A World Transformed By AI | Global Summit 2018 | Singularity University

[Music] I thought maybe we could kind of start off with a brief statement from you all you know talking a little bit about machine learning and an AI and and kind of maybe some of the areas where you're seeing the the greatest impact around those you want to need to start us off maybe we'll just work right down the line here and if I could ask you to keep your your answers you know brief we've got a lot of material to go through and want to make sure we get a good chance to kind of connect with everyone yep thank you so one of the areas I'm very excited about right now is actually kind of the human aspect of it and how AI kind of forces us a little bit to look at what it means to be human when we talk about the future of AI and that's one of the things I'm preoccupied with like what does it mean in you know in a future where we're at some point going to reach you know artificial general intelligence what does it actually mean to be human and what is our humanity compared to an artificial intelligence great thank you we're gonna talk some more about AGI a little bit later as well and the difference is I'm well I'm really interested in robots as some of you probably heard and I'm really excited about how AI and robotics are now coming together in the past it hasn't been easy to integrate AI with robotics and robots have been controlled just using standard programming but we're now seeing robots that learn more and more of them popping up all over the place so I'm really excited about those two fields coming together thank you you know I'm really interested in a eyes ability to improve our decision-making so when we work with AIS we can do a better job with situation assessment and decision-making under fire under really stressful conditions and I think as the world accelerates that is going to become more and more valuable and now we have hardware that is faster we have more data and we have better algorithms so we're we're really moving forward with this in terms of impact of AI I'm seeing it everywhere really it's hard to find an industry where it's not on the verge of being revolutionized by AI some things I am personally involved with or excited about there's an AI in agriculture so we need to you know this is one of humanity's global challenges and you know we're looking at AI systems that can you know across the board basically got automatically you know harvest the harvested crops detect plant signals interview what's going on in plants I think we're gonna have you know yield increases of like you know maybe double yield from AI so in the AI also breaks I'd about AI in health care AI is hitting Pharma in a big way people are designing drug discovering drugs and managing clinical trials better all kinds of medical devices basically it's affecting our health on a daily basis so I'm really seeing it you know old-school industries are all being changed by a right now so pretty much it's hard to find a place that is not being impacted right now great thank you so maybe be helpful for the audience to kind of define some of the things that are happening around AI maybe we can have some folks kind of talk about machine learning versus AI let's give a a general description about AG AI vs. AI and maybe help that will help frame up a little of our talk down the road anyone want to dive in AGI versus AI all right so yeah the the idea the initial idea of AI so that was conceived back in the 50s was to try and create machines that think like we do and what's happened is that AI has sort of split into many subfields like pattern recognition reinforcement learning deep learning and really none of these you know in you know as individual things really making machines that think like humans but what we're seeing now is that a lot of these fields are now kind of merging back together and people are starting to think how can we make collaborations between these fields you're seeing this kind of like what what I've heard referred to as this third wave of AI with the first wave being symbolic style AI symbolic reasoning and logic systems the second wave being deep learning neural nets very cool pattern recognition systems and the third wave is hopefully going to be these things coming together to create more brain like architectures that really are truly machines that think like us yeah I would just clarify that the brain like architectures and building human-level AI is still a research frontier and and right now the business opportunities are in applying machine learning not just deep learning but many different machine learning algorithms to solve a wide variety of problems across the board that were very hard to crack before I think that there's there's been you know quite a revolution in terms of deep learning and that it's been able to buy a vast amount of computing power investment of data that we didn't have before so the lot of these algorithms and ideas were around from the earliest days in fact you know AI didn't start there so with the symbolic approaches it started with the perceptron and Rosenblatt and these kind of things so it's now you know compute things came along where this stuff could actually apply and start working and well I think what a lot of people don't realize is that lots of other aspects of AI have also been waiting for the Cape for they're sort of prerequisites to happen and they've all happened as well so just as we see the steep learning revolution you're gonna see the same kind of revolution as all these other aspects that they out people working on for 30 years or more as well also come of age and then they start to get integrated so that's it's all to come right now I think also one of the things because we keep talking about like modeling AI after the human brain and I think it's just also highly important to highlight that we don't quite know how the human brain works fully and so it is kind of a biomimicry thing where we're looking at the you know neuroscience and trying to understand the human brain but we don't quite know how that works either which is the interesting part that you have the two fields kind of going a little bit back and forth and we can use AI algorithms to better understand the human brain and and vice versa so I think there's a lot of synergy effects but at the end of the day we might not be able to replicate how the human brain works or we might find that that was that was incredibly limiting yeah do we be one of our better right do we even want to go down that direction right should it be modeled on the human brain and and how humans think or should it be a new way of thinking like would that potentially serve us better well we have a proof point with the human brain baseline to be able to get to we don't have to stop there and we don't have to emulate every little aspect of human brain processing but it certainly is useful to have something that we can use as a reference point and no doubt there are all kinds of intelligent processing capabilities that we don't have that we could imagine creating so I look forward to doing that and I think the human brain one of the issues is all of the biases that you know that comes from like not having you know and I'm not a neuroscientist but like not having like all of our memories stored and we kind of read we patched up our memories to kind of recreate them a little bit and and and that creates an enormous amount of bias and let's let's not build that into our algorithms so is this crew here optimistic or are you highly concerned about what you're seeing in terms of some of these developments I think or do you fear AI and machine learning are we taking it you know paying enough attention to the potential downside risks here or Society really not it's not really going here it asks you right we basically study you know the positive aspects and opportunities and you know and the big threats and risks I'm still probably more worried about the risk of synthetic biology you know that I'd be about the risks of AI but because of where AI is right now or and just hasn't developed to a point where you think it poses well first of all I mean you know it's funny there was a friend of mine was putting together a you know sort of a worst-case scenarios what might be the really bad things that can happen about AI want to pull a bunch of the AI experts together and I was saying well just imagine what would happen if you know if a an entire country decided to try to take over other country by harnessing AI to learn everything and basically influence everyone's beliefs you know and basically influence elections and take our political system like that's happened I can't imagine and that doesn't need all the bases in latest AI and we're on this verge of this whole new fake news thing where now you're gonna make videos and you really will be very hard to tell the videos the articles everything so this whole notion of what is truth that you know people are talking about in the media well we haven't really seen it hit yet but it's coming and it's gonna come hard and we're not prepared for that so that's that's a big threat so I think a lot of these ones aren't the natural ristic that's the next couple years is that fire next couple years okay now it's now now we now have the tools and the data that bad people can use them just like good people can use them and so that's present and we have to address all the factors that go into you know criminal activity using all these tools it and there's a lot of people out there that wanting you to do bad stuff and AI can help them so people are really too focused on evil AI and not focused enough on human intent yeah I often say that I don't stay up late at night worrying about machine intelligence I worry about human stupidity and malevolent and so yeah yeah and so I think I think that we shouldn't underestimate that a eyes could go off the rails and there's a whole community in the AI community that's committed to building in safeguards multiple redundant safeguards of different classes can you talk a little about that excuse me can you talk a little bit about that yeah so one of the things that you want to be able to do is to detect malicious behavior so that's not random I mean if you have a life support system and suddenly you see an AI going after your life support system turning off the oxygen opening up the airlock you know things like that you want to be able to shut it down very quickly and and if it starts interfering with your ability to shut it down you want to have another annular ring of control that can counter beam that move so you want multiple redundant layers of control and this is a predator-prey problem in other words you know the the Predators get better in the prey gets better so it's not a problem that you can finally solve evolution but it is a problem that you can address systematically and we certainly if you're building an AI product you certainly should build it in a sandbox environment so that you test it under a lot of different conditions and and not be surprised at the first 1,500 things that it could do wrong you know you have thought about those things at least and then you're dealing with corner cases after that great I do think there's another kind of there is an issue that we're becoming more and more dependent on just that software right that's kind of the next software and just we're dependent on software being fed in on AI driven software affecting all of our different systems and there is the challenge that we really don't understand how the stuff is working a deep level especially the ones that are trained by machine learning they're trained on data and there are so many actual sources of bias in our systems and we don't have a good way of knowing what's going on so you know imagine all these self-driving cars out on the road and they work well enough in the simulations and the testing and we didn't really know how to test them through all the different conditions and then they're just basically big structural problems and now how do you even go out and fix them right so it's not like there's going to be some oh if I understood it now that's just a patch we may have no idea what to patch our systems and you know the AI driven things may take over and replace the ones that were more traditional and now we've been pinning them and there's no way to easily go back and turn off the switch so that's more the kind of you know unintended consequences and it's not that they were all evil or bad it is we didn't understand how they work and you know if you look at all this great lists of biases in AI you know the you know you don't see red blue stoplights it's not in training at all and now you're in a place in the country of the having blue blue stop sites no one thought to test that what does the system actually do no one knows about it now it's just out there and deployed and how you go back and suddenly train it you don't have the data many reasons to be concerned about that and the education of the people are now trying to deploy AI they don't have any training or background and even thinking about that right people are kind of scramble you gonna use things so that's a big is improving there it does help to run self-driving cars millions and millions of miles under lots of test conditions including really wacky unusual test conditions that helps it doesn't solve the problem and the other thing that helps is the DARPA sponsored xai program that has multiple University teams working on better explanation and transparency from machine learning systems so that's a really important program these are things that we can do to make our systems safer and I think there's also a very human element to it which is the teams that are building the ai's the teams are selecting the datasets to actually kind of and I'm gonna say this like check your priveledge a little bit because there is a tendency that there's a lot of white males in this industry and just be very aware when selecting it maybe not so much with the self-driving cars or though there's definitely scenarios there to where you want to you know check that but when you're dealing with human beings we're dealing with with applications for image recognition for example the fact that our iPhones you know if you're if you're white male the iPhone face ID works a lot better than if you're if you're black and female right and and all of these things is simply because of the humans that are building this they don't really think that all the way through and they don't necessarily have the right framework for going oh wait so here's my here's my box in my understanding of the world what am I missing like what is not part of my frame of reference when I select the data set when I check it for bias and I think that's also really important to have those conversations with everyone who's involved and say you know what let's just like take five minutes and say you're five hours or five days and say you know what are we not thinking about like what lived experiences do we not have right here right now that we should bring in and consider I'm actually very optimistic about AI in general but one thing I think that hasn't been mentioned yet is most AI today has a really well-defined sort of single goal or motivation and so when people say there's things like bias in the data what they're saying is that that's the thing that's now screwing up the AIS ability to get to this goal that's been defined but what I'm more worried about is when those goals structures start to become more and more complex and it's not just like you know the car has to identify whether it's a stop sign or not anymore but the AI has to make very nuanced decisions in in sort of social situations and you have a very big hierarchical goal structure so AI is sort of in two parts there's the intelligence part which is how well can I learn from this data in the service of my goals but the other part is designing good goal systems for AI is in the first place I don't think we've really even hit into that problem very much X we've been thinking about these very simple goal systems and so I think that's going to be really interesting a few years ahead thank you parents of young children have some experience so I think one of the things might be interesting also explore a little bit or just some of the use cases Peter Diamandis was talking about AI is kind of out on the edge as the inner face the ultimate interface with humans and and potentially with other machines maybe the the panelists here can kind of give us some examples to kind of identify the breadth of tasks and problems that AI is addressing these days so I mean what I'm working on these days is applying AI to scientific research and helping us make sense of all of that incredible knowledge we have in the world and and so what we're working on in my company is doing you know a semi-automated literature review and eventually semi-automated peer review to measure the validity of research and then there are other companies like benevolent AI working on you know finding specific target proteins from from scientific literature and they have this amazing case study of going from you know someone who isn't an domain expert going from something like you know in four days finding five different target proteins that would take someone a lifetime of research to achieve and you have amazing breakthroughs like that and also you know companies doing more kind of entity tracking and looking at you know what can where can we find you know one paper that talks about one specific chemical and other papers that do the same even though they don't necessarily use the same name etc which makes us you know able to to use all of the knowledge we have in a much more efficient way and not waste all of that time and effort and and really what is that what that is doing is speeding up scientific breakthrough speeding up research speeding up innovation which is going to rapidly accelerate the progress of the world where is that in the development phase right now is that people can access that and use that right now absolutely absolutely awesome Suzanne W oh one of the things I'm really excited I need to touched on it earlier is the interface between neuroscience and AI so as we try and build AI systems that are more and more human-like it's still in the very very early research phases but it's actually encouraging AI people to talk to more neuroscientists it's encouraging us to ask questions about the brain like can we create a simulation of this part the brain does it work like the real brain does so almost by accident this this field is helping us to learn about our own brains and driving research in neuroscience a little bit so I'm very excited about these these emergent effects that are becoming cross-disciplinary that that AI is having an impact on just not directly great one of the interfaces that I think is really exciting is between medical devices and medical sensors and AI interpretation of those devices and sensors so that now we can empower nurses and patients and doctors to look at these at the data from these devices and reach much more evidence-based conclusions and those experiments are going on all over the world now and that is going to change our relationship between medical data and how we manage our health interesting so how do you think Barney gonna come back and get your your your input in just one second here but given that there's lots of errors still with software code there's questions around really what's happening in the black box of machine learning how do you think about I mean you know you have people's lives in your hands right how do you think about that exposing that data set and recommendations coming out and making these these recommendations based on oppression there are two key approaches to that one is to make sure that the AI that you're using in the medical arena has been tested extensively and has the ability to explain how it got to its conclusions that's a research frontier but I think it's a very promising one and the other thing is to make sure that currently if you have a brain condition or a heart condition that's a critical condition you have a well-trained doctor mediating with the AI and you so that you make sure that if it gives you an answer that's run off the rails you can use your critical good judgment and 15 or 20 years of training to say no no I you know we need to rethink that how quickly do we lose that though if you think about what's happening with maps right I mean no one uses that's gonna be a watch it anywhere right it's a Google map away their critical judgment in these situations yeah interesting well in terms of you know areas I'm interested or involved with and in breadth of areas give a couple examples that could be called heart flow based out of here in the Bay Area heart flow is addressing the problem of diagnosing whether someone may have a heart condition that needs surgery or not and normally when they're going through CT scans or other kinds of tests a huge percentage of the time it's almost like a die roll you know a crapshoot that the doctor says yeah go let's go and they open up the person's chest and they didn't actually need to okay and it turns out this great case for AI so they can now take the CT scan and they can run an AI system that will figure out the structure of all the heart vessels from that scan and then they run computational fluid dynamics on it to actually model the fluid flow and detect whether actually are issues or not and then they can make the recommendation that the doctor can make a thought and decision on and say yes you know go and open up this person let's look deeper and it turns out based on now huge amount of clinical trials and studies that they're able to remove all the cases where the doctor would referred someone they didn't need to open their heart and not lose any of the good cases right so this thing is now rolling out it has a chance to basically change the game and take actually you know routine heart screening all right so everyone I wouldn't have my heart checked all right I had high cholesterol the doctors saying here we put you on statins now you're you know you're 50 and welcome to 50 here's your statins for your high cholesterol you know I wouldn't I did this hard flow scan and the system said and with the doctor said no your heart's great you don't need to do anything okay so it took the whole mystery out of the whole game that's a really neat example and that's fda-approved right and now rolling out in the world so that's kind of a big interesting healthcare game-changer I'm on the board of a company called eco ation and this is combining the biology of plant signals with AI and robotics so in greenhouses this makes a robotic sensor that runs up and down the lanes and is actually looking and strobing the plants with all kinds of lights and collecting all the sensory of information all these plants and it turns out that every plant leaves a different each species of plants give a very different unique signature for every malady the plant may have so you may not know but a tomato plant with a single caterpillar on it will give a different signal okay and you can now read this and match it against your big database that's been assembled and then say okay this panel and has a problem let's go and get that caterpillar right now so you can actually catch problems with your crop in the earliest stages before they get worse and that again is just massive game changer it's combining AI and robotics and data and also flows canvas analytics as a start up that ion mentored and invested in on just announced a series a funding round led by gradient Ventures is google's AI firm and this is applying applying AI in machine learning to industrial internet of things there's this every single industry making every kind of thing and they've got all the sensory did all this data that's been collecting the machines that have just been sitting in the databases and no one's been doing anything with it now you can come in and you can rapidly use AI to build a model of the system and then predict what's actually going to happen with the system what state are we in predict the problems and then enable people to optimize the systems and we're optimizing industrial systems a 5% impact can mean like sort of you know millions of dollars every single day and this little company a couple years ago was brand new and went into the space and now they're starting to have bigger companies getting these like million-dollar-a-day savings and you know signing up for bigger deals so those are a few examples of where I see AI it shows the spec the breath but really it is everywhere and super exciting times that's amazing yeah some of these examples are really pretty mind-blowing right to think about being able to get a signal from a plant that a single caterpillar is on there and then react to it right who would have imagined that so so let's try to imagine I don't know five years out or ten years out what what is the world of AI and machine learning look like you know how how what kind of revolutionary products and services are we seeing and how is it touching our lives I think one of the biggest things is that right now we all use smart phones and if we lose our smart phone we think ah you know this is a big problem because we are used to being augmented by our smart phones and yet smart phones in their current form are very weak augmentation so I think we're going to see progressively stronger forms of augmentation so that the things that we want to monitor are monitored and we get signals about how things have changed so that they act as our assistants as our partners as our partners in doing new kinds of research so that instead of it being like a weak assistant it's really stepping up to interacting with you on tough questions so I think we're going to see major new forms of augmentation there's these recent announcements about some of these actual robotic scientists the first one is called Adam and then the second one was appropriately called Eve both of these out of England and these are robots that can actually run experiments and in the real-time loop formed their own hypotheses test them out and discover things and they just run these things in a way you know humans are basically couldn't you know couldn't possibly do it and they're now actually discovering you know novel science driven by these things so that's just getting started imagine in ten years that's just the way science is gonna be done you know large amounts of data and screening AI systems supported by humans interacting the s and throwing out theories humans are throwing in theories collaborating together and we're gonna be sharing the knowledge in a worldwide basis doing high-end you know computational simulations as well these things probably using quantum quantum system simulations and will basically just be doing science at a speed that we've never imagined possible exciting I think there's also a certain level of personalization in it because right now when you interact with Siri or Alexa or any of these chat BOTS it's very impersonal right it doesn't actually know you and I think we're gonna move be moving into a world where more and more your your AI knows you and potentially 10 years from now better than you know yourself so that you know when you're like oh it's a home you just got F of work it's like 7:00 p.m. and you kind of need to go to the gym but you kind of don't wanna and having an AI assistant that actually knows you better than yourself and so it can actually know exactly what button to push to get you off of that couch and go you know go workout or or make healthy food instead of unhealthy or whatever whatever it is you need help with to give you that little extra nudge whether it's like having your your mom call you or whether it's you know turning off the TV and not allowing it back on or whatever it is that you need right so that personalized and that goes you know both kind of personal decisions but also like banking you know can you have a personal banking assistant that isn't just telling you you know oh here's you know the amount of money you have in your account but you could ask it like hey can I afford to take my partner out to you know fancy restaurant tonight and then the system can look at your account your your earnings history how old your car is whether it needs repair soon are you saving up for a vacation like all of those things and take all of that into account and then actually give you a financial recommendation whether it's for taking someone out to a restaurant or you know investing your money or whatever it is and so have that kind of very kind of almost personal relationship with an AI which of course leads us into the whole discussion on you know if someone watch the movie her or machina you know at what point are we going to start feeling like we actually have a not necessarily romantic relationship but a personal relationship with our AI assistant how far off do you think that is I mean you already have people talking to Siri as if she was their girlfriend or you know that I mean I think I think we're seeing that happen and and it's it's still kind of just a you know funny you know yeah funny things but then you know and then we get into sex robots and that's a whole other topic of discussion but you know late night for the I think party I think well we're talking about the conversational assistance this becomes such an important part of our daily lives and the rate of adoption of you know the Amazon echo Google home all these different kind of things I mean they're growing super fast we're just now you know using them and the capabilities are so early in rudimentary and they're already basically popular and successful we think about 10 years from now these things will you know should really be able interruptin much more powerfully much more natural ways get a lot of jobs done and across the board be saying hey you really can ask questions right now you think I can ask five questions and I'll use those five questions and be happy about it be sure to ask large numbers of questions and it's gonna be kind of routine 30,000 skills or on Amazon Alexa right now you know how quickly and those skills those skills are all fragmented they're sort of hand designed a little scripted skills imagine those 30,000 skills turning into a million skills a knowledge layer connecting all these different skills conversational systems able to operate at that knowledge layer and coordinate across these tasks so we've seen visions that people have put out I think the you know viv you know who was acquired had a you know vision of this kind of stuff but that probably is realistic within the next sort of 10 years and this is going to be totally part of our daily life across our own lives and across the workplace it's gonna be really excited we have any sort of sense as to how quickly Amazon or Alexa's sorry Alexa or Google home how quickly their skills are growing I mean is it exponential when they're doubling in capability every six months or exponential and accelerating yeah and it started out with 14 skills yeah yeah yeah for those of you who are not familiar with viv that was Adam shires company and the Adam was one of the architects of Siri they created a next-generation conversational assistance system demonstrated it and about six months later were snapped up by Samsung so stay tuned interesting which is also and then I mean this is also an interesting kind of ethics discussion right because we're talking Amazon we're talking Google and we're talking Apple right but alone all of this and and Microsoft Bob decided right yeah yeah four or five companies kind of really owning this right having such tremendous insight into who we are and being able to wield some scary power and so the switching costs for some of these very low hmm so you might see some new player move into the marketplace and be a very effective competitor interesting so the opportunities are still there yeah Suzanne Hubbell you what's your I think that within 10-15 years will have human-like robots living living with us working alongside us being our best friends yeah interesting so you all are the the experts here tell me you know what happened we talked about what should we talk about what he excited about sharing with with this audience well I'm I'm excited I'm my PhD work was on games on games and learning and so when I was doing this and you know the late late 80s I was thinking would be great to have systems that could learn to play all these different games and interact with people you know just from their experience in playing the games try to do go and chess and those kind of things and now the rate at which these things are happening is super exciting I mean for me personally it's like well thirty years later you know you have a vision and now all this stuff is coming true alpha zero you know so there's alphago beating you know beating the best go players in the world and with incredible insights that people like well we don't understand this game of go and then making alpha zero making more and more general and then be able to do what outdo the previous program in a matter of like a few more days and then say well let's apply that same thing to chess basically the same system almost unmodified applied to chess and within really like one day was better than every new program and human that's ever existed in the game of chess and then you know a couple weeks ago there's a game called dota 2 and dota 2 is a large scale real time strategy game the few little things in eSports I mean people are now watching more eSports and they're watching the other sports it's becoming a really big business and this is a different kind of game you have all these kind of different player characters all these complexities everyone's doing things in battle in real time you've got a lot of complexity and a team of AI bots beat a team of human professionals and completely killed them and the humans are like well we don't even understand we know this is never no one's ever thought about doing so we don't know what hit us and you know when they have it they plan to have three matches and then the AI is beat the humans so badly on match 1 and match to that on game 3 they had the audience say well let's pick the team of characters that the eyes have to play with and they made the worst possible team and the AI system said yeah we think we're gonna lose we think we're gonna lose in them you know than they did but for me that was pretty amazing I mean no one would have thought I think that at this time we'd have AI systems playing real-time strategy games and being better and in the next I think tomorrow maybe even today no tomorrow is against all the top professionals that event is about to happen and I would say I think the eyes are gonna beat the professionals and kill them so that's really a watershed moment that's happening and you think the funny part I thought is when I was training with games I thought well we like games and once you can really solve these games you can probably do it too like lots of other applications and so when I saw now this dota 2 milestone being head then I started thinking all right well now it's time to put up or shut up I mean now we can really do this stuff let's look at what are all the applications one can possibly do with this stuff but it's actually it's a very exciting times now really exciting I think the most important application for a eyes is making the world work for everyone we have a lot of big problems that we need to solve and we're kind of in a race between entropy and education and problem solving and we need to win that race and if we do we can have a world that is really outstanding a world that we would be proud to show someone from outside earth but the way it is now it really is problematic and we need to apply these powerful machine learning technologies to solving each one of these problems like climate change and pandemic disease poverty etc and I think we can use them effectively to accomplish just that one of the things I'm really excited about in the future is how we're going to have humans like telly operating robots not just to for the purpose of training AI but that in itself will be a huge market there'll be people wanting to work remotely using these systems there'll be people wanting to you know have their surrogate robot go out for them to do this dangerous job instead of sending their real body there'll be people that just want to look different than the way they look so they'll be able to have like a new sleeve made that's exactly the way they want to look like you can customise your your video game character now and so that's all gonna happen but what that does is it enables a really large online real time streaming data set of data trunk being transferred between human the human brain and a robot body and AIS can learn from that day through it's a beautiful beautiful resource and I think that foundation is what's going to allow us to get to human-level AI I think one of the things that is important to talk about and Neil talked about that in the beginning is kind of this kind of crazy futuristic sci-fi movie that we're talking about ten years from now and kind of the very applied concrete things that are happening right now and and especially for people who are kind of running a business being tasked with like you know and I've spoken to people are like from their manager told you're you're in charge of AI in this company come up with an AI strategy and implement it and they'll go around to like different departments and like ask the default question like where are we wasting kind of where are we wasting too much human power like where are we sitting and doing these like manual tasks again and again and they go in there and they want to you know do an AI project and surprise surprise the human the humans don't want to be automated and and I think we're seeing this kind of this difficulty in kind of applying the day to day actually you know doing that and I think the the discrepancy between what you actually need to do right now as a company owner as a you know executive of a company what you need to do right now to apply these AI strategies so that you're prepared for what is coming that's kind of where where it gets real difficult because we're dealing with human beings that don't want to be automated we're dealing with you know slowness of movement in your organization in fact is there's a lot of startups and there's a lot of researchers that are doing incredible things and and if you don't you know act on this right now you're probably going to be very quickly out competed and I think that is a real challenge that a lot of kind of the incumbents are struggling with right now I mean kind of more traditional industries not that the big incumbents that we talked about in terms of the the forefront but you know all of the big the big companies of the world and I think trying to grasp that mindset and actually start implementing these these kind of futuristic but applic applicable technologies today so that you are prepared for the the ten years from now future it's always going to be kind of a massive tension between the two interesting thank you I think we will end on that but I want to thank our panelists for a really broad and balanced conversation obviously AI and machine learning are gonna have absolutely extraordinary impacts on the world ahead and I appreciate you sharing your thoughts with us so thank you so much [Applause] [Music] you [Music]

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Views: 19,692

Rating: 4.4929576 out of 5

Keywords: Singularity University, Singularity Hub, Education, Science, leadership, technology, learning, designing thinking, future forecasting, Ray Kurzweil, Peter Diamandis, 3D printing, AI, artificial intelligence, AR, augmented reality, VR, virtual reality, automation, biotechnology, blockchain, computing, CRISPR, entrepreneurship, future, futurist, futurism, future of work, future of learning, genetics, health, healthtech, medtech, fintech, nanotechnology, robotics, talks, Machine Learning, Global Summit

Id: kehoxzQQihc

Length: 37min 12sec (2232 seconds)

Published: Tue Sep 18 2018

Reddit Comments

[u/mmaatt78]

Very interesting video!

[u/8BOXX]

great video