This Image Is Fine. Completely Fine. 🤖 

But the network was trained with shuffled inputs right? If humans practice like that they might also be able to cope with the shuffled inputs.

@@sebastianjost I agree a human could learn to play the game with these inputs. They may never realise they are playing pong - but they will be able to work out the rules. You won't get the benefit of using the priors that the visual cortex has, but so what.

In fact, the neural network handles such problems poorly. Pong always arrives at one point, and the car is not driving along the road, albeit in the right direction. It seems to me that this is not a complete victory!

I would argue that the reverse steering bike is more complicated. Turning the handlebars involves moving your center of mass, and on typical bikes it moves in a way that is contusive to steering in that direction. To ride the reverse bike, it's not just relearning that clockwise = left and counter-clockwise = right, it's entirely relearning how to posture yourself on the bike. Humans are actually pretty good (albeit slow) at purely switching inputs (see, well, many examples of video games in which this happens as a "status effect").

In the case of backwards bike our internal brain networks was only trained on one way turning bikes to make if some rode both backward and forwards bikes there internal model accommodate that. for it 100% fair the neural net work should be trained based off same away if also for a human to learn a scrambled version of the game they could. but it would take longer because our brains already levvage older models to build new ones

That’s a really good point. Also there are no physical limitations on an AI other than the circuitry it runs on.

Yeah, that makes sense. Also human brain is entirely optimized for the earth environment where these strange things never happen, but AIs are trained from the ground up to handle these things.

Yes we are hunter gatherers not kaleidoscopic pong players 😂

But sometimes we gets bursts of energy too

this is really funny but imagine an ai that is efficient.. it would become so much better! we have evolved over millions of years to be efficient meat machines lol

7:05

Reminds me of Boosting Stop-Motion to 60 fps using AI (ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-sFN9dzw0qH8.html)

Lol

Now imagine this is a robotic dog shooting guns at people - vendor's ad: "no issues whatsoever!"

Wow Just like me with my broken screen

It took Destin 8 months or so, practicing 5 minutes every day. His son did it much faster because a child's brain learns easier.

@@realmetatron And that's what we should be comparing it too. An adult's brain is basically solidified and almost unable to progress with these kinds of things (not to mention being riddled with all the useless data and conventions not helpful for the task at hand), but a clean slate kiddo brain is much closer to an artificial neural network in its learning ways.

The question is as well what is a failure-reset. A normal human will usually 'hit a reset' before failure, which in case of a bike ride is falling off and getting injured.

I noticed learning how to tightrope walk - a foot off the ground, it's ok - aged (55) was made easier by simply ignoring what I was trying to do, chatting with friends and family etc. Concentrating on the task was worse than useless. Curious, that.

​@@tim40gabby25 The unconscious mind is processing 500,000 x more information per second then your puny frontal cortex can muster. By not directly focusing on a task you are no longer slowing down the unconscious mind. This is also why taking a break from solving a hard problem actually helps you solve said problem.

Watch more closely, that’s only somewhat true.

5:08 I agree, the ball starts at center, and it is the same everytime. Other simulations are fine, pong is broken

We're also slow because of biology and chemistry. Silicon is much much faster at calculating things.

Well, in racing that grass patch so i mean a little off road is not rlly a big deal

A.i would never do that in all a.i road traffic. The conclusion you came up with is a human idea, human error. A human would drive into a ditch and complain about something irrelevant to avoid responsibility of facing reality.

@@Andytlp 7:04 is an example of what they were actually trying to say

I remember that video on YT where a guy puts on glasses with a mirror to see the world upside down. The beginning was awful, but after a few days he adapted and was able to do things like pouring milk into his cup just fine. Then the inverse happened when he took them off^^ (although adaptation was much faster)

"A child that grows up with glasses that randomly shuffle their vision every second". Good writing prompt for a sci-fi story, given basic understanding of the topic. Would a human brain that adapted to these conditions perceive more reliably than everyday humans? Conversely, can any common optical illusions in human vision be traced to our vision NOT being trained to deal with perturbation? I'm a migraine sufferer, and that messes with my vision quite often: can it explain my moderately-above-average artistic and mathematical aptitudes? What a time to be alive.

Yeah... Humans can definitely adapt to stuff like this. It just doesn't happen as fast. I don't think the shuffling bit shows a qualitative difference between human and AI processing methods... it just shows that AI can adapt to changes faster.

I think just maybe your experiment ins't going to pass the ethics committee : /

If human neurons were that quick to adapt, they might adapt too much into some random non-sense and be in troubles 99% of the cases.

That is true, and actually a general trend for how neural networks fit a function: they exploit differential motion (tweaking nobs slightly) to see how they affect one another, gradually mapping the function space.

but we could already simulate insects to 100%. human brain for sure some day. the greater problem is that if you want a human like beeing, you would have to raise it like a human.

I don't know if we will ever need to do this. The goal is not to create a virtual human. The goal is to solve a task in a best way. I think that the idea of that video: "Machines doesn't think like us and they shouldn't". The ability to solve a task in cases, where humans can not is not human-like, but this is what we want from machine.

Yes, but still long way to go. I predict in the 250-500 years.

You don’t FEEL we can mirror human thinking.

Why mirror a human brain when you can make algorithms like these, that do things humans never could? I think that's actually much more helpful to us than just digitized versions of ourselves.

You can compare it or even mimic human behavior but it can never be identical because of how deep rooted our behavior is to biology we’d have to understand psychology and neurology a lot more as a species

Yeah I also think about it. If you provided with this task you'll try to find meaningfull parts which were several blocks, while others were distraction

@@1xdreykex1 I mean, I don't think our biology naturally accounts for driving behaviors

Sure, but a human could not navigate the shuffled road: Our understanding of the game of ‘steering an object’ demands a continuous environment, simply because we have no experience with counter examples. Things tend not to teleport spatially in our daily lives! But to this AI it is business as usual.

@@anywallsocket I think human could adapt to this after training. Blind people or people with visual diparities could navigate in environment , also some people have hallucination but still could. AI more adaptable but it isn't so much different

If we're smart enough to use some sort of conditional warnings, so they don't just continue performing until they're in complete ruins, it could allow some flexibility in maintenance. AI in production line bots... I just have a scene in my head of somebody trying to take one offline and it throwing a tantrum. "But I wanna solder! Nooooooooo!"

@@SwervingLemon 😂

If the quantum computer is integrated, it is an answer to the problems facing artificial intelligence. It is very fast, super fast, a penalty of a second, very complex information, and very huge information in seconds. It is a future with artificial intelligence. The solution to many human problems is very complex

I think what this reveals is that humans have a built in reward function for throwing information down strongly-established neural pathways, even if the data doesn't fit. Since the learning algo doesn't have this - its reward function is tied strictly to whatever it's programmed for - it can adapt much more rapidly. This is what's behind anthropomorphization, CBT-style "thought distortions" and so on.

it's great to see the leaps humans have made in AI but AI itself isn't magic. it's all predetermined, based on the inputs and limitations given to the program. it's only as good as we make it. a purpose built AI is going to iterate much faster on it's sole task then a human could ever simply because a AI doesn't need to think about anything else but what's it's programed for. however because of this fact it cannot actually ever compete with a human brain. computers are recreations (attempt) of our brains. however they too are far more focused with a single task and that task is computation. however computation can be used to simulate other areas of our brains but they are still no where near our level as these simulations are not purpose built like a computer is. you would be quite surprised by how much information our brains process on a daily basis. the level of data our brain's sort through every second is quite hard to grasp. most of that data you don't even notice because it's just not something people think about but is required in order to stay alive. like take all of your organs for instance, the brain keeps all of that stuff running but you never have any sort of idea of the data involved in doing such a thing. walking, talking, hearing, seeing, eating, sleeping. they all sound simple but they are not. ask any robot AI expect how hard it is to get something to walk around without falling over. computation is great but it's only a part of what the human brain can do. computation is pointless if you can't form a thought about why you would even want/need such a thing. lastly our brains are very power effective compared to a computer. it's kinda amazing.

Is a bat's brain beyond a human brain just because it can process sound through echolocation? Just like it's be impossible for a human to make sense of trillions of 0s and 1s, our brain is simply not wired for being a sonar. Just because computers are better than humans for some specific tasks, I wouldn't say they're "beyond Human" in their ability to adapt. Not to mention that the definition of "interpreting a raw computer stream" is somewhat loose.

@@manzell literally every cognitive bias

Not to mention that our optical sensor suite is way far from ideal.

You should be clear about what you’re suggesting here. Minimizing the difference between our model of the world and the world is baked into natural selection. If you’re suggesting however that our model for an apple doesn’t itself resemble an apple, then quite clearly yes, of course, this isomorphism is unnecessary. On the other hand, these AIs are deep learning, meaning they are playing with hyper parameters within their response to given information, which is about relations between components, regardless of overall structure. I’m sure there are examples where human minds can do similar things, e.g. abstracting the notion of ‘something coming at you’ no matter the angle, environment, or thing, but it is in no way obvious that’s generally the case.

@@anywallsocket Your assumptions seem reasonable, obvious even...right up until Hoffman demolishes them ¯\_(ツ)_/¯

If our sense of reality was widely different from the way that it really is, we wouldn't be able to predict it and thus likely die off as other species have. I would say we are actually the best so far at seeing reality for the way it truly is, at least at the scale of humans.

@@laykefindley6604 By all means, go tell Hoffman he's wrong without hearing his argument.

Yes. For pong also, I'd like to see a control experiment where the model has no input at all. If the model fails, it would confirm that it's using "vision" to play, and not only blindly moves to the same spot each game.

At least it can mantain the car near the road, thats more what a human can do

But is there a penalty for driving on the grass?

You can actually do that with a bit of practice, depending on the game. Some of them allow the ball to squirt through if you hit the exact corner between a block and the wall.

@@SwervingLemon Sure, but that would be a glitch, not how it actually works. ;)

No the training does not start over.

did you just say a snail is fast compared to snails. what does that mean 🤣

@@Rybz I meant that the human brain adapting is slow compared to the AI adapting, but not necessarily slow when comparing to the initial training time of each. It's like comparing acceleration and top speed of a human with that of a car. The car can accelerate faster and has a higher top speed than a human, but that doesn't mean a human has to take more time before he can accelerate to his top speed than a car requires to accelerate to its top speed.

@@Lttlemoi But what does that matter if the AI is better than the human brain in this task?

I wouldn't call it flawed, more like "greedy" (in the computer sense of the word), humans make a lot of assumptions and optimizations to quickly be able to process the world around them, and that's usually helpful, computers are, in a sense, more polyvalent in that they can be completely reprogrammed very easily, and that makes me hopeful of what we'll be able to achieve with them once they become more powerful, but right now, and until computers have as much computing power as a human brain and more (we're a ways off that), AI will have to make assumptions and optimizations too, just not the same ones as we do

In the end, it's all about complexity, the more information, the more processing power, the more memory it has, it will be able to look at more complex patterns, it's not like humans are any different in that sense, in fact, if you grab a human that doesn't know what snow/dogs/wolfs are and do this to them, I'm pretty much sure they will reach the same conclusion as the computer. the way I see it, getting computers to think like humans once we have computers that are as powerful as a human brain, will be pretty much trivial, seeing the things we are doing with them having fractions of that

@@geli95us In the end I had a perfectly functional table calculations program (better than almost all versions of excel) and a word processor (better than all versions of word) on the very first pc I had access to (it was my Dad's around 1990, late '80s). And even then the adage "garbage in, garbage out" held true. And if you program a woke AI, it will put garbage out no matter what the input. Like Michael Mann's hockey stick graph: No matter the input, his algo always spit out the same graph. Thus my "less enthusiasm, more care" to guard against the Terminator-Matrix events timelines. Oh, and even if the machines do not become self-aware, somebody who controls the machines might be evil. Or the person listening to the machine might be stupid: Like the countless people who got stranded in the wilderness after listening to their Nav.com. that said "turn right on the next" and misunderstood what the next right tuen was. Btw, saw it happen, heard about it happening and happened to me myself, so not dissing anybody, just saying to be careful and think for yourself.

@@geli95us I think their greatest value is in how they DON'T think like us. They've arrived at novel solutions to challenges that I don't think would have occurred to a human.

This channel is always overhyping "AI", or more accurately, unintelligent machine learning. It is very useful, but not 'artificial intelligence'.

If a person with no general knowledge was only given training photos of wolves in snow, they would also think that the only difference between dogs and wolves was that wolves are dogs in snow. Your example seems to be a failure in giving the algorithm adequate training data.