found in your mind in that goldmine between your ears try for five ideas every morning and write them down and save those sheets of paper in a special idea file many perhaps most of your ideas will be worthless but some of them will be very good a few will be excellent and every once in a while you will come up with something really outstanding you see five ideas a day is


25 a week if you don't think on weekends that's more than a thousand ideas a year one idea can get you to that income you shooting for the law of averages swing so far in your favor you just can't miss try to develop a sense of expectancy that is try to hold the feeling that the goal you're shooting for is a sure thing and that it's only a matter of time before it's realized you know Henry Ford

Change Your Life in 19 Minutes with Earl Nightingale


didn't start making cars until he was 45 a friend of mine started a new company at 65 he's still going strong and his new company has sales of better than 300 million dollars a year it's almost never too late try not to think of things outside of your own line of work or whatever it is you're most interested in to think well and profitably you must discipline your thinking keep it on


course controlled he put in one field specialized now for the final item on your checklist it appears as the word and the word is attitude attitude has been called the most important word in the language William James put it this way he said the greatest discovery of my generation is that human beings can alter their lives by altering their attitudes of mind now this is something


to think about can alter their lives by altering their attitudes of mind it's another way of saying we become what we think about look at it this way you're a total environment if you've been an adult for any appreciable period of time your total environment is a reflection of you as a person the house or the neighborhood in which you live the car you drive the clothes you wear the job


you do the people with whom you regularly associate your total environment is an exact and merciless mirror of you as a human being how have you feel your environment can stand some improvement you have only to improve your attitude and your world will gradually change to reflect the changing prison is how to change your attitude beginning now begin to act as


would the person you most want to become now that is if you were already in possession of the goal you're shooting for how would you conduct yourself at all of your affairs we'll do it now and tomorrow and the next day begin now how to act the part of the person you most want to become and you learn by becoming that person subtly in little ways in the way you dress in the way you talk in the


unfailing courtesy you show to every person with whom you come in contact begin to act the part of the person who has already achieved that which you're shooting for the German philosopher Goethe gave us the secret when he said before you can do something you must first be something when you behave like the person you most want to become the things that person would have will tend


to come to you is simply cause and effect don't be in too big a hurry it takes longer to build a skyscraper than a chicken coop they'll slowly steadily and well then when you make it you'll keep it you will stay on top always be suspicious of the so-called get-rich-quick scheme or sudden success never forget that word attitude it's your attitude toward the people with


whom you come in contact that will determine their attitudes toward you the person with a great attitude toward life in the world is the person other people call lucky he's not lucky he's just using our old friend cause and effect his causes are excellent and his effects has to be just as good well that's it three things to remember three things to practice every


day if you spent sixteen hours a day seven days a week practicing your golf swing in a relatively short time you'd have a grooved beautiful swing like the pros so practice your new attitude every day every in our practice thinking a few minutes every morning and you'll find yourself thinking all day long to remember the formula our rewards in life will always


thank you thank you very much for the warm introduction it's an honor to be here and participate in this lecture series I want to first clear up any possible misconceptions I have one kid just one and he has one head this was this two-headed thing was taken with photo booth on the iPad and it's his favorite thing to do is to take pictures of himself with two heads so anyway

Thinking Algorithmically About Impossibility


that's impossible and he's thinking about it okay all right all right okay so so I have two goals for this talk and they're kind of conflicting a little bit so the first goal is I want to tell you about the state of the art in in circuit complexity lower bounds in what particular areas of certain complexity at least and the second goal is to give a general thought for a scientific


audience so I'm I'm going to try to navigate these two things and hopefully it will turn out okay all right let's begin so as a young computer science theorist one is taught that there are roughly two kinds of people there are algorithms people and there are complexity people okay the algorithms designers asked the following very positive upbeat questions they asked


when can we solve problems quickly what can we do with computers when can we find a good algorithm what what does good mean good could mean many sorts of things generally speaking it means that your algorithm is time efficient it runs very fast okay on the other hand there's this other group of people called complexity theorists okay the complexity theorists are negative nancies they ask


where our problem is difficult to solve okay when when can we prove the problem is not easy okay in more slightly more technical terms when can we prove a lower bound on the amount of time that we need to solve a problem or the amount of resources needed to be consumed in order to solve a problem so so this sort of lower bound thing versus algorithms think it's going to be pretty prevalent


this talk so here are some intuitions about theory that you build up again as a young computer scientist you learned that the elegant design and the complexity theorists have opposing goals and tasks okay certainly the way I presented it I mean one is trying to say there's an algorithm the other ones trying to say nope you know so so the way I presented there really a opposite


to each other okay and the second thing you learn when way or another so the algorithms tribe has an easier life somehow somehow designing algorithms is just much easier than proving lower balance and this intuition is really really strong I mean you want to say look if I design an algorithm I just have to give you one piece of code it's just going to work and solve the problem


no matter what but if just to show a lower bound I have to show that no matter what code you wrote down even code I will never see like just there's just only so much code I could see in my life oh well no matter what you write down it's just not going to work it's just not going to solve the problem fast enough so just from that point of view it seems designing Aram's is easier okay


so some of our lower bound says have you have to reason about all possible algorithms right so I assume many of you know about algorithms so I'll spend some time talking more about lower bounds at least the beginning so the most famous lower bound question the Grandmamma of them all is P equals NP okay and just to give you a very high-level idea of what P and NP are not to go in the


definitions NP is the set of problems where verifying a solution to the problem is easy and P are the problems where finding a solution is easy to do okay and so by easy we mean the following technical sense of Paul meal time so there's some algorithm that on inputs of length n runs in some polynomial and an amount of steps think of this P of n is N squared or in cubed


something like that so this is what we mean by easy most of the things that you see when you're learning our than they're easy sorting easy so this problem was articulated in 1970s by Steve cook and the carp and also in the Soviet Union by leonid 11 okay so the rather than going into definitions and things like this I will just give you an illustration of this and probably the


best illustration of the difference between P and NP is the problem of factoring numbers so suppose I give you this number okay it's 232 digits and I want you to I want to know like x and y so the x times y equals this number okay well turns out there are two such numbers okay X of this length Y of this length all right now the problem of giving such a number what are x and y right so this is


an EMPI problem because if I gave you x and y to begin with if I gave you the solution of the problem the X and the y then it's easy to check that next times y equals the given number you can definitely multiply these things in less than a second on your cellphone check they equal the number okay what about finding the x and y given just this number what can you do well I


gave you a very special number here actually so this is the RSA 768 number it took two years and a team of number theorists and programmers to find this X&Y a lot of famous people crushed really really hard to find this X&Y despite the fact that once it's given to me takes no time at all to verify their items okay so in so MP is this kind of problem where you I give


you the number and I want to know the two the two factors x and y well with P you just you know I'm just I just want to like find the answer okay so the problem of people's emptiness asking whenever it's possible to check that x times y equals a number could I find could I do the inverse and figure out x and y efficiently as well okay so this is a very stark contrast between


these two kinds of problems okay so P equals NP is asking a verifying a solution a problem is easy to do then can we find a solution the problem also easily so here's another number okay it's been slightly longer it's 240 decimal digits alright so we don't actually don't know how to factor this number well okay as far as we know perhaps our friends the NSA have


factored this number a long time ago okay and and if you want to you know a bigger list of numbers that we don't know how to factor there's a whole page and Wikipedia with lots and lots of numbers going up about six hundred digits big lists of numbers okay so if we could factor integers easily so this problem is looks a simple number theoretic problem but you


probably know that if we could factor these interests easily we could break all our essay based cryptography so we could break anything that says there's just a little lock here on your web browser yeah forget it it's not really I mean this is crack it open okay well HTTPS SSL totally broken okay these are as same numbers actually is a great resource for someone like me


if someone send emails me and say they have a proof people's MP I just forwarding this link and say I'll be waiting you know just all you got to do just tell me these you know short 100 something digit numbers from everything in this list okay then oh then I read your paper okay so so that that makes them so very very makes it easy for me right okay so we believe the answer is no okay hope this


is you know somewhat convincing just this this problem of multiplication versus factoring a number but how that we mathematically prove that how could we prove there's just no way to do it there's no way to break a number in the protectors we have to show that some problem that's easy to verify it somehow impossible to efficiently solve okay so this requires us to somehow reason about


all easy programs one way or another so let me emphasize it P where's MP is only one of many questions of this kind many of which just I simply can't mention this talk I just want to mentioned the most prominent one there but they all sort of hint or at the same kind of thing reasoning about efficient computation we don't know how to do it okay so where lower bounds good for so


they're impossibilities they'll say you can't do something well the first sort of very useful way in which the lower bounds were uses for algorithm engineering so if I prove that a lower bound on a particular mathematical formalization or a problem that just often just means that the way you set the problem formally is not the right sort of way it's math it just not going


to help you if you try for all this if you set if you wrote the problem down in this way so so you could try something else you try to read for me at the problem go back to it and and see if you can steer yourself away from things which MP hard and things which are more like P but lower bounds have actually been extremely useful in other areas such as machine learning theory useful


in in learning functions for which you know lower bounds for those kinds of functions for cryptography as as demonstrated with RSA so you sort of need lower bounds just to get off the ground you need to be able to say that you know I have have concocted a problem efficiently but you you can't crack it you can't you know turn around and solve it for me and also more grounds come up


in an unexpected way and the construction of so-called pseudo-random generators so these come up when you have a randomized algorithm arete the album which tosses coins and you can argue that you can mathematically prove that okay if you have uniform independent random coins then this algorithm is going to work well maybe maybe you know you can't rely on independent random coins well lower


bounds can actually be used to remove the randomness from algorithms replace randomized armed systematically with something that just uses a purely deterministic sequence of coin flips and still works well this is a very nice connection and sort of unexpected when you first think about it but one day I really want to try at home is that we don't really know what all


lower bounds are good for because we have no idea what the proofs of them look like okay I mean this almost all this is just from sort of knowing just that the lower bound holds what you can do but what if you actually had a mathematical proof something that actually did the reasoning and you could study it then there's no telling what we could we could learn about computation


that way and so lower balance to me are one of the great scientific mysteries of our time there are conjectures about them everywhere ok Internet security relies on them being true but knowing them for sure is mostly inaccessible to us ok so so yeah this is just a great mystery why is easy computation so hard to understand okay why are lower bound so hard to


prove well why are we so stuck here so actually we have a pretty good understanding of why we're stuck so there are many known nogo theorems in complexity theory with various technical names cultivation natural proofs algebra zation so on what they say in a nutshell is that the common proof techniques that we use are just not going to be enough to prove things much much weaker than p9


wimpy things much weaker than this just like very simple things we can't we can't prove allowance for and this has cost a great deal of weeping and wailing and gnashing of teeth a great pessimism in complexity theory and so the big question here is how will we make progress how do we get smarter how are we going to get off the ground and start really proving that some problems are


hard to solve ok so so this is what I'm going to try to say something about okay I mean obviously I don't have all the answers but I would like to say something that I hope can be conveyed to everyone yeah well sort of the main insights in work that I participate in so far is in connections between arms and lower bounds so we're talking about how arm designs are the


easier life then lower bound improvers in fact we are deep connections between the two subjects that we just don't understand yet we know for sure that they are there we have sort of glimpses of connections we but there is certainly something more under the hood that we've got to know and so I'll tell you about the glimpses that we have so the main idea I guess the one thing I would like


to you to try to take away from this talk is this thing in this box designing algorithms is as hard as proving lower bounds sometimes now this is one of those ideas that depending on who you talk to is either complete heresy or they thought of it already okay so that means it's a good idea from from my from my informal judgment okay so yeah so it's a so it but it's sort of


provocative so it's very provocative actually because if because lower bound designer doesn't have it as hard as the album designer okay so somehow the design event certain algorithms is going to actually give you lower bounds so maybe the our designer doesn't have it so easy after all it just they managed to solve other problems or maybe it's just psychological maybe we


just are wired to think about algorithms and some you know more easily than lower bounds but there's not so much different between them in the end so here's here's just that my vague attempt to draw some parallel here so a typical resultant album design says here is an algorithm a that solves some problem on all possible instances of the problem now it's actually amazing how


many statements of this form hold but the fact that like you can have a algorithm that say solves a a problem all possible graphs no matter which graph you see even graphs you will never be able to see you can still prove it's going to work and give you the right answer for this graph okay on all possible instances okay now a typical theorem from lower bounds will say


something like here is a proof P that this problem can't be solved on all possible by all above two albums of some class some weak class of algorithms that I've sort of box over here and I can rule all of them out from solving this problem here so what I would like to promote is to draw a parallel between the algorithm here in the album design problem and the proof here in lower


bounds and all possible instances of the problem in the album design problem with all possible albums with some class okay so you think about this it sort of automatically suggests the kind of algorithms we want to design so the kind of problems we want to solve our problems which take some kind of algorithm as input itself and perform some computation so we wanted to solve


some kind of problem that is taking an algorithm at an input we have to be very careful of how we do this I mean like if you just took the usual kind of algorithm as input like the sort of terrain type algorithm then there are lots of results an undergrad computability theory that just says no matter what you try to do analyzing the things it's not going to work


so it's make this kind of connection tractable we have to slightly change the model of algorithm we're looking at and this is what I'm going to talk about okay so so I have a very high level very high level here's the specific kind of connection that I'm going to talk about so suppose you have there exists some non-trivial circuit analysis our okay so think of this is just some albums some


fixed program maybe a Turing machine what have you it receives as input some circuit encoded in some nice way some logical circuit and for all logical circuits it can do the some kind of non-trivial analysis on this thing let's say it can determine whether that this circuit is satisfiable in other words whether the circuit is computing a trivial function or this computing something


interesting let's suppose I can do this in some nice way something just better than trying all possible inputs to the circuit and seeing what happens if you can set this kind of thing of it you can solve this kind of album design problem then you can say there is a function f compute with respect to certain Turing machines for which for all circuits they cannot


compute this function for all circuits that cannot compute this function so you get circuit complexity lower amounts so from the design of particular kinds of algorithms problems ones which take as some kind of algorithm or circuit as input and analyzing them non trivially you can then prove that for whatever it is I analyzed non-trivial e I now have a new function that can't be computed by


it so that this is a very very counterintuitive if you look at it in the wrong way if you look at it it's quite a far away maybe it's so slightly more into okay so this is the basic kind of connection that I and others have been able to draw and we've been able to actually prove lower new lower bounds new limits on computing using this kind of connection so here's an outline for


the rest of the talk I'll give you a very quick introduction to just boolean circuits and sort of the problems we're thinking about here I'll compare circuits to algorithms in the usual sense that people learn them and then I'll talk briefly at the end about how circuit analysis algorithms can imply circuit limitations okay all right so bullion circuits look you know something


like this so this is a boolean circuit which is taking three bits of input okay it's taking the aunt of each pair of bits and then taking the or of those bits and so it's outputting a and B or B NC or ANC okay in general a circuit of size s for us will take a fixed number of zero one inputs and it will output let's say a single bit you can generalize it to outputting mobile bits


for SS a single bit then for F times it's going to take two previously computed bits maybe some input bits such as here a and B and you compute a function of those two bits and that will be some new bit that I can use in subsequent computations such as here I'm taking this or if those things okay so just to give an example this above circuit on three inputs outputs one if


only if at least two the inputs of one okay now the size of the complexity of this circuit is going to be like four four gates in it and so this is how we can measure computation but for finite functions so it's important here that the number of bits of input is something finite okay and then we can talk about if I want to compute some given function how many times do I have to take some


previously computed bits and give up another but before I finally get the output of the function I want okay and so trying to minimize this as the number of little no gates you have in here is the core problem in certain complexity all right so most functions most require humongous circuits okay so a very old theorem of Shannon and matched by loop on/off says it with high probability if


you randomly choose a function by just sort of flipping a bunch of coins generating a function by coin flipping this will require a very large circuit but still gonna nearly a size like to the N over N okay it's another number of inputs to this thing so this is just saying I take a bit zero one and you know I take n of those bits and I output a single bit okay so in fact every


function has a circuit of size about to the you know gram okay so this think of it this way so both um I'm flipping two to the N points and I generate some very very long two to the N bit string this is just defining some function f okay what this is saying is that there's no way in general with high probability to compress this 2 to the N bit thing and a small very small representation this


is basically proved by a counting argument is too many small series too few small circuits and too many randomly chosen functions so a random one is just not going to be represented by something small there's a simple incompressibility type argument so okay the universe is littered with you know these really really hard functions from the circumflex II point of view my question


is which natural functions ones that we actually want to solve exhibit this kind of exponential behavior they require a large number of gates in order to compute them okay so this is a very central question in certain place you just trying to understand which functions can be solved at all we know that random ones cannot can we can we get is there some function of interest


you know that we that we that doesn't have that we cannot to solve the small sir okay that's it so that's a sorry complex see that show so the kinds of things I want to do is circuits I is I want to analyze them so I'm going to talk about computational problems that take a circuit encode it in some way as an input and compute something on that circuit so the circuit analysis problem


is a problem where the input is a circuit so here we have some logical circuit written down in some encoding the P that our program can read and we want to output some property of the function computed by this circuit so we want to know something about what function is it computing in you know inside this little description so the canonical example of this is a circuit


satisfiability problem also known as circuit set okay it's really like the simplest circuit analysis problem in a particular sense here you're given a logical circuit see so you wrote you've written down some description of some circuits got wired together in some funky way and you want to know is it computing the all zeroes function is it the case that no matter what input I


give it is always going to print zero or is there some input which will make it print one okay that would usually when something makes it print one we call it a satisfying assignment and that's the origin of satisfiability but really want to know Jesus is it computing a trivial function or not so just tell me that okay so a circuit set is a so-called np-complete problem and so it's very


unlikely to be solvable efficiently okay unless P equals NP okay like this so probably we're not going to be able to solve this circuit analysis problem very well but we could ask still if there is some way to solve it faster than the obvious self what is the obvious algorithm just try every possible input to the circuit okay so here we're leveraging the fact that there's a


finite number of inputs so the circuit has a finite number of bits coming in so you could just if it's got n bits coming in there to the impossibility is just try them all see if any of them make this thing print one okay so we can answer this something faster this obvious brute-force search our okay we and maybe this is an interesting question maybe it isn't I always thought it was a


very interesting question turns out it actually is interesting for other reasons it actually is connected to circuit complexity but just the question of it you have us you have a silly algorithm you have a obvious algorithm is it the best you can do this is just a very fundamental question by itself all right so that's a quick introduction to circuits okay now I want to talk about


how circuits compare to algorithms so in the usual everything model where you think of you know writing a single program it's going to solve a problem you have some finite description of some program and this program can take an arbitrarily long inputs and still solve the problem okay if you give it an input of length 10 no problem input of length of thousand no problem a million no


problem 100 million no problem this thing always solves the problem single thing okay that is in contrast with the circuit model where we only allow you to take in fixed length inputs okay so so if we're thinking about how our circuits can relate to one another we've got this sort of correctness type mismatch where algorithms can take an arbitrarily long input and circuits can


only take in the fixed length inputs and there's a very easy way that complexity theorist came up with sort of unifying the two and the idea is to talk about a computational model called a circuit family the circuit family has is an infinite collection of circuits infinitely many and you have a circuit on one bit of input a circuit on two bits of input circuit on ten bits of


input for every possible size of input you've got a circuit with that many inputs okay so for each end you have a circuit C sub n that's going to bring you on on inputs of lengths in and so it's obvious what I'm going to do and I want to compute something I'm you give me an input I measure its length I feed it to whatever the circuit is in the collection that matches that length as I


run it it gives me some answer okay that's how the circuit family would compute on on some problem that would normally be solved by now okay all right so in this model the circuit family model programs have infinitely long descriptions a priori okay there is nothing bounding what these circuits could be you get a separate program every single time here input changes so it's possible that you


know if your input if your input is you know a thousand there's something really clever you can do just when in facility two thousand one thousand and one you gotta restart everything do something completely different and get something you know very good okay so that there's this infinite description can be really really powerful okay but our notion of efficiency here is this class P poly so


efficiency for algorithms is just the class P P poly is a set of problems solvable with one of these infinite circuit families so separate circuits ease of in for each input length where for every end the size of the in circuit is some polynomial in it this polynomial is fixed once and for all let's say it's N squared and so this circuit sees up a thousand be a thousand


squared sighs okay so this is our notion of efficiency here for this or infinite model okay and the idea is that each circuit here it's going to be small relative to its input but you get a separate circuit for each input length that's sort of the extra thing you get you get this infinite description okay so this circuit family right here alright that's a notion of P poly so you


anything programs have infinite link descriptions huh excuse me why study this model okay Theory dork that's all fine and good but why don't you go play off in theory land well all the rest of us go change the world okay okay well I have an answer for you all right okay so so proving limitations on what circuit families can compute is a step towards a non asymptotic complexity theory a


complexity it doesn't talk about polynomials or Exponential's or whatever when have you talked about numbers how big does the computation get if I want to run it and if I want to run it on inputs of length a million can I fit it inside the known universe so so concrete limitations on computing within the known universe is would be is the the thing you would like to have that's sort


of the ideal thing you would like to have something not as I'm sorry we don't care about polynomials exploration we just want to know how big is the computation gonna get so think something like any computer solving most instances might tend to for a bit problem these at least 10 to the 125 bits to be described so if you've got you know this kind of statement then your problem is just


simply not solvable period okay so actually such a statement was proved by Meyer and stockmeyer in the 70s for a particular logic problem and they derived it we by reverse engineering a circuit lure about because circuit lower bounds are actually explicitly considering the trade-offs between the size of an input and the size of the computation you got a throw at the input to solve whereas


Peters MP is actually not talking about this time at a fixed algorithm that's gonna work on all inputs okay and so the universe turns out stores a less than 10 to the 125 bits this is the this is the famous bekenstein bound in 1970s let's derive from that and so what you're saying is that any computer solving most instance my problem just won't fit within the known universe okay


that's a pretty good lower bound okay so that's the kind of thing we would like to have and so the circuit family is just some stepping stone to getting some non asymptotic things were just talking about numbers that is the ultimate goal we are really this is des soon okay all right so a lot of algorithms compared to circuit families okay so we define this thing how does it compare to the uniform


one program during low so if you got a program that runs and say T of n time when inputs of length n so in the squared time then it's well-known that you can always get a circuit family that will do the same thing and it has about TN in size as well there's some extra little factors don't see it in size so you can simulate efficient albums with efficient circuit families okay so time


will scale the size all right so so that's all fine good in fact if you flip random coins in your algorithm and it gives you the right answer with high probability you will still be able to get infinite family no circuits has about the same size so in a sense you can remove the RAM this when you allow for an infinite circuit fan this is nothing more than the statement okay in


complexity theory terms that bvp is in P Polly randomized phone real-time as efficient bonafide circuit changes okay suppose I'm just looking at circuit families I want to know can they be simulated by algorithms well turns out there is a family we in fact every circuit has size about in some linear size circuits so there's definitely in ppalli there's only a linear size as so


as the input gross it's only just grows linearly in the transit slice however that family has no algorithm at all okay there is just simply no algorithm that will solve the problem that is solved by the circuit family okay so you you can't like hope to make them equivalent okay the main key here is that the circuit family gets an infinite description you get a different circuit for every input


link and here you have a finite description a single element right and this is just a statement of some undecidable problems in fact many of them are in P poly okay so undecidable meaning there's no our at all that's just technical so finally I want to talk about okay suppose you've got a really complicated algorithm what what can you say about the size of the


circuit family solving it so let's suppose I have some album running in exponential time so to the in step so given an in bit input it takes to the in step so like something just solving the trivial rm4 circuit set so I'm just going through all possibilities through the the inputs and just seeing what happens okay so it's actually open it's an open question whether every


exponential time algorithm I'm saying extraordinary amount of time can be solved by circuits with a circuit family with N squared size every circuit has this is this is a really really remarkably open question so is it somehow possible that extremely long time consuming computation can be split up into implementing tiny little you know chunks we're on every single


input length when I solve that problem optimally I actually get only N squared amount of computation this is possible it's actually possible we don't believe it's true but it's still a possibility we cannot rule it out and this is saying in complexity terms that X in ppalli is an open question so exponential time being in poly size there is the open question okay so just to give you


just a even more crazy way in which we still don't know how like the power circuit families talk about exponential time versus shallowness some of you might have attended ms or a workshop on neural net okay this is also we don't want shallow Nets here so this is a very very shallow neural networks so suppose you've got an algorithm and this algorithm is solving a problem that


solvable in so-called non-deterministic exponential time so this is like some just gigantic unfathomable class so this is problems where the solution takes exponentially many bits to describe and then verifying the solution is exponentially so this is just some gigantic class of problems ok the solutions themselves are gigantic and then verifying them takes a gigantic


amount of time ok so this is a huge class of problems so it's actually possible this is gigantic class problems every single problem in this class could be again simulated by an infinite family of circuits one for every input length with the following property so this is zooming in ok so every circuit looks like the following very very simple object okay we've got N squared neurons


here and so in some layer we've got n inputs so this is the amp circuit so it's going to take n inputs and output a bit and then from those n squared neurons they're gonna give me a 0 or 1 and they want to take one more neuron some function of those and output 0 1 ok so if each neuron we're just computing some linear form of the input so there's some weights here


being some real weights being multiplied to 0 one values we're checking whether that exceeds some particular threshold value let's say T part if it does we we fire it doesn't you know what if it doesn't we fire as you just this very simple activation function okay so these are neural networks with one hidden layer small neural networks with one hidden layer there should be a very very


weak class ok in fact if the weights are really small the weights are like minus 1 1 we do have very strong Louisville but if we allow the weights to be anything we want this is still possible it's a gigantic gap in our knowledge so we don't yet understand very simple neural networks okay just even things like just one hidden layer just one hidden layer with


a simple activation function it's so so I like to just give an analogy here of an alien brain versus implementing for libraries okay so so think of this not as a central time thing is as you got some super advanced you know alien brain no no think of it is like something times 100 that okay something just just some brains and solving problems unfathomable to you okay okay but it's


one brain it's just one of them okay and that you want to know if there are any mini little fly brains you know just just one just two layers of depth you know anslee many of them though which will also solve the problem no matter what this no matter what the the big alien brain okay so this so the only advantage you have is that there are implementing our brains and there's a different for


each input length is that enough almost certainly not but it's the implementing that's that trips us up alright okay so now I want to talk and remaining turn about how certain analysis algorithms can actually imply circuit limitations so circuit islands algorithms can imply cellular balance and particular situations and in a particular sense this was well known a long time ago


there's a theorem of car flipping and Meyer from 1980 which says suppose we had extremely efficient circuit now serums like basically perfect sureños arms okay so then there are problems solvable by hours in exponential time say to the end time long enough time that cannot be solved the polynomial size circuit family so this would resolve this X and P poly question we were talking about really


okay so just a notation if P equals empty say circuit side is in P that is a good circuit an office algorithm then we would have it this class X was not in people okay this is a very interesting implication very interesting that that an algorithm analysis could even the other P would be okay that's part not gonna be there but some error analysis can actually prove a lower bound can


actually lead to a lower bound at all okay but the thing is we don't believe that hypothesis is true so it's kind of like saying if pigs can fly then pigs can wink I mean like we we don't believe I bought is this we expect the conclusion to happen okay so so it's it's you know it's it could be useful but it seems to be a limited utility it seems like we have to assume you've got


like this amazing like circuit analysis algorithm let's go study that I mean okay maybe extra people not in people ollie happens but okay this is you know by far the more interesting of the of the tooth to me okay so so what we want to do this you know take the wings off this i'll just dude and you know bring him back down to earth and like maybe maybe he can like you know like a lot no


you know something that is actually possible and then you know we can prove something that we expect anyway yeah so a lower a circuit lower about that that's what we would like to do alright and so we were able to do this in particularly restricted situations so this is work of myself and many others who are working on this too and and a very high level what we can say is it a


slightly faster algorithm for the circuit set problem just slightly faster than exhaustive search already implies lower balanced against circuits solving these problems gigantic class non-deterministic exponential time this class were which like it's possible that even neural Nets is one hidden layer could just solve the class so in pictures what we're saying is that


suppose you know I'll give you just some arbitrary circuit well and there's some way to inspect this circuit and you can find an input which makes it print one whenever it exists yeah and the one extra thing I want to say is instead of taking say the exponential cost of two to the end time as you would have exhaustively you take to the end over end of the ten time okay and you do


this for all polynomial size circuits so let's just say you do this for every circuit in particular s you shave off some polynomial into the tenth is enough okay for for our purposes suppose you can do that just a tiny sliver off exhaustive search then you'll be able to prove for the same circuit class that there it that it cannot compute sorry in X cannot be so so X is not in people


okay so so what's the intuition behind this the there's two part the basic intuitions what is it faster circuit set algorithms uncover a weakness in and what circuits can do so in particular it says it high-level that small circuits can't offer you skate the all zeros function they can't hide it from you so I suppose this where a black box okay and you can't actually appear inside the


circuit and look at what's going on the only thing you can do is take inputs stick them in and get outputs that's why that's the only thing you could do and now you want to solve this a problem you want to know there's an input which makes the circuit print one right then you can prove that you you would need to call this black box at least to the end times to know for sure by there ways you


could trip up any particular strategy for querying this black box getting inputs and outputs without looking in it so that you've got to take to the end time so if we can solve the problem into the end over a ten time then there is some fundamental way in which we are opening up the guts of this circuit and getting some advantage over a black box and turns out this little advantage this


weakness in circuits of the algorithm is uncovered is already enough to start proving the lower bounds alright so that's it that's the first intuition for why is such an implication should even hold the second intuition is that faster circuits set algorithms show a strength of faster than to the N algorithm so they saw some tasks that you didn't think could be done namely find an input


which makes the circuit print one it does it faster than to the end so there's some nice algorithm that can efficiently tell us when a given circuit computes the all zeroes function so so given these two intuitions you can think of this problem in general as some kind of gain between ours and in circuits there's a circuit set problem that algorithms would like to solve and there


are circuits that are you know inherently devious and try you know trying to fool any given algorithm and wired in some way to keep the algorithms from telling whether it's and when we can be exhaustive search we are winning the game the algorithm is winning the game and no matter what circuits given it can somehow drill down through the back black box and and get the answer


okay so with the algorithm winning the game we running a lesson to the end time we hope to say circuits or a week less than to the entire albums are strong and somehow we're going to turn this into some function in non interesting exponential time that doesn't have your smallest area so now the circuits algorithm is showing that circuits are weak and this theorem is more less


making that formal ok so we can actually apply this kind of theorem to prove lower bounds for restricted classes of circuits see fancy see ok I won't go into what the definitions of these things are but the the way in which they're proof lays out exactly as you might expect we show that faster circuits at algorithms for this for particular classes can imply circuit


lower bounds for that particular class ok so just some generic kind of theorem if see circuits sat on circuits with n inputs can be solved and to the N over in ten steps then this class in X or some function this big class in X it doesn't have circuits all that kind whatever it is that we prove this circuit settling for okay and the second step is just design


the element now this is just an in conditional sort of thing so just we can prove for many interesting certain classes ones will renew no lower bounds whatsoever we can solve the SAP problem faster than exhaustive search and then plugging that into the above connection you get lower bounds for them so you can improve over exhaustive search in some cases and so far this is kind of the


only way we know how to prove certain lower balance is through the design of the right kind of set over all right so I'd like to conclude with the following challenge so how do we become smarter about computation so the outsiders it may seem that we already know maybe too much about computation like computers are getting smarter and smarter than us all the time yet from the theory level


we actually don't know precisely how powerful that we are understanding this is really coarse so the dirty secret we still don't know too much about the limits of computers so how can algorithms help prove lower bounds so this is just in general a direction I would like people to think more about what kinds of algorithms could help to lower bounds because if we can set


things up in this way then the lower bound problem does not become so intimidating anymore it just becomes the problem of designing the right kind of our something that we have done as computer scientists for many years and then how can lower bounds help design algorithms I didn't have time at all here to talk about how that how the connections of that form but there are


there are many such connections namely in this thing I briefly mentioned about how lower bounds can imply D randomization removing the roundness from algorithms and making them fully deterministic there are many more connections to be film I'm sure so just earlier this year my student Brin more Chapman and I showed that lower bound circuit lower bounds can in a particular


sense be equivalent to a particular design problem so you prove a lower bound circuit lower bound if and only if you desire test data for testing whether a given function of given circuit computes a particular function okay so trying to minimize the test data you need to test whether a circuit computes of function is actually equivalent to proving a lower bound so this is very nice in the


sense that that it gives a very constructive way of thinking about how you prove an impossibility result a very algorithmic way how you do and so in general I think we will make serious progress by studying alguns and complexity as a whole and not as competing fields for anything we're really doing I really think we're doing a lot of the same thing more than we


think okay that's all I have to say thank you sorry can you repeat it again right here oh so like the work of prasada you know yeah I mean so I think what's going on there is they're explaining the fact that I'm at least to me is that improving something is np-hard is actually an algorithms problem and so it's like from my understanding is you know either something can be done or it can't be


done you get and you get a hardness reduction so it's so I mean in one case you get an algorithm or you get a hard introduction I mean in both cases you get some kind of algorithm but but I don't know I mean or any of them unconditional I don't know I need a lower bound you get unconditional yeah yeah I think it's a different kind of instance it's very interesting kind


of instance but yeah I think I think it's different from what I'm trying to say but that's I didn't think I thought you still need unique games are or P not equal NP or something like this so I mean I would like to say something stronger than this so it's not just algorithm or hardness reduction yeah yes yes yes yeah yeah yeah yeah yeah so that's what makes the problem so


immensely difficult is that it could just be anything in you know in and the hardest case is where each one is sort of incompressible by itself yeah if they're uniform if there's an algorithm generating those then you know this kind of dichotomy I was talking about becomes a completely different picture it was a very different picture I mean there's still interesting questions I mean the


open questions around it but the picture is very different see I thought it was very careful not to say that I'm not trying a equals not hmm oh so yeah I mean even at the undergrad automata Theory level like when I teach them my hole in the road theorem I say well this means that for every language either it's got a fire automaton it's regular or there's this weird infinite


object called distinguishing set and this proves that there's no final total and so in that case it is some sort of constructive way in which you're showing that something is not regular something does not have a DFA so yeah it does come up elsewhere I just yeah I only have so much time yeah yeah there's a whole community of people that would disagree with I mean that solving


is quite an enterprise actually yeah yeah oh well I mean there are things like this so-called exponential time hypothesis is false so then for example counting independent sets if it's in sub exponential time you can prove nonlinear circuit size lower bounds that that connection actually carries over in that kind so if you could count independent sets in sub exponential time you would


on April 21st jurgen schmidhuber tweeted out stop crediting the wrong people for inventions made by others at least in science the facts will always win at the end as long as the facts have not yet won it is not yet the end no fancy award can ever change that hashtag it self-correcting science hashtag plagiarism and links to an article of his own website where he

[Drama] Schmidhuber: Critique of Honda Prize for Dr. Hinton


wrote critique of Honda Prize for dr. Hinton so this is on Schmidt Hoover's own website and it's by himself and don't you love this how to pronounce his name jurgen schmidhuber you again sorry this is this is absolutely great so both actually Schmid over and Hinton are on Twitter you can tweet at them and follow them this article here is a basically a critique of the press release of Honda


when they awarded geoff hinton for his achievements and it goes through it step by step and we won't look at the whole thing but just two for you to get the flavor so here honda says dr. Hinton has created a number of technologies that have enabled the broader application of AI including the backpropagation algorithm that forms the basis of deep learning approach to AI and schmidhuber


just goes off its he basically claims him while Hinton and his co-workers have made certain significant contributions to deep learning he claimed above is plain wrong right he did not invent back propagation the person who invented back propagation was settled in linear MA and the many papers he says basically many papers failed to cite linin MA and this who was the original inventor of back


prop and so on and he go kind of goes through a history of this and how it's even earlier I always have a bit of a trouble with claims like who invented what because when it is an algo them really the same thing right and when he when is it a variation on another algorithm and when is it something completely new it's never entirely clear but the the points here


made that the things the backpropagation algorithm existed before Hinton and also that some of the papers some of the seminal papers did not cite the correct origin statement to in 2002 he introduced the a fast learning algorithm for restricted Boltzmann machines that allowed them to learn a single layer of distributor representation without requiring any labeled data these methods


allow deep learning to work better and they led to the current deep learning revolution and he is no dr. Hinton's interesting unsupervised pre training for deep neural networks was irrelevant for the current latif learning revolution in 2010 our team showed that the feed-forward networks can be trained by plain backprop do not at all require pre training and he basically again says


apart from this Hinton's unsupervised pretending was conceptually a rehash of my unsupervised pre training for deep recurrent neural networks so he you know as you know she made Ober has done a lot of work in recurrent neural networks and he basically says it it was just a rehash of his algorithm now I I have to say I have so first look first of all he he makes a point here right that we


don't really do unsupervised pre-training him or until now of course but you like for to train an amnesty law fighter you don't have to do that but it's also doubtful that this this was a step even though even if it wasn't on the exact path to the current situation it was a thing that got people excited maybe and so the critique is like half valid and also it doesn't help me to burn that


he always compares it to his own things like it just it just like either criticized them for you know in general things but then avoid bringing your own things in because it just sounds like I did this before and also I read some papers from from these times people just wrote papers sometimes I haven't read this specific one but sometimes people just wrote papers writing down their ideas


like one could do this and this and this never doing any experiments or actually specifying exactly what they mean they just kind of wrote down a bunch of ideas and that got published especially like there's some some reinforcement learning papers where people are just like oh one I imagine agents doing this and learning from that so it is again it is never really clear in ideas or just had by


everyone I think people people mistake this that think that the ideas are unique it's not ideas that are unique many people have the same ideas but some there's also execution and exact formalization and so on and exact level of specificity this all of this is really hard and then the honda says in 2009 dr. Hinton and two of his students used multi-layer neural nets to make major


breakthrough and speech recognition that led directly to greatly improved and this of course Schrader who goes off by this because speech recognition is of course prime LS TM territory so you don't want to go near this and the Honda further says revolutionized computer vision by showing that deep learning worked far better than existing state of the art and again he says the basic


ingredients were already there and so on and the our team in Switzerland already used his first superior award-winning GPU based CNN and so on that's what it's called dan net was produced by his group and again this seems correct right this seems when he lays it out like this but it doesn't change the fact that Alex net1 imagenet in 2012 and that was like the start of


the deep learning revolution it was like wow you can cut the learn like the error rate by something like 30% simply by doing this deep learning stuff so again even if Dan that he says it blew away the competition it just seems it it always seems like Schmidt Hooper's kinda right but then also he's not he's like a cadet exact academic work and and the idea being


there on a paper isn't the only thing that drives progress and says to achieve their dramatic results dr. Hinton also invented a widely used new method called dropout which reduces overfitting no like no and like no just no like randomly dropping parts in order to make something more robust that is surely not a new thing and he also says much early it is there's this stochastic Delta rule


and so on and he also critiques that this paper did not cite this they just gave it the name right this is an idea that is kind of so simple that you you wouldn't even necessarily think about researching whether that has existed already I think they just did it and then because it's a natural idea and then they gave it a name and the name stuck right it's not about the idea


itself and then lastly they say of the countless AI based technological services across the world it is no exaggeration to say that few would have been possible without the results dr. Hinton created I love this name one that would not have been possible and he just gives a list of their own group and that are basically possible without Hinton's contributions and this is just it's a


bit of a cheap shot right clearly honda if they're not saying it would have been you know physically him possible without his contributions its but certainly Hinton has has if even if he hadn't invented any of those things he certainly has created like a spark and his these things created a splash got people excited people thinking about new ways of applying things even you


know if this is all true so right and but but I would like you to I'd like you to notice this is a critique of what Honda says about Hinton and if I read through the statements of Schmidt who were most of them are technically correct right and you know that so that was that and then I thought okay cool but then someone posted II didn't read it and then Hinton replies and this is


okay don't you love this so Hinton says having a public debate with schmidhuber about academic credit is not at advisable because it just encourages him and there is no limit to the time and effort that he is willing to put into trying to discredit his perceived Arrivals he is even escorted to tricks like having multiple aliases in Wikipedia to make it look as if other


people agree the patient on his website about Alan Turing is a nice example of how he goes on trying to these are like these are shots fired and he says I'm going to respond once and only once I have never claimed that I invented backpropagation David Romo hard invented it independently after other after other people in other fields had invented it it's true when you first published we


did not know the history so he basically says okay we did forget decided when we first published about rock crop but he doesn't say he invented it what I've claimed is that I was the person to clearly demonstrate that back prop could learn interesting in turn represent and that that this is what made it popular right so this goes into into the direction schmidhuber is very much on


academic contributions idea was there before and hint and basically says no what we did is kind of we showed that it works in this particular way and we can have got people excited about it I did is by forcing that blah blah blah and it is he says it is true that many people in the press have said I invented back prop and I've spent a lot of time correcting them here's an excerpt from


2018 where this is I guess a quote from this book that quotes Hinton where he says lots of people invented different versions of back prop before day with normal heart they were mainly independent inventions something I feel I've got too much credit for it's one of these rare cases where an academic feels he has got too much credit for something my main contribution was to sure you can


use it for learning distributed representations so I'd like to set the record straight on that and then he said maybe Jurgen would like to set the record straight on who invented LST M's boom boom crazy shot shots fired by Hinton here this is I mean this is just great but again look at what Hinton says Hinton basically says yes I have not invented that I have corrected this on


public record in the past and yeah so so that's what Hinton says and I mean the the the comments here are just gold I really invite you to read it and then schmidhuber of course being Schmidt who replies again down here he has a a response to the reply and I don't expect Hinton to reply again so I waited for a bit but but I I believe him when he says he does it only once so he goes into


this summary the facts presented in sections 1 2 3 4 5 are still valid so he goes what kind of statement by statements is having a public debate blah blah blah and he says this is an ad hominem attack which is true right this is true and he says he even has multiple aliases in Wikipedia and he just says another ad hominem attack and then he goes into that schmidhuber tries to discredit Alan


Turing and then shmita goes into this big long big long basically claim that Alan Turing wasn't as important as people made him out to be and people invented this kind of Turing machine equivalents before that again it's kind of showing tubers take that the idea basically was already there and these people don't get the correct credit and also he's correct that this is a this is a true it's an ad


hominem attack right so you know be it as it may this is correct and then when when Hinton goes that he doesn't stay and invent backdrop and me to persist this is finally response related to my post which is true right however he does not at all contradict what I wrote and it is true that he credited his co-author Rommel Hart with the invention but but neither cited alanine MA and


also the statement lots of people he says it wasn't created by lots of different people but exactly one person so this I find como like can you really say now this is the exact time when backprop was invented even though it probably wasn't in the current exact current formulation and it probably existed someone like this so but again and he his main claim is dr.


Hinton except the Honda Prize although he apparently agrees that Honda's claims are false he should ask Honda to correct their statements and like in the end maybe you're going would like to set the record straight who invented LST M's and you know as we as you may know seppo writer it kind of invented LST ms under jurgen schmidhuber as a as a PhD advisor but the to


summarize dr. Hinton's comments and ad hominem arguments diverged from the contents of my post and do not challenge the facts and so on and i have to say after reading this this this is a this is correct right hinton basically replies to hey i I never claimed I invented back prop and other people have invented it and Schmidt Hoover doesn't criticize hinton in this particular post


he may otherwise schmidhuber doesn't create as as Hinton for claiming that he criticizes Honda for claiming that Hinton did and it doesn't hidden basically agrees with him and also schmidhuber says dr. Hinton accepted the Honda Prize although he apparently agrees that the claims are false he should ask Honda to correct their statements and it is true that Hinton


accepted this price under this release right now you might be able to say him Hinton also says he's on the record basically saying he didn't do this and I guess if you're Hinton and you know you've had this you've had a successful career and so on and you have previously really publicly stated that you didn't invent these things and you know made it clear and then you get a prize and they


write this thing maybe you just don't want to go after every single press statement and correcting that but you know in essence basically Hinton and understood this as an attack on himself that he claims he invented back prop and schmidhuber says Honda claims he invented back rub and Hinton accepted the price so agrees with it and he basically agrees with it but doesn't say


Honda should have corrected at which I can understand so this is my take on this issue it's kind of both or correct and they just kind of talk past each other and schmidhuber is always on the the idea existed before and Hinton is correct when he says it's not always just about the idea progress is also made by people being excited people actually getting something to work


people you know doing something at the right time in the right place which is also correct but it is fun it is fun so so I just I enjoyed I enjoy this honestly like because ultimately this is the kind of discussions also need to happen in science because credit assignment is an important thing in science and even though sometimes it's over the top like schmidhuber always


The human voice: It's the instrument we all play. It's the most powerful sound in the world, probably. It's the only one that can start a war or say "I love you." And yet many people have the experience that when they speak, people don't listen to them. And why is that? How can we speak powerfully to make change in the world? What I'd like to suggest, there are a number of habits that we need to move away from.

How to speak so that people want to listen | Julian Treasure


I've assembled for your pleasure here seven deadly sins of speaking. I'm not pretending this is an exhaustive list, but these seven, I think, are pretty large habits that we can all fall into. First, gossip. Speaking ill of somebody who's not present. Not a nice habit, and we know perfectly well the person gossiping, five minutes later, will be gossiping about us.


Second, judging. We know people who are like this in conversation, and it's very hard to listen to somebody if you know that you're being judged and found wanting at the same time. Third, negativity. You can fall into this. My mother, in the last years of her life, became very negative, and it's hard to listen. I remember one day, I said to her, "It's October 1 today,"


and she said, "I know, isn't it dreadful?" (Laughter) It's hard to listen when somebody's that negative. (Laughter) And another form of negativity, complaining. Well, this is the national art of the U.K. It's our national sport. We complain about the weather, sport, about politics, about everything, but actually, complaining is viral misery. It's not spreading sunshine and lightness in the world.


Excuses. We've all met this guy. Maybe we've all been this guy. Some people have a blamethrower. They just pass it on to everybody else and don't take responsibility for their actions, and again, hard to listen to somebody who is being like that. Penultimate, the sixth of the seven, embroidery, exaggeration. It demeans our language, actually, sometimes. For example, if I see something that really is awesome,


what do I call it? (Laughter) And then, of course, this exaggeration becomes lying, and we don't want to listen to people we know are lying to us. And finally, dogmatism. The confusion of facts with opinions. When those two things get conflated, you're listening into the wind. You know, somebody is bombarding you with their opinions as if they were true. It's difficult to listen to that.


So here they are, seven deadly sins of speaking. These are things I think we need to avoid. But is there a positive way to think about this? Yes, there is. I'd like to suggest that there are four really powerful cornerstones, foundations, that we can stand on if we want our speech to be powerful and to make change in the world. Fortunately, these things spell a word.


The word is "hail," and it has a great definition as well. I'm not talking about the stuff that falls from the sky and hits you on the head. I'm talking about this definition, to greet or acclaim enthusiastically, which is how I think our words will be received if we stand on these four things. So what do they stand for? See if you can guess. The H, honesty, of course,


being true in what you say, being straight and clear. The A is authenticity, just being yourself. A friend of mine described it as standing in your own truth, which I think is a lovely way to put it. The I is integrity, being your word, actually doing what you say, and being somebody people can trust. And the L is love. I don't mean romantic love, but I do mean wishing people well, for two reasons.


First of all, I think absolute honesty may not be what we want. I mean, my goodness, you look ugly this morning. Perhaps that's not necessary. Tempered with love, of course, honesty is a great thing. But also, if you're really wishing somebody well, it's very hard to judge them at the same time. I'm not even sure you can do those two things simultaneously. So hail.


Also, now that's what you say, and it's like the old song, it is what you say, it's also the way that you say it. You have an amazing toolbox. This instrument is incredible, and yet this is a toolbox that very few people have ever opened. I'd like to have a little rummage in there with you now and just pull a few tools out that you might like to take away and play with,


which will increase the power of your speaking. Register, for example. Now, falsetto register may not be very useful most of the time, but there's a register in between. I'm not going to get very technical about this for any of you who are voice coaches. You can locate your voice, however. So if I talk up here in my nose, you can hear the difference. If I go down here in my throat,


which is where most of us speak from most of the time. But if you want weight, you need to go down here to the chest. You hear the difference? We vote for politicians with lower voices, it's true, because we associate depth with power and with authority. That's register. Then we have timbre. It's the way your voice feels. Again, the research shows that we prefer voices which are rich, smooth, warm,


like hot chocolate. Well if that's not you, that's not the end of the world, because you can train. Go and get a voice coach. And there are amazing things you can do with breathing, with posture, and with exercises to improve the timbre of your voice. Then prosody. I love prosody. This is the sing-song, the meta-language that we use in order to impart meaning.


It's root one for meaning in conversation. People who speak all on one note are really quite hard to listen to if they don't have any prosody at all. That's where the word "monotonic" comes from, or monotonous, monotone. Also, we have repetitive prosody now coming in, where every sentence ends as if it were a question when it's actually not a question, it's a statement?


(Laughter) And if you repeat that one, it's actually restricting your ability to communicate through prosody, which I think is a shame, so let's try and break that habit. Pace. I can get very excited by saying something really quickly, or I can slow right down to emphasize, and at the end of that, of course, is our old friend silence. There's nothing wrong with a bit of silence in a talk, is there?


We don't have to fill it with ums and ahs. It can be very powerful. Of course, pitch often goes along with pace to indicate arousal, but you can do it just with pitch. Where did you leave my keys? (Higher pitch) Where did you leave my keys? So, slightly different meaning in those two deliveries. And finally, volume. (Loud) I can get really excited by using volume.


Sorry about that, if I startled anybody. Or, I can have you really pay attention by getting very quiet. Some people broadcast the whole time. Try not to do that. That's called sodcasting, (Laughter) Imposing your sound on people around you carelessly and inconsiderately. Not nice. Of course, where this all comes into play most of all is when you've got something really important to do.


It might be standing on a stage like this and giving a talk to people. It might be proposing marriage, asking for a raise, a wedding speech. Whatever it is, if it's really important, you owe it to yourself to look at this toolbox and the engine that it's going to work on, and no engine works well without being warmed up. Warm up your voice. Actually, let me show you how to do that.


Would you all like to stand up for a moment? I'm going to show you the six vocal warm-up exercises that I do before every talk I ever do. Any time you're going to talk to anybody important, do these. First, arms up, deep breath in, and sigh out, ahhhhh, like that. One more time. Ahhhh, very good. Now we're going to warm up our lips, and we're going to go Ba, Ba, Ba, Ba,


Ba, Ba, Ba, Ba. Very good. And now, brrrrrrrrrr, just like when you were a kid. Brrrr. Now your lips should be coming alive. We're going to do the tongue next with exaggerated la, la, la, la, la, la, la, la, la. Beautiful. You're getting really good at this. And then, roll an R. Rrrrrrr. That's like champagne for the tongue. Finally, and if I can only do one,


the pros call this the siren. It's really good. It starts with "we" and goes to "aw." The "we" is high, the "aw" is low. So you go, weeeaawww, weeeaawww. Fantastic. Give yourselves a round of applause. Take a seat, thank you. (Applause) Next time you speak, do those in advance. Now let me just put this in context to close. This is a serious point here. This is where we are now, right?


We speak not very well to people who simply aren't listening in an environment that's all about noise and bad acoustics. I have talked about that on this stage in different phases. What would the world be like if we were speaking powerfully to people who were listening consciously in environments which were actually fit for purpose? Or to make that a bit larger,


Okay, now I don't want to alarm anybody in this room, but it's just come to my attention that the person to your right is a liar. (Laughter) Also, the person to your left is a liar. Also the person sitting in your very seats is a liar. We're all liars. What I'm going to do today is I'm going to show you what the research says about why we're all liars, how you can become a liespotter

How to spot a liar | Pamela Meyer


and why you might want to go the extra mile and go from liespotting to truth seeking, and ultimately to trust building. Now, speaking of trust, ever since I wrote this book, "Liespotting," no one wants to meet me in person anymore, no, no, no, no, no. They say, "It's okay, we'll email you." (Laughter) I can't even get a coffee date at Starbucks. My husband's like, "Honey, deception?

How to spot a liar | Pamela Meyer


Maybe you could have focused on cooking. How about French cooking?" So before I get started, what I'm going to do is I'm going to clarify my goal for you, which is not to teach a game of Gotcha. Liespotters aren't those nitpicky kids, those kids in the back of the room that are shouting, "Gotcha! Gotcha! Your eyebrow twitched. You flared your nostril. I watch that TV show 'Lie To Me.' I know you're lying."

How to spot a liar | Pamela Meyer


No, liespotters are armed with scientific knowledge of how to spot deception. They use it to get to the truth, and they do what mature leaders do everyday; they have difficult conversations with difficult people, sometimes during very difficult times. And they start up that path by accepting a core proposition, and that proposition is the following: Lying is a cooperative act.

How to spot a liar | Pamela Meyer


Think about it, a lie has no power whatsoever by its mere utterance. Its power emerges when someone else agrees to believe the lie. So I know it may sound like tough love, but look, if at some point you got lied to, it's because you agreed to get lied to. Truth number one about lying: Lying's a cooperative act. Now not all lies are harmful. Sometimes we're willing participants in deception

How to spot a liar | Pamela Meyer


for the sake of social dignity, maybe to keep a secret that should be kept secret, secret. We say, "Nice song." "Honey, you don't look fat in that, no." Or we say, favorite of the digiratti, "You know, I just fished that email out of my Spam folder. So sorry." But there are times when we are unwilling participants in deception. And that can have dramatic costs for us.

How to spot a liar | Pamela Meyer


Last year saw 997 billion dollars in corporate fraud alone in the United States. That's an eyelash under a trillion dollars. That's seven percent of revenues. Deception can cost billions. Think Enron, Madoff, the mortgage crisis. Or in the case of double agents and traitors, like Robert Hanssen or Aldrich Ames, lies can betray our country, they can compromise our security, they can undermine democracy,

How to spot a liar | Pamela Meyer


they can cause the deaths of those that defend us. Deception is actually serious business. This con man, Henry Oberlander, he was such an effective con man, British authorities say he could have undermined the entire banking system of the Western world. And you can't find this guy on Google; you can't find him anywhere. He was interviewed once, and he said the following.

How to spot a liar | Pamela Meyer


He said, "Look, I've got one rule." And this was Henry's rule, he said, "Look, everyone is willing to give you something. They're ready to give you something for whatever it is they're hungry for." And that's the crux of it. If you don't want to be deceived, you have to know, what is it that you're hungry for? And we all kind of hate to admit it. We wish we were better husbands, better wives,

How to spot a liar | Pamela Meyer


smarter, more powerful, taller, richer -- the list goes on. Lying is an attempt to bridge that gap, to connect our wishes and our fantasies about who we wish we were, how we wish we could be, with what we're really like. And boy are we willing to fill in those gaps in our lives with lies. On a given day, studies show that you may be lied to anywhere from 10 to 200 times.

How to spot a liar | Pamela Meyer


Now granted, many of those are white lies. But in another study, it showed that strangers lied three times within the first 10 minutes of meeting each other. (Laughter) Now when we first hear this data, we recoil. We can't believe how prevalent lying is. We're essentially against lying. But if you look more closely, the plot actually thickens. We lie more to strangers than we lie to coworkers.

How to spot a liar | Pamela Meyer


Extroverts lie more than introverts. Men lie eight times more about themselves than they do other people. Women lie more to protect other people. If you're an average married couple, you're going to lie to your spouse in one out of every 10 interactions. Now, you may think that's bad. If you're unmarried, that number drops to three. Lying's complex. It's woven into the fabric of our daily and our business lives.

How to spot a liar | Pamela Meyer


We're deeply ambivalent about the truth. We parse it out on an as-needed basis, sometimes for very good reasons, other times just because we don't understand the gaps in our lives. That's truth number two about lying. We're against lying, but we're covertly for it in ways that our society has sanctioned for centuries and centuries and centuries. It's as old as breathing.

How to spot a liar | Pamela Meyer


It's part of our culture, it's part of our history. Think Dante, Shakespeare, the Bible, News of the World. (Laughter) Lying has evolutionary value to us as a species. Researchers have long known that the more intelligent the species, the larger the neocortex, the more likely it is to be deceptive. Now you might remember Koko. Does anybody remember Koko the gorilla who was taught sign language?

How to spot a liar | Pamela Meyer


Koko was taught to communicate via sign language. Here's Koko with her kitten. It's her cute little, fluffy pet kitten. Koko once blamed her pet kitten for ripping a sink out of the wall. (Laughter) We're hardwired to become leaders of the pack. It's starts really, really early. How early? Well babies will fake a cry, pause, wait to see who's coming and then go right back to crying.

How to spot a liar | Pamela Meyer


One-year-olds learn concealment. (Laughter) Two-year-olds bluff. Five-year-olds lie outright. They manipulate via flattery. Nine-year-olds, masters of the cover-up. By the time you enter college, you're going to lie to your mom in one out of every five interactions. By the time we enter this work world and we're breadwinners, we enter a world that is just cluttered with Spam, fake digital friends,

How to spot a liar | Pamela Meyer


partisan media, ingenious identity thieves, world-class Ponzi schemers, a deception epidemic -- in short, what one author calls a post-truth society. It's been very confusing for a long time now. What do you do? Well, there are steps we can take to navigate our way through the morass. Trained liespotters get to the truth 90 percent of the time. The rest of us, we're only 54 percent accurate.

How to spot a liar | Pamela Meyer


Why is it so easy to learn? There are good liars and bad liars. There are no real original liars. We all make the same mistakes. We all use the same techniques. So what I'm going to do is I'm going to show you two patterns of deception. And then we're going to look at the hot spots and see if we can find them ourselves. We're going to start with speech. (Video) Bill Clinton: I want you to listen to me.

How to spot a liar | Pamela Meyer


I'm going to say this again. I did not have sexual relations with that woman, Miss Lewinsky. I never told anybody to lie, not a single time, never. And these allegations are false. And I need to go back to work for the American people. Thank you. (Applause) Pamela Meyer: Okay, what were the telltale signs? Well first we heard what's known as a non-contracted denial.

How to spot a liar | Pamela Meyer


Studies show that people who are overdetermined in their denial will resort to formal rather than informal language. We also heard distancing language: "that woman." We know that liars will unconsciously distance themselves from their subject, using language as their tool. Now if Bill Clinton had said, "Well, to tell you the truth ..." or Richard Nixon's favorite, "In all candor ..."

How to spot a liar | Pamela Meyer


he would have been a dead giveaway for any liespotter that knows that qualifying language, as it's called, qualifying language like that, further discredits the subject. Now if he had repeated the question in its entirety, or if he had peppered his account with a little too much detail -- and we're all really glad he didn't do that -- he would have further discredited himself.

How to spot a liar | Pamela Meyer


Freud had it right. Freud said, look, there's much more to it than speech: "No mortal can keep a secret. If his lips are silent, he chatters with his fingertips." And we all do it no matter how powerful you are. We all chatter with our fingertips. I'm going to show you Dominique Strauss-Kahn with Obama who's chattering with his fingertips. (Laughter) Now this brings us to our next pattern, which is body language.

How to spot a liar | Pamela Meyer


With body language, here's what you've got to do. You've really got to just throw your assumptions out the door. Let the science temper your knowledge a little bit. Because we think liars fidget all the time. Well guess what, they're known to freeze their upper bodies when they're lying. We think liars won't look you in the eyes. Well guess what, they look you in the eyes a little too much

How to spot a liar | Pamela Meyer


just to compensate for that myth. We think warmth and smiles convey honesty, sincerity. But a trained liespotter can spot a fake smile a mile away. Can you all spot the fake smile here? You can consciously contract the muscles in your cheeks. But the real smile's in the eyes, the crow's feet of the eyes. They cannot be consciously contracted, especially if you overdid the Botox.

How to spot a liar | Pamela Meyer


Don't overdo the Botox; nobody will think you're honest. Now we're going to look at the hot spots. Can you tell what's happening in a conversation? Can you start to find the hot spots to see the discrepancies between someone's words and someone's actions? Now, I know it seems really obvious, but when you're having a conversation with someone you suspect of deception,

How to spot a liar | Pamela Meyer


attitude is by far the most overlooked but telling of indicators. An honest person is going to be cooperative. They're going to show they're on your side. They're going to be enthusiastic. They're going to be willing and helpful to getting you to the truth. They're going to be willing to brainstorm, name suspects, provide details. They're going to say, "Hey, maybe it was those guys in payroll that forged those checks."

How to spot a liar | Pamela Meyer


They're going to be infuriated if they sense they're wrongly accused throughout the entire course of the interview, not just in flashes; they'll be infuriated throughout the entire course of the interview. And if you ask someone honest what should happen to whomever did forge those checks, an honest person is much more likely to recommend strict rather than lenient punishment.

How to spot a liar | Pamela Meyer


Now let's say you're having that exact same conversation with someone deceptive. That person may be withdrawn, look down, lower their voice, pause, be kind of herky-jerky. Ask a deceptive person to tell their story, they're going to pepper it with way too much detail in all kinds of irrelevant places. And then they're going to tell their story in strict chronological order.

How to spot a liar | Pamela Meyer


And what a trained interrogator does is they come in and in very subtle ways over the course of several hours, they will ask that person to tell that story backwards, and then they'll watch them squirm, and track which questions produce the highest volume of deceptive tells. Why do they do that? Well, we all do the same thing. We rehearse our words, but we rarely rehearse our gestures.

How to spot a liar | Pamela Meyer


We say "yes," we shake our heads "no." We tell very convincing stories, we slightly shrug our shoulders. We commit terrible crimes, and we smile at the delight in getting away with it. Now, that smile is known in the trade as "duping delight." And we're going to see that in several videos moving forward, but we're going to start -- for those of you who don't know him,

How to spot a liar | Pamela Meyer


this is presidential candidate John Edwards who shocked America by fathering a child out of wedlock. We're going to see him talk about getting a paternity test. See now if you can spot him saying, "yes" while shaking his head "no," slightly shrugging his shoulders. (Video) John Edwards: I'd be happy to participate in one. I know that it's not possible that this child could be mine,

How to spot a liar | Pamela Meyer


because of the timing of events. So I know it's not possible. Happy to take a paternity test, and would love to see it happen. Interviewer: Are you going to do that soon? Is there somebody -- JE: Well, I'm only one side. I'm only one side of the test. But I'm happy to participate in one. PM: Okay, those head shakes are much easier to spot once you know to look for them.

How to spot a liar | Pamela Meyer


There are going to be times when someone makes one expression while masking another that just kind of leaks through in a flash. Murderers are known to leak sadness. Your new joint venture partner might shake your hand, celebrate, go out to dinner with you and then leak an expression of anger. And we're not all going to become facial expression experts overnight here,

How to spot a liar | Pamela Meyer


but there's one I can teach you that's very dangerous and it's easy to learn, and that's the expression of contempt. Now with anger, you've got two people on an even playing field. It's still somewhat of a healthy relationship. But when anger turns to contempt, you've been dismissed. It's associated with moral superiority. And for that reason, it's very, very hard to recover from.

How to spot a liar | Pamela Meyer


Here's what it looks like. It's marked by one lip corner pulled up and in. It's the only asymmetrical expression. And in the presence of contempt, whether or not deception follows -- and it doesn't always follow -- look the other way, go the other direction, reconsider the deal, say, "No thank you. I'm not coming up for just one more nightcap. Thank you." Science has surfaced many, many more indicators.

How to spot a liar | Pamela Meyer


We know, for example, we know liars will shift their blink rate, point their feet towards an exit. They will take barrier objects and put them between themselves and the person that is interviewing them. They'll alter their vocal tone, often making their vocal tone much lower. Now here's the deal. These behaviors are just behaviors. They're not proof of deception.

How to spot a liar | Pamela Meyer


They're red flags. We're human beings. We make deceptive flailing gestures all over the place all day long. They don't mean anything in and of themselves. But when you see clusters of them, that's your signal. Look, listen, probe, ask some hard questions, get out of that very comfortable mode of knowing, walk into curiosity mode, ask more questions, have a little dignity, treat the person you're talking to with rapport.

How to spot a liar | Pamela Meyer


Don't try to be like those folks on "Law & Order" and those other TV shows that pummel their subjects into submission. Don't be too aggressive, it doesn't work. Now, we've talked a little bit about how to talk to someone who's lying and how to spot a lie. And as I promised, we're now going to look at what the truth looks like. But I'm going to show you two videos,

How to spot a liar | Pamela Meyer


two mothers -- one is lying, one is telling the truth. And these were surfaced by researcher David Matsumoto in California. And I think they're an excellent example of what the truth looks like. This mother, Diane Downs, shot her kids at close range, drove them to the hospital while they bled all over the car, claimed a scraggy-haired stranger did it. And you'll see when you see the video,

How to spot a liar | Pamela Meyer


she can't even pretend to be an agonizing mother. What you want to look for here is an incredible discrepancy between horrific events that she describes and her very, very cool demeanor. And if you look closely, you'll see duping delight throughout this video. (Video) Diane Downs: At night when I close my eyes, I can see Christie reaching her hand out to me while I'm driving,

How to spot a liar | Pamela Meyer


and the blood just kept coming out of her mouth. And that -- maybe it'll fade too with time -- but I don't think so. That bothers me the most. PM: Now I'm going to show you a video of an actual grieving mother, Erin Runnion, confronting her daughter's murderer and torturer in court. Here you're going to see no false emotion, just the authentic expression of a mother's agony.

How to spot a liar | Pamela Meyer


(Video) Erin Runnion: I wrote this statement on the third anniversary of the night you took my baby, and you hurt her, and you crushed her, you terrified her until her heart stopped. And she fought, and I know she fought you. But I know she looked at you with those amazing brown eyes, and you still wanted to kill her. And I don't understand it, and I never will.

How to spot a liar | Pamela Meyer


PM: Okay, there's no doubting the veracity of those emotions. Now the technology around what the truth looks like is progressing on, the science of it. We know, for example, that we now have specialized eye trackers and infrared brain scans, MRI's that can decode the signals that our bodies send out when we're trying to be deceptive. And these technologies are going to be marketed to all of us

How to spot a liar | Pamela Meyer


as panaceas for deceit, and they will prove incredibly useful some day. But you've got to ask yourself in the meantime: Who do you want on your side of the meeting, someone who's trained in getting to the truth or some guy who's going to drag a 400-pound electroencephalogram through the door? Liespotters rely on human tools. They know, as someone once said, "Character's who you are in the dark."

How to spot a liar | Pamela Meyer


And what's kind of interesting is that today, we have so little darkness. Our world is lit up 24 hours a day. It's transparent with blogs and social networks broadcasting the buzz of a whole new generation of people that have made a choice to live their lives in public. It's a much more noisy world. So one challenge we have is to remember, oversharing, that's not honesty.

How to spot a liar | Pamela Meyer


Our manic tweeting and texting can blind us to the fact that the subtleties of human decency -- character integrity -- that's still what matters, that's always what's going to matter. So in this much noisier world, it might make sense for us to be just a little bit more explicit about our moral code. When you combine the science of recognizing deception with the art of looking, listening,

How to spot a liar | Pamela Meyer


you exempt yourself from collaborating in a lie. You start up that path of being just a little bit more explicit, because you signal to everyone around you, you say, "Hey, my world, our world, it's going to be an honest one. My world is going to be one where truth is strengthened and falsehood is recognized and marginalized." And when you do that, the ground around you starts to shift just a little bit.

How to spot a liar | Pamela Meyer


all right today we're going to find out why ai is much better at governing people why poor people really should pay more taxes and how donald trump is just a normal human all right we'll dive into it we're looking at the ai economist by salesforce research now salesforce research has kind of created a simulated world environment where they can place agents in it and the agents

The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies (Paper Explained)


they can move around they can collect resources they can trade those resources and they can use those resources to build houses and that will earn them coins and each agent wants to maximize its own coins but also there's the government and the government can set taxes so they collect money from everyone and they redistribute it and the goal now is to going to be


that the ai handles both the agent and the taxes and we want to maximize the social welfare of the entire population all right that's the goal so the paper here is called the ai economist improving equality and productivity with ai driven tax policies by stefan cheng and alexander trott and other people from salesforce research and harvard university so as i said this is a simulated


environment and the simulated environment works like this there is a 2d plane kind of like a game playing field and in this game there are agents here you can see the agents there are always four agents where oh down here what are you what are you doing in the corner come on be productive um the the the agents are in this world and they can do certain things they have certain actions at


their disposal so first of all they can move around they can move down left right and so on whenever they walk past a resource tile they collect the resource this is stone and this is wood so there are two kinds of resources and then the last actions the agents have is building a house one wood and one stone will create one house and the house gives you coins


so this is a house and that will give you coins but how much coins you get is different from agent to agent and this represents the agent's different skill levels this is an abstraction and the kind of economic theory behind it is that the income inequality in people one of the main drivers of it is that they are skilled differently and therefore are able to


they are able to convert one unit of labor into more money than another lower skilled worker so this is here represented by the fact that maybe if this agent here builds the house they'll get 50 coins but if this agent here would build the same house they'll only get 10 coins so we'll call this here a high skilled worker and this here a low skilled worker now the last thing sorry i thought last


thing before but the very last thing the agents can do is they can trade so if one agent has too many resources and the other one has not enough they can trade those resources among each other for those coins so once you build a house you collect some coins you can then either go and collect more resources or you can use those coins in order to buy resources off of


other people this guy this is unlucky no coins no houses and no resources look at them oh yeah so you also can't move across the water here um you can only move on the grass you can also not move through a house which gives you some interesting abilities because you can just build a house right here and um yes so and you can't move over other players but these are so


the rules are pretty simple and the goal here is for the agents to maximize the number of coins they get in a thousand steps so the number h here is one thousand which is the number of steps that the agents can take before the game is over and it restarts again so each agent is using reinforcement learning in order to learn how to achieve the maximum number of


coins now the policies of course going to be different depending on whether that is a high or a low skilled worker the catch here is that outside of this there is the government the government here let's draw this big house with the flag of our fictitious nation which is like this that's the flag and the government will observe what's happening here and they will issue a tax um


taxes so it will issue a tax distribution now how do you imagine that so if you imagine the government says something like this for the first ten coins you own you owe us five percent of that um for the next 10 coins so from 10 to 20 you earn you owe us 10 and so on so if you earn even more you owe us more and more percent of those extra coins this is what you might know as a


progressive tax schedule the more you earn the more percentage-wise you pay on that extra earned money this is what you might be used to but there are other tax schedules and the exact histogram you see or the exact how many percent for which amount of coins that is the action of the government so the government decides on the taxes and the taxes are just collected from