, num_return_sequences=1, do_sample=True, temperature=0.7, top _p=0.9 ) print( "Standalone LLM-like Response:") print(response_standalone[0] [ 'generated_text']) What Is This Code Doing? The Knowledge Base is "literally" passed into the prompt. This simulates the effect of CAG, where the LLM's entire input includes the necessary context without needing a separate retrieval step during inference. Notice how the cag_input has the context directly embedded. When the generator processes this, it does not need to perform any external search; all the information is immediately available within its input window. The contrast with the "standalone" example highlights that without that immediate, preloaded context, the LLM relies solely on its internal training, which might be outdated or insufficient for specific queries. 28 ENDTOKENS ° scramble the Signal: The Al Word Play Can you decode these jumbled names of real AI tools and tech terms? 1..MGEIIN 2.AGOITLMHR 3.DOCDREE 4.PMTPOR 5.DTASTEA 6.FEFNOTROR How | experiment with the Design using Al Generated two separate images using JSON prompts in ChatGPT—one futuristic tech card and one sports-themed card. Then, using ChatGPT again. | combined elements from both to create a cohesive visual. No external tools—just prompt engineering and Al magic within a single platform. Mn =| 29 * END TOKENS Prompt of the Month Try it out and let us know This one’s for the thinkers, builders, and quiet rebels. Try it in ChatGPT, on a whiteboard, or during your next strategy sprint. This month's prompt: “What's a problem in my industry that Al hasn't solved yet — and what would it take to build it?” Flip the script: “What's a problem in my industry that shouldn't be solved by Al — and why should it remain human?’ a Al for Good Global Summit 2025 | July 8-11 | Geneva, Switzerland Organised by: United Nations' ITU (International Telecommunication Union) Theme: Al to accelerate progress toward the UN Sustainable Development Goals (SDGs) Key Takeaways: 1. Al for Global Impact: Al is being used to solve real-world challenges like disaster prediction, remote healthcare, and food security. 2. Ethical & Inclusive Al: There was a strong call for fairness, transparency, and global participation in how Al systems are built and deployed. 3. Power of Collaboration: Tech giants, governments, and UN bodies announced new partnerships to advance Al for the UN Sustainable Development Goals. 30 AIEDGE ISSUE 3] JULY’25 by ¥ GeskyAnts