Artificial General Intelligence (AGI) remains a nebulous and contentious concept within the AI community. Despite its growing prominence as a goal among major AI companies and its portrayal in popular media, we lack a clear, universally accepted definition. Ask five different people, and you’ll get ten different answers. This lack of clarity poses a significant challenge to our progress towards achieving AGI. The term ‘AGI’ is often perceived as a goal without a solid scientific foundation, a crucial aspect we must address. The term gained traction around 20 years ago but has, in my opinion, become more of a buzzword than a well-defined objective. At least in the fears of the many, I observe a conflation between AGI and Artificial *Sentience*. However, the current trajectory of AI research, which focuses on transitioning from narrow AI to broader AI, barely scratches the surface of what would be required to create artificial sentience. Enhancing intelligence alone does not equate to creating an entity capable of sentient experiences, and true intelligence isn’t exactly linear. You don’t plot a line of intelligence; and then — beyond a certain point, call something alive or sentient. Ongoing research into growing the intelligence of a system towards superhuman capabilities isn’t a route to a judgemental, all-encompassing AGI that the science fiction movies of the world tell us will kill us. Indeed, we have already seen computers be super-human in many scenarios, and we’re still around! :) True AGI would need to encompass advanced cognitive abilities and a deep understanding of and interaction with the world, akin to human consciousness. Moreover, the quest for AGI involves far more than developing smarter algorithms; it requires a profound leap in our understanding of consciousness, emotions, and self-awareness. Current advancements in AI, such as large language models, represent incremental progress rather than revolutionary steps toward artificial sentience. These systems, while impressive, operate within the confines of their programming and lack true understanding or subjective experiences. They simulate that through language, and we infer they are ‘thinking’ or ‘reasoning’, but it’s artificial. In conclusion, the realization of AGI is a distant dream, primarily due to our struggle to define it precisely and the vast disparity between current AI capabilities and the intricate nature of human-like intelligence and sentience. As the New Scientist article rightly points out, AGI remains more of a speculative concept than an imminent reality, and our journey toward it is just beginning. And perhaps this is why companies are disbanding any safety or research around the potential impact of AGI -- because, for now, at least, perhaps it's a fallacy to do so. #ArtificialIntelligence #AGI #Futurism
Understanding Artificial General Intelligence and Narrow AI
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Summary
Understanding the difference between artificial general intelligence (AGI) and narrow AI is crucial in demystifying the current state and future possibilities of artificial intelligence. While narrow AI focuses on specific tasks like language translation or image recognition, AGI aspires to replicate human-like intelligence, capable of learning and reasoning across a broad range of subjects—a concept that remains far from reality.
- Focus on narrow applications: Current AI excels in specialized tasks, so prioritize using it in areas where it can make a tangible difference, like data analysis or customer support.
- Acknowledge AGI’s complexity: Understand that AGI requires a deeper scientific breakthrough in consciousness and cognition, making it a long-term research goal rather than an imminent technology.
- Stay grounded in facts: Avoid falling for AGI hype and focus on the practical benefits of present-day AI advancements to drive value and innovation.
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🚨 The buzz around Artificial General Intelligence (AGI) is louder than ever. But are we fooling ourselves with the hype? In my latest column for InfoWorld, I dig into why AGI—machines with true human-like intelligence—remains far more fantasy than reality. While breakthroughs in narrow AI are impressive and genuinely transformative, the leap to generalized, human-level cognition is not just a technical leap, but may be a fundamental illusion. I explore: The difference between today’s practical, focused “narrow AI” and the elusive goals of AGI Why moving from advanced automation to real, adaptable intelligence requires more than scaling up current models How misplaced expectations could slow actual progress and invite unnecessary risk I encourage fellow IT leaders, decision-makers, and technologists to read this article with a critical eye. Separating hype from real opportunity is key to driving value—not just headlines—in your business. Take a look and let me know your thoughts in the comments. Are we chasing a promise that may never be fulfilled, or are you seeing something different in the field? 🔗 https://lnkd.in/eBPb4Nsv #AI #AGI #ArtificialIntelligence #TechLeadership #DigitalTransformation #InfoWorld Artificial general intelligence is an artificial general illusion | InfoWorld
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Not All Algorithms are 'AI' (part 3): General Intelligence Scared that AI will take over your job (and the world)? Don't be. In the last part of my Forbes series, we walk through why today's generative AI technologies are a "dead end" architecture for true intelligence. AGI is probably 15-30 years away in my opinion. I outline 6 precursors that must exist for artificial general intelligence (AGI) otherwise known as machine consciousness: 1. Hierarchical design: perceptions to ostensive objects to high-level concepts. 2. Consciousness must infer causality, and understand sequence (time)...today's models do not 3. Consequences matter. You need penalty and reward at an "emotional" level for machines to care about wrong answers and hallucinations. 4. AI needs to interact with the world (or a simulation). Same reason your toddler needs to drop a ball a hundred times to infer gravity. 5. You need to model the consciousness of others. 6. You need an "attention" model of your own consciousness, aka "focus". If you prefer video as a way to learn about algorithms, check out my past posts on AI for healthcare: machine learning vs. AI, LLMs for healthcare, rules-based algorithms vs. AI. https://lnkd.in/gtySNPMM #healthcare #ai #forbes
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