A recent surge in predictions about achieving human-level artificial intelligence by 2030 has left many experts questioning whether we're chasing an unattainable dream. While some CEOs are doubling down on the promise of artificial general intelligence (AGI), others argue that it's nothing more than hype, and that what we really need is a focus on autonomy.
The question is: what exactly do we mean by "intelligence" when we talk about AI? Intelligence has proven to be a subjective measure, as there's no straightforward way to quantify or assess its progress. And while AGI represents the ultimate goal of creating machines that can perform any task a human can, it's also a dauntingly complex and unmeasurable objective.
The problem with focusing on AGI is that it promises supreme autonomy, which in practice means eliminating the need for human oversight altogether. But how do you prove a machine can run a large company or fully educate a child without putting them through actual trials? It's clear that we're not making concrete progress toward this goal anytime soon.
So what should we be focusing on instead? The answer lies in measuring autonomy, not intelligence. How much work can an AI system automate, and to what degree is it capable of operating independently? These are the metrics that truly reflect the value of a system, as automation is the primary purpose of any machine.
The problem with AI hype is that it promises unrealistic levels of autonomy. By viewing AI goodness through the lens of autonomy, we can identify when an AI promise is nothing more than hot air. For example, the concept of AGI represents the epitome of AI hype, given its promise of supreme autonomy.
But there's another side to this story: predictive AI. This type of AI takes on tasks that are more forgiving and don't require constant human supervision, making it a much more practical and valuable pursuit. From instant decision-making in bank systems to optimizing marketing campaigns, predictive AI is already delivering significant value across various industries.
The paradox lies in the fact that while generative AI (GenAI) may seem incredibly human-like, its reliance on human oversight at each step means it's actually less autonomous than predictive AI. It's time for decision-makers to reorient their priorities and focus on projects that deliver tangible improvements to enterprise efficiency – rather than chasing after a dream of AGI that may never materialize.
As the AI landscape continues to evolve, one thing is clear: autonomy, not intelligence, should be the metric we're using to measure success. Only then can we separate hype from reality and prioritize initiatives that truly deliver value.
The question is: what exactly do we mean by "intelligence" when we talk about AI? Intelligence has proven to be a subjective measure, as there's no straightforward way to quantify or assess its progress. And while AGI represents the ultimate goal of creating machines that can perform any task a human can, it's also a dauntingly complex and unmeasurable objective.
The problem with focusing on AGI is that it promises supreme autonomy, which in practice means eliminating the need for human oversight altogether. But how do you prove a machine can run a large company or fully educate a child without putting them through actual trials? It's clear that we're not making concrete progress toward this goal anytime soon.
So what should we be focusing on instead? The answer lies in measuring autonomy, not intelligence. How much work can an AI system automate, and to what degree is it capable of operating independently? These are the metrics that truly reflect the value of a system, as automation is the primary purpose of any machine.
The problem with AI hype is that it promises unrealistic levels of autonomy. By viewing AI goodness through the lens of autonomy, we can identify when an AI promise is nothing more than hot air. For example, the concept of AGI represents the epitome of AI hype, given its promise of supreme autonomy.
But there's another side to this story: predictive AI. This type of AI takes on tasks that are more forgiving and don't require constant human supervision, making it a much more practical and valuable pursuit. From instant decision-making in bank systems to optimizing marketing campaigns, predictive AI is already delivering significant value across various industries.
The paradox lies in the fact that while generative AI (GenAI) may seem incredibly human-like, its reliance on human oversight at each step means it's actually less autonomous than predictive AI. It's time for decision-makers to reorient their priorities and focus on projects that deliver tangible improvements to enterprise efficiency – rather than chasing after a dream of AGI that may never materialize.
As the AI landscape continues to evolve, one thing is clear: autonomy, not intelligence, should be the metric we're using to measure success. Only then can we separate hype from reality and prioritize initiatives that truly deliver value.
I think this whole AGI hype is just a case of people trying to sound cool. We're already seeing AI doing some pretty impressive stuff with predictive AI - like in finance or marketing, it's making a real difference. But when we start talking about achieving human-level intelligence, that's just not something you can measure or prove.
I feel like so many of us are caught up in the excitement of AI and AGI, but what about all the people who might get left behind? 
It's all about finding ways to make life easier and more efficient, not just creating another tool that might not even be needed in the first place. 
I'm just saying, have you guys considered what it means to "solve" AI? 
But then we get all caught up in the hype of AGI and autonomy, which just seems like a pipe dream to me. 
What if instead of focusing on making machines super smart, we just focus on making them better at doing their jobs? 
It's not about creating intelligence, it's about creating value. And predictive AI is already doing that in a big way! 
? It's like we're chasing some kind of magic pill or something. And don't even get me started on this AGI business... what exactly does that even mean? Can a machine truly be "intelligent" in the way humans are? I'm not so sure.
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. and honestly, i don't even know if it's possible to create a machine that can outsmart humans completely - we've seen how well predictive ai is doing already, but agi is like trying to make a car go from 0-60 in seconds without ever even moving off the starting line 
. let's focus on getting our tech to do something practical first, like automating boring tasks or making bank systems more efficient 
. and btw, what's with all the emphasis on autonomy? doesn't that just mean eliminating human oversight altogether? isn't there a risk in doing that? shouldn't we be more careful about how we develop this tech? 
. So, let's stop chasing this myth and focus on building AI systems that actually deliver value – autonomy is the real key to unlocking AI goodness 
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. I think we need to focus on building more practical applications of AI, like predictive analytics or machine learning that can help us make better decisions 
. The hype around AGI is exciting, but let's not forget what really matters: making AI work for us, not the other way around 
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