AGI Future and Impact

The Argument for an AGI Horizons Team In early 2023, shortly after the release of ChatGPT, a major software company reached out to me to try and understand what was happening with Gen AI and how it might impact their product roadmap and business strategy. We talked through how ChatGPT worked, compared notes on what it could mean to their product development plans, and I shared insights into what else the AI labs were working on that might affect their company and its customers in the coming years. One of my key recommendations for them was to form what I termed an “AGI Horizons Team” tasked with monitoring advancements toward artificial general intelligence (AGI) and assessing potential threats and opportunities. While there continues to be a lack of agreement on how to define AGI, I consider it an AI system that is generally capable of outperforming the average human at most cognitive tasks (e.g. ChatGPT being able to perform 90% or more of marketing tasks better than an average marketer). For more than 70 years, researchers have pursued this idea of human-like general intelligence. They were driven by a belief that we could give machines the ability to think, reason, understand, create, and take actions in the digital and physical worlds. But, progress was often slow, and the impact on our professional lives was minimal. Then, everything changed—and accelerated—with the release of ChatGPT in November 2022, and the rise of Gen AI. By early 2023, the tone and positioning on AGI from the leading AI labs had changed. They no longer talked about AGI as something that might be possible in a decade or more. They were conveying increasing confidence that there was a clear path to achieving AGI within 3 - 5 years. My point to the software company was that while everyone was racing to understand the impact of the current forms of AI on their business, far smarter and more generally capable models were on the horizon that might force them to reimagine and reinvent their products and business model. That if AGI was unlocked by OpenAI, Google Deepmind or another AI lab, it would change everything. And while the probability of that occurring in the 3 - 5 year window was relatively unknown, it certainly wasn’t zero. In other words, there was a potential transformative event (maybe even an extinction-level event for their company) possible within half of a decade. I felt that it was worth putting a team of their best people together to assess, along with outside advisors who can be more objective about the path forward. I now believe there is a greater than 50% chance of an AI lab claiming they have achieved AGI with 1 - 2 years. What that means to your business and industry is unknown. What that means to society and the economy is also unclear. But, I think it’s significant enough that we should all be doing more to consider the possibilities. Maybe it’s time for an AGI Horizons Team in your organization.

The Problem with AI Isn't Just Data, It's the Nature of Intelligence! I am often asked about my opinion on AGI and the true capabilities of AI. I am not pessimistic about AI and neither optimistic about our current scaling of AI. Here are my views shaped by my humble understanding of AI and my passion and interest to have better AI systems that are safe and trustworthy, and that augment our capabilities but not replaces us! One of the most pressing challenges in AI today isn’t just its ability to process information, it’s how it learns. Most of today's AI models are trained on massive datasets and optimized to infer patterns within that training data. But here's the real test: What happens when AI encounters something it hasn’t seen before? Will it truly understand, or will it guess? Can it reason beyond the boundaries of its training data? It does not seem so! This is the limit of current AI - it is not intelligence in the human sense. It’s powerful pattern recognition, not reasoning. It lacks intuition, context awareness, and a true sense of meaning.   The challenges with AI including: - Overfitting to data rather than learning abstract concepts. - Hallucinations and false inferences when faced with unfamiliar scenarios. - The illusion of intelligence without true understanding or common sense. The current trends in AI reasoning aim to move from shallow pattern recognition to deeper, structured thinking - more akin to how humans solve problems. Techniques like chain-of-thought prompting, neuro-symbolic reasoning, and agent-based architectures are early attempts to replicate human-like deductive steps. While promising, these methods often mimic how we think without actually understanding why. Human reasoning is built on lived experience, emotion, and adaptability - dimensions AI still struggles to grasp. The dream of Artificial General Intelligence (AGI) includes reasoning, deduction, adaptability, and understanding context across domains - hallmarks of human intelligence. But will AGI ever truly embody those traits? That remains uncertain. Even as models grow more capable, their cognition lacks self-awareness, intentionality, and moral grounding. AGI may someday match or exceed humans in narrow tasks, but replicating the richness of human reasoning - including empathy, ethics, and meaning - may require breakthroughs not just in engineering and neuroscience, but in our philosophical understanding of intelligence itself. As we build the next generation of AI, we must ask not just how well it performs in known tasks, but how gracefully it fails when facing the unknown. True progress lies in generalization and common sense, not just memorization. Let’s keep pushing the boundaries—but responsibly, and with a clear-eyed understanding of what current AI is and what it isn’t! #ai #artificialIntelligence #agi #responsibleai #aifutures #machinelearning #aiethics #reasoning #humanintelligence #topvoice #

On a personal note... I finally got the chance to meet the brilliant Professor Daniela Rus, Director of MIT CSAIL, and a contributing author to The Digitalist Papers, Vol. 2. For over a decade, I've followed her work in robotics and AI. See two slides from my AI presentation I gave in 2017 (AI TechWorld) below. It was a genuinely starstruck moment to shake her hand. Her recent essay, "Private Physical AI for the Edge," isn't just a technical discussion; it's a critical roadmap for the next phase of AI development and, potentially, the necessary step toward achieving true Artificial General Intelligence (AGI). Read the essay here: https://bit.ly/4qbfjWd The current era of AI, dominated by Large Language Models (LLMs), has achieved incredible fluency and creativity. However, as Professor Rus argues, this is only part of the intelligence puzzle. These models are "hungrier" for electricity than ever and lack a fundamental, built-in understanding of the real, physical world. Their intelligence is not grounded in physics. The work led by Professor Rus, as detailed in her essay, represents the vital shift to Private Physical Edge AI (often simply referred to as Physical AI). This paradigm moves intelligence away from sprawling, distant, energy-intensive data centers and places it directly on the devices that sense, decide, and act in the world... the "Edge." ⭐ Intelligence per Watt: It’s about building systems that are small, fast, and radically energy-efficient. This is crucial for sustainability and widespread deployment. ⭐ Grounded in Physics: Physical AI, as exemplified by breakthroughs like Liquid Neural Networks (LNNs) , is designed to be causal and physics-aware. They are inspired by the compact, adaptable brains of small species (like the C. elegans worm) and learn the task rather than just the context. ⭐ The AGI Connection: This causal, adaptable, and energy-efficient intelligence is what makes the work so critical for AGI. True general intelligence must be able to: - Understand the Physical World: It must reason about materials, motion, and uncertainty... the "messiness of the real world." - Generalize Zero-Shot: As Rus points out, LNNs can seamlessly transfer skills learned in one environment (e.g., a drone hiking in summer woods) to an entirely different one (the same task in winter or an urban setting) without retraining. This level of generalization is a hallmark of true intelligence, which current large models struggle with. By making AI compact, efficient, and inherently grounded in the world's physics, Professor Rus is helping to democratize intelligence and weave it seamlessly into the physical and social fabric of everyday life... from personal AI glasses for the visually impaired to decentralized energy grid management. This is a future where intelligence is measured not by trillions of parameters, but by intelligence per watt, and where it belongs to everyone. Thank you, Daniela Rus, for your groundbreaking vision!

Multimodality of AI for Education: Towards Artificial General Intelligence published at arxiv.org from Cornell University This paper presents a comprehensive examination of how multimodal artificial intelligence (AI) approaches are paving the way towards the realization of Artificial General Intelligence (AGI) in educational contexts. It scrutinizes the evolution and integration of AI in educational systems, emphasizing the crucial role of multimodality, which encompasses auditory, visual, kinesthetic, and linguistic modes of learning. This research delves deeply into the key facets of AGI, including cognitive frameworks, advanced knowledge representation, adaptive learning mechanisms, strategic planning, sophisticated language processing, and the integration of diverse multimodal data sources. It critically assesses AGI's transformative potential in reshaping educational paradigms, focusing on enhancing teaching and learning effectiveness, filling gaps in existing methodologies, and addressing ethical considerations and responsible usage of AGI in educational settings. The paper also discusses the implications of multimodal AI's role in education, offering insights into future directions and challenges in AGI development. This exploration aims to provide a nuanced understanding of the intersection between AI, multimodality, and education, setting a foundation for future research and development in AGI.   5️⃣ key takeaways from the study: #Multimodal AI in Education: The paper discusses the integration of multimodal artificial intelligence (AI) in educational contexts, highlighting its potential to achieve Artificial General Intelligence (AGI). #CognitiveFrameworks: It emphasizes the importance of cognitive frameworks, knowledge representation, and adaptive learning mechanisms in developing AGI for education. #StrategicPlanning: The study explores strategic planning and sophisticated language processing as crucial elements of AGI that can enhance teaching and learning effectiveness. #Ethical Considerations: Ethical, explainable, and responsible usage of AGI in educational settings is critically assessed, addressing the transformative potential and challenges. #Future Directions: The paper offers insights into future directions for AGI development, including the implications of multimodal AI’s role in education and the challenges ahead.

New on AI Snake Oil: Arvind Narayanan and I argue that AGI will not lead to rapid economic effects, the race to AGI is not relevant for great power competition, we won't know AGI when we have built it, and AGI does not imply impending superintelligence. In other words, AGI is not a milestone: https://lnkd.in/exDQbafU 1) Even if general-purpose AI systems reach some agreed-upon capability threshold, we will need many complementary innovations that allow AI to diffuse across industries to realize its productive impact. Diffusion occurs at human (and societal) timescales, not at the speed of tech development. 2) Worries about AGI and catastrophic risk often conflate capabilities with power. Once we distinguish between the two, we can reject the idea of a critical point in AI development at which it becomes infeasible for humanity to remain in control. 3) The proliferation of AGI definitions is a symptom, not the disease. AGI is significant because of its presumed impacts but must be defined based on properties of the AI system itself. But the link between system properties and impacts is tenuous, and greatly depends on how we design the environment in which AI systems operate. Thus, whether or not a given AI system will go on to have transformative impacts is yet to be determined at the moment the system is released. So a determination that an AI system constitutes AGI can only meaningfully be made retrospectively. 4) Businesses and policy makers should take a long-term view. Businesses should not rush to adopt half-baked AI products. Rapid progress in AI methods and capabilities does not automatically translate to better products. Building products on top of inherently stochastic models is challenging, and businesses should adopt AI products cautiously, conducting careful experiments to determine the impact of using AI to automate key business processes. A “Manhattan Project for AGI” is misguided on many levels. Since AGI is not a milestone, there is no way to know when the goal has been reached or how much more needs to be invested. And accelerating AI capabilities does nothing to address the real bottlenecks to realizing its economic benefits. We plan to keep writing on this topic, and have a series of essay planned on the theme of AI as Normal Technology. Follow the AI Snake Oil substack for more.

The recent $500B AI infrastructure announcement may subtly signal a shift toward viewing artificial general intelligence (AGI) as primarily an infrastructure challenge, with a focus on scaling up large language models (LLMs). While infrastructure and scaling have their place, I think this perspective risks oversimplifying the complexities of achieving AGI. True progress will require, I think, significant investment in fundamental research and the creation of high-quality, diverse datasets tailored to support AI development. Also, the absence of a clear definition of AGI or a concrete vision of what a world with AGI might look like further complicates and hinders its pursuit. If we frame AGI as a system capable of making autonomous discoveries, it’s worth noting that some of the most exciting advancements in AI-driven discovery —such as AI-driven material discovery (e.g., recently published MatterGen)—come from models that aren’t based on LLMs. This underscores the importance of exploring diverse AI architectures rather than relying solely on scaling up LLMs. Infrastructure alone cannot substitute for the creative rethinking and foundational breakthroughs required to achieve AGI.

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

Why is Artificial General Intelligence (#AGI) such a big deal? It’s not about making machines that are “human-like” because humans are so exceptional. The power of AGI is all in the generality—the ability to adapt to new environments and solve new problems, not just repeat what’s already been learned. For example, a machine that optimized for superhuman bagel-making is impressive, but if you ask it to switch to croissants, or move from the factory to your kitchen, it fails (https://lnkd.in/gfTWbW2c). That’s because narrow #AI is only good “within distribution”—it works where and when the data matches its training, but breaks down in new situations. Humans are remarkable not because our intelligence is so powerful, but because it’s so general. It’s amazing that beavers can build a dam when they hear water, or that wasps are born able to build paper nests. However, these animals are stuck with instinct: building dams or nests, but only those things, and only in the right setting. Humans have a different capacity: we can figure out how to build an igloo, a skyscraper, a hut, or a log cabin—whatever the environment or materials demand. The real promise of AGI is unlocking this kind of flexibility for machines. Imagine an AI that can adapt to new jobs, environments, and challenges—one that doesn’t go haywire the moment something unexpected happens. That’s the difference between a helpful/valuable tool and a truly transformative technology. We don’t need AGI to think or be architected like a human, but rather to generalize like one. The transformative future opens up when AI can learn, improvise, and handle the unknown—not just the expected. Bottom line: Would you trust a power grid, a domestic robot, or a defense planning system that works perfectly—right up until the moment you threw it a curveball?