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I don't know about you guys but computers have always been faster at math than my brain.
by KeplerBoy 3y ago
I don't know about you guys but computers have always been faster at math than my brain.
- hardware2win 3y agoHow about learning e.g car driving?
- firecall 3y agoIt's just a matter of knowing how to teach it...
- ed25519FUUU 3y agoWhy not have it teach itself if it’s so smart!
- echelon 3y agoIt took humans 300,000 years to learn to drive the car. Much longer than that, if you consider our lineage.
- hardware2win 3y ago>It took humans 300,000 years to learn to drive the car. False, to invent car. People manage to learn to operate cars in less than 50 hours
- detrites 3y agoYou're comparing training a model from scratch in ML, to the equivalent of model fine-tuning in humans. It's unfair and incorrect. Eg, no human can drive without first learning to operate their limbs, recognise shapes etc. The GP is pointing out that training in fine muscle motor skills, self-awareness and ability to project self-awareness to other objects under ones control etc, all took many thousands of years to develop. AI is faster. However, it's again unfair, as AI only knows what it knows from us, so in that sense any comparison is built on shaky ground. But for the purposes of comparing a stock human brain as hardware, versus a current high-end GPU specifically in terms of ingesting information and then perform tasks, the GPU beats the human brain "hands-down" in any category. The only categories it doesn't are simply ones that no one has trained it to yet - so the argument on a pure hardware capability basis stands.
- incrudible 3y agoStill, the 300,000 years figure is way off. That's an anatomically modern human and they most likely could be trained to operate a car much like a human of today can. Getting to that point took billions of years, but producing the driver of a car was never the goal of that process. It's just the wrong way to look at the problem. You're not trying to develop the generic system that can learn how to drive a car, you're trying to develop the specific system that can safely drive a car occupied by humans, naturally employing machine learning. I would argue that we're 95% there, but solving those last 5% is exponentially more expensive, but not commensurately more valuable. There's a "profit ceiling" imposed by the cost of a human driver, which appeats to make solving the problem economically intractable.
- gzer0 3y agohttps://www.youtube.com/watch?v=DjZSZTKYEU4 https://www.youtube.com/watch?v=DjZSZTKYEU4 Here's a video of Tesla FSD driving through the complicated streets of Los Angeles for an hour straight, with 0 human intervention.
- hardware2win 3y agoAnd how many learning-years did it need to achieve that? In compare to human's tens of hours?
- TeMPOraL 3y ago> In compare to human's tens of hours? On top of typically around 18 years of learning to process and fuse vision, sound, proprioception and other inputs, to navigate the world and reason about it.
- hardware2win 3y ago18 years because this is more due to cultural/law requirements, not brain capability
- TeMPOraL 3y agoAnd those cultural/law requirements are not arbitrary - they've been set at around the brain capability is sufficient for the task. There's both science and a ton of experimentation behind them.
- TN1ck 3y agoTens of hours? I wasn't aware that newborns are allowed to drive :D. Jokes aside, if you want to compare the training time, you should also include the time it takes us to learn our surroundings and navigate the world.
- qwytw 3y agoEven if we assume 10-15 years a human brain is still astronomically more efficient at this. Unfortunately we can't yet copy-paste of our brain so everyone has to learn it from scratch...
- MrBuddyCasino 3y agocall me back when they can iron my shirts
- TeMPOraL 3y agoThey can, you just won't like the price tag.
- gtirloni 3y agoThe original comment was about the human brain, not a single specialized task.
- TeMPOraL 3y agoI'm not going to bet against GPT-4, or its multi-modal successor that's supposed to be out soon, being up for this task. The hard bit is manipulator hardware, which is out of scope for this topic, and the tricky bit is teaching a generative model how to use it, which I think is entirely within capabilities of current state-of-the-art models.
- sylware 3y agoYou totally missed the point: the connectome of a human brain is still orders of magnitude bigger and faster than that. Power efficiency does not matter for those. Those are interesting only for the implementation of specialized cognitive functions. Additionnaly, the connectomes of those chips are 2D and very localized. Human brains are 3D and much less localized. Simulating 3D connectome with those 2D chips slows everything down by a lot.
- tstrimple 3y agoFor conscious math yeah that's true. We also do a lot of unconscious math. Catching an arcing ball flying through the air for example. We don't think of that as math because it feels so natural to us, but our brain is tracking trajectory and speed and you're coordinating your body to intercept based on those details. Similar story with throwing a pass to where you know someone will be. Those sorts of skills become ingrained to the point where they become a reflex, but it's math computation behind the scenes.
- casey2 3y agoWhat math exactly does the brain do while you are trying to catch a ball? Just because you can mathematically model the trajectory of a ball doesn't mean your brain is using that model. The standard method humans use to catch a ball is stabilization at a few updates every second, while the classic method used to tract the trajectory of a ball is newton's method of fluxions. Humans suck at both compared to computers; though the former isn't as much a shameful a loss as the latter.
- tstrimple 3y agoBecause that's what studies into this suggest. https://news.mit.edu/2018/study-reveals-how-brain-tracks-objects-motion-0306 https://news.mit.edu/2018/study-reveals-how-brain-tracks-obj...
- boringuser2 3y agoThis is simply ridiculous.