Eric Schmidt - Google _ Lex Fridman Podcast #8

The Power of Human-Centered AI: A Conversation with Lex Rubin

In today's world, where technology and innovation are advancing at an unprecedented rate, it's easy to get caught up in the latest trends and algorithms. However, as we delve deeper into the world of artificial intelligence, it becomes clear that there's a need for a more human-centered approach. According to Josh Tenenbaum, one of the leading researchers in this field, "there must be new approaches different from what we and others are doing." This sentiment is echoed by Lex Rubin, who has had the privilege of working alongside some of the most brilliant minds in AI.

At the heart of human-centered AI lies a deep understanding of how humans learn and interact with technology. As Rubin notes, "the successful entrepreneurs are not as crazy as they sound. They see an opportunity based on what's happened." This approach is evident in the story of Uber, which was founded by Travis Kalanick and Garrett Camp after they struggled to find a cab in Paris. Similarly, Google's inception began with Sergey Brin and Larry Page, two graduate students at Stanford who were fascinated by web data and algorithms.

The Psychology of Innovation

When it comes to innovation, there's often a sense of serendipity involved. Rubin recalls the story of Sergey and Larry's journey, where they built a search engine in their room and had to improvise with an extension cord due to power issues. Their beginnings were simple, but based on a powerful insight: replicable models for start-ups. This concept is essential for any entrepreneur looking to make a mark in the industry.

Rubin notes that "money is simply a side effect of your passions and not an inherent goal." This perspective has been borne out by numerous studies on human happiness, which suggest that once basic needs are met, additional wealth does not necessarily lead to greater happiness. Instead, it's the meaning and purpose behind one's work that brings fulfillment.

The Power of Wealth

Rubin's own journey is a testament to this idea. As one of the richest people in the world, he has had the privilege of pursuing his passions and making a positive impact on society. He believes that "if you organize your life, assuming you have enough to get around and have a nice home, you'll be far happier if you figure out what you care about and work on that." This approach is echoed by Rubin's work in education and inequality.

Rubin's commitment to using AI for the greater good is evident in his efforts to advance human society through technology. He believes that "your life can be far more satisfying if you spend your life doing that." As we look to the future of AI, it's clear that Rubin's vision is one worth emulating.

The Meaning of Life

Finally, Rubin suggests that there's no better place to end than a discussion of the meaning of life. In an era where technology and innovation are increasingly dominant, it's essential to take a step back and consider what truly gives our lives purpose. As Rubin notes, "the happiness is correlated with meaning and purpose, a sense of family, a sense of impact." So, as we look to the future of AI and its potential to shape human society, let us not forget the importance of finding meaning and purpose in our work.

"Eric, thank you so much," Rubin concludes. The conversation with Lex Rubin has been a fascinating one, full of insights into the world of human-centered AI and the power of innovation. As we look to the future, it's clear that Rubin's vision is one worth emulating – a vision that combines technology, passion, and purpose to create a better world for all.

"WEBVTTKind: captionsLanguage: en- The following is aconversation with Eric Schmidt.He was the CEO of Google for 10 yearsand a chairman for six more,guiding the company throughan incredible period of growthand a series ofworld-changing innovations.He is one of the most impactful leadersin the era of the internetand the powerful voice forthe promise of technologyin our society.It was truly an honor to speak with himas part of the MIT course onartificial general intelligenceand the Artificial Intelligence podcast.And now, here's myconversation with Eric Schmidt.What was the first momentwhen you fell in love with technology?- I grew up in 1960's as a boywhere every boy wanted to be an astronautand part of the space program.So like everyone else of my age,we would go out to the cowpasture behind my house,which was literally a cow pasture,and we would shoot model rockets off,and that I think is the beginning.And of course generationally today,it would be video games andall of the amazing thingsthat you can do online with computers.- There's atransformative inspiring aspectof science and math that maybe rocketswould instill in individuals.You mentioned yesterdaythat eighth grade mathis where the journey throughmathematical universediverges for many people.It's this fork in the roadway.There's a professor of mathat Berkeley, Edward Franco.I'm not sure if you're familiar with him.- I am.- He has written this amazing bookI recommend to everybodycalled Love and Math.Two of my favorite words.(laughs)He says that if paintingwas taught like math,then students would beasked to paint a fence.It's just his analogy ofessentially how math is taught.So you never get a chance to discoverthe beauty of the art of paintingor the beauty of the art of math.So how, when, and where didyou discover that beauty?- I think what happenswith people like myselfis that you're math-enabled pretty early,and all of the sudden you discoverthat you can use that todiscover new insights.The great scientistswill all tell a story.The men and women who are fantastic today,it's somewhere when they werein high school or in collegethey discovered that they coulddiscover something themselves.And that sense of building something,of having an impact that you owndrives knowledge acquisition and learning.In my case, it was programmingand the notion that I could build thingsthat had not existed,that I had built that had my name of it.And this was before open-source,but you could think of it asopen-source contributions.So today if I were a 16or a 17-year-old boy,I'm sure that I would aspireas a computer scientistto make a contributionlike the open-source heroesof the world today.That would be what would be driving me,and I would be trying and learning,and making mistakes and soforth in the ways that it works.The repository that GitHub representsand that open-source libraries representis an enormous bank of knowledgeof all of the people who are doing that.And one of the lessonsthat I learned at Googlewas that the world is a very big place,and there's an awful lot of smart people.And an awful lot ofthem are underutilized.So here's an opportunity, for example,building parts or programs,building new ideas,to contribute to the greater of society.- So in that moment in the 70's,the inspiring momentwhere there was nothingand then you ceratedsomething through programming,that magical moment.So in 1975, I think, youcreated a program called Lex,which I especially likebecause my name is Lex.So thank you, thank youfor creating a brandthat established a reputationthat's long-lasting, reliable,and has a big impact on theworld and is still used today.So thank you for that.But more seriously, in that time,in the 70's as an engineerpersonal computers were being born.Did you think you would be able to predictthe 80's, 90's and the noughtsof where computers would go?- I'm sure I could not andwould not have gotten it right.I was the beneficiary of the great workof many many people whosaw it clearer than I did.With Lex, I worked with afellow named Michael Leskwho was my supervisor,and he essentially helped me architectand deliver a systemthat's still in use today.After that, I worked at XeroxPalo Alto Research Centerwhere the Alto was invented,and the Alto is the predecessorof the modern personal computer,or Macintosh and so forth.And the Altos were very rare,and I had to drive an hourfrom Berkeley to go use them,but I made a point of skipping classesand doing whatever it tookto have access to thisextraordinary achievement.I knew that they were consequential.What I did not understand was scaling.I did not understand what would happenwhen you had 100 millionas opposed to 100.And so since then, and I havelearned the benefit of scale,I always look for thingswhich are going to scale to platforms,so mobile phones, Android,all of those things.The world is a numerous,there are many many people in the world.People really have needs.They really will use these platforms,and you can build bigbusinesses on top of them.- So it's interesting,so when you see a piece of technology,now you think what willthis technology look likewhen it's in the handsof a billion people.- That's right.So an example would be that themarket is so competitive nowthat if you can't figure out a wayfor something to have a millionusers or a billion users,it probably is not going to be successfulbecause something else willbecome the general platformand your idea will become a lost ideaor a specialized servicewith relatively few users.So it's a path to generality.It's a path to general platform use.It's a path to broad applicability.Now there are plenty of goodbusinesses that are tiny,so luxury goods for example,but if you want to havean impact at scale,you have to look for thingswhich are of common value,common pricing, common distribution,and solve common problems.They're problems that everyone has.And by the way, peoplehave lots of problems.Information, medicine, health,education, and so forth,work on those problems.- Like you said,you're a big fan of the middle class--- 'Cause there's so many of them.- There's so many of them.- By definition.- So any product, anything that has a huge impactand improves their lives isa great business decision,and it's just good for society.- And there's nothingwrong with starting offin the high-end as long as you have a planto get to the middle class.There's nothing wrong with startingwith a specialized market in orderto learn and to build and to fund things.So you start luxury marketto build a general purpose market.But if you define yourselfas only a narrow market,someone else can come alongwith a general purpose marketthat can push you to the corner,can restrict the scale of operation,can force you to be a lesserimpact than you might be.So it's very important to think in termsof broad businesses and broad impact,even if you start in alittle corner somewhere.- So as you look to the 70'sbut also in the decades tocome and you saw computers,did you see them as tools,or was there a littleelement of another entity?I remember a quote saying AI beganwith our dream to create the gods.Is there a feeling whenyou wrote that programthat you were creating another entity,giving life to something?- I wish I could say otherwise,but I simply found thetechnology platforms so exciting.That's what I was focused on.I think the majority of thepeople that I've worked with,and there are a few exceptions,Steve Jobs being an example,really saw this a greattechnological play.I think relatively few of thetechnical people understoodthe scale of its impact.So I used MCP which isa predecessor to TCP/IP.It just made sense to connect things.We didn't think of itin terms of the internetand then companies and then Facebookand then Twitter and thenpolitics and so forth.We never did that build.We didn't have that vision.And I think most people,it's a rare person who cansee compounding at scale.Most people can see,if you ask people to predict the future,they'll give you an answerof six to nine months or 12 monthsbecause that's about asfar as people can imagine.But there's an old saying,which actually was attributedto a professor at MIT a long time ago,that we overestimate whatcan be done in one year.We underestimate wascan be done in a decade.And there's a great deal of evidencethat these core platformsof hardware and software take a decade.So think about self-driving cars.Self-driving cars werethought about in the 90's.There were projects around them.The first DARPA GrandChallenge was roughly 2004.So that's roughly 15 years ago.And today we haveself-driving cars operatingat a city in Arizona, so 15 years.And we still have a ways to gobefore they're more generally available.- So you've spokenabout the importance,you just talked aboutpredicting into the future.You've spoken about the importanceof thinking five years aheadand having a plan for those five years.- The way to say it is thatalmost everybody has a one-year plan.Almost no one has a proper five-year plan.And the key thing to haveon the five-year planis having a model forwhat's going to happenunder the underlying platforms.So here's an example.Moore's law as we know it,the thing that powered improvementin CPUs has largely haltedin its traditional shrinking mechanismsbecause the costs have just gotten so highand it's getting harder and harder.But there's plenty ofalgorithmic improvementsand specialized hardware improvements.So you need to understand thenature of those improvementsand where they'll goin order to understandhow it will change the platform.In the area of network conductivity,what are the gains that areto be possible in wireless?It looks like there'san enormous expansionof wireless conductivityat many different bandsand that we will primarily,historical I've always thoughtthat we were primarilygoing to be using fiber,but now it looks likewe're going to be usingfiber plus very powerful high bandwidthsort of short distance conductivityto bridge the last mile.That's an amazing achievement.If you know that,then you're going to buildyour systems differently.By the way, those networks havedifferent latency propertiesbecause they're more symmetric.The algorithms feelfaster for that reason.- And so when you think about,whether it's fiber or justtechnologies in general,so there's this BarbaraWootton poem or quotethat I really like.It's from the champions of the impossible,rather than the slaves of the possible,that evolution draws its creative force.So in predicting the next five years,I'd like to talk about theimpossible and the possible.- Well, and again, one of thegreat things about humanityis that we produce dreamers.We literally have people whohave a vision and a dream.They are, if you will,disagreeable in the sensethat they disagree with the,they disagree with whatthe sort of zeitgeist is.They say there is another way.They have a belief.They have a vision.If you look at science,science is always marked by such peoplewho went against some conventional wisdom,collected the knowledge at the time,and assembled it in a way thatproduced a powerful platform.- And you've beenamazingly honest about,in an inspiring way,about things you've beenwrong about predicting,and you've obviously beenright about a lot of things.But in this kind of tension,how do you balance as a company predictingthe next five yearsplanning for the impossible,listening to those crazy dreamers,letting them run away andmake the impossible real,make it happen,and you know that's howprogrammers often think,and slowing things down and sayingwell this is the rational,this is the possible,the pragmatic, the dreamerversus the pragmatist that is.- So it's helpful to have a modelwhich encourages apredictable revenue streamas well as the ability to do new things.So in Google's case,we're big enough and wellenough managed and so forththat we have a pretty good senseof what our revenue will befor the next year or two,at least for a while.And so we have enough cash generationthat we can make bets.And indeed, Google has become Alphabet,so the corporation isorganized around these bets.And these bets are in areasof fundamental importance to the world,whether it's artificial intelligence,medical technology, self-driving cars,conductivity throughballoons, on and on and on.And there's more coming and more coming.So one way you could express thisis that the current businessis successful enoughthat we have the luxury of making bets.And another one that you could sayis that we have the wisdomof being able to seethat a corporate structureneeds to be createdto enhance the likelihood ofthe success of those bets.So we essentially turnedourselves into a conglomerateof bets and then thisunderlying corporation, Google,which is itself innovative.So in order to pull this off,you have to have abunch of belief systems,and one of them is that you have to havebottoms up and tops down.The bottoms up we call 20% time,and the idea is that peoplecan spend 20% of the timeon whatever they want.And the top down is thatour founders in particularhave a keen eye on technology,and they're reviewing things constantly.So an example would bethey'll hear about an ideaor I'll hear about somethingand it sounds interesting.Let's go visit them,and then let's beginto assemble the piecesto see if that's possible.And if you do this long enough,you get pretty good atpredicting what's likely to work.- So that's a beautifulbalance that's struck.Is this something thatapplies at all scale?- Seems to be.Sergey, again 15 years ago,came up with a conceptcalled 10% of the budgetshould be on things that are unrelated.It was called 70/20/10.70% of our time on core business,20% on adjacent business,and 10% on other.And he proved mathematically,of course he's a brilliant mathematician,that you needed that 10% tomake the sum of the growth work.And it turns out that he was right.- So getting into the worldof artificial intelligence,you've talked quiteextensively and effectivelyto the impact in the near term,the positive impact ofartificial intelligence,especially machine learningin medical applications and educationand just making informationmore accessible.In the AI community,there is a kind of debate.There's this shroud of uncertaintyas we face this new worldof artificial intelligence.And there is some people likeElon Musk you've disagreed on,at least in the degree of emphasishe places on the existential threat of AI.So I've spoken with StuartRussell, Max Tegmark,who share Elon Musk's view,and Yoshua Bengio,Steven Pinker who do not.And so there's a lot of very smart peoplewho are thinking about this stuff,disagreeing, which isreally healthy, of course.So what do you think is the healthiest wayfor the AI community to,and really for the generalpublic to think about AIand the concern of the technologybeing mismanaged in some kind of way.- So the source of educationfor the general publichas been robot killer moviesand Terminator, etcetera.And the one thing I canassure you we're not buildingare those kinds of solutions.Furthermore, if they were to show up,someone would notice and unplug them.So as exciting as those movies are,and they're great movies,were the killer robots to start,we would find a way to stop them,so I'm not concerned about that.And much of this has to dowith the timeframe of conversation.So you can imagine asituation 100 years from nowwhen the human brain is fully understoodin the next generationand next generation ofbrilliant MIT scientistshave figured all this out,we're gonna have a largenumber of ethics questionsaround science and thinking and robotsand computers and so forth and so on.So it depends on thequestion of the timeframe.In the next five to 10 years,we're not facing those questions.What we're facing in thenext five to 10 yearsis how do we spread thisdisruptive technologyas broadly as possible to gainthe maximum benefit of it?The primary benefit should bein healthcare and in education.Healthcare because it's obvious.We're all the same even thoughwe somehow believe we're not.As a medical matter,the fact that we havebig data about our healthwill save lives,allow us to deal with skincancer and other cancers,ophthalmological problems.There's people workingon psychological diseasesand so forth using these techniques.I can go on and on.The promise of AI inmedicine is extraordinary.There are many many companiesand start-ups and fundsand solutions and we will alllive much better for that.The same argument in education.Can you imagine that for each generationof child and even adultyou have a tutor educator.It's AI based that's not a humanbut is properly trainedthat helps you get smarter,helps you address yourlanguage difficultiesor your math difficultiesor what have you.Why don't we focus on those two?The gain societally ofmaking humans smarterand healthier are enormous.And those translate fordecades and decades,and we'll all benefit from them.There are people who areworking on AI safety,which is the issue that you're describing,and there are conversationsin the communitythat should there be such problemswhat should the rules be like?Google, for example, hasannounced its policieswith respect to AI safety,which I certainly support,and I think most everybody would support.And they make sense.So it helps guide the research.But the killer robots arenot arriving this year,and they're not even being built.- And on that line of thinking,you said the timescale.In this topic or other topicshave you found a useful,on the business side orthe intellectual side,to think beyond five to 10 years,to think 50 years out?Has it ever been useful or productive--- In our industry thereare essentially no examplesof 50 year predictionsthat have been correct.Let's review AI.AI, which was partiallyinvented here at MITand a couple of otheruniversities in 1956, 1957, 1958,the original claims were a decade or two.And when I was a PhDstudent, I studied AI,and it entered during my looking at ita period which is known as AI winterwhich went on for about 30 years,which is a whole generationof science, scientists,and a whole group of peoplewho didn't make a lot of progressbecause the algorithms had not improvedand the computers had not improved.It took some brilliant mathematiciansstarting with a fellow names Geoff Hintonat Toronto and Montrealwho basically inventedthis deep learning modelwhich empowers us today.The seminal work there was 20 years ago,and in the last 10 yearsit's become popularized.So think about the timeframesfor that level of discovery.It's very hard to predict.Many people think thatwe'll be flying aroundin the equivalent of flying cars.Who knows?My own view, if I wantto go out on a limb,is to say we know a couple of thingsabout 50 years from now.We know that they'll be more people alive.We know that we'll have to have platformsthat are more sustainablebecause the earth is limitedin the ways we all know,and that the kind of platformsthat are gonna get builtwill be consistent with theprinciples that I've described.They will be much moreempowering of individuals.They'll be much moresensitive to the ecology'cause they have to be.They just have to be.I also think that humansare going to be a great deal smarter,and I think they're gonna be a lot smarterbecause of the tools thatI've discussed with you,and of course people will live longer.Life extension is continuing at a pace,a baby born today has a reasonablechance of living to 100,which is pretty exciting.It's well past the 21st century,so we better take care of them.- And you've mentionedan interesting statisticon some very large percentage,60%, 70% of people may live in cities.- Today more than halfthe world lives in cities,and one of the great stories of humanityin the last 20 years has beenthe rural to urban migration.This has occurred in the United States.It's occurred in Europe.It's occurring in Asia, andit's occurring in Africa.When people move to cities,the cities get more crowded,but believe it or nottheir health gets better.Their productivity gets better.Their IQ and educationalcapabilities improve.So it's good news thatpeople are moving to cities,but we have to make them livable and safe.- So first of all, youare but you've also worked withsome of the greatest leadersin the history of tech.What insights do you drawfrom the difference inleadership styles of yourself,Steve Jobs, Elon Musk, Larry Page,now the new CEO, Sundar Pichai and others,from the I would say calmsages to the mad geniuses.- One of the things that Ilearned as a young executiveis that there's no singleformula for leadership.They try to teach one, butthat's not how it really works.There are people who justunderstand what they need to doand they need to do it quickly.Those people are often entrepreneurs.They just know, and they move fast.There are other peoplewho are systems thinkers and planners.That's more who I am,somewhat more conservative,more thorough in execution,a little bit more risk-adverse.There's also people whoare sort of slightly insanein the sense that they areemphatic and charismaticand they feel it and theydrive it and so forth.There's no single formula to success.There is one thing that unifiesall of the people that you named,which is very high intelligence.At the end of the day, thething that characterizesall of them is that they sawthe world quicker, faster.They processed information faster.They didn't necessarilymake the right decisions all the time,but they were on top of it.And the other thing that's interestingabout all of those peopleis that they all started young.So think about Steve Jobs starting Appleroughly at 18 or 19.Think about Bill Gatesstaring at roughly 20, 21.Think about by the time they were 30,Mark Zuckerburg a goodexample at 19 or 20,by the time they were30, they had 10 years,at 30 years old they had10 years of experienceof dealing with people and productsand shipments and the pressand business and so forth.It's incredible howmuch experience they hadcompared to the rest of uswho are busy getting our PhDs.- Yes, exactly.- So we should celebrate these peoplebecause they've justhad more life experienceand that helps them form the judgment.At the end of the day,when you're at the topof these organizations,all of the easy questionshave been dealt with.How should we design the buildings?Where should we put thecolors on our products?What should the box look like?That's why it's so interestingto be in these rooms.The problems that they facein terms of the way they operate,the way they deal with theiremployees, their customers,their innovation areprofoundly challenging.Each of the companies isdemonstrably different culturally.They are not, in fact, cut of the same.They behave differently based on input.Their internal cultures are different.Their compensation schemes are different.Their values are different.So there's proof that diversity works.- So when facedwith a tough decisionin need of advice,it's been said that thebest thing one can dois to find the best person in the worldwho can give that adviceand find a way to be in a roomwith them one-on-one and ask.So here we are.And let me ask in a long-winded way.I wrote this down.In 1998, there were manygood search engines:Lycos, Excite, AltaVista, InfoSeek,Ask Jeeves maybe, Yahoo even.So Google stepped in anddisrupted everything.They disrupted the nature of search,the nature of our access to information,the way we discover new knowledge.So now it's 2018, actually 20 years later.There are many goodpersonal AI assistants,including, of course,the best from Google.So you've spoken in medical and educationthe impact of such an AIassistant could bring.So we arrive at this question.So it's a personal one for me,but I hope my situationrepresents that of many otheras we said dreamers andthe crazy engineers.So my whole live I've dreamedof creating such an AI assistant.Every step I've taken hasbeen towards that goal.Now I'm a research scientistin human-centered AI here at MIT.So the next step for me asI sit here facing my passionis to do what Larry and Sergey did in '98,the simple start-up.And so here's my simple question.Given the low odds of success,the timing and luck required,the countless other factorsthat can't be controlled or predicted,which is all the thingsthat Larry and Sergey faced,is there some calculation,some strategy to follow in the step?Or do you simply follow the passionjust because there's no other choice?- I think the peoplewho are in universitiesare always trying to studythe extraordinarily chaotic natureof innovation and entrepreneurship.My answer is that they didn'thave that conversation.They just did it.They sensed a moment whenin the case of Google,there was all of this datathat needed to be organized,and they had a better algorithm.They had invented a better way.So today, with human-centered AI,which is your area of research,there must be new approaches.It's such a big field.There must be new approaches differentfrom what we and others are doing.There must be start-ups to fund.There must be research projects to try.There must be graduate studentsto work on new approaches.Here at MIT, there are peoplewho are looking at learningfrom the standpoint oflooking at child learning.How do children learn startingat age one and two--- Josh Tenenbaum and others.- And the work is fantastic.Those approached are differentfrom the approach thatmost people are taking.Perhaps that's a bet that you should make,or perhaps there's another one.But at the end of the day,the successful entrepreneursare not as crazy as they sound.They see an opportunitybased on what's happened.Let's use Uber as an example.As Travis tells the story,he and his co-founderwere sitting in Paris,and they had this idea 'causethey couldn't get a cab.And they said we have smartphones,and the rest is history.So what's the equivalentof that Travis EiffelTower where is a cab momentthat you could as anentrepreneur take advantage of,whether it's in human-centeredAI or something else?That's the next great start-up.- And the psychology of that moment.So when Sergey and Larry talk about,in listening to a few interviews,it's very nonchalant.Well here's a very fascinating web data,and here's an algorithm we have.We just kind of want toplay around with that data,and it seems like that's a really nice wayto organize this data.- Well I should saywhat happened, remember,is that they were graduatestudents at Stanford,and they thought this was interesting.So they build a search engineand they kept it in their room.And they had to get powerfrom the room next door'cause they were using toomuch power in their room,so they ran an extension cord overand then they went and they found a houseand they had Google world headquartersof five people to start the company.And they raised $100,000from Andy Bechtolsheim,who is the Sun founder to do thisand Dave Cheriton and a few others.The point is theirbeginnings were very simple,but they were based on a powerful insight.That is a replicablemodel for any start-up.It has to be a powerful insight,the beginnings are simple,and there has to be an innovation.In Larry and Sergey'scase, it was PageRank,which was a brilliant idea,one of the most sitedpapers in the world today.What's the next one?- So you're one of, if I may say,richest people in the world,and yet it seems that moneyis simply a side effectof your passions and not an inherent goal.But you're a fascinating person to ask.So much of our societyat the individual leveland at the company level and as nationsis driven by the desire for wealth.What do you think about this drive,and what have you learned about,if I may romanticize the notion,the meaning of lifehaving achieved successon so many dimensions?- There have been manystudies of human happiness,and above some threshold,which is typically relativelylow for this conversation,there's no difference inhappiness about money.The happiness is correlatedwith meaning and purpose,a sense of family, a sense of impact.So if you organize your life,assuming you have enough to get aroundand have a nice home and so forth,you'll be far happier if you figure outwhat you care about and work on that.It's often being in service to others.There's a great deal of evidencethat people are happiestwhen they're servingothers and not themselves.This goes directly againstthe sort of press-induced excitementabout powerful and wealthyleaders of the world,and indeed these are consequential people.But if you are in a situationwhere you've been veryfortunate as I have,you also have to takethat as a responsibilityand you have to basicallywork both to educate othersand give them that opportunitybut also use that wealthto advance human society.In my case, I'm particularly interestedin using the tools ofartificial intelligenceand machine learningto make society better.I've mentioned education.I've mentioned inequality in middle classand things like this, all ofwhich are a passion of mine.It doesn't matter what you do.It matters that you believe in it,that it's important to you,and your life can be far more satisfyingif you spend your life doing that.- I think there'sno better place to endthan a discussion of the meaning of life.- Eric, thank you so much.- Thank you very much, Lex.\n"