[HN Gopher] DeepMind AI learns simple physics like a baby
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DeepMind AI learns simple physics like a baby
Author : mdp2021
Score : 58 points
Date : 2022-07-11 20:14 UTC (2 hours ago)
(HTM) web link (www.nature.com)
(TXT) w3m dump (www.nature.com)
| mdp2021 wrote:
| Another divulgative article:
|
| -- DeepMind AI learns physics by watching videos that don't make
| sense - An algorithm created by AI firm DeepMind can distinguish
| between videos in which objects obey the laws of physics and ones
| where they don't -
| https://www.newscientist.com/article/2327766-deepmind-ai-lea...
|
| > _Luis Piloto at DeepMind and his colleagues have created an AI
| called Physics Learning through Auto-encoding and Tracking
| Objects (PLATO) that is designed to understand that the physical
| world is composed of objects that follow basic physical laws. //
| The researchers trained PLATO to identify objects and their
| interactions by using simulated videos of objects moving as we
| would expect [...] They also gave PLATO data showing exactly
| which pixels in every frame belonged to each object. To test
| PLATO's ability to understand five physical concepts such as
| persistence..., solidity and unchangingness..., the researchers
| used another series of simulated videos. Some showed objects
| obeying the laws of physics, while others depicted nonsensical
| actions_ [with the latter, correctly the AI returned wrong
| predictions, showing an acquired intuition of physics]
|
| From the submitted one:
|
| > _[Jeff Clune, Uni British Columbia, Vancouver: ]<<[Comparing AI
| with how human infants learn is] an important research direction.
| That said, the paper does hand-design much of the prior knowledge
| that gives these AI models their advantage>>. // Clune and other
| researchers are working on approaches in which the program
| develops its own algorithms for understanding the physical world_
| Jeff_Brown wrote:
| Data and analysis alone, without experimentation, don't seem like
| enough to achieve real intelligence. From its title this article
| sounded like it would be about progress in learning by doing.
| Alas, it's not.
| mdp2021 wrote:
| > _experimentation_
|
| Right. "You have to tell yourself stories", as the late Prof.
| Patrick Winston said (you are intelligent because you can
| predict the unexperienced). Because you need concept
| development and critical thinking - an active process.
| thomasjudge wrote:
| I feel like there is an inferential leap implied, to greatly
| simplify, from "A does X and B does X" to "A and B must operate
| relevantly similarly." For example, walking and flying are both
| modes of transportation, but you can't really learn anything
| interesting about one from studying the other
| sebzim4500 wrote:
| >For example, walking and flying are both modes of
| transportation, but you can't really learn anything interesting
| about one from studying the other
|
| You can figure out newtonian mechanics entirely on the ground
| and that clearly helps you understand flight. By analogy,
| getting more understanding about what limits exist in ANNs
| could plausibly help understand how the brain works (and vice
| versa).
| ChikkaChiChi wrote:
| Once you understand that walking gets you from where you are to
| where you want to be, you start to define the characteristics
| of motion. Then, seeing other forms of conveyance that are
| faster underpin the concept of efficiency.
|
| Walking and flying may not have a lot in common to you and I,
| but to a thing learning to crawl, there is a lot to be
| understood.
| jonbaer wrote:
| Looking at the photo I don't think the AI is going to realize
| eating the piece it is about to pick up and it will choke. I
| would actually like to see more reinforcement learning agents
| like that, the action space on infant movement is quite small so
| it's even really about "action space discovery" to some point.
| Things it discovers are way more interesting, like if food were
| not on the floor/level and it has to stand to get it, it will
| eventually get there after N attempts (over time), and then if
| you introduce another agent if learning to block the other agent
| will award more food, etc, then it discovers 50/50 and
| equilibriums (better to eat now than wait). PLATO seems like a
| step in that direction.
| danielmorozoff wrote:
| Similar work has been pursued for a number of years now in a
| Darpa program called Machine Common Sense:
| https://www.darpa.mil/news-events/2018-10-11
|
| I recall Tenenbaum's lab had a similar paper a few years back.
| mdp2021 wrote:
| Also https://www.machinecommonsense.com/ ,
|
| in which animations are shown which reveal the close similarity
| to the DeepMind project.
| mikolajw wrote:
| Clickbait title.
|
| I wish ML researchers stopped using anthropomorphizing language.
| This has decades of solid tradition, but that's no excuse. Any
| comparison of a machine to a human misleads the public. Machines
| aren't like babies, artificial neural networks aren't like actual
| neural networks or brains. Machines shouldn't be given human
| names (PLATO is a borderline case).
|
| I know this is like talking to a wall -- money requires hype --
| but still, please stop doing that.
| mdp2021 wrote:
| A further article and a commentary just appeared on The
| Conversation from a Professor of Psychology and Infant Studies:
|
| https://theconversation.com/researchers-trained-an-ai-model-...
|
| > _Typically, AI models start with a blank slate and are trained
| on data with many different examples, from which the model
| constructs knowledge. But research on infants suggests this is
| not what babies do. Instead of building knowledge from scratch,
| infants start with some principled expectations about objects
| [...] The exciting finding by Piloto and colleagues is that a
| deep-learning AI system modelled on what babies do, outperforms a
| system that begins with a blank slate and tries to learn based on
| experience alone_
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