Compliant Mechanisms that LEARN! - Mechanical Neural Network Architected Materials

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This video introduces the world’s first mechanical neural network that can learn its behavior. It consists of a lattice of compliant mechanisms that constitute an artificial intelligent (AI) architected material that gets better and better at acquiring desired behaviors and properties with increased exposure to unanticipated ambient loading conditions. It is a physical version of an artificial neural network used in current machine learning technologies.
To learn more about the content of this video, I encourage you to read the following publications, which can be accessed at the provided links:
[1] Lee, R.H., Mulder, E.A.B., Hopkins, J.B., 2022, “Mechanical Neural Networks: Architected Materials that Learn Behaviors,” Science Robotics, 7(71): pp. 1-9
www.science.org/stoken/author...
[2] Lee, R.H., Sainaghi, P., Hopkins, J.B., 2023, “Comparing Mechanical Neural-network Learning Algorithms,” Journal of Mechanical Design, 145(7): 071704 (7 pages)
asmedigitalcollection.asme.or...
Part files to fabricate the mechanical neural network can be downloaded on Thingiverse using this link:
www.thingiverse.com/thefactso...
Donate to help support my channel:
If you’d like to make a one-time donation, you can use the following link:
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Thank you for your support! It is much appreciated and helps enable me to make more content.
Acknowledgements:
Special thanks to Ryan Lee, Erwin Mulder, and Pietro Sainaghi who helped fabricate, test, and simulate the mechanical neural network in the video. I am also grateful to my AFOSR program officer, “Les” Lee, who funded the research that this video features.
Brain Scan Attribution:
Christian R. Linder, CC BY-SA 3.0 creativecommons.org/licenses/b..., via Wikimedia Commons
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Microstructure Image Attribution:
Edward Pleshakov, CC BY 3.0 creativecommons.org/licenses/..., via Wikimedia Commons
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Body Armor Attribution:
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Disclaimer:
Responsibility for the content of this video is my own. The University of California, Los Angeles is not involved with this channel nor does it endorse its content.

Опубликовано:

9 июл 2023

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Комментарии : 853

@mrmurphymil 10 месяцев назад

at 11 minutes I realised this was a research paper in an easily digestable and widely available format, great work.

@turolretar 10 месяцев назад

It was so easily digestible that I went to shit right after finishing this video

@watcherofvideoswasteroftim5788 10 месяцев назад

Accessible cutting edge research is humanity at its best

@Hoptronics 10 месяцев назад

15 mins I decide to read comments .

@Ensign_games 10 месяцев назад

I noticed that at 18 minutes but I like me a good research paper

@sudsierspace9010 10 месяцев назад

@@turolretar lmao man

@BenFitz7897 10 месяцев назад

As a mechanical engineer who is learning computer science and machine learning, this is an amazing bridge between the two worlds! I cant wait to print some and play with the concept myself. The applications are truly endless, I wonder how long until this is made microscopically, and applied everywhere.

@ch1pnd413 10 месяцев назад

This sounds like fiction, but makes total sense when you think about what else we’ve done recently with neural networks.

@zombieregime 10 месяцев назад

As a mechanical engineer you should recognize that all they are doing is recognizing the displacement of one node and then directing other nodes to form the final shape. Its like if you had a human like statue, rigged with motors for human like motion and programmed it so that it would want to return to a neutral stance but if someone slowly pushed it over it would transition to a different pose based on how far it was being pushed. Its a basic transform function, like blending between two key frames. This video is trying to make something that should be cool on tis own sound futuristic by relating it to neural nets. Its not, and it isnt. Kids these days need to learn the difference between a transform algorithm and a neural network. If anything it sounds like the first step to having a T-1000. By the way.....Skynet was so afraid of the T-1000 liquid metal it kept it in a box at the bottom of the ocean surrounded by terminator hardware..... So making some is probably not a good idea....

@zombieregime 10 месяцев назад

@@ch1pnd413 It sounds like you're either a sycophant that is buying way too hard into their swinging bologna, or a purchased comment. There is nothing revolutionary here other than the material science that allowed the springy....sorry, 'compliant' elements to be so easily manufactured.

@CTimmerman 10 месяцев назад

@@zombieregime If Skynet has feelings, why doesn't it respect the feelings of others? Trauma is a poor excuse to harm innocent beings.

@zombieregime 10 месяцев назад

@@CTimmerman the part you skipped over was establishing why it should respect the feelings of others. Having feelings does not inherently imply an empathy towards other beings who express an adequate level of sentience. The cold hard truth lost on the youth of today, and honestly anyone else who hasnt given the world a think from an unbiased third party point of view, is that no entity is inherently obligated to act in your best interest. Also, it is impossible to regulate away unsavory behavior. Lastly, when the powers that be share your sensibilities its call progress, when they dont its called oppression. Oppression can come from any side, and is inched along by the refusal to consider concessions for those lifestyles you disagree with. Punishing the many for the sins of the few by way of wild assumptions compounded with the inability or unwillingness to hear and understand those whos rights, freedoms, and liberties policy built on assumptions affects. Your rights have a limit, and they end where another's begin. As theirs are limited to where yours begin. However, while that gives us a framework for how we may approach coexistence (but is not intended to be a instructional pamphlet, telling us how we should feel or behave in general. That is up to the person to conduct themselves respectfully. And if they cant figure out how to do that for themselves, to have their own thoughts, and feelings, separate from the zeitgeist, then maybe commentary on societal convention is something they shouldn't be engaging in....) What does any of that have to do with a machine and whatever behavior it exhibits that we might classify as 'feelings' or 'intent' or 'desire'? Why should a computer 'care' about you? Or anyone for that matter? And I do challenge you to avoid the trap of assuming any algorithm, however complex and misnomered, is actually sentient on any level....

@x.khann.x 8 месяцев назад

My heart goes out to the graduate students who did all this work. You guys are ferocious, you deserve only the best in life.

@blacklistnr1 10 месяцев назад

This is an incredible combination of an entertaining youtube video and a technical paper presentation! I wish more articles were presented like this

@6acosta9 10 месяцев назад

Watch @twominutepapers it’s similar I think

@blacklistnr1 10 месяцев назад

@@6acosta9Thanks, for the suggestion! It was interesting in the beginning, but it feels a bit mainstream nowadays, presenting the results instead of diving into the paper's details

@whatilearnttoday5295 10 месяцев назад

It immediately went off the rails at "Similar to biological brains"

@Hexcede 9 месяцев назад

@@whatilearnttoday5295 Not really

@etunimenisukunimeni1302 10 месяцев назад

I went from complete "what is this I don't even" to "okay this makes sense, cool" in 20 minutes. Very well presented, super interesting and understandable even to someone with zero experience in mechanical engineering.

@Dan-dy8zp 10 месяцев назад

Interesting, yet my instinctive reaction is that using a digital computer and sensors is going to be more cost effective than this 'compliant material' stuff.

@Hexcede 9 месяцев назад

@@Dan-dy8zp I don't believe the goal is for computation, I believe the goal is more physically focused. The compliant materials are pretty necessary for utilizing this stuff at a smaller scale, especially cheaply.

@michalchik 10 месяцев назад

In a general sense this is what bone and connective tissues do. They have built-in stress sensors that look for electrical signals that appear in weak spots in the bone and connective tissue. They rebuild the structure to fix those weak spot s and redistribute load.

@Castle3179 10 месяцев назад

Using these materials for robot bodies might help them walk better.

@omargoodman2999 10 месяцев назад

@@Castle3179 In the most extreme case, this is what nanotechnology would accomplish at some point. Instead of a solid bar used as a leg, for example, a composite of nano-scale versions of these nodes and beams which can independently function and reorient with, say, a goal of "optimize stress distribution", and a combination of load-bearing and stress absorbing materials around them could result in a synthetic version of bone tissue. And, if run in the opposite direction, power could be applied to the linkages in such a way that they contract on demand to make synthetic muscle tissue. Furthermore, the lattice isn't limited to two-dimensional organization. Tetrahedral lattice, I would anticipate, would likely be a highly optimal way to distribute forces throughout a volume rather than just across a plane. Though, when it comes to organic growth, like bone tissue, the structure tends to orient itself _along_ lines of stress so it's more like it would determine which paths require flexion and develop flexible connections along those lines, and which lines of stress require maximum stiffness and concentrate the most load-bearing material along them. So nano-cells within the material would periodically be redistributing stiff load-bearing material and soft cushioning material around within it to accomplish the creation of micro-struts and micro-cushions inside the composite material just as osteoblasts, osteocytes, and osteoclasts do for bone.

@poipoi300 10 месяцев назад

This is insane. Soon we'll be doing this kind of stuff with photolithography. Perhaps it'll be the next step in neural networks as a whole to increase efficiency.

@EmceeJoseph 10 месяцев назад

There are other ways to make Neural accelerator chips, so I think miniaturising this would be better for materials science like the video suggests.

@poipoi300 10 месяцев назад

@@EmceeJoseph Yes and those other ways aren't enough of an improvement over GPUs to be worth considering right now lol. That's a sentiment from Ilya Sutskever himself. There's nothing stopping it from being used for both applications.

@generalpurposevehicl6100 10 месяцев назад

@@poipoi300 The fact that this team made a tool that to simulate larger nets says a lot to how this not very useful for computation.

@poipoi300 10 месяцев назад

@@generalpurposevehicl6100 The simulation tool they've made is useful because it allows for rapid prototyping without the need of physical assembly or materials. I don't understand how you arrived to the conclusion you did, because there is no link with the premise.

@generalpurposevehicl6100 10 месяцев назад

@@poipoi300 I am refering to the material for use in computing.

@Sazoji 10 месяцев назад

I wonder if you could use plant cells to do something like this. Have a gas-filled vacuole inflate/deflate across a uniform foam of cells, which alters the tension against the cell walls, allowing for control over the material stiffness. plants already do this naturally to grow twards light, but imagine it being used as an organic wing. I imagine it would be made up of something like cactus flesh, filled with a microfluidic network to control local stiffness.

@hedgehog3180 10 месяцев назад

If nothing else plants probably serve as a good model.

@Sazoji 10 месяцев назад

@@hedgehog3180 I'm imagining you could compare it to the varioshore 3d printer filament, where you can change how dense the material can be. Just with living material that you'd produce in cell culture. plants normally change their cell size as they grow towards light, but the mechanism I'm thinking about is how cactus will uptake water and change their stiffness. maybe, something like those fruit molds farmers use could make a model object to compare. melon farmers sometimes use acrylic molds to make a cube shaped fruit or the like.

@ciruelo5921 10 месяцев назад

That's reallt smart

@Joseh-le4yl 10 месяцев назад

Interesting. What have you studied to be able to come up with something like that?

@Sazoji 10 месяцев назад

@@Joseh-le4yl my degree is in molecular biology, but I work in cell culture. I have an interest in microfluidics and 3d printing. this video is fascinating tho, I heard about complaint mechanisms, but trying to program them as if it's a neural net is crazy.

@cougarten 10 месяцев назад

I guess after trying the dynamic learning you could (mass) produce a hard-coded version with the same values and just 3D printing :)

@DigitalJedi 10 месяцев назад

I was thinking about the same thing. This is the FPGA for metamaterials.

@ShiroKage009 10 месяцев назад

I mean, you can mass produce ASICs made for a specific model (or type of model) and distirbute it with a copy of the software. It has many fewer points of failure.

@claws61821 10 месяцев назад

@@ShiroKage009Less than the FPGA or less than the mechanical array? I believe what @cougarten meant was to dynamically test the array for the target conditions and then send your client or manufacturing department a 3D print or a schematic model of the final lattice.

@ShiroKage009 10 месяцев назад

@@claws61821 a chip has fewer failure points than a mechanical system just because it's not a mechanical system.

@affegpus4195 10 месяцев назад

you probably can do it with proteins

@jamespray 10 месяцев назад

This is amazing. Miniaturized / nanoscale applications of this really could drive world-changing metamaterial developments. It's also a very helpful way to unpack and visualize the fairly opaque world of learning neural networks in general. I never mind waiting for content like this. Thanks so much for the walkthrough!

@dougaltolan3017 10 месяцев назад

Unfortunately, neural network learning comes under the heading of don't believe the hype. Psychosis and gaming are serious issues with neural networks, the consequences of which can, and are likely to, be catastrophic. Other machine learning paradigms exist, and may well be more appropriate.

@Blayzeing 10 месяцев назад

Absolutely fantastic! I look forward to seeing this get progressively miniaturised.

@Jamelith 10 месяцев назад

I look forward to it being developed in 3D.

@Jamelith 10 месяцев назад

What I mean is right now it processes esentially in a plane, an x, y axis. Wait until we can do this on an x, y, z axis!

@Schadrach42 10 месяцев назад

@@Jamelith Being serious, wouldn't that just require a different and significantly more complex hub design?

@cubicengineering4715 10 месяцев назад

Very interesting! Though it feels like there will be a lot of problems with miniaturising this type of system. My intuition tells me that most miniature things wouldn't be tunable by the connections between nodes, but rather the nodes themselves. For example I could imagine a theoretical case where each node has some sort of "pressure" that it applies universilly to all of its neighbors. It may even be as simple as laying out a latice of beads either of different materials, or hollow with different air pressures or wall thicknesses. Thus, what I would be most interested in seeing next is simulating a node-pressure centric model, to see if changing the adjustable factors from the beams to them would still be able to produce the behaviours that were exhibited in this video.

@dorotabudzyn7636 10 месяцев назад

This is fantastic way to present your paper. Very interesting research, I am looking forward to more work from your lab!

@smoothmidnightfudge7450 10 месяцев назад

Materials Science and Engineering dropout here. I couldn’t hack it in academia at that level, I had the smarts but it was too much stress and pressure. But I still love the subject matter, I think it’s absolutely fascinating, and stuff like this video is what sent me into that field in the first place. Thank you for the detailed breakdown, this was awesome to watch.

@flyingpotatoe1299 10 месяцев назад

In sweden you can study at university level at a slower pace if you wanted to, do you have that opportunity where you live? Such a shame to let it go if you liked it

@smoothmidnightfudge7450 10 месяцев назад

@@flyingpotatoe1299 in theory the option exists but it would have cost me a fortune. I’m in the US, tuition for the school I was attending is around 80,000 USD annually. Most of that was covered by financial aid but that only lasts 4 years, so if I took 5 or 6 to get my degree I’d have to pay near-full tuition. In any case, I have no desire to go back, at least not into MatSci. Career-prospect wise, it’s a bad fit for me, as I don’t have any interest in doing research and the job options outside of that are extremely competitive. I’m over having that stress in my life. Currently, I’m working on going back to college for a degree in English, with the end goal of going into technical writing. Much more my speed.

@spencert94 10 месяцев назад

I thought the whole point was it's a neural net where the weights have a physical meaning (i.e. the displacement), but you don't represent it that way or use gradient descent to optimize the weights. The main benefit of neural networks is that they are differentiable and so can be efficiently trained with gradient descent.

@emockensturm 10 месяцев назад

Yep. Agreed.

@dougaltolan3017 10 месяцев назад

The weights do have physical meaning, the beam stiffness. It is not sensible to have the weights define any dimension, since there are many impossible configurations, which would require calculation to avoid damage.

@avnertishby 10 месяцев назад

This bothered me too. In fact, if I understand correctly, there is no error back-propagation occuring in this setup. By using a genetic algorithm in the way that was described, this crucial step is simply avoided. Perhaps this is not true for the other optimisation methods studied? It seems like such a system would benefit from more rigorous weight tuning procedures.

@avnertishby 10 месяцев назад

@@dougaltolan3017how is beam stiffness information back propagted? The genetic algorithm appears to avoid this, if I understand correctly.

@xzendon 10 месяцев назад

You should be able to manufacture a much cheaper and easier to scale version of this by using electro-osmotic cells (cellulose membrane tube with internal electrode between two plates is probably the simplest) as the stiffness altering actuator. Simply increase the voltage on the cell to increase the internal pressure.

@davedsilva 9 месяцев назад

Cool. How did you figure this out?

@xzendon 9 месяцев назад

Just occurred to me while watching the video, but I think the slowed down thought process was something like this; ok, the minimum easy to control input is an electric impulse, which also allows us to sense the structure as well, so how to we translate electricity into force? Well there's no movement needed, so the actuator doesn't have to actually move, just increase the pressure it's exerting. Osmosis through a semipermeable membrane can be directly modulated by electric charge...

@astral6749 9 месяцев назад

As others have already mentioned, the weights/stiffness could probably be simulated and trained on a computer so that it would be cheaper and faster. Then, once training has finished, the resulting model could be manufactured with the determined stiffness between the nodes. Regardless, this is a really great paper and video. Good job on getting featured on the front cover as well.

@novahyper6731 7 месяцев назад

We need more research papers presented in more accessible formats like this. Great work.

@droko9 10 месяцев назад

I feel like having a lattice of adjustable stiffness beams is the much, much more impressive feat than the neural network part. Like, does such a lattice exist in usable ways (ie building or clothing scale devices)?

@ExtantFrodo2 10 месяцев назад

It was this underplayed note that rung out through the whole video. It was the tour du force that made possible the investigation of their tunability. As I remarked above I'd be very curious to see the tuned parameters fixed (glued) in place to see if the unpowered network behaves the same way. There are other videos on variable stiffness 3d prints producing non-linear behaviors. Using these principles to predict the behaviors of given prints would go a long way to making that become a standard engineering practice.

@MM3Soapgoblin 10 месяцев назад

@@ExtantFrodo2 That's pretty analogous to practical application of neural networks today. In many applications where the network needs to be deployed at the edge (not in a datacenter), the network is designed and trained on large purpose built servers. After the weights and biases are established for the network, a fixed voltage gate chip can be created that is small in size, low in power requirement, and extremely fast. That chip can then be deployed at the edge in small devices. It just requires a complete replacement if the network is later optimized. I can see that applying here. Use a complicated setup in the video to determine optimal parameters for the network design and task, then transfer those parameters to a fixed system as you described that can be easily and cheaply deployed.

@1Chitus 9 месяцев назад

This is fantastic way to present your paper.

@jake-o3843 10 месяцев назад

this is one of those things that is first off awesome to share with the world in this format (no way in hell i would have ever read the paper) and also an extremely interesting idea with genuine potential to change the world, thank you so much for taking the time to make such an entertaining and informative video!

@the.original.throwback 10 месяцев назад

The joys of turbulence and material science continue. It is interesting to contemplate where and how nature employs similar functions in organism behaviors.

@patrickryckman3867 9 месяцев назад

Whoever made this video if I had One billion dollars I would share it with you and develop this with you. Excellent explanation, rare I find something of such high quality.

@CliveBagley 10 месяцев назад

Very thought-provoking. Jolly good work from this team.

@jimmehdean012 10 месяцев назад

This is incredible. Bravo. So much to learn from this!

@dinhero21 10 месяцев назад

This is an idea that I had I wanted to share with yall. This idea has been partially implemented in the video but I want to extend it. What if instead of optimizing the model in the real world you created a computer simulation that would give you more accurate results and a much faster interface (because it's software software instead of software real world). Now that you are doing the simulation part purely digitally you don't really need such a complicated mechanism to vary the stiffness. Instead, you could export the result of the computer simulation in a format readable by 3D printers. Instead of your current mechanism, you could have something like a coil that could be stiffness-manipulated by varying its width. Now, yes, this is a much less "dynamic" approach because it does not allow you to change the values on-the-fly and requires you to 3D print your material every time you want to test it in the real world but as long as your Simulation -> Real World process is accurate enough you should not need to 3D print your material every time you want to test it and should be able to do it using only software and only need to 3D print it when you want to be absolutely sure that the material behaves as it should.

@JoshuaValerio 10 месяцев назад

Congrats on the front cover of Science!

@codyfan7161 10 месяцев назад

Thank you for making this video Professor Hopkins!

@kellymoses8566 10 месяцев назад

This almost feels like it should win some kind of award.

@mrmurphymil 10 месяцев назад

This format needs to be the standard for research papers going forward

@phamnuwen-wi5qh 10 месяцев назад

This was the most mind blowing thing I've learned in the past few years! Thankyou.

@cheaterxl243 10 месяцев назад

The most detailed video I have ever seen. I have only understand 1% but it is so beautiful to watch because it’s so well explained.

@RasberryPhi 10 месяцев назад

I´d loved to learn more about the interface of neuronal networks and machines! It was a really cool progect!

@henrylouis5143 10 месяцев назад

That's a fascinating idea! Instead of deploying the learning mechanics directly, we could potentially use computer simulation and optimization to design our desired model. By simulating and optimizing the design, we can determine the best configuration without the need for a trainable machine. Once we have the optimized model, we can then build it physically, thereby bypassing the energy-intensive process of training a machine from scratch. This approach has the potential to save a tremendous amount of energy while still achieving the desired final state.

@thingsarelifeis 10 месяцев назад

Found the computer scientist

@BaronVonScrub 10 месяцев назад

Thanks for this, this is super cool! It's given me inspiration for a potential project of my own, albeit much lower budget and tech. Consider a PLA 3D printed lattice in a similar configuration as the triangular one you used here, but using a slight curve on the beams to allow them to bend. Consider then pressing the lattice into a mold with a force, to the point of plastic deformation. The plastic deformation of the compliant mechanisms - the damage the beams suffer - could serve as a kind of learning process, reducing the weights of certain beams, and increasing the strain and thus weights on others. Setting this apart from traditional machine learning - aside from the medium - is that the training is not easily reversible; the plastic deformation can't be undone, and for a weakened beam to become relevant again can only happen within the context of other interacting beams becoming relatively weaker too. Thus, I don't think it could learn many behaviours, as the system is essentially lossy. I'm not aware of any literature that tests neural networks whose weights can only ever shift in one direction; they would naturally be less accurate, and you would have to take a very slow and conservative learning approach so as not to totally collapse the system, but I would be very interested to see how it goes. Perhaps I'll start off with that kind of computational model. I'm also not sure how effectively it would work with simply molding it to shape, as it could use the mold as a crutch with different output forces on different locations, resulting in a different shape when not constrained by the mold. Perhaps rather than a primitive mold, then, a rig of fource gauges at the output locations could be there and seek to find where the output force is zero at the desired location; if the material is overpressuring a certain output location, you can apply a counteractive force at JUST that output location to create plastic deformation until said output force IS zero. This would have to be done conservatively and stepwise, as reducing the error at that location will inevitably create more error across the other locations; the maximal error output would have to be tweaked slightly, then the next, etc. Would love to know your thoughts, and thanks again! :)

@HappyJackington 7 месяцев назад

This is an amazing idea. Thank you for synthesizing the concept from something that existed in software to something in the mechanical world. As this technology gets developed and shrinks in size, its applications will be limitless. This is so cool!

@achpek13 9 месяцев назад

This idea worth a Nobel prize! Great job, guys! In the future we will create a metamaterial that can morph into anything and be controlled by brain. This is real deal, I must say as an engineer.

@SamChaneyProductions 9 месяцев назад

Such incredible stuff. I love it when work in one field is applied to another seemingly unrelated field. Just goes to show that everything in life is interconnected

@ZenPyramid 10 месяцев назад

...mind totally blown! Mechanical neural networks, and you just demonstrated it! In my face! Oh goddess that's so beautiful, tyvm...x

@JessWLStuart 10 месяцев назад

Wow! The idea of making a material that can change its configuration based on learned input is amazing!

@althuelectronics5158 7 месяцев назад

Powerful💪😎 video amazing brother🌹🌹🌹

@ravemonkey7872 9 месяцев назад

Great. 90% research for defense industry. 🙌🙌

@fathom6424 9 месяцев назад

This is glorious. Not least of all because I thought of it forty years ago - but I shouldn't say that. The presentation is first rate and the narrator is very easy to listen to. To see a working model of this concept is truly beautiful.

@syahrul9282 10 месяцев назад

This is a very well made documentary! Felt like I'm watching a discovery channel or reading a publication.

@Seiffouri 10 месяцев назад

Interestingly I was thinking about a mechanical neural network made up of nodes and springs with variable tensions and now I see this!! Fascinating!

@graemecook8131 10 месяцев назад

I really commend the accessibility and transparency of this content. Excellent work, this seems like very promising technology!

@PartykongenBaddi 10 месяцев назад

This is really interesting and impressive! Your video also brought some methods to my attention that may be useful to me when making topology optimization add-ons for FEM packages where only the output and not the underlying stiffness matrix is available.

@richardshillam7075 9 месяцев назад

Well done all. Thoroughly enjoyable.

@bubbasplants189 10 месяцев назад

Amazing work, looking forward to seeing the progress on this and if it can be made using other methods.

@stefanguiton 10 месяцев назад

Excellent Work!

@argfasdfgadfgasdfgsdfgsdfg6351 9 месяцев назад

In all aspects: Great work. From the design, to the scientific exploration, to the visual presentation - excellent!

@aerobyrdable 10 месяцев назад

Just incredible. Thank you.

@caiobortoletto4363 6 месяцев назад

There are people that legitimately think that going to space is so crazy that we havent done it. Meanwhile, were doing this. Its nuts

@devlabz 10 месяцев назад

that has to be one of the most amazing things I've seen in a while

@GokuLevelKi 10 месяцев назад

This is fascinating research with amazing use cases via further development.

@isaaclinn2954 10 месяцев назад

This is beautiful! So many different concepts from different engineering classes are demonstrated in this video with elegant visual effects. Something especially promising seems to me to be vibration dampening. I recall that the LIGO has active vibration dampening to isolate its sensitive sensors from Earthly disturbances. If this could be trained to negate lots of different frequencies at every temperature, it would probably save some engineers somewhere a lot of work.

@Noble909 10 месяцев назад

Incredible! So cool. I'd love to see static models developed this way

@Axiomatic75 9 месяцев назад

Wow, this is a wondrous example of engineering.

@kylenolan3138 10 месяцев назад

I was a little surprised that what seemed to be a natural next step wasn't mentioned. I thought that they would construct a simple network of beams with the resultant fixed stiffneses to demonstrate that the target behaviors would be achieved.

@mylittleparody2277 10 месяцев назад

Super interesting video! Thank you a lot for sharing!

@MrSaemichlaus 10 месяцев назад

Excellent work and presentation! I felt hooked all the way through. I guess these lattices will at some point be etched or 3d-printed so the stiffnesses will be "hardcoded" into the geometry of the lattice elements. Basically a compliant structure with set stiffnesses. Maybe at some point the "resolution" of the lattice will become high enough so you could talk about a continuous stiffness distribution with an analytic description rather than a matrix of distinct values. Maybe behaviours could be trained in order of importance, first learning a common movement and then refining that by overlaying more precise modes of movement. Or maybe I have it backwards. On the topic of stiffness distribution, it could be represented by a bitmap. The layered compression technique in JPG format would likely go hand in hand with my previous point of layered precision.

@Scobbo 10 месяцев назад

This is absolutely amazing! And you just give us the designs for free. Thankyou for doing such great works!

@entropy_labs 9 месяцев назад

Awesome engineering!

@sam-is-a-human 10 месяцев назад

i remember the feeling of seeing pictures of the earliest computers, with their large, clunky electromagnets for bits, slow clock speeds, and room sized casings and thinking "look how far we've come". i hope in 60 years, i'll walk back into this video and think the same.

@Pedritox0953 10 месяцев назад

Great project!

@marinepower 10 месяцев назад

Is there a reason why something like gradient descent / backpropagation wasn't used to calculate the values as opposed to evolutionary search? Was the issue that the 'hard stops' prevented backpropagation from being used?

@dougaltolan3017 10 месяцев назад

Isn't gradient descent a feature of Nelder-Mead method?

@Embassy_of_Jupiter 10 месяцев назад

It might seem hard to compute, but in reality many neural networks are fully connected, meaning every node connects to every node in the next layer, while here each node only connects to 3 nodes in the next layer.

@petevenuti7355 10 месяцев назад

What do you mean by "in reality"‽ I'm actually serious, do you mean in practical use in a machine learning environment or do you mean biological systems? In biological systems even though long axons can connect to groups of neurons at a distance, I would not in any way consider it fully connected. If you know any references plotting actual connectivity vs proximity I'd be interested. As for machine learning environments I'd still argue, when you get to large models that need to be distributed amongst many systems, then being fully connected is an unlikely option.

@dougaltolan3017 10 месяцев назад

Yes, and no..... You are right that the nodes are not fully connected, but while there is only direct connection to 3 nodes in the next layer, there are also lateral connections, the effect will propogate sideways beyond those three nodes (attenuating with distance). In a contemporary NN there is no equivalence of that lateral connection.

@dougaltolan3017 10 месяцев назад

@@markaspen What is that 'good reason' and how does that relate to a network that is not fully connected? As for 60 years, you are glossing over the decade+ hiatus during the 70s and early 80s, during which little or no development was done. The post 80s work was so significantly more advanced than previous contributions, the difference is like modern cpus vs the first "computers" that were no more than programmable calculators. It was late 80s, early 90s when I first really became aware of neural networks and machine learning. Virtually right away I proposed the concept of NN psychoses. The idea was shot down, out of hand, by PhD researchers in the field. 20 years later there were reams of academic papers detailing exactly what I had put forward. So do consider my extensive knowledge, understanding, and scepticism of the topic in any reply.

@TonyOstrich 10 месяцев назад

Were other lattice configurations considered or tested at any point? I'd be curious how something like a hexagonal lattice performs.

@FixedAFT 10 месяцев назад

definitely helped me understand neural networks more even if not intended, Bravo!

@user-eq4hr5uk3f 10 месяцев назад

You could use the simulated network to generate a stiffness map for a certain behavior and then wire EDM a big aluminium plate compliant mechanism with these stiffness values. This results in a preprogrammed mechanical network that is easier to manufacture and scaleable.

@rklauco 10 месяцев назад

Now to replace the magnets with ceramics for piezzo-ceramic effect to minimize the size, use the piezzo effect for both actuating and measuring the position, make it microscopic and new era of materials is here. Amazing video, great explanation and excellent visualizations. Thank you!

@ragnarmarnikulasson3626 9 месяцев назад

this is amazing!

@andresdelapena1285 10 месяцев назад

Can't wait for those machine learning pillows.

@atomer13cz 8 месяцев назад

This is a great invention, thanks

@VirtuelleWeltenMitKhan 9 месяцев назад

awesome stuff

@sliver170 9 месяцев назад

The transistor was once quite a bit larger than it is now. Hopefully this material can be shrunk down and mass produced to that degree too. Will etching be the way to go?

@JackLe1127 9 месяцев назад

Have you guys thought about something like a tensegrity structure but modified so that 1 string can determine the tension of the joint? That way you don't need to sense the force into to apply the magnetic field dynamically. I imagine you can just rotate an axle to tighten or relax the string.

@HoogbyRuligan 10 месяцев назад

Great concept and the presentation is very very good and clear

@KalijahAnderson 10 месяцев назад

Interesting demonstration. Though I'd say the material itself isn't learning anything, just being tuned by a computer. Maybe I'm just nitpicking though. Either way, this is fascinating.

@lightspeed6302 6 месяцев назад

Mechanical Neural Network who knew so cool !

@KevinLarsson42 7 месяцев назад

This is awesome, great work!

@volodymyr3169 10 месяцев назад

Next level stuff right there!

@bouipozz 10 месяцев назад

This is excellent learning material

@Virtualblueart 10 месяцев назад

This made me think of the experiment where a programmable array was used to "evolve" a basic radio. In the end they bended up with a functional 2 way radio, but it contained components that weren't connected to any part of the circuit but could not be removed because the radio would stop working. It showed that "real world devices" might get results simulations would miss because we never thought of adding them in. I might have fuzzed up some of the details, it was some time ago I came across it.

@avnertishby 10 месяцев назад

Do you remember the name of the study or its authors?

@schvanger 10 месяцев назад

Hey John Hopkins! Nice work!

@jayneshpatel7925 10 месяцев назад

incredible👍

@semicell 7 месяцев назад

Incredible video and research. Truely next level work

@i_never_asked_for_an_alias 10 месяцев назад

Fascinating.

@jeremycochoy7771 10 месяцев назад

This idea is just amazing. Amazing.

@asantehunter 10 месяцев назад

Insanely cool concept,

@biobuu4118 10 месяцев назад

Amazing work and channel I'm glad to find ! A few months ago came to me this idea of mechanical computing kind of the same way you do here but with much more clumsy mechanics because I'm not engineer lol I couldn't figure out it was a neural network problem and was thinking more about a kind of crappy manual qbit processor if it makes sense to you. So I got the idea while looking at scissor extension arm and imaging that if all hinges could slide along both scissors it links, it will vary the position of the end of arm, the X,Y outputs, the start of each links of scissors being the inputs. I have the intuition that if the hinges, or node, could be controlled by some arduino and servos to slide onto the pair the result can be interesting and maybe able to achieve some of the computing you're doing here. But I now see flaws in my design that the scissor is rigid to a line and a lenght so the node hasn't as much freedom of movement as in this clever design. Insights needed for improvements and if someone wants to realise a prototype of my design please feel free but tell me :) Subscribed !

@milckshakebeans8356 9 месяцев назад

This is amazing. I wonder how far away is a two dimensional version of this

@automationsolution 10 месяцев назад

This is valuable! To all those people who would build on(a.k.a copying) this information, its my humble request that you please acknowledge this guy. Acknowledgement is like speaking the truth, its absence equivalent to thievery. As an EE, who absolutely depends on physics and engineering to learn a lot of math, I am thrilled to watch this. 🙏IND

@user-hi2xv3nw6y 10 месяцев назад

wow!! this is next level stuff right here well done!

@R67K 10 месяцев назад

mindblowing

@Creepyslandofdreams 10 месяцев назад

Congrats on getting into sceince! Amazing stuff.

@ultravidz 10 месяцев назад

Ridiculously fascinating

@theangry0077 10 месяцев назад

im in disbelief that this video has so few views, extremely fascinating technology, professional presentation and an entertaining video all in one!

@automationsolution 10 месяцев назад

Yes, exactly. The channel name and the unassuming title of the video is untainted by even an iota of marketing true to the engineering tradition.

@justinklenk 9 месяцев назад

Bravo - brilliant work, groundbreaking resulting implications - truly magnificent, you guys. A spectacular achievement for the world, and for material design. Presentation and video? Satisfyingly awesome.