Generative AI + Education: Reinventing the Learner Experience

to start off uh I'd like to introduce uh Randy Williams she is a PhD student in the media lab uh who's really been doing pioneering work in K12 AI Literacy for children as young as kindergarten preschool up through middle school and high school um she's been in inventing a number of innovative AI Technologies and curriculum uh and her work is deeply motivated uh by Passion for equity and inclusion uh in an increasingly powered world so Randy please take it away I think if you yeah if you're comfortable speaking from there that's fine or great yeah no I have the slides in front of me hello good morning everyone my name is Randy Williams as Cynthia shared I'm a PhD candidate in the person robots group Cynthia is my adviser um and today I'll be sharing uh Sparky which is a tool I've been working on it's an interactive agent that supports K12 AI education and pretty much I'm just going to Ste a bunch of videos because I only have a very little bit of time I don't have a clicker so oh I say next oh excellent perfect um so for context the work that I do is in the field of K12 AI education why because in the words of one of my heroes Belle hooks I think of classrooms as a radical space for reimagining the future and in particular the future with AI thinking about who gets included in designing this technology who does not and what would happen if more people were able to participate in the creation of these Technologies um as an example here's a student by a stu we'll call her B seventh grader in Massachusetts who is learning about natural language processing Robotics and text classification inside a course that I ran this summer and one of the conversations that kept coming up was be is bilingual so speaks um different languages many students in the classed and they kept saying that Alexa is great but it doesn't always understand my accent or it doesn't speak speak in the language that I speak in and so be designed a doctor because they want to be a doctor when they grow up they think baymax is pretty cool um basically a jbo doctor that was able to speak many different languages and so the kinds of curriculum that I'm developing are where students are developing technical skills yes but also ethical skills because they're working on passion projects that have very big real world impacts um so how do we in that space where students are working on these open-ended projects support them in their technical skills ethical reasoning but also their creativity so on the right there there's some screenshots of Sparky in different forms I'm definitely playing around and experimenting with what I want the technology to be but in essence it's meant to be a creative companion that learns alongside students so less of a tutor less of a coach more of a tool or resource that they can pick on when they want it provides coding and machine learning support of course based on this knowledge base of scratch which it learned I think from reading the whole scratch Wiki um but also it has this knowledge of AI best practices so what makes a difference and last it facilitates design thinking so it helps students think about debugging and breaking problems down it proves their design choices and offers feedback so that they can continue to persist in their ideas so yeah video one let's see it hopefully this works yes awesome Sparky is a tool that provides guidance on your AI project ideas a blank project proposal form will open this is one form your goal is to come up with an idea for an AI project Let's do an example I will propose a project that helps teenagers deal with stress first I will fill out my project description the Box asks what does your project do I already know what I want my project to do I type this project helps teenagers deal with stress I am off to a good start but I would like to get feedback on this idea on the bottom right of the description text box there is a button I can press to bring up Sparky a chat bot I can discuss my ideas with there are buttons I can press to have Sparky show me an example of a project description give me some ideas or give me feedback I press progress check and Sparky shows a checklist of goals that I have accomplished and ones that I still need to work on plus Sparky gives me specific positive and negative feedback I can also type in the chat to ask specific questions I ask Sparky how can I make this project more about AI then I can go back and update my project description and ask Sparky again for feedback you can work with Sparky to improve your ideas or to ask for help getting started look back at the progress checklist to make sure you complete all questions thoroughly so that's one form of Sparky um one that I've already started using in classrooms Middle School classrooms in this case um but I'm also playing around with other ideas so here's another demo featuring some of my lab mates this one is definitely more in the works yes so they're working on a project where they're programming Jibo hey hey J can you help me brainstorm a good idea for J is also using Sparky sure I'd be happy to help you brainstorm project ideas what are some of your interests or passions that we can incorporate into your project we have idea that combine panda with the peanut butter certainly how about a project where you help a virtual to find its favorite peanut butter treat so yeah onani gives them some ideas how can I help they get stuck so they head to a tutorial um to try and figure out these new gbo blocks how can I add a panda icon add a panda icon to jbo screen you can use the show block from the jbo category and select the panda icon here's an example so it helps them get through that then later they're training a text classifier um about peanut butter and pandas um and you us more words that's related to peanut butter certainly here are three more wordss related to peanut butter one spreadable two NY three protein Rich so they're able to make a more robust text classifier with those additional examples um what I sort of love about this work is this is my sort of summary slide is a that I'm thinking about different ways that this can exist especially think about group work because that's so important in the curricula that I'm building but I think the most meaningful part of this work is actually getting to use it with students and teachers and get their feedback to reshape how this technology works and I'll end by saying thank you so much to my collaborators prer Robie sfh Ali Hal abson and of course my advisor Cynthia thank you great thank you awesome so just a couple quick questions so I know you have again a passion around greater diversity around stem education and AI as you've been doing this work are there particular kinds of learning experiences that you think are particularly important uh to engage uh underrepresented students yes um I think what I often see um with the way that these Technologies are spoken about is that they're for everyone and they'll make a difference in all of our lives but when actual students start to use these Technologies they bring up pain points and they say well it's great but it kind of doesn't work in this way and I think that's an opportunity for creation and Innovation and so can these Technologies critique themselves is kind of like the weird question that I'm asking about them can they be used to actually create something better um Can the ownership of them sort of be transferred more to the people who maybe weren't included the first time around that they were designed um sort of broad big questions but those are the kinds of things that come up a lot when students are using them that they wish that it worked in a slightly different way and they wish that they could build something a little better for them yeah so maybe we can dig into that a little more deeply I mean Justin was was calling out that that maybe students don't want to talk to computers you know maybe they really want to talk to each other this is obviously experience that's trying to I think bring both of those together and I know you do a lot of codesign in your work so maybe you can talk a little bit about your own process and how you dig into and find uh these experiences to make sure that they're they're they're achieving the goals that you hope they will yes so what's sort of missing from these demos is the before where students are learning about the Technologies and learning how they work um then they use them and then they come back and they talk about and discuss and reflect on them um and then they critique them and they say oh but it should work like this or oh it should work like that and I'm like awesome let's build it like how do we do that um and so the codesign process very much looks like uh presenting a prototype that is uh something that's transparent they can break apart put back together in different ways and sort of frames the technology in a way where it's uh something that we're all tinkering on together as opposed to something that they just have to use in the form that it exists and I think that's particularly powerful for the Educators because they're often being told that their students using these Technologies and they have to do something about it or they have to use these Technologies and there's training next week um can we also give Educators the opportunity to crique and build and think about how they want to bring them into their classrooms and integrate them with their own teaching practices um that's something that I'm able to do my work because you smaller classes I'm not changing Cal systems yet but I think what we learned from that can be very powerful looking at broader skilles great thank you so much all right thank you next we have Jesse Thor who is a professor in the MIT physics department he's also the director at the NSF Institute for artificial intelligence and fundamental interactions wow he's been doing a lot of innovative work at the intersection of generative I AI in physics education and Outreach Jesse great well thanks so much thanks so much for having me uh and good morning everyone and this is a very very unfamiliar learning environment for me um I'm a theoretical physicist I'm most at home with a piece of chalk and a chalkboard um and I was very skeptical about the power of AI uh in my own research field but my mind changed in part because of interaction with graduate students who were teaching me the way that Computing can affect the research that we're doing and then also of course uh affecting the way that we can do education so um I'm the director of this institute for artificial intelligence and fundamental interactions if you haven't heard of us it's because we started during the pandemic in 2020 uh but we're a joint effort between MIT Harvard nor Eastern and tus and what we're trying to do is fuse kind of the advances and deep learning with the kind of deep thinking that we do in fundamental physics the principles that govern our universe to gain both a deeper understanding of how the universe works but also a deeper understanding of way that intelligence works and you can think about certain AI systems as complex physical phenomena that have emerging behavior um and actually thinking about AI through that scientific lens has been very uh helpful in our research so we have research that's at the intersection of AI in physics we're also empowering the next generation of talent through various educational efforts that I'll talk about in a moment as well as building a community and part of the way that we've got into generative AI has been in our engagement with uh with the community so let me just start on the on the research end uh generative AI that phrase has a very rigorous meaning in terms of sampling from probability densities and generative AI has been absolutely transformative and will continue to be transformative for scientific discovery So within IFI uh we're using generative models actually create digital twins of our universe and studying astrophysics and cosmology by generating synthetic data sets about the distribution of uh galaxies in our universe and then generative AI it turns out to be a strategy for doing first principles calculations of the structure of fundamental matter and in nuclear and particle physics we're using generative AI in a very different context than synthetic image generation rather we're generating synthetic gauge field configurations for Quantum field Theory yet the mathematics behind that is actually relatively similar though the Technologies behind them actually have to be quite different because of that uh different scientific application and what we can do is now take these generative AI developments that are happening in the research sphere and bring them into the education sphere and my colleague Phil Harris in the physics department has developed a course that's both available on mitx but is also part of the MIT course catalog as as 816 where we're actually bringing data science into physics um where we have for modules 1 two and three more traditional data science and uh machine learning and then module four is actually based on what I told you about about first principles calculations in nuclear and particle physics where the same type of generative AI That's at the Forefront on the research is now bringing uh being brought into the classroom and uh in general this intersection between physics statistics data science is very rich and we're proud to partner with the MIT statistics and data science center to bring an interdisplinary PhD program to our MIT students now um when we thinking about gender of AI I just talked about gender of AI in the kind of rigorous sense something you can actually use for scient scien ific Discovery um what about in the more kind of creative space um image generation text generation uh and we've uh been uh working with the Cambridge Science Festival we had two events that happened this past September uh one was a lunch and learn about ethics and Ai and art where we talked about the science behind generative AI but then also the ethical implications and then at the carnival uh there was a a chatbot which you'll be able to play with in the uh in the hallway out there um that was actually started off as an April Fool's joke uh so my name is Jess theor and you've heard of chat GPT but have you heard of chat Jesse T you can go to chat jess.com and as a kind of April Fool sendup uh they did fine-tuning of uh I think it was of uh of gbd4 uh in order to make it know all of the papers that I've ever written my website my Wikipedia page and it responds very enthusiastically about topics in physics and Ai and it is a pleasure to to use but it's but it's but it's it's kind of a a joke of course it's a fun joke and we when we saw people engaging with trap JCT and the type of questions that it would be asked we realized that actually we could go one step further and instead of taking a a uh a scientist like myself how about taking a historical figure who's very important um and that thus was born uh open Heimer um so it was an April full sendup but then a public engagement opportunity and uh this is an example of a uh of a of a query you can do you can ask like kind of fun things to open imer and and ask you know telling a joke uh so uh this joke let me see if I can read it uh a neutron walks into a bar and says to the bartender how much for a drink and the bartender replies for you no charge okay but because this is Oppenheimer the neutron uh feeling quite please says Ah now have I have become debt destroyer of wallets okay so you have this kind of creative engagement and now you think about how you going to bring this into the learning space and so our II project manager uh who wanted to learn something about about physics she herself comes from the academic publishing world knows basically nothing about about physics but she heard me and my students talk about the born Oppenheimer approximation ah so maybe the virtual Oppenheimer should be able to answer what the born Oppenheimer approximation is um and so she asked the question and it responds it's a simplification of the mathematical treatment of molecules in quantum mechanics that is correct then it gives a list of three papers the first one exists the second one does not and the third one is not really appropriate for a research cont context and this for me was a little bit disappointing because we had actually trained this on a bibliography of all of oppenheimer's works and then me looking back and seeing the failures of that bibliography actually uh the uh the bibliographic entries were actually missing for the papers that should have been there I indeed the the original first paper actually is not in the database that was used for for for searching which is a failure of kind of the information behind it so you know in thinking about bringing of gener into the education space is a couple things that I think about um one is that you know we teach every student two semesters of physics because physics provides a universal language that can be applied to a range of scientific problems but similarly I believe that statistics data science computation it offers a similarly universal language and that needs to be brought very much into the education space now generative AI in this rigorous sense offers new pathways through the physics curriculum we start with Calculus we start with with um mechanics and electricity and magnetism but if students become more versed in probability and sta Statics we have an opportunity to introduce them to quantum mechanics and statch much earlier on in the curriculum so that's an interesting opportunity but then in this more creative sense there are new learning opportunities which I'm happy to tell you about and also just a further advertisement for II that we're trying to build this you know Common Language that transcends the intellectual borders because there's actually a lot of intellectual similarities between what we're doing in the physics space and what's going on in the AI Community thank you great thank you so Jesse so you you've talked about hallucinations um and in our back and forth you also mentioned you know in primary school students first learn to read before they can read to learn so uh what do you think students really need to know about generative AI uh to be able to use it as an effective learning tool for themselves so there there's two things one which is an unfortunate design choice that is actually easily solvable that there was nothing stopping uh Oppenheimer from actually giving links to the primary source material and the first thing is that you know we are hyperlink up the Wazoo why are we not hyperlink the Wazoo in the kind of generative space why is there not everything clickable to say where are things coming from where is that information coming from and so understanding the connection to primary source material and actually the the joy of discovering you know digging down into the literature and finding uh opportunities in literature that's one thing that I think we we can do and that students kind of need to learn and the other thing that not only students need to learn but we all need to learn and as a physicist this is very natural to me but it turns out to be not so natural to other people that I talk to which is that generative AI is not a deterministic Computing tool it's not a calculator that every time you do 3 plus three you always get nine it's a probabilistic distribution and of course it is it's genitive that's the definition of generative modeling is like probability distributions but somehow we don't understand that we don't understand that this is a complex emergent behavior and that's something that it turns out I find surprising that uh someone will you know only ask a question once or only generate an image once and not think about what if I generated thousands of images of peanut butters and pandas what would what would the distribution of that look like and understanding that kind of more distributional probabilistic thing I think would be helpful and this is a c change that hopefully will happen in education away from calculus-based only deterministic things to more probability staty ways of engaging with with education great thank you so much all right so next uh often uh students are the lead innovators and power users of these Technologies to advance their own learning so our next speakers uh Rachel har haraki is a graduate student at MIT slone School of Management and David kopla was an MIT senior and co-presidents of the HK and Honor Society and we really wanted them to be on the panel to share their perspective as students and how they're using Jer AI uh in in in their learning process and what we can learn from them in that so maybe start with you Rachel yeah absolutely uh thank you for having me here today very excited to get a chance to kind of talk about my experience as a student um I don't have anything as exciting as Sparky or Jessie PT to talk to you'all about but I can talk about what the transition was like coming from working in a tech startup back to school so I'm a dual degree student in MIT Sloan and the School of Engineering and computer science and the difference between those two is apparent in a lot of ways but everyone is using chat GPT and I think that was the most surprising thing for me coming back to school we had just started using it in my startup to kind of work on you know getting personalized content to members Etc but I wasn't expecting to come back to school and have the landscape be completely changed from when I was an undergrad so assignments as an undergrad that I would dread that would take me two three hours of writing all of a sudden ask chat GPT edit it into your own words 25 minutes and so I've really been struggling with some of the ethical implications as a grad student I came back to school to further my own learning I'm not here because anyone's making me be here this is something I'm paying for out of pocket so having a tool like chat GPT available and then figuring out how to use it to not take away from your own learning has been really challenging for me um in things that I'm more confident conf in like coding I love using it I love using it as a debugger I know how to code I don't need any help but starting in my MBA classes using it to read cases probably something I should be able to do on my own without chat GPT um and so it's been a really interesting balance for me trying to figure out as an adult where that line is and I think the things that I've been thinking about have been how does that look for middle school students or high school students who Maybe don't have the same learning goals that I do if I'm struggling with it as a 26-year-old so really excited to be here today and listen to everyone in the field of Computer Science Education physics talk about their experiences and kind of helping myself to untangle what that looks like great thank you and David can you share your experiences yes sure so my name is David cppo uh I am the co-president of HK Honor Society I am also the co-president of AI at MIT which is mit's largest AI student organization uh but perhaps what's most important for my experience today is that I'm a senior one who might be taking what might be my final final exam in 23 days so it's a little bit surreal for me to be up here speaking to all of you uh so close to the end of my forceable academic Journey uh about Ai and how it can be used to Aid a student education especially considering that I might not be at MIT today if it weren't for this technology you say I'm dyslexic and uh this conversation this realization that a lot of people are having over the course of the past year with using these large language models to help uh promote their learning and access material in a different way is similar to a realization that I had about a decade ago when I started using text to speech software and it kind of meant that overnight I was no longer limited by my speed of reading I was limited by my speed of understanding and it allowed me to really pursue and dive deeply into the things that I excelled at the things that I loved uh and and really just go forward with that and well in parallel I tried to improve my reading ability and all the other things associated with dyslexia I knew that I would never be at that same level as many of the other people probably in this room and that was okay because I would always be interacting with the world through these tools that allowed me to interact in the world in a way that I had more power and the way that I was more effective and what I see happening right now is a similar transformation now there are some that are worried about these large language models replacing entirely the role of what a student could be and you know there there was a paper public or widely spread uh over the summer that claimed MIT could a or gp4 could Ace MIT zek's curriculum um and I think that's like an example of people really pushing like jump the gun here in that sense uh that paper turned out not to be um totally accurate but it's very clear that these large language models are radically transforming what the role is of being a student and I think given my experience uh this should all be addressed in the form of what ways what is the purpose of what we are learning and how can we use these large language models in ways to better get to that purpose and that might mean that we don't need to learn some things that we used to learn or not as deeply so for example maybe it's not as important to learn SQL very deeply as long as you a working understanding of how to code now because what becomes more important now uh is the ability to determine when something is wrong and how to fix it not to generate uh something from scratch and I'm really excited for all the ways that this is starting to uh take place and Academia and all the ways that it's going to be uh changing the world of Education in the coming years Earth great thank you so much all right so I have a question for both of you which is given all the experience and using these tools and using it to further your learning what do you most want Educators to know about how to how to harness these things in in from their side uh to further their their their their ability to help you learn more effectively with these tools as well uh so I guess I can go first I I think that these tools should be really embraced and that Educators should be looking at ways to uh use these tools and even refine what they're choosing to teach in the context of these tools uh the these are tools that all of your students will have access to for the rest of their lives and so it's important to be asking always the question with every everything that you're teaching why are you teaching this uh and how can uh going forward like maybe there's another way to teach it or another thing that you weren't able to cover in the original class that you can now start talking about because you're able to move through material more quickly because students have access to these tools yeah I think from my perspective it's Clarity of expectations so is this a class where you want us to be using generative AI to help us with our homework I've had professors that have chat GPT policies that is just use it period and that's really helpful because that shows that they're thinking about it and they understand students will be using it and so hopefully they're designing their lessons and their assignments with that in mind in a way that chat GPT isn't doing all of the work for you um so I find that if teachers have done the thinking around how to use it as a tool ahead of time it's incredibly helpful for the student to then know where the boundary is of yeah summarize that reading this is not a exercise on reading comprehension this is an exercise on then writing the essay from it and so if a teacher tells you that UPF front it's a lot easier for you to go do your homework and say okay okay whatever I don't need to read that article instead I'm going to focus on this summary make sure it's correct from the reading and then I can write my paper um and so that's been by far the most useful thing for me great thank you so much all right all right we also now have Andie sastri she's a faculty director of the J World education lab and Associate Dean for open learning she's also a senior lectur at Sloan and she brings a global Workforce perspective particularly when you think about uh developing countries hi yes thank you so um you just mentioned I don't need it I don't have slides you just mentioned um thinking about your students and part of my job is to think about the world students uh so the Jam World education lab connects uh ideas from MIT a community that's anchored at MIT that has a relationship to MIT with Educators all over the world and we probably have one and a half million this is my guest students who are at our collaborating institutions and well over 100,000 faculty alone not to mention um staff postdocs T Etc so we have a potential for massive reach uh what we often do is work directly in a sort of go the business route B2B model working with the universities to help them um innovate address challenges come up with new ideas for how they arrange their curricula and their learning experiences student experience for instance all around the world first year learning could really stand to be improved at many universities we see high Dropout rates we see students struggling to kind of understand and onboard into universities especially when they come from varied backgrounds all over the world too universities are being asked to innovate and serve Society in new ways um so a great graduate who's ready for the Work World new research but are we actually also improving our environment and our community and that's putting some really interesting new educational ideas that have a long Tradition at MIT into the mix can we do real world projects for instance that get students tackling problems and challenges that they see right in front of them how could a I help with all these things so our University Partners ask us for they are hungry for knowledge about how to instructors adapt their current assignments to address AI or could we get a quick course so we can understand better what this is what are the MIT tools that exist to help us bring AI MIT level AI teaching directly to our students and we also experiment at jwell with working directly with um doing teaching ourselves so we're not all B2B uh one of our Flagship projects uh efforts is now six years old built on an earlier program that's still informing this effort called react our emerging Talent program serves refugees uh internally displaced uh people um migrants and others who lack access to formal education and it's a really interesting use case for AI because students come into that wanting a ton we have massive excess demand for taking some MIT courses and getting an MIT certificate and then learning a bit about the work world how to position what they're learning with respect to jobs and this is clearly one opportunity that everyone would love to think more about how could we pair education with a pathway into a job and it's been become very controversial so I'm going to give you your like quote of the week to remember I wrote it down um both Texas and Mississippi are pushing universities to spend they're trying to use funding as a mechanism to push universities to encourage students to do useful Majors right and with with the Advent of AI in sort of monitoring and managing what students are doing you could see mechanisms for recommendation and pathway mapping becoming ever strong in terms of shaping choices that students make and the the quote of the week is um we need to get rid of useless um degrees in garbage Fields so who what are the useless degrees what are the garbage Fields right and what do we lose when we say those things should not exist so I think there's a moment here to bring back into the conversation the theme we've already been hearing about how do we link Humanities how do we link critical thinking how do we link teamwork and um essential skills of discernment of debate of of um resolving complex and breaking down complex issues how do we bring them into the mix I think I'm very much in keeping with what others have said there but done well you could imagine an AI watching a student and say hey do you realize you're really good at this try this course next or looks like you're really stuck here let me help you out with this additional reading so you could have tutors that are responsive where a student would inquire or you could have stors tutors that are sort of monitoring and guiding and we know that um navigating even here at MIT we have a wealth of courses how do you figure out which course to take or if you are a self-directed learner and you open the door to ocw how do you know where to go because there's so much there guidance could be really helpful and guidance that you have some faith in could be fantastic you could imagine then using that method to think of new ways we could deliver education at scale what if adults in the workforce could take small courses and get some real world project experience that was calibrated nicely to the course that they were taking and then bundle that together into a credential that would allow ow them to do sort of episodic learning that link to interests and perhaps Market demands or real world opportunities and accumulate portfolios of qualifications that went beyond classroom learning or moo learning and included Real World engagement and we're actually experimenting with exactly such things in our emerging Talent program I wrote nine more ideas down but I know we don't have time for all of them um but I do want to put in a pitch for a form of for thinking about inclusion in a in a way that's that I think really will change the world for the better for all of us so one challenge we have is we're looking at tools and needs that we have even within our team it's very tempting for um folks who are supporting us to say you have a small program there's much big programs here so we're going to focus on the bigger programs and make sure we're meeting their needs it seems like a very rational decision but if we're not working at the edges and and serving students from extreme conditions maybe someone who would never make it to MIT how are we then testing our knowledge how are we building the most robust possible platform and the best possible learning routes for people so I'd urge us to look for edge cases and to look at ways in which we can work with the students that we're already serving through jwell in Tanzania or in usbekistan or in lvia or in Mexico or in Indonesia or in India so we have probably two dozen organizations all over the world we work with but just think of what we could accomplish if we could tap into all of them and really take their student experiences and their faculty uh ideas seriously great thank you all right I'll ask one question maybe as Angy answers it if anyone from the audience wants to approach the microphones we can take a couple questions from the audience as well uh so Andie uh if generative AI were actually able uh to deliver this revolution in digital learning and teaching that we aspire to um is that going to get us far enough towards enabling opportunity for all so I've been thinking about this question a lot as Cynthia knows um because again our own experien is showing us what the challenges are so I read a a recent uh un report that argued that if we wanted to get all of the world's Learners online with easy access devices and internet availability it would cost a billion dollars a day so I don't know if that's true but it's in a recent report um so there's going to be there's still the issue of access to data like as in minutes of data connectivity to the internet access to the devices we do know that we have to design for mobile phone based applications but we also know that mobile phones limit the kinds of information and immersion that's possible so I think access is going to be a huge issue we just making great AI T tutor tools is not going to get us far enough great thank you sorry it's not good sorry we're here we're here to unearth unearth the reality are there any questions uh from the audience at okay maybe if we can hand the mic over somebody can grab the mic yeah it's right there here we go yeah all right so there's a couple of questions that I think I want to just throw out to the team I think in my mind is as this thing gets smarter and smarter and it will be smarter than the entire human race um I'm thinking about how are we going to be able to control it mhm and what comes to my mind it's it's MIT we're we're like the best in the world and being able to build gen you know I went to MIT so like best in the world to be able to build technology uh that we can allow us to be able to do that and you know 20 years ago I was building Ai and AI that was controlling the black boxes trading in the in the stock exchange and if they went sideways we shut them down mhm so we should be building some newer technology that helps us to be able to shut these things down if they go sideways and maybe have some other AI that comes monitors that those things and I was wondering if number one if somebody is working on that problem because that's going to be a problem that's going to hit us and it's going to hit everybody in the entire planet right just like Co did and if we don't come up with you know we don't put the smartest people in the world to work on these problems not going to get solved so that that's you know number one thing that I would love to be able to ask if anybody's even thinking about that and or coming up with solutions for those things and the next thing is you know you mentioned something about um you know having hallucinations and you know we we've solved this problem in AI before where we we asked 10 experts you know what's the problem and then nine out of 10 disagree or agree on something and then we pick the one that you know that the one that does the most so maybe we use eight or nine generative AI things that that we ask it and we filter out a lot of the noise so we can actually so if there's some research that's working on that piece to be able to to solve those problems because we see the patterns already of what we've done before you know and so I just want to ask some some of those open questions and and have the team come you know come up with some comments on those things thank you great thank you maybe uh Jesse have you filled that one yeah so um you know I come from the world of curiosity driven research um and so you know the fears that I would have uh are related to things like data falsification you know giving the wrong person a Nobel Prize um and not to mention any names right U but but uh but you know one of the things uh I mean you mentioned kind of AI monitoring other AI like at least in in information space Providence seems to be something that's really important and lacking um and so I mentioned before kind of the ability to hyperlink everything understanding where the information is coming from is essential and um I'm I'm going to get the the quote wrong um from Einstein um but you know was was talking about like all these scientists the majority of scientists attacking his work and and Einstein said something like all you needed was one person you know if I was actually wrong and so so majority rules is not a good strategy for for safety um rather you really need to have Providence uh you really need to have uh you know logical inquiry um and that that's something that I think we need to be building in much more into our tools MH great thank you I think we're actually at time let's thank the panel I know thank you

Channel: Massachusetts Institute of Technology (MIT)

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Id: k68m0ifhPvA

Length: 40min 40sec (2440 seconds)

Published: Mon Dec 18 2023