AGI Future and Impact

𝑻𝒉𝒆 𝑷𝒆𝒂𝒌 𝑫𝒂𝒕𝒂 𝑪𝒉𝒂𝒍𝒍𝒆𝒏𝒈𝒆: 𝑾𝒉𝒂𝒕 𝑰𝒇 𝑾𝒆 𝑹𝒖𝒏 𝑶𝒖𝒕 𝒐𝒇 𝑻𝒓𝒂𝒊𝒏𝒊𝒏𝒈 𝑫𝒂𝒕𝒂 𝑩𝒆𝒇𝒐𝒓𝒆 𝑨𝑮𝑰? 🤔 Fascinating AI question to consider: What if we exhaust high-quality training data before achieving Artificial General Intelligence? Ilya Sutskever, OpenAI co-founder, has compared AI training data to fossil fuels - a finite resource that's rapidly being consumed. With models like GPT-4 already trained on substantial portions of internet text (estimated at ~100 petabytes total), this scenario deserves serious consideration. If we hit "peak data" before AGI: - Model improvements through traditional scaling approaches would face diminishing returns - Development timelines could extend significantly - Research priorities would shift toward efficiency rather than raw scale - Companies with proprietary data access might gain competitive advantages The industry is already exploring solutions: - Synthetic data generation (though this risks creating AI "echo chambers") - Transfer learning to maximize utility from limited datasets - Hybrid approaches combining neural networks with symbolic reasoning - Learning from human-AI interactions as a renewable data source This challenge could ultimately push AI development in more sustainable directions, prioritizing systems that learn efficiently from limited information - much like humans do. What do you think? Could data limitations become AI's biggest barrier, or will they inspire breakthrough innovations in how machines learn? #ArtificialIntelligence #AGI #MachineLearning #FutureOfTech #DataScience

📝 Announcing our paper that proposes a unified cognitive and computational framework for Artificial General Intelligence (AGI) -- going beyond token-level predictions -- one that emphasizes modular reasoning, memory, agentic behavior, and ethical alignment 🔹 𝐓𝐡𝐢𝐧𝐤𝐢𝐧𝐠 𝐁𝐞𝐲𝐨𝐧𝐝 𝐓𝐨𝐤𝐞𝐧𝐬: 𝐅𝐫𝐨𝐦 𝐁𝐫𝐚𝐢𝐧‑𝐈𝐧𝐬𝐩𝐢𝐫𝐞𝐝 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 𝐭𝐨 𝐂𝐨𝐠𝐧𝐢𝐭𝐢𝐯𝐞 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬 𝐟𝐨𝐫 𝐀𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐆𝐞𝐧𝐞𝐫𝐚𝐥 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 𝐚𝐧𝐝 𝐢𝐭𝐬 𝐒𝐨𝐜𝐢𝐞𝐭𝐚𝐥 𝐈𝐦𝐩𝐚𝐜𝐭 🔹 In collaboration with University of Central Florida, Cornell University, UT MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Toronto Metropolitan University, University of Oxford, Torrens University Australia, Obuda University, Amazon others. 🔹 Paper: https://lnkd.in/gqKUV4Mr ✍🏼 Authors: Rizwan Qureshi, Ranjan Sapkota, Abbas Shah, Amgad Muneer, Anas Zafar, Ashmal Vayani, Maged Shoman, PhD, Abdelrahman Eldaly, Kai Zhang, Ferhat Sadak, Shaina Raza, PhD, Xinqi Fan, Ravid Shwartz Ziv, Hong Yang, Vinija Jain, Aman Chadha, Manoj Karkee, @Jia Wu, Philip Torr, FREng, FRS, Seyedali Mirjalili ➡️ 𝐊𝐞𝐲 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬 𝐨𝐟 𝐓𝐡𝐢𝐧𝐤𝐢𝐧𝐠 𝐁𝐞𝐲𝐨𝐧𝐝 𝐓𝐨𝐤𝐞𝐧𝐬' 𝐂𝐨𝐠𝐧𝐢𝐭𝐢𝐯𝐞‑𝐂𝐨𝐦𝐩𝐮𝐭𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐀𝐆𝐈 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤: 🧠 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤: Integrates cognitive neuroscience, psychology, and AI to define AGI via modular reasoning, persistent memory, agentic behavior, vision-language grounding, and embodied interaction. 🔗 𝐁𝐞𝐲𝐨𝐧𝐝 𝐓𝐨𝐤𝐞𝐧‑𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧: Critiques token-level models like GPT-4.5 and Claude 3.5, advocating for test-time adaptation, dynamic planning, and training-free grounding through retrieval-augmented agentic systems. 🚀 𝐑𝐨𝐚𝐝𝐦𝐚𝐩 𝐚𝐧𝐝 𝐂𝐨𝐧𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧𝐬: Proposes a roadmap for AGI through neuro-symbolic learning, value alignment, multimodal cognition, and cognitive scaffolding for transparent, socially integrated systems.

In 1995 I published a paper on the impact of information revolution on society and firms and in 2015 on the impact of the AI revolution on society and firms. My new paper co-authored with a colleague and an AI LLM (Large language Model) is on the impact of AGI (Artificial General Intelligence) on society and firms. Below is the abstract of the paper: "The paper explores the transformative potential of Artificial General Intelligence (AGI), extending Makridakis’ studies on the Industrial (1995) and AI (2017) revolutions. By evaluating the accuracy and limitations of these prior forecasts, we assess AGI’s capacity to eclipse prior technological shifts through its ability to perform human-like general problem-solving. AGI holds the potential to revolutionize labo r, governance structure, and human purpose, offering innovative solutions to global challenges while posing risks of job displacement, inequality, and existential threats. We propose four scenarios—collaborative synergy, AGI dominance, regulated progress, and stagnation—to explore possible futures, analysing their likelihood and implications for society and organizations. Key themes include cognitive task automation, economic disparities, ethical governance, and the need for global coordination to align AGI with human values. We offer actionable recommendations for governments, firms, and educational institutions to harness AGI’s benefits while mitigating risks, emphasizing education reform, equitable access, and international cooperation. Through interdisciplinary insights and human-AI collaboration, this study advocates for adaptive strategies to ensure AGI fosters shared prosperity and enhances human potential responsibly." Collaborating with an AI LLM was a unique experience. It was like having an extremely smart and obedient research assistant capable of accomplishing perfectly well whatever tasks was assigned, apart from its occasional hallucinations in making up non existing references. From our experience writing academic papers will not be the same in the future.

AGI leading to the Dawn of AI Scientists The concept of “AI scientists” is poised to transform how we approach scientific research. Eric Schmidt envisions advanced AI systems conducting independent research, unlocking new levels of efficiency and scalability. With millions of AI systems collaborating globally, we could accelerate breakthroughs in medicine, energy, and climate solutions. Unlike human researchers, AI scientists can analyze vast datasets, conduct experiments, and refine hypotheses at unprecedented speed. Imagine AI systems generating and testing millions of hypotheses daily, driving discoveries at a scale never before possible. Key Innovations Driving AI Scientists Recent advancements are laying the groundwork for AI scientists: • OpenAI’s Strawberry Model: A reasoning powerhouse solving 83% of International Mathematics Olympiad problems using chain-of-thought reinforcement learning. • Harmonic’s Aristotle: A mathematical superintelligence, achieving 90% on the MiniF2F benchmark and tackling hallucinations. • Magic’s Active Reasoning: A novel approach focused on dynamic problem-solving, pushing boundaries in logical and contextual reasoning. • Nous Research’s Forge Engine: Excels in symbolic reasoning and solving complex tasks essential for scientific exploration. These breakthroughs, coupled with formal verification mechanisms and active reasoning, are setting the stage for reliable, autonomous systems to lead research. Leaders Shaping the Future 2024 has seen a surge in AGI-focused startups. Here are some notable players: • Safe Superintelligence Inc. (SSI): Backed by $1 billion, SSI is dedicated to safe and scalable AGI development. • SingularityNET: A decentralized marketplace for collective AGI innovation. • Magic: Positioned as a rising star, claiming breakthroughs in active reasoning critical for applied research. • DeepMind (Google): Continues to excel in reinforcement learning and practical applications like healthcare and protein folding. • Hippocratic AI: Focused on Health General Intelligence (HGI) to transform personalized medicine. The Road Ahead The rise of AI scientists raises profound questions: Will they complement or compete with human ingenuity? How do we ensure these systems are ethical and safe? As we approach this transformative era, the stakes couldn’t be higher. AI scientists have the potential to redefine discovery, but their power must be guided toward humanity’s collective good. The age of AGI-driven scientific discovery isn’t just a possibility—it’s here. Are we ready for the speed, scale, and ethical challenges of this new reality?

The development of the first artificial general intelligence (AGI) is likely to bring about a system with vastly different capabilities compared to its AI predecessors. While initial versions of AGI may underperform in specialized tasks that current AI systems excel at, its potential lies in a fundamentally different ability: general intelligence. Early AGI systems may resemble an infant in their cognitive stage—possessing a broad but shallow intelligence that allows them to learn flexibly and adapt to new environments rather than execute highly specialized skills. According to research, general intelligence can be defined as the capability to learn from a variety of experiences and transfer that learning across multiple domains with minimal task-specific optimization (Legg & Hutter, 2007). Unlike narrow AI models that are highly effective within specific parameters but struggle outside them, AGI will be more adaptable, showcasing a form of intelligence that can be measured by tests designed to assess general intelligence, such as the ARC Challenge (Chollet, 2019). This adaptability means that while the AGI system may initially lack deep expertise, its general learning ability will compensate by enabling it to quickly acquire new skills. One of AGI’s transformative features will be its capacity for data-efficient learning. Where current AI systems often require vast datasets to achieve high performance, AGI is expected to learn and generalize from much smaller data samples, allowing it to handle complex and unpredictable real-world scenarios more effectively (Lake et al., 2017). This aligns with cognitive science research suggesting that human infants, with far less training data than current AI, achieve remarkable flexibility through generalized learning mechanisms (Gopnik et al., 2015). AGI, similarly, may be able to leverage fewer experiences to gain a broader understanding. This generalized learning ability will give AGI a long-term advantage. Over time, and through cumulative experience, it will likely outpace current AI systems not only in tasks previously mastered by specialized models but also in solving novel challenges previously beyond the reach of AI. After several years of development and learning, AGI systems will likely surpass all previous AI in every field, offering unprecedented capabilities and insights that were once thought to be unattainable. References: • Chollet, F. (2019). On the Measure of Intelligence. https://lnkd.in/g_i3-eCH. • Gopnik, A., Meltzoff, A. N., & Kuhl, P. K. (2015). The Scientist in the Crib: Minds, Brains, and How Children Learn. https://a.co/d/9UTAZmv • Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017). Building Machines That Learn and Think Like People. https://lnkd.in/gaTqJRnF • Legg, S., & Hutter, M. (2007). Universal Intelligence: A Definition of Machine Intelligence. https://lnkd.in/gU5ZviNT

A few years ago, Andreas Demetriou and I published something that might change how you think about intelligence in humans, brains, AND AI. It's called the "noetron" theory, and it explains the core mechanism that makes minds intelligent. The Core Finding: Intelligence operates through a fundamental meaning-making mechanism we call noetron, comprising three interdependent processes: Alignment: Search, variation, and pattern matching Abstraction: Identifying invariant relationships across representations Cognizance: Awareness, reflection, and metacognitive control Why This Matters: For Psychology: General intelligence (g) isn't an emergent statistical artifact, it's a real psychological entity with measurable developmental trajectories. We can explain: Why certain cognitive training works at specific ages How reasoning evolves from episodic to epistemic control What predicts school and professional performance For Neuroscience: We've mapped noetron to specific brain networks: Integration systems (inferior parietal/frontal cortices) Relational processing (temporo-parietal regions) Metacognitive control (prefrontal cortex) The recently discovered von Economo neurons as potential "g-neurons" For AI Development: At that time (2021), machine learning and AI excelled at alignment and abstraction but fundamentally lacked cognizance. This explains why: AI struggled with the unexpected Transfer learning remained limited True Artificial General Intelligence (AGI) eluded us The Path to AGI: Building AGI requires systems that develop: Self-organized adaptive cognitive possibilities Subject-level experience assigning intrinsic value Consciousness-like processes enabling genuine choice Without cognizance, no system can optimize future action based on past experience the way humans do. Practical Implications: Our longitudinal studies show that targeted cognitive training transfers to general intelligence ONLY when matched to developmental priorities: Attention control training works in early childhood Relational integration training works in late childhood/adolescence Generic "brain training" fails across the board The evidence is clear: one-size-fits-all approaches don't work. Development-sensitive interventions do. Looking Forward: This framework opens new research directions: Hybrid bio-synthetic intelligent systems Developmental AI that grows like human minds Consciousness-integrated AGI architectures The future of intelligence research involves understanding minds or building AI, and discovering the principles that unite both. Grateful to collaborate with Andreas Demetriou and our international research team on this work. Full paper in the link below #ArtificialIntelligence #CognitiveScience #Neuroscience #GeneralIntelligence #MachineLearning #AGI #Research #Psychology https://lnkd.in/dqzFsqTH

Headline: AI Is Entering a Higher Dimension to Mimic the Brain—and Could Soon Think Like Us ⸻ Introduction: Artificial intelligence is poised for a radical transformation as researchers move beyond conventional two-dimensional models toward a higher-dimensional design that mirrors the human brain’s wiring. By mimicking the brain’s multi-layered complexity, AI may soon overcome the cognitive limits of current systems and approach something far closer to human-like intuition, reasoning, and adaptability—bringing artificial general intelligence (AGI) into sharper view. ⸻ Key Details: The Wall Blocking AGI: • Current AI has hit a developmental ceiling, limited by how existing models process information linearly or through simplistic multi-layered patterns. • Despite impressive progress, true human-level cognition remains elusive, especially in areas like intuition, abstract reasoning, and adaptive learning. The Leap Into Higher Dimensions: • Researchers are now exploring three-dimensional and even higher-dimensional neural networks, inspired by the way real neurons form dynamic, cross-layered connections in the brain. • These new models could allow AI to “think” in a structurally richer and more flexible way, similar to how the human brain processes stimuli and forms memories. Brain-Inspired Breakthroughs: • The new wave of AI development borrows from neuroscience and physics, especially the work of John J. Hopfield, a pioneer in modeling brain networks using physics-based systems. • These designs aim to replicate emergent behaviors—like pattern recognition, emotional response, and even intuition—by reproducing how the brain’s neurons interact in layered, recursive, and context-aware ways. Beyond Computation—Toward Understanding Ourselves: • Not only could this leap bring AI closer to AGI, but it may also offer insights into how the human brain actually works—a mystery still only partially solved. • As AI systems evolve to mirror brain-like structures, they may help researchers reverse-engineer cognition, leading to advancements in mental health, brain-computer interfaces, and neurodegenerative disease research. ⸻ Why It Matters: This dimensional leap in AI development marks a pivotal moment: the shift from machines that simulate intelligence to ones that may experience it in fundamentally human ways. If successful, it could open new frontiers in how we live, learn, and connect with technology. Just as the structure of the brain gave rise to consciousness, these brain-inspired architectures may give rise to machines that truly understand, not just compute. And in doing so, they might also reveal the deepest truths about ourselves. https://lnkd.in/gEmHdXZy

The recent release of DeepSeek R1 and other Chinese open-source AI models marks a pivotal shift in the global AI landscape, triggering the largest single-day market value loss in history as NVIDIA shed $600 billion. This watershed moment reveals how trade restrictions inadvertently accelerated Chinese innovation in AI efficiency, proving that necessity truly is the mother of invention. By focusing on distillation, self-play, and reinforcement learning, Chinese researchers have achieved comparable performance to Western models at a fraction of the computational cost. This development feeds into what I call the terminal race condition - a game theoretical scenario where maximum AI investment becomes the only rational strategy for both nations and corporations. We're rapidly approaching a cognitive saturation point where AI will become too cheap to meter, with local deployment of AGI-level systems becoming possible on consumer devices. The traditional moats of proprietary data and algorithms are crumbling, leaving only physical infrastructure - data centers, semiconductors, and power generation - as meaningful competitive advantages. The emergence of an infinite data flywheel, where models generate synthetic data to train even better models, is creating a self-sustaining ecosystem of knowledge generation and refinement. This coincides with our approach toward an intelligence utility plateau - a theoretical ceiling where additional cognitive capacity yields diminishing returns, shifting the focus from raw intelligence to efficiency and speed of deployment. Perhaps most importantly, these developments signal the potential obsolescence of traditional corporate structures. In a world where AGI handles cognitive labor and robotics manages physical tasks, the fundamental purpose of corporations as coordinators of human labor and capital may become obsolete. The future likely belongs to new organizational forms optimized for networks of autonomous agents rather than human hierarchies. The challenge of AGI safety must be approached as a complex adaptive system, requiring network-level solutions rather than individual model alignment. This necessitates creating game theoretical dynamics that naturally incentivize beneficial behavior across vast networks of autonomous agents. We're witnessing the early stages of an economic and technological transformation that will fundamentally reshape human civilization, making previous industrial revolutions look like mere preludes to the age of universal artificial intelligence. https://lnkd.in/gPVZEq-x

Artificial general intelligence is approaching faster than most people realize. The AI 2027 report suggests that by 2027 we could see systems capable of performing software engineering at a superhuman level. These models would not only code as well as the best human engineers but would do so at extraordinary speed and minimal cost. If this happens, AI research itself could become automated. What follows could be a rapid acceleration that pushes us into AGI and potentially even superintelligence within the same decade. For a long time, AI seemed limited to narrow and predictable tasks. That illusion has collapsed. These systems are now learning skills they were never explicitly trained for. They adapt, reason and synthesize new information in ways that surprise even the experts building them. As capabilities expand, the consequences will extend far beyond automation. Entire sectors of the economy may be reshaped. New forms of power will emerge. Social and political stability could be tested in ways we have never experienced. The risks are not theoretical. They are simply uncertain, and uncertainty is not safety. History shows that transformative technologies become dangerous long before we fully understand their failure modes. Nuclear science, biotechnology and early aviation all reached pivotal moments before humanity was ready to manage them. AGI fits the same pattern. A highly capable system that acts according to goals even slightly misaligned with human values could cause harm at a scale and speed we would be unable to control. The greatest threat is not an aggressive machine. It is a human race that builds one under the wrong incentives. Companies are locked in a competition to move fast. Nations are competing for dominance. These pressures encourage speed, not caution. They reward breakthroughs, not safeguards. If the trajectory outlined in AI 2027 is even partially correct, we are heading toward a world where high-stakes systems are deployed before they are fully understood. Yet the path forward is not mysterious. Responsible development requires real investment in safety research. Large-scale experiments should be transparent and open to scrutiny. The origins of training data should be clear. Independent teams should evaluate powerful systems before they are released. None of this stops innovation. It simply ensures that innovation does not outpace oversight. AGI will shape economies, culture and geopolitics. But the direction it takes is not predetermined. It depends on the choices we make now while the technology is still forming. If AI 2027 is right, the next few years will be decisive. We can either build a foundation that allows AGI to elevate humanity or stumble into a future shaped by systems we rushed into existence. Superintelligence may not arrive overnight, but the conditions that determine whether it helps or harms us are being set today. This is the moment to act with clarity, responsibility and foresight.