Let Robots Do Your Lab Work

Dina Genkina: Hello. I’m Dina Genkina for IEEE Spectrum‘s Fixing the Future. Earlier than we begin, I wish to let you know which you could get the most recent protection from a few of Spectrum’s most necessary beeps, together with AI, Change, and Robotics, by signing up for considered one of our free newsletters. Simply go to spectrum.ieee.orgnewsletters to subscribe. At present, a visitor is Dr. Benji Maruyama, a Principal Supplies Analysis Engineer on the Air Force Research Laboratory, or AFRL. Dr. Maruyama is a supplies scientist, and his analysis focuses on carbon nanotubes and making analysis go quicker. However he’s additionally a person with a dream, a dream of a world the place science isn’t one thing completed by a choose few locked away in an ivory tower, however one thing most individuals can take part in. He hopes to begin what he calls the billion scientist motion by constructing AI-enabled analysis robots which can be accessible to all. Benji, thanks for approaching the present.

Benji Maruyama: Thanks, Dina. Nice to be with you. I admire the invitation.

Genkina: Yeah. So let’s set the scene a bit bit for our listeners. So that you advocate for this billion scientist motion. If every part works amazingly, what would this appear to be? Paint us an image of how AI will assist us get there.

Maruyama: Proper, nice. Thanks. Yeah. So one of many issues as you set the scene there may be proper now, to be a scientist, most individuals must have entry to an enormous lab with very costly tools. So I believe prime universities, authorities labs, business people, a lot of tools. It’s like 1,000,000 {dollars}, proper, to get considered one of them. And admittedly, simply not that many people have entry to these sorts of devices. However on the similar time, there’s in all probability numerous us who wish to do science, proper? And so how can we make it in order that anybody who needs to do science can attempt, can have entry to devices in order that they’ll contribute to it. In order that’s the fundamentals behind citizen science or democratization of science so that everybody can do it. And a technique to think about it’s what occurred with 3D printing. It was that as a way to make one thing, you needed to have entry to a machine store or possibly get fancy instruments and dyes that might price tens of 1000’s of {dollars} a pop. Or if you happen to wished to do electronics, you needed to have entry to very costly tools or providers. However when 3D printers got here alongside and have become very cheap, abruptly now, anybody with entry to a 3D printer, so possibly in a faculty or a library or a makerspace may print one thing out. And it could possibly be one thing enjoyable, like a sport piece, however it may be one thing that bought you to an invention, one thing that was possibly helpful to the neighborhood, was both a prototype or an precise working machine.

And so actually, 3D printing democratized manufacturing, proper? It made it in order that many extra of us may do issues that earlier than solely a choose few may. And in order that’s the place we’re making an attempt to go along with science now, is that as a substitute of solely these of us who’ve entry to large labs, we’re constructing analysis robots. And once I say we, we’re doing it, however now there are numerous others who’re doing it as nicely, and I’ll get into that. However the instance that we have now is that we took a 3D printer which you could purchase off the web for lower than $300. Plus a few additional components, a webcam, a Raspberry Pi board, and a tripod actually, so solely 4 elements. You will get all of them for $300. Load them with open-source software program that was developed by AFIT, the Air Force Institute of Technology. So Burt Peterson and Greg Captain [inaudible]. We labored collectively to construct this absolutely autonomous 3D printing robotic that taught itself learn how to print to raised than producer’s specs. In order that was a extremely enjoyable advance for us, and now we’re making an attempt to take that very same concept and broaden it. So I’ll flip it again over to you.

Genkina: Yeah, okay. So possibly let’s speak a bit bit about this automated analysis robotic that you simply’ve made. So proper now, it really works with a 3D printer, however is the large image that sooner or later it’s going to present folks entry to that million greenback lab? How would that appear to be?

Maruyama: Proper, so there are totally different fashions on the market. One, we simply did a workshop on the College of— sorry, North Carolina State College about that very downside, proper? So there’s two fashions. One is to get low-cost scientific instruments just like the 3D printer. There’s a few totally different chemistry robots, one out of College of Maryland and NIST, one out of College of Washington which can be within the type of 300 to 1,000 {dollars} vary that makes it accessible. The opposite half is form of the person facility mannequin. So within the US, the Division of Power Nationwide Labs have many person services the place you’ll be able to apply to get time on very costly devices. Now we’re speaking tens of thousands and thousands. For instance, Brookhaven has a synchrotron mild supply the place you’ll be able to join and it doesn’t price you any cash to make use of the ability. And you may get days on that facility. And in order that’s already there, however now the advances are that by utilizing this, autonomy, autonomous closed loop experimentation, that the work that you simply do shall be a lot quicker and rather more productive. So, for instance, on ARES, our Autonomous Analysis System at AFRL, we truly had been in a position to do experiments so quick {that a} professor who got here into my lab stated, it simply took me apart and stated, “Hey, Benji, in per week’s value of time, I did a dissertation’s value of analysis.” So possibly 5 years value of analysis in per week. So think about if you happen to preserve doing that week after week after week, how briskly analysis goes. So it’s very thrilling.

Genkina: Yeah, so inform us a bit bit about how that works. So what’s this method that has sped up 5 years of analysis into per week and made graduate college students out of date? Not but, not but. How does that work? Is that the 3D printer system or is {that a}—

Maruyama: So we began with our system to develop carbon nanotubes. And I’ll say, truly, once we first considered it, your remark about graduate college students being absolute— out of date, sorry, is attention-grabbing and necessary as a result of, once we first constructed our system that labored it 100 instances quicker than regular, I assumed that could be the case. We referred to as it type of graduate scholar out of the loop. However once I began speaking with individuals who specialise in autonomy, it’s truly the alternative, proper? It’s truly empowering graduate college students to go quicker and in addition to do the work that they wish to do, proper? And so simply to digress a bit bit, if you concentrate on farmers earlier than the Industrial Revolution, what had been they doing? They had been plowing fields with oxen and beasts of burden and hand plows. And it was onerous work. And now, after all, you wouldn’t ask a farmer as we speak to surrender their tractor or their mix harvester, proper? They might say, after all not. So very quickly, we count on it to be the identical for researchers, that if you happen to requested a graduate scholar to surrender their autonomous analysis robotic 5 years from now, they’ll say, “Are you loopy? That is how I get my work completed.”

However for our authentic ARES system, it labored on the synthesis of carbon nanotubes. In order that meant that what we’re doing is making an attempt to take this method that’s been fairly nicely studied, however we haven’t found out learn how to make it at scale. So at tons of of thousands and thousands of tons per 12 months, type of like polyethylene manufacturing. And a part of that’s as a result of it’s sluggish, proper? One experiment takes a day, but additionally as a result of there are simply so many various methods to do a response, so many various mixtures of temperature and stress and a dozen totally different gases and half the periodic desk so far as the catalyst. It’s simply an excessive amount of to only brute drive your manner by. So although we went from experiments the place we may do 100 experiments a day as a substitute of 1 experiment a day, simply that combinatorial house was vastly overwhelmed our capability to do it, even with many analysis robots or many graduate college students. So the thought of getting artificial intelligence algorithms that drive the analysis is vital. And in order that capability to do an experiment, see what occurred, after which analyze it, iterate, and continuously be capable to select the optimum subsequent greatest experiment to do is the place ARES actually shines. And in order that’s what we did. ARES taught itself learn how to develop carbon nanotubes at managed charges. And we had been the primary ones to try this for materials science in our 2016 publication.

Genkina: That’s very thrilling. So possibly we are able to peer below the hood a bit little bit of this AI mannequin. How does the magic work? How does it decide the subsequent greatest level to take and why it’s higher than you possibly can do as a graduate scholar or researcher?

Maruyama: Yeah, and so I believe it’s attention-grabbing, proper? In science, numerous instances we’re taught to carry every part fixed, change one variable at a time, search over that total house, see what occurred, after which return and take a look at one thing else, proper? So we cut back it to at least one variable at a time. It’s a reductionist method. And that’s labored very well, however numerous the issues that we wish to go after are just too complicated for that reductionist method. And so the good thing about having the ability to use synthetic intelligence is that top dimensionality is not any downside, proper? Tens of dimensions search over very complicated high-dimensional parameter house, which is overwhelming to people, proper? Is simply principally bread and butter for AI. The opposite half to it’s the iterative half. The fantastic thing about doing autonomous experimentation is that you simply’re continuously iterating. You’re continuously studying over what simply occurred. You may additionally say, nicely, not solely do I do know what occurred experimentally, however I’ve different sources of prior data, proper? So for instance, ultimate gasoline regulation says that this could occur, proper? Or Gibbs section rule would possibly say, this could occur or this could’t occur. So you should utilize that prior data to say, “Okay, I’m not going to do these experiments as a result of that’s not going to work. I’m going to attempt right here as a result of this has the most effective probability of working.”

And inside that, there are lots of totally different machine studying or synthetic intelligence algorithms. Bayesian optimization is a well-liked one that can assist you select what experiment is greatest. There’s additionally new AI that individuals are making an attempt to develop to get higher search.

Genkina: Cool. And so the software program a part of this autonomous robotic is accessible for anybody to obtain, which can also be actually thrilling. So what would somebody must do to have the ability to use that? Do they should get a 3D printer and a Raspberry Pi and set it up? And what would they be capable to do with it? Can they only construct carbon nanotubes or can they do extra stuff?

Maruyama: Proper. So what we did, we constructed ARES OS, which is our open supply software program, and we’ll ensure to get you the GitHub link in order that anybody can obtain it. And the thought behind ARES OS is that it gives a software program framework for anybody to construct their very own autonomous analysis robotic. And so the 3D printing instance shall be on the market quickly. But it surely’s the place to begin. In fact, if you wish to construct your personal new form of robotic, you continue to need to do the software program growth, for instance, to hyperlink the ARES framework, the core, if you’ll, to your specific {hardware}, possibly your specific digicam or 3D printer, or pipetting robotic, or spectrometer, no matter that’s. We have now examples on the market and we’re hoping to get to a degree the place it turns into rather more user-friendly. So having direct Python connects so that you simply don’t— at present it’s programmed in C#. However to make it extra accessible, we’d prefer it to be arrange in order that if you are able to do Python, you’ll be able to in all probability have good success in constructing your personal analysis robotic.

Genkina: Cool. And also you’re additionally engaged on a instructional model of this, I perceive. So what’s the standing of that and what’s totally different about that model?

Maruyama: Yeah, proper. So the academic model goes to be– its type of composition of a mix of {hardware} and software program. So what we’re beginning with is a low-cost 3D printer. And we’re collaborating now with the University at Buffalo, Materials Design Innovation Department. And we’re hoping to construct up a robotic based mostly on a 3D printer. And we’ll see the way it goes. It’s nonetheless evolving. However for instance, it could possibly be based mostly on this very cheap $200 3D printer. It’s an Ender 3D printer. There’s one other printer on the market that’s based mostly on University of Washington’s Jubilee printer. And that’s a really thrilling growth as nicely. So professors Lilo Pozzo and Nadya Peek on the College of Washington constructed this Jubilee robotic with that concept of accessibility in thoughts. And so combining our ARES OS software program with their Jubilee robotic {hardware} is one thing that I’m very enthusiastic about and hope to have the ability to transfer ahead on.

Genkina: What’s this Jubilee 3D printer? How is it totally different from an everyday 3D printer?

Maruyama: It’s very open supply. Not all 3D printers are open supply and it’s based mostly on a gantry system with interchangeable heads. So for instance, you may get not only a 3D printing head, however different heads which may do issues like do indentation, see how stiff one thing is, or possibly put a digicam on there that may transfer round. And so it’s the flexibleness of having the ability to decide totally different heads dynamically that I believe makes it tremendous helpful. For the software program, proper, we have now to have a great, accessible, user-friendly graphical person interface, a GUI. That takes effort and time, so we wish to work on that. However once more, that’s simply the {hardware} software program. Actually to make ARES a great instructional platform, we have to make it so {that a} trainer who’s can have the bottom activation barrier doable, proper? We would like he or she to have the ability to pull a lesson plan off of the web, have supporting YouTube movies, and truly have the fabric that could be a absolutely developed curriculum that’s mapped towards state requirements.

In order that, proper now, if you happen to’re a trainer who— let’s face it, lecturers are already overwhelmed with all that they need to do, placing one thing like this into their curriculum could be numerous work, particularly if you must take into consideration, nicely, I’m going to take all this time, however I even have to satisfy all of my instructing requirements, all of the state curriculum requirements. And so if we construct that out in order that it’s a matter of simply wanting on the curriculum and simply checking off the bins of what state requirements it maps to, then that makes it that a lot simpler for the trainer to show.

Genkina: Nice. And what do you assume is the timeline? Do you count on to have the ability to do that someday within the coming 12 months?

Maruyama: That’s proper. This stuff at all times take longer than hoped for than anticipated, however we’re hoping to do it inside this calendar 12 months and really excited to get it going. And I’d say to your listeners, if you happen to’re inquisitive about working collectively, please let me know. We’re very enthusiastic about making an attempt to contain as many individuals as we are able to.

Genkina: Nice. Okay, so you might have the academic model, and you’ve got the extra analysis geared model, and also you’re engaged on making this instructional model extra accessible. Is there one thing with the analysis model that you simply’re engaged on subsequent, the way you’re hoping to improve it, or is there one thing you’re utilizing it for proper now that you simply’re enthusiastic about?

There’s a variety of issues that we’re very enthusiastic about the potential of carbon nanotubes being produced at very massive scale. So proper now, folks might bear in mind carbon nanotubes as that nice materials that type of by no means made it and was very overhyped. However there’s a core group of us who’re nonetheless engaged on it due to the necessary promise of that materials. So it’s materials that’s tremendous sturdy, stiff, light-weight, electrically conductive. A lot better than silicon as a digital electronics compute materials. All of these nice issues, besides we’re not making it at massive sufficient scale. It’s truly used fairly considerably in lithium-ion batteries. It’s an necessary software. However apart from that, it’s type of like the place’s my flying automotive? It’s by no means panned out. However there’s, as I stated, a gaggle of us who’re working to essentially produce carbon nanotubes at a lot bigger scale. So massive scale for nanotubes now could be type of within the kilogram or ton scale. However what we have to get to is tons of of thousands and thousands of tons per 12 months manufacturing charges. And why is that? Nicely, there’s an excellent effort that got here out of ARPA-E. So the Division of Power Superior Analysis Initiatives Company and the E is for Power in that case.

So that they funded a collaboration between Shell Oil and Rice College to pyrolyze methane, so pure gasoline into hydrogen for the hydrogen economic system. So now that’s a clear burning gasoline plus carbon. And as a substitute of burning the carbon to CO2, which is what we now do, proper? We simply take pure gasoline and feed it by a turbine and generate electrical energy as a substitute of— and that, by the best way, generates a lot CO2 that it’s inflicting international climate change. So if we are able to do this pyrolysis at scale, at tons of of thousands and thousands of tons per 12 months, it’s actually a save the world proposition, that means that we are able to keep away from a lot CO2 emissions that we are able to cut back international CO2 emissions by 20 to 40 p.c. And that’s the save the world proposition. It’s an enormous endeavor, proper? That’s an enormous downside to deal with, beginning with the science. We nonetheless don’t have the science to effectively and successfully make carbon nanotubes at that scale. After which, after all, we have now to take the fabric and switch it into helpful merchandise. So the batteries is the primary instance, however occupied with changing copper for electrical wire, changing metal for structural supplies, aluminum, all these sorts of purposes. However we are able to’t do it. We are able to’t even get to that form of growth as a result of we haven’t been in a position to make the carbon nanotubes at ample scale.

So I’d say that’s one thing that I’m engaged on now that I’m very enthusiastic about and making an attempt to get there, however it’s going to take some good developments in our analysis robots and a few very good folks to get us there.

Genkina: Yeah, it appears so counterintuitive that making every part out of carbon is nice for reducing carbon emissions, however I assume that’s the break.

Maruyama: Yeah, it’s attention-grabbing, proper? So folks speak about carbon emissions, however actually, the molecule that’s inflicting international warming is carbon dioxide, CO2, which you get from burning carbon. And so if you happen to take that methane and parallelize it to carbon nanotubes, that carbon is now sequestered, proper? It’s not going off as CO2. It’s staying in stable state. And never solely is it simply not going up into the ambiance, however now we’re utilizing it to exchange metal, for instance, which, by the best way, metal, aluminum, copper manufacturing, all of these issues emit a lot of CO2 of their manufacturing, proper? They’re power intensive as a fabric manufacturing. So it’s form of ironic.

Genkina: Okay, and are there every other analysis robots that you simply’re enthusiastic about that you simply assume are additionally contributing to this democratization of science course of?

Maruyama: Yeah, so we talked about Jubilee, the NIST robotic, which is from Professor Ichiro Takeuchi at Maryland and Gilad Kusne at NIST, Nationwide Institute of Requirements and Expertise. Theirs is enjoyable too. It’s LEGO as. So it’s truly based mostly on a LEGO robotics platform. So it’s an precise chemistry robotic constructed out of Legos. So I believe that’s enjoyable as nicely. And you’ll think about, similar to we have now LEGO robotic competitions, we are able to have autonomous analysis robotic competitions the place we try to do analysis by these robots or competitions the place everyone type of begins with the identical robotic, similar to with LEGO robotics. In order that’s enjoyable as nicely. However I’d say there’s a rising variety of folks doing these sorts of, to begin with, low-cost science, accessible science, however particularly low-cost autonomous experimentation.

Genkina: So how far are we from a world the place a highschool scholar has an concept they usually can simply go and carry it out on some autonomous analysis system at some high-end lab?

Maruyama: That’s a extremely good query. I hope that it’s going to be in 5 to 10 years, that it turns into moderately commonplace. But it surely’s going to take nonetheless some vital funding to get this going. And so we’ll see how that goes. However I don’t assume there are any scientific impediments to getting this completed. There’s a vital quantity of engineering to be completed. And typically we hear, oh, it’s simply engineering. The engineering is a big downside. And it’s work to get a few of these issues accessible, low price. However there are many nice efforts. There are individuals who have used CDs, compact discs to make spectrometers out of. There are many good examples of citizen science on the market. But it surely’s, I believe, at this level, going to take funding in software program, in {hardware} to make it accessible, after which importantly, getting college students actually up to the mark on what AI is and the way it works and the way it may also help them. And so I believe it’s truly actually necessary. So once more, that’s the democratization of science is that if we are able to make it accessible to everybody and accessible, then that helps folks, everybody contribute to science. And I do consider that there are necessary contributions to be made by bizarre residents, by individuals who aren’t you understand PhDs working in a lab.

And I believe there’s numerous science on the market to be completed. In case you ask working scientists, nearly nobody has run out of concepts or issues they wish to work on. There’s many extra scientific issues to work on than we have now the time the place individuals are funding to work on. And so if we make science cheaper to do, then abruptly, extra folks can do science. And so these questions begin to be resolved. And so I believe that’s tremendous necessary. And now we have now, as a substitute of, simply these of us who work in large labs, you might have thousands and thousands, tens of thousands and thousands, as much as a billion folks, that’s the billion scientist concept, who’re contributing to the scientific neighborhood. And that, to me, is so highly effective that many extra of us can contribute than simply the few of us who do it proper now.

Genkina: Okay, that’s an excellent place to finish on, I believe. So, as we speak we spoke to Dr. Benji Maruyama, a fabric scientist at AFRL, about his efforts to democratize scientific discovery by automated analysis robots. For IEEE Spectrum, I’m Dina Genkina, and I hope you’ll be part of us subsequent time on Fixing the Future.