The AI Revolution

by US News and World Report U happy to report that UC Berkeley is retaining this position as the top Public University in this country yeah of course that wouldn't be possible with outstanding alumni and students um and the students are the ones who attract and keep our outstanding faculty here you're you are our inspiration so um just wanted to mention because the topic is AI today uh we do have consistently top ranked programs in computer science and electrical engineering these are the foundational fields for artificial intelligence which will be the focus of today's uh presentation um so Berkeley you might know is also known for entrepreneurship so artificial intelligence today is really having a revolutionary impact not only on our personal and social lives but also in pretty much every field of human endeavor and at Berkeley we really liked this C like this culture of collaboration to solve the world's biggest problems because as Engineers we want to really benefits people and Society so a lot of multi-disciplinary problems that we are working on today are include Health sustainability and um democracy and equality um so as in people depend more and more on Information Systems Ai and so on the the challenge of maintaining a democratic and Equitable uh future for all becomes greater so AI is accelerating progress but also presents new challenges in itself so that I think these will be themes that come out of the panel today and to translate our Innovations here more quickly to commercial products we have become a very vibrant engine for Innovation and Entrepreneurship so today at Berkeley we have a really full entrepreneurship ecosystem oh you might little known fact is that Berkeley today is now the the university that has produced the most alumni who go on to F to start uh companies that are venture-backed so we're the top um entrepreneurship um university in the world today um the amount of money that our graduates raise is less than the second ranked uh school which is Stanford but I think that that shows that we train our students to do more with less and I think that's important if we care about sustainability right using up the world's resources faster is not necessarily a good thing so I think it's all very consistent um I just want to point out the satara center for entrepreneurship and technology has all kinds of programs for our students in the College of Engineering but we welcome students from across the campus so the sataria center educates over 2,000 students every year on entrepreneurship the Berkeley method of Entrepreneurship give them opportunities to work together to come up with new ideas for new products and those 2,000 over 2,000 students represent over 170 Majors across the campus so this is this is pretty much open to any student across the campus Because we want future engineering leaders to be able to work together together collaboratively with all people across society and thear Center will be moving to the new engineering center which is under construction today and so that's like the most visible project that that is underway and that's just um uh epitomizes or or embodies the cultural transformation that has been happening within the college we want engineering to be welcoming and inclusive um because that's the best way we can ensure that a future shaped by Engineers is going to be Equitable and sustainable we have the me management entrepreneurship and Technology Program which helps students earn both a engineering Bachelor's of Science degree and a a business bachelor's science degree within all within four years and uh to help our faculty we have a baker fellow program to help give them funding to bridge that Gap from the research lab stage to the VC or you know commercialization stage there's kind of a valley of death um where you need some funding it's not really basic research but you need funding to show proof of concept before you can raise money to start a company so Baker fellows program has been very successful most of most of the faculty in that program are engineers and we have a Berkeley Sky Deck which is a joint venture between the College of Engineering High School of Business and the vice Chancellor for research we um it's an incubator and accelerator for startup companies um and a lot of the companies actually have uh AI based products so without further Ado I'd like to move on to introduce our oh uh one interesting um good piece of good news is that Time Magazine recently published its list of 100 most influential people in Ai and among these people uh out of 100 11 are members of the Cal Community that's pretty amazing right includes our current students so current students alums faculty and a member of our uh engineering my engineering Advisory board so to give give you an even deeper picture of the work Happening Here in the college I'm delighted to have my colleagues here with us today so let me start to introduce them first is uh Jill finon she's the managing director of the Citrus Innovation Hub she which is ex Jill just say so the Innovation Hub is dedicated to accelerating it research in the interest of society so Jill co-leads the UC systemwide inclusive Innovation and Equitable entrepreneurship initiative she um produces the edge in Tech blog and the future of work podcast and she's also a cal Alum thank you Joel for joining us next I'd like to introduce profess Professor Saif salahudin he is the tsmc distinguished distinguished professor of electrical engineering Computer Sciences um he's he's really doing Leading Edge research advancing Next Generation microelectronics uh which essentially includes the integrated circuits uh under pending the AI Revolution so the work he's doing is really opening doors to enable these computer chips to do more with less energy and less power less time um so much more energy efficient uh Computing devices in the future now you might have seen last week uh an announcement from the Department US Department of Defense their chips act program The microelectronics Commons it turns out uh well Saif is a uh the leader of the um Berkeley uh portion of that program so Berkeley and Stanford together are collaborating to lead one of the regional Innovation hubs for the Northwest um Pacific region of the United States so sa is a leader of that program which is bringing many millions of dollars U for research and to upgrade our research facilities here so really appreciate his leadership in in the foundational technology that underpins Ai and then finally Professor Clare Tomlin is here with us she's the chair of the Department of electrical engineering and Computer Sciences thank you for your warm welcome she's also she holds a distinguished um James and Katherine La chair in in engineering and she's a member of the US National Academy of engineering and she is one of our I won't say many but not uncommon uh MacArthur genius Award winners um so she yeah this is a really prestigious award um that and we're really proud to have her on our um among in our community um so she Claire has led the research of hybrid systems and control theory um emphasizing unmanned aerial Vehicles so this includes drones and she really has helped to make sure that air traffic control systems work safely and also her control theory and hybrid systems pertain to power grid the electric grid uh control and modeling and and finally modeling of biological systems so she's really an expert in um modeling and Engineering Systems and no matter from biological to physical to Virtual so please join me in welcoming all of our panelists and I'd like to just introduce have them up each come up to give a really short introductory presentation starting with Jill and then then sa and then Claire and then after that we'll have them sit down and we can start asking them some questions okay so Joel would you like to start us off please welcome great to see everybody here and thank you suj it's so wonderful to work with with you um really a champion of expanding diversity and gender equity in Tech and so I'm thrilled to be here and I'm going to give you kind of the thousand foot View and then our colleagues will go into some of the more nitty-gritty engineering stuff that will get you very excited uh as you might see here these images are generated by generative AI the dolly not chat GPT but I put in things like I want to see a real student and a bear in artificial intelligence on a berkele on a camp campus right and so you play around with these Technologies and you see what you can get but you also see what doesn't work and what the problems are and so these were a couple images generated and it's interesting because now ai crosses everything so I'm part of citrus which is as she said the center for it research in the interest of society and we focus on Aviation health and climate we used to have a separate initiative called people in robotics but we've decided that's so important across all of these features all of these categories that it's really a crosscutting initiative so how do we look at Ai and health how do we look at Ai and climate and so those are crosscutting along with things like diversity equity and inclusion Workforce Development and policy issues so Citrus actually has a policy lab to look at Tech policy to look at how are we creating the guard rails that we need with these expansive and growing Technologies so I got really curious about AI from this equity and inclusion lens and I started to look at the fact that AI was being used by almost every company to do hiring and I'm this is very interesting let's look at all the different ways it's deciding who sees the job because you use AI to optimize where it's shown it's deciding who is applying and is it increasing efficiency it's filtering and one of the big challenges with filtering is if you take an old job description and keep adding requirements to it it becomes really hard to fill that job if you have to meet every requirement it becomes this unicorn job description because there are so many requirements so we really need to rethink how we write job descriptions in the era of AI because you don't want to filter out all of the people who are qualified for the job or people who meet 98% of the requirements you don't want to filter those folks out but that can happen today so it was really interesting for me to think about this and it really raised the question of equity how is AI going to affect Equity but it's even bigger than that it's survival and so this was a statement that was put out and signed by many many scientists including Don song Here who's uh part of the computer science and they say specifically mitigating the risk of Extinction from AI should be a global priority right up there with pandemics and nuclear war and this is the important part it's not an either or it's a yes and we have to deal with climate change and we have to deal with AI so how are we going to do that so this is what we've been told about you know what's happening because of all of the Automation and AI that's happening so 85 million jobs displaced and primarily that's going to be low skilled jobs that are impacted right anything that can be automated will be automated especially postco and we talk about the skills needed the workforce needed all of these things are going to change and then generative AI came out and that was gamechanging right because now it's 300 million jobs now it's this idea that it's not blue collar jobs it's white collar jobs it's Green Collar jobs it's blue collar jobs every collar job is going to be impacted by AI so we have to again think about this a little bit differently so I had the opportunity to speak with itai Shu who is a a faculty the Berkeley extension and he was talking about the fact that AI is creeping into what the students turn in at school right in the University it's in their in their papers that they're turning in and he asked the question you know how do academic institutions respond to the fact that AI is out there and I loved what he said he said I don't know what the right answer is but I know what the two wrong answers are one is to put the responsibility on the individual teacher to to figure out a policy and the second is zero tolerance you can't use it in my class why is that such a bad approach because when they graduate the companies are going to expect them to be able to use AI to optimize productivity to be a better programmer to do their work better and more efficiently they you can't say don't use it in school and then expect them to be ready to use it in the workplace so students don't see this as a problem um this again was with II he said 72% of his students don't associate it with dishonesty or cheating and that by a significant margin they see it as a way to get unstuck to figure out what they're doing wrong to problem solve to get answers to see different ways of solving things so the other thing I find super interesting about AI is that this is not a tool in the traditional sense of a tool it's really a collaboration partner and what do I mean mean by that I mean that when you ask a generative AI to do something you're going to revise they're going to give you an answer you're going to look at the answer you're going to say no I need it to be shorter I need to be more professional I need it to be more scientifically based I need this you want to create guard rails so you're what they call engineering The Prompt but you're also iterating it's a back and forth collaboration and so I noted a couple of things to highlight here code generation you'll start coding and it'll I think you're trying to do this will this code work and that code will pop up and you'll be like yeah perfect saves me time no typos it's out there or you might look at it and go nope that's not going to work I need to change it I need to iterate on it I need to adapt it to what it needs to be and then I also wanted to highlight this one storytelling because I saw another panel that I thought was absolutely fascinating this kindergarten teacher at a low resource School said he was using AI in the kindergarten and I'm like whoa take a step back tell me more why is this the case and he said I'll sit there with my students and the little four-year-old boy I'll say what do you want to tell a story about and he'll say I want to tell a story about a four-year-old boy who goes to the moon and plants flower seeds and so they'll be like AI write a story kindergarten reading level for a little boy who goes to the moon and plants flowers boom there it is that's how fast it is and he's like is that my story and he's excited what has it done it's debunked the technology it's made them comfortable with the technology it's also made them want to read it's also made them creative I want to write a different story let's come up with a different prompt it really fuels the imagination so I think this is part of thinking about this is not a grammar checker right this is not just a run through and see what you get and you have to be very critical about what you get back so as we were discussing earlier Berkeley is addressing this in a couple of ways one we want everyone to innovate we want want everyone to think about AI as their responsibility and so we do that by having this very robust ecosystem and yes this does say number two but as she said boom number one so this is new and yeah so this is fantastic and this speaks to not only um the robustness of the ecosystem but the collaboration in the ecosystem how do we work together how do we make sure we hand hand off entrepreneurs to other people and how do we bring more people into it so as you were saying we have the satra center for entrepreneurship and technology and part of that is we run challenge Labs we ask students to create startups in 15 weeks it's crazy it's amazing and it pulls from all of these different Majors so you as a technologist can leverage a journalism student for communication and customer interviews and you can utilize um you know somebody from soci sociology really understands the problem space it's fantastic and so we actually use the students here as innovators and residents to peer mentor other students who are doing startups because they've been through the cycle of it we also really focus on that applied learning so our Workforce Innovation program placing them in jobs in semiconductor companies placing them in jobs where they're using data to solve real problems and we also try to debunk the myth so this is brand Niki who heads the Citrus policy lab and her podcast is all about those myths that we believe about technology and how can we debunk them so really building in the change making so everybody who comes to Cal sees thems as a change maker they have agency to be make change what do you want to change I want to change education I want to change government I want to change policy whatever it is you can be a change maker and we're trying to get them when they first come to Cal and when they transfer to Cal because we want every body thinking this way right when they come in so I would be remiss if I didn't leave you with five things for all of you in the audience to think about right now so the first is in the era of AI major and being human so this is about empathy creativity unpredictableness because you're the ones who are going to problem solve learn continuously this is suj running a LinkedIn course on applied AI so this is about responsible AI but looking at in domains like Health looking at climate looking at social media looking at HR and looking at how do we need to think differently about these sectors and you need to play with it because if you don't know what doesn't work you can't solve for problems and so this was actually Brandy naaki she is a ballet person when she's not running the Citrus policy lab and she wanted to design a shoe that would have uh play different music depending on the angle of the shoe and so she wrote the code but it didn't work she went to chat gbt 4 it debugged it for her it taught her what she was doing wrong and now she has this shoe that use that can actually do what she intended it to do so think about it as unleashing potential and think about it as teaching critical thinking because you really have to ask questions it hallucinates it comes up with credible answers but is it true and so we really have to teach those critical thinking you need to understand the bias in the data right because underlying all of this amazing technology is data and if we're not mitigating for bias in the data sets we could actually amplify and make problems much much worse so these are some of my favorite ones and unmasking AI just came out and lastly the reason you have to read those books is because we need everyone in this room to be the voice in the rooms that you're in we need you to be asking the good questions championing inclusion safety and ethics by Design upfront and it's so important because otherwise you know we're going to have a future that looks like this when we want to have a future that looks like this so thank you very [Music] much all right next SI all right so uh AI is everywhere today right so uh everybody knows about it uh in fact uh even things that uh prob probably traditionally we would not call ai ai is so popular today that we even call those AI right so uh so yeah so so we we live in this new reality that um AI um is um everywhere and it is enabling U many many things for us which is which is fantastic but there is a cost everything has a cost right so AI has a cost and if you if you think about you know we we just heard about chat gbt di all kinds of models that you are running and you're doing things something is Computing to give you all those results you have these large computers that are Computing that are looking at data that are doing all kinds of mathematical calculations so that it can generate all those results for you and that Computing needs energy so U how many of you um have tried to do Bitcoin mining some of you right and so th those of you who have tried to do that in the recent years you know that at some point it became very very difficult to do so because the because the miners that you will use like ant Miners and other things that you will put in uh their energy cost became uh too much in terms of how much coins you can mine and how much power you are spending and what is your electricity bill so actually to me that was um the first time I think that rank can file uh we kind of experience the energy cost of computing typically these big servers are held by the big companies they are taking care of their their servers the electricity that that is needed to run those servers we don't see them but this Bitcoin mining when people started buying U specialized machines to put in their garage uh and do this Computing to do the mining very very soon they realized as the Bitcoin mining became more and more difficult so you needed more computations uh people realize that paying for that electricity is becoming the uh uh the roadblock okay and so in the uh for our um servers that are doing these AI calculations um one has to um actually take care of it so uh if you think about for example these big servers that department of energy runs they often call this Peta scale servers so basically they're doing 10 days to 15th um uh instructions in these servers today these take 60 me wat okay and the projection is that in the next 10 years they would like to go to Exile scale which is 1,000 times more than that which means that if we keep doing what we are doing one of those machines will take 60 gwatt that's just that's that's not possible that's just not um physically possible so if you look at um some some of the data in terms of how much energy we need so the this is where I'm showing it uh this is how it is increasing over the years and it is projected that by the middle of uh next decade uh sorry I just did what I was told not to do uh which is to click um on the slide um yeah so if you if you look at this data what it shows is by the middle of uh next decade um if we keep going this way uh the energy taken by our Computing machine this is just the servers not even you know all the other gadgets that we are using just the servers will become single percentage of world energy need and world energy needs uh uh include everything like lighting you know the all the electricity that you need in production flows every everything so that is unsustainable it's just U not possible to give single percentage of our energy needs to just doing um Computing although computing is very important the second part of it is that it also shows uh let's see if I can point to it without clicking uh yeah uh this green U uh Trace there that shows the rate at which the world energy production is going up so you can see that um the energy taken by the computers U is much faster the rate of growth is much faster than the world energy production so if it keeps going like this uh um you know again that just shows that it is not sustainable right so we have to do something about that and definitely uh uh there is a there is an heightened awareness about U the energy required for our Computing there are a lot of um um there's a lot of research all around the world which is looking into the Basic Hardware that builds our computers and um that tries to increase the Energy Efficiency okay and there um the exciting part is you really have to work with nature uh because where we are today our basic building blocks for our computers uh we have to work with Dimensions that are 40 to 45 atoms okay there there basically if you look at the minimum size that we have to work with there are 40 to 45 atoms there so you are really trying to put atoms exactly where you want them to build these computers and once you go there the classical physics that um helps us to do Computing also starts to become somewhat Marky because you are really at the atomic level and you have to understand that physics and try to think about how U you can control that uh to improve the Energy Efficiency but Energy Efficiency is um at least in my view is going to become one of the pressing challenges of our lifetime uh because of all these um all these reasons that we just talked about and so um we it's an exciting time to be around because whatever we do in that direction is going to have a long-lasting legacy and of course US government is also very much um uh uh very much serious about this and uh you probably have heard about chips act and many initiatives that US government is starting so we are happy to say that we will be one of the eight hubs Nationwide uh that looks into uh Next Generation Computing hardware for AI and U starting from um these basic building blocks all the way up to how we design energy efficient um Computing systems um and so I'll just say that um again uh that this is an exciting time to be around um if you are interested in um controlling nature uh for energy efficient computers thank [Music] you [Applause] welcome everybody it's a pleasure to um be here and to see all of you um especially uh former students current students and uh your families um my name is CLA Tomlin I'm a professor I'm the chair of the electrical engineering and Computer Sciences department and um maybe just a little bit of a story so I was a graduate student here working in control theory and I worked a lot on safety critical systems so throughout my PhD I was working with NASA on air traffic control how do you automate some of what air traffic controllers now or then did manually um I graduated a got a job at Stanford I was a professor at Stanford for 10 years and then Berkeley gave me an offer to come back um and I did and one of the reasons I came back is so this is around 2005 um Berkeley was building up one of the best AI groups in the country and they were they'd always had a very strong like theoretical AI group machine learning but they were bringing in people who worked in AI systems um really committed to the development of AI systems and from a control systems perspective when you're thinking about automating things and this is back in 2005 it was pretty clear that AI was going to be an extremely important component in that and we had to think about designing control systems that took into effect Ai and understood how to integrate those systems in particular integrate them safely so that's what I work on um I've built up a lab in safe AI thinking about how you design control systems that integrate learning but do it safely so that the systems that you're designing operate safely and this has become a huge topic so um you know we're familiar with autonomous cars but a lot of I mean I've got an interest in aircraft and airspace and air traffic control and so um in addition to working in autonomous cars um we've got some pretty exciting uh systems that are now being automated from um autonomous aircraft um really interesting new air taxis that are tilt rotor you take the aircraft takes off in a a vertical mode like a helicopter and then the rotors tilt so that the aircraft becomes a you know goes into forward flight um working with autonomous ships and thinking about you know how we protect our um our Waters around the US um and also continuing to think about air traffic control and and also thinking about you know autonomous vessels that are doing other things like for example towards Energy Efficiency as sa was was saying okay so um so we think about kind of control and AI from this point of view of you've got a system so we do a lot of work with Boeing and um this is actually a picture of a Boeing aircraft it looks like a Cessna because it is a Cessna it's one of their research aircraft so they've equipped this aircraft with a bunch of cameras and we work with them on designing algorithms that you put on board the aircraft so that you can do autonomous flight autonomous Landing using that onboard perception from the cameras only so for example next month we're going out to Montana to do a number of flight tests of our algorithms on this aircraft as it's coming into land when you have a bunch of different things going on the runway going on on the runway like other vehicles moving around so we think about kind of the design of control systems that integrate AI from the safety filter point of view how to design safety filters such that you know in all of the other actions that you're doing from perception you know what your sensors are looking at and how you interpret those to prediction what other vehicles around you might be doing so perception and prediction are two blocks that are now primarily done by Machine learning algorithms they perform much better than the traditional design like the traditional computer vision stack or the traditional model-based prediction um through planning and control planning and control are still and I believe will remain to be largely model-based um uh methodologies um so how do we integrate that with safety filters that basically ensure that the actuation that the vehicle that's flying around is going to is going to is going to perform so that it it completes its uh its Mission or its task or its flight is safe and so these safety filters um you know this is this is actually from Stanford so um I don't know if you recognize roele field but I mean that's where I started but they have um you know these are um these are actually four quad rers this was there we built these so this was before you could um buy a quad rotor on every on every street corner I guess but they um they're running our algorithm so they're flying around actually students are sitting there each one is controlling a quad rotor and when they get within a distance from which you can't prove that the vehicles will stay safe anymore the automation on board each vehicle takes over and guides the vehicle away from the other vehicles and then it gets to a point where the you know the human pilot one of those students sitting there Under the Tent takes over and controls the vehicle again so these are these safety filters that we designed these are modelbased methods they use traditional optimal control um Dynamic Game Theory to take into account disturbances um so this is kind of more like historical control um so how do we use these so I'm just going to now the um for the last few minutes just talk about these couple of examples so this is um this is actually a picture from Joby Aviation it's one of the air taxi companies that's in the Bay Area um and uh one of my students spent the summer there but we're working with them to understand safety of that transition maneuver when you're up you've you've taken off you're in vertical flight and your rotors are tilting so that you can go to forward flight it's an incredibly difficult maneuver that they'd like to autom M but the flow the air flow around the rotors and how that affects the lift of the aircraft is unknown and using machine learning is a way to better develop models for these systems so that you can design safe controllers for that regime so basically trying to understand how to compute these safety filters throughout that transition regime so that you can always guarantee that the vehicle will recover lift as you're transitioning um we're also working um on a project that I mentioned earlier to think about how you might learn the flows of oceans and then design vehicles and design control systems for them which are kind of hitchhiking these flows so that they can in a very low energy way maneuver to Regions with high nutrients and basically use the vehicle as a platform to grow seaweed which is a a a a way to collect carbon so do carbon sequestration and then at some point that seaweed is cut off and it's deposited in the deep ocean floor so um this is a project that we're working on with um well it was Google now it's a spin-off from Google called fos um where we're actually looking at how you learn you we have a lot of data about the flows but how you need to predict ahead of time so usually the these forecasts have errors in them how do you learn what those errors are over time so that you can basically get very um energy efficient control to guide the vehicles through these flows so for example if you just used a naive you didn't actuate it at all um if you just you know pointed the vehicle towards the goal for example where the goal is that Green Dot representing High uh nutrient areas or if you hitchhike the flows you predict them accurately enough that you can just nudge the system onto one of those nice flows to get you to your goal and use very little energy to do that um finally this is um you know this is a series of experiments that we've done with Boeing where we're going um first from Taxi to Landing to autonomous flight where this vehicle has cameras on board and we're designing safe Control Systems which even if the perception fails the vehicle will be able to safely either taxi or land or fly in in the presence of other aircraft so here the vehicle is um taxiing there's another vehicle that um that is actually on the runway it's Guided by its cameras and all of a sudden the cameras you know there's some error the camera blacks out there's something blocking the camera you'd still like the vehicle to operate safely in those regimes um you'd like it to be able to um additionally understand if there are other vehicles around even if it hasn't been trained on those types of vehicles so you want to be able to use the uncertainty in perception as kind of a first class opponent in your control system and uh and create a safe control law under you know this this uncertainty and then finally you know you want the vehicle to be able to land even when there's other vehicles on the ground and I think um Jane is standing here we're going to move over to no I can go okay well anyway so so this is the the test that we're going to be um running next month in Montana where you've got a number of vehicles uh run moving around on the runway and basically the aircraft is coming into land it's doing an autonomous land it has to decide whether or not the runway is clear and so it has to make a go or noo decision based on its pce perception of what's going on on the runway so we need to be able to have accurate um and safe prediction algorithms that are typically as I said before they use machine learning uh to be able to do that prediction and then you want to be able to land uh safely okay so where are we now we're doing a lot of work in automating platforms um we're not there yet but I think we're on a really good track to think about how to automate platforms so that you can make guarantees about these individual systems and these are some of the systems we've worked with but you also have to think about how they interact with others and so finally as we move into the future and as part of of this Berkeley AI research lab I think one of the big things we're thinking about are systems um so now you know um we we talk about diff systems at different levels but here we're thinking about um Vehicles interacting with with each other um humans in vehicles interacting with each other how do we design that automation so that these systems interact correctly and interact safely so that's um that's what my lab is doing a bunch of um current and former students working on on this and um again thanks very much for being here and it's great to participate in this panel thank you so much Claire and I'd like to invite all of our panelists to come back up here um thank you for giving us an overview of the many applications of AI including Automation and how to make automation safe and secure and also some of the challenges for making AI as it proliferates more sustainable so I can start off by asking just one question of the do that sorry panelist okay and I hope the audience will will have a lot of questions to and we'll give you the opportunity to ask so maybe my my first question hopefully open my last will be what is the what is the greatest challenge in in your area of focus whether or not it's um you know impact on society or sustainability or safety um and yeah what is the biggest challenge your mind that that needs to be tackled and how we can work together with you know involve our current students our alumni um our parents like how can people get involved to help solve that most complex challenge in your area of specialty yeah so I would actually say there are two uh areas and they're intersecting so one is we don't have enough Workforce and the second is we're not including everyone so you can see how those two work together if we can expand diversity and gender equity and Technology bring more people and cross-disciplinary if we can bring people from the humanities into working on these AI Solutions and make sure that we're designing systems that work for everyone we're actually solving two problems because we have a giant lack of uh Workforce I actually was talking with a gentleman at UC Davis he's starting a master's in power engineering and he said the reason I'm doing this is we can actually solve our energy needs we just don't have enough power engineers to redo the grid right to fix the problems and so this is why we need to think about inclusive on-ramps non-traditional ways into technology and really leveraging the skills of everyone to develop AI that works for [Music] everyone yeah so I mean um from you know I will go to my comfort zone which is the hardware and from a hardware point of view uh the thing is that uh it's it's very similar to this this uh the the previous uh comment that you heard is that in the end the Innovations come from human beings so we are still not at a place where machines will uh come up with all the innovations that we need and so we need very smart people um to uh to come into uh this field and uh essentially participate in this uh in in this uh Innovation um a journey to some extent to figure out how we can do uh Computing more efficiently uh in in Computing side U you know if you if you follow you will often um uh hear that people are saying okay we are reaching the limits of physical dimensions I I mentioned that you can only right now we are working with minimum feature sizes which are only 40 to 50 uh atoms uh so you you can keep you can try to keep uh going down but at some point there you will be running out of atoms right so we definitely need U completely uh new ideas uh often in this field people uh use the word radically new uh so we need radically new ideas but that needs uh uh smart people from all fields to come in and if you again look at Computing it is not only an electrical engineering only discipline uh if you think about how chips are made how uh they work and all of that it requires the the the entire Village you it needs physics chemistry Material Science um and in fact you know uh some of the principles that we use today came up came from mechanical engineering you will not often think that that is relevant for computing but uh that's um that's what we do so uh we definitely need uh uh people from all different disciplines to participate and uh to recognize that this is uh becoming um a very important challenge for for um our time yeah so I think the two biggest challenges technically in in my area are you know to traditionally to be able to prove safety of a system you have to be able to predict all of the possible things that could happen and that's impossible right because all of a sudden some accident happens and you're like wow nobody ever predicted that could happen so it's it's been I think a kind of you know before AI came in a modeling and enumeration thing and the the the thing that people are quite excited about with AI is that there is although limited an ability to generalize that you know that AI learns if if it you know if it's done properly which it's still not done properly that it could predict things that people haven't thought of and that allows the you know the incorporation of new cases that we could prove safety against but we're still not there yet so it's like continually reaching and trying to develop methods for capturing all possible cases of things that could happen um I think that this and and this ties to saif's point the other thing is computation I mean we are limited in the amount that we can do by the computation that we have and we're crunching you know we're most of our grant money is being spent on computation now um it's it's you know more than the cost of salaries so I think that that is a huge challenge to Jill's point I think that the um you know we don't we don't just need more people who are narrowly trained in AI we need people who understand like mathematics more generally coming you know that's why I feel like coming from a control systems background where we did math math math all the time before we were allowed to actually do something um I felt like that you know it's it's a really it's really important to have a a broad background to be able to think about new ways to solve these problems too often people get into niches of their specialty and it gets you know there's so much work to do that you get more and more narrow and you forget how important it is to have ideas coming from other areas so so I really um endorse what both of you said in in my area too thank you so much all right why don't we open up for questions from the audience if we can get the microphone to you we will I'll take you thank you I really appreciate this was fantastic I have two questions I'll ask one and then wait wait for my turn if it comes to the second um there's a lot of uh discussion concern about Singularity when AI will essentially be able to have higher intelligence than humans there are various measures of intelligence but let's start with one which is being able to do hypothesis generation not just hypoth hthis testing even generative AI Etc that we have today is essentially doing hypothesis testing and putting options for that not necessarily hypothesis generation I'm curious to know what do you think in your mind will be the leading indicators for hypothesis generation being done by AI machine learning that is not happening today thank you well maybe I'll start I don't I don't this is not hypothesis generation but one of the key methods for doing control system safety is um to develop a control barrier function these are um you know barriers are basically what they look like they're they create spaces on which you can prove properties and that is so we do that computationally but there's methods to develop low-dimensional representations of those but there's no constructive method to do those so as soon as this started coming out or my students started hey can we asked GPT 4 how to construct barrier functions how do we do that so that is going beyond the testing and it's going beyond the um the kind of prediction it's more at the level of generation but what is it doing it's searching all sources and it's been trained on those sources so you have to prompt it really well through a sequence of prompts to get it to its expert region there and then it generated a control barrier function I mean that was kind of surprising to us this was back in April so I think that you know being able to do something that that that is something that we don't know how to do now um computationally and so there was a lot of you know there was a lot of computation that went into that but that was my first surprise of how these tools might be used in a way that allows us to go beyond what we as designers have done we we have a question back here hi I had a question about the automation that you're doing in terms of the data that we have right is the uh research approach to throw a lot of complex data at it because that's where you have to eventually get to solve right in a real world scenario is the research approach to go with smaller data sets solve the simpler problems and then build it up to complex or is there another approach starting with the comp from my point of view that's a well it's a great question how do we it I mean it's as we start to introduce learning based components into our control systems data is all important right because that's these These are basically uh components that are encapsulating the data that it's been trained from there's different approaches and both of the ones you mentioned are being used I think it's still very much the Wild West trying to figure out what the what works what what generalizes is what what can you make some statement about guarant probabilistic guarantees about but I think what maybe one point is one of the methods that we've seen to be very successful is when you design you you com you you develop a structure for a deep neural net so you use an architecture that already has some of the modelbased some of the kind of systems that some knowledge of the system model in it um whether it be through the structure of the layers or the the the L function that you're using to train it and then you use the data that you have but the data is then being almost filtered through this model that has a representation of the system so it still allows a generalization hopefully but it um but it it it it kind of Gears you towards the problem that you're trying to solve deal did I cut you off from an earlier answering s well I was just going to add kind of to both of these points an example from uh professor Professor Grace goo so she was looking at bioinspired technology so she looked at the Mako Shark cuz it's super fast it goes through the water incredibly fast and they're like how is it doing that and so they looked at the microscopic level of the skin and they found that there were these denticles that allowed it to go swiftly through the water and so she used that inspiration to create denticles that can go in infrastructure so like pipes where there might be swirling or there could be clogging and so by putting these tentacles in it reduces that from happening but then also other applications on propellers or windmills different things like that where reducing friction is really valuable and the interesting part about that was that she used bio inspired but AI optimized so then she would run different scenarios she would see what can we do within our manufacturing limits right so I think this human in a loop and and really putting these prompts together and asking these questions I think is really key yeah I I have not much to add I'll I'll just say that one thing that often gets ignored in all all these discussions is that most of our AI models these are datab based on all all models are databased and data a is not cheap and B um it it actually needs physical things to go out and collect the data so that's where I think that you know any Singularity and other things in my mind is um difficult to see in the in the in the description of what we will find in popular science books is because you actually need to collect data after that your math tries to train so that you can uh Train by the guidance of the data but the data needs to come from somewhere right now I think Chad GPT and Di and all these U GPT kind of models um have really gotten out you know have really appealed to our imagination because there collecting data from all the things that are already existing in the internet but if you think in terms of a conventional hypothesis based even research right the hypothesis needs to be tested if it's a new hypothesis somebody needs to go and do that experiment and I don't think we are at that uh position or even close to that position where then uh I mean to some extent you can then call that humans are those robots who are collecting data to train and to some extent we are train our brain by do by collecting data from from the environment so uh so from that point of view I I think that uh at least in the in the sense that where we find in uh popular uh literature the what Singularity means we are very far from it we are at a very high level of automation we are we are learning how to automate things in a better and better way but uh but that's what question over here hi there uh I'm curious about the defense implications of the technology that's being developed here and in other locations um you all are talking about applications in commercial situations with the environment so on so forth what do you think the first examples will be of AI involved in defense and what of the most concerning concerning applications for that technology well that's a loaded question I'm not sure I'm um even qualified enough to answer that question but I would say that uh if you just look at today there is a war going on and you all already see drones are being used right in in large numbers and um so that gives us some indication right that how automated machines if you want to call them AIS and others uh that's fine um are going to be used but I mean definitely I don't have a lot of background in that direction to comment I think it's already being used so and I I I kind of know for a fact it's already being used so um so computer vision has been replaced I mean computer vision is still there but it's gone through a complete Revolution it's done only or 99.9% with neural Nets now and um and these systems are being used um broadly they're being used in defense systems as well and I think that um as researchers it's a question that comes up a lot because we're supported by the research branches behind the Department of Defense and we're working on largely civilian applications and and applications that you know are important for society um but these Technologies are being used also in defense systems I I'm clearly not qualified to speak to this but I'm going to make a comment nonetheless and that is that this is a people problem so we're designing these systems where are we putting the people in the loop are we doing the continuous monitoring to make sure that where we can intercept we actually can intercept and that AI doesn't go around the human and so I think these are really important questions and it does speak to the importance of how do we put the safeguards in where are the safeguards how are we monitoring and ensuring that things are functioning as designed now sometimes functioning as design can be problematic but that's another question um unfortunately because of time and I know a lot of people have other programs to go to at homecoming weekend we're going to have to wrap this but I might be able to impose on our panelists to just stay here if you want to come up and ask them a question informally but we do have to sort of wrap this up for time for the homecoming weekend CJ so just want to thank you all again for coming hopefully you got some insights from our panelists thank you again and please enjoy the festivities of the day go Bears Bears be thank you guys thank you take the than you I just yeah saw and

2023-10-13 18:33

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