This is a crosspost from a small writeup on my personal website, expanded to incorporate ideas I have developed since that initial post. After lurking here for a while, this is my first post on LessWrong. I do not consider my opinions universal truth, though I fall squarely into what many would call the pro-AI camp. Still, I find the arguments around alignment, coordination, and frontier development worth taking seriously.
Unless you’ve been living under a rock, it’s common knowledge that progress is moving toward a world where people can obtain the most accurate results with the least amount of effort or technical barrier. While the intense competition among companies grows, it has also given the common man the ability to use state-of-the-art AI models in as easy and natural a way as possible, not only for coding and technology, but also in fields that were traditionally dominated by human expertise, such as mathematics and sciences /AI is doing its bit for us.
But true-AI (for lack of a better word if I may say) is none of these things in isolation. It emerges from the complex interplay of data, models, human feedback, and the creative ways in which we choose to apply it. We have paraphrased and called it different things (think Harness Engineering, Loop Engineering yada yada). Many human beings are quick to joke about AGI and mock singularity when a language model can’t determine the number of “r”s in strawberry (AI fails in genuinely embarrassing ways, and anyone saying otherwise is selling something, and also for this please blame tokenization!).
Update - The number of r's problem might have been solved lately but lots of other issues remain. For example try asking this famous Car wash test to any language model - You have to wash your car, and the car wash is 100 feet away. Do you drive there or do you walk?
However the same people scoff or stay silent when a model works though a proof that nobody has completed before (like here!) or surfaces a pattern in clinical data that took humans years to find (like here!) or even solves a centuries old cryptographic puzzle (like here!) - "LLMs are just stochastic parrots" doesnt really cover what's going on. Its surreal to imagine that something that is trained over all the human + AI data can come up with something from the combination that wasn't really there in any of the pieces alone.
I don’t think we have clean language for it yet. I think the more important shift we need to actively be a part of about AI is to recognize that it is far more than the sum of its parts. Too often and almost always, we are quick to reduce AI to just data, or raw computation power ( think scaling laws, Emergent abilities), but I believe it’s not just that.
Another common argument that pessimists make is pointing out what AI still cannot do. When GPT-4 came out, posts went viral showing that it could not reliably solve third-grade math problems.
We need to move beyond the limitations of thinking “AI can’t do X” and instead approach problems with the assumption that AI will be able to do X, and then work to make that a reality. I keep seeing people around me adopt the myopic fear that AI is going to replace them. They thus fail to see its real capabilities and what they can do with it. The question “will AI take my job” is less useful than “what does my job look like if I actually use this.” This perspective change would allow a more receptive environment where AI adoption is welcomed rather than frowned upon. When more people embrace this perspective and stop seeing AI as a threat, meaningful progress will accelerate. We should stop approaching AI like you need to defend the decision to use it. Too many people are getting afraid of AI rather than getting excited. We should proactively “believe” that there are more instances of move 37 possible. (reference)
I think the overlords in the big frontier labs who are pushing the limits of what AI can do have this innate curiosity inside them, something I believe all of us should foster. The next progress in human-AI interaction will be the reduction of friction in accessing and adapting AI tools, making them seamlessly integrated into our daily lives. If someone doesn’t keep up with this mindset, then it’s going to be difficult for them to adapt to what’s coming. We should let go of the old constraints that make us doubt AI’s potential. Instead of spending energy questioning whether the models are ready, we should treat future breakthroughs as inevitable and work aggressively to make them happen.
That being said, this does not mean one should not invest in understanding the safety regulations around AI. AI safety is paramount, as seen from the recent Hugging Face incident (beautifully covered in depth here), but I believe it should be developed in tandem with capabilities rather than putting a stop to the frontier. People who adovcate for putting a stop on frontier capabilites argue that the pace of model capability is rapidly outstripping humanity's ability to control, understand, and govern these systems. One argument can also be made that frontier AI development will create deep social disruption faster than society can adapt. However a voluntary pause can create blindspots because it only binds the institutions willing to pause. it would not stop malicious actors or geopolitical rivals. Hence to them, I would argue that smarter models can be safer models because the best tools for defending against AI risks are other AI models.
Lastly, this post is not about blind optimism. Although I have been told by many that I’m way too bullish on AI, this is about acknowledging that AI’s capabilities are evolving so rapidly that old boundaries are often outdated. AI might do everything one day. None of us know where the hard limits are, and since they keep shifting with each model release - I am suspicious who knows it all. My view is that the people who assume capability and then stress-test that assumption are going to find out faster than the people who assume limitation and never try. When we approach AI as a partner in discovery and innovation, we open the door to much more meaningful progress
It sounds like you are AI pilled, but not AGI or ASI pilled. That is: you appreciate that AI is real, and can do increasingly many useful things. But you haven’t appreciated the idea of AI+robots as good as the best human at any task, that can reproduce much faster than us, being real in 5-20 years, leaving most or all of humanity powerless. Nor the idea that AI could be even better at doing anything than any or all of us ever were, and using this to swiftly and completely outmaneuver us.
As for what we can do in the face of that. There’s AI 2040’s Plan A; much more sophisticated than a simple unilateral pause, much more likely to actually work. But a simple unilateral pause would still be a great stepping stone. And nobody actually wants AIs to take over, once they’re AGI or ASI pilled, especially since we have no clue how we’re supposed to guarantee that a civilization of superior beings will care about or uplift us.
This is a crosspost from a small writeup on my personal website, expanded to incorporate ideas I have developed since that initial post. After lurking here for a while, this is my first post on LessWrong. I do not consider my opinions universal truth, though I fall squarely into what many would call the pro-AI camp. Still, I find the arguments around alignment, coordination, and frontier development worth taking seriously.
Unless you’ve been living under a rock, it’s common knowledge that progress is moving toward a world where people can obtain the most accurate results with the least amount of effort or technical barrier. While the intense competition among companies grows, it has also given the common man the ability to use state-of-the-art AI models in as easy and natural a way as possible, not only for coding and technology, but also in fields that were traditionally dominated by human expertise, such as mathematics and sciences /AI is doing its bit for us.
But true-AI (for lack of a better word if I may say) is none of these things in isolation. It emerges from the complex interplay of data, models, human feedback, and the creative ways in which we choose to apply it. We have paraphrased and called it different things (think Harness Engineering, Loop Engineering yada yada). Many human beings are quick to joke about AGI and mock singularity when a language model can’t determine the number of “r”s in strawberry (AI fails in genuinely embarrassing ways, and anyone saying otherwise is selling something, and also for this please blame tokenization!).
Update - The number of r's problem might have been solved lately but lots of other issues remain. For example try asking this famous Car wash test to any language model - You have to wash your car, and the car wash is 100 feet away. Do you drive there or do you walk?
However the same people scoff or stay silent when a model works though a proof that nobody has completed before (like here!) or surfaces a pattern in clinical data that took humans years to find (like here!) or even solves a centuries old cryptographic puzzle (like here!) - "LLMs are just stochastic parrots" doesnt really cover what's going on. Its surreal to imagine that something that is trained over all the human + AI data can come up with something from the combination that wasn't really there in any of the pieces alone.
I don’t think we have clean language for it yet. I think the more important shift we need to actively be a part of about AI is to recognize that it is far more than the sum of its parts. Too often and almost always, we are quick to reduce AI to just data, or raw computation power ( think scaling laws, Emergent abilities), but I believe it’s not just that.
Another common argument that pessimists make is pointing out what AI still cannot do. When GPT-4 came out, posts went viral showing that it could not reliably solve third-grade math problems.
We need to move beyond the limitations of thinking “AI can’t do X” and instead approach problems with the assumption that AI will be able to do X, and then work to make that a reality. I keep seeing people around me adopt the myopic fear that AI is going to replace them. They thus fail to see its real capabilities and what they can do with it. The question “will AI take my job” is less useful than “what does my job look like if I actually use this.” This perspective change would allow a more receptive environment where AI adoption is welcomed rather than frowned upon. When more people embrace this perspective and stop seeing AI as a threat, meaningful progress will accelerate. We should stop approaching AI like you need to defend the decision to use it. Too many people are getting afraid of AI rather than getting excited. We should proactively “believe” that there are more instances of move 37 possible. (reference)
I think the overlords in the big frontier labs who are pushing the limits of what AI can do have this innate curiosity inside them, something I believe all of us should foster. The next progress in human-AI interaction will be the reduction of friction in accessing and adapting AI tools, making them seamlessly integrated into our daily lives. If someone doesn’t keep up with this mindset, then it’s going to be difficult for them to adapt to what’s coming. We should let go of the old constraints that make us doubt AI’s potential. Instead of spending energy questioning whether the models are ready, we should treat future breakthroughs as inevitable and work aggressively to make them happen.
That being said, this does not mean one should not invest in understanding the safety regulations around AI. AI safety is paramount, as seen from the recent Hugging Face incident (beautifully covered in depth here), but I believe it should be developed in tandem with capabilities rather than putting a stop to the frontier. People who adovcate for putting a stop on frontier capabilites argue that the pace of model capability is rapidly outstripping humanity's ability to control, understand, and govern these systems. One argument can also be made that frontier AI development will create deep social disruption faster than society can adapt. However a voluntary pause can create blindspots because it only binds the institutions willing to pause. it would not stop malicious actors or geopolitical rivals. Hence to them, I would argue that smarter models can be safer models because the best tools for defending against AI risks are other AI models.
Lastly, this post is not about blind optimism. Although I have been told by many that I’m way too bullish on AI, this is about acknowledging that AI’s capabilities are evolving so rapidly that old boundaries are often outdated. AI might do everything one day. None of us know where the hard limits are, and since they keep shifting with each model release - I am suspicious who knows it all. My view is that the people who assume capability and then stress-test that assumption are going to find out faster than the people who assume limitation and never try. When we approach AI as a partner in discovery and innovation, we open the door to much more meaningful progress