Making Sense - Sam Harris - #40 — Complexity & Stupidity

00:00:00.000 welcome to the making sense podcast this is sam harris just a note to say that if you're hearing

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00:00:38.840 today i'm going to be speaking with david krakauer who runs the santa fe institute one of the most

00:00:52.340 interesting organizations scientifically anywhere and david is a mathematical biologist he has a

00:01:00.620 phd in evolutionary theory from oxford but being at the santa fe institute puts him at the crossroads

00:01:07.880 of many different areas of inquiry we talk a little bit about what the institute is but given that its

00:01:14.420 focus is on complex systems the people there attempt to understand complexity using every

00:01:21.760 scientific and intellectual tool available so david knows a lot about many things as you'll hear in

00:01:29.880 this conversation we start by covering some foundational concepts in science like information

00:01:37.500 and complexity and intelligence then move on from there to talk about the implications for

00:01:43.820 society and culture and the future in any case i love talking to david and i hope you enjoy the

00:01:50.180 ground we covered and now i give you david krakauer

00:01:53.780 i have david krakauer on the line david thanks for joining me on the podcast pleasure to be with you

00:02:05.040 so david you gave a really fascinating lecture in los angeles that i want to talk about and i essentially

00:02:11.380 want you to just track through that as much as you can without without your visuals and i'm especially

00:02:18.360 interested in the the the importance of culture and the importance of of artifacts that we create 0.93

00:02:24.420 for human intelligence and resisting our slide into stupidity which you which you talked about which was 0.90

00:02:30.800 the focus of your talk but before we get there let's just set the stage a little bit tell us a little

00:02:36.200 bit about your scientific interests and background so well it's great to be with you first um my scientific

00:02:44.200 interests as i've come to understand them uh are essentially grappling with the problem of the

00:02:51.760 evolution of intelligence and stupidity on earth and it's quite common for people to talk about 0.99

00:02:58.820 intelligence it's less common for people to talk about stupidity even though arguably it's more common 0.98

00:03:04.580 and um and so my background is in uh mathematical evolutionary theory and um i essentially work on 0.85

00:03:13.340 information and computation in nature um that would include the nature that we've created that we call

00:03:21.880 technology um and where it came from uh what it's doing today and where it's going in the future

00:03:28.620 and so you would would you describe yourself as a mathematical biologist is that the right category

00:03:34.500 yeah i think it's reasonable i think unfortunately all of these categories are starting to strain a

00:03:38.840 little yeah well and you're at now you're running the santa fe institute which i think quite happily

00:03:44.900 is its its existence seems to be predicated on the the porousness of these boundaries between

00:03:52.020 disciplines or even their non-existence and so maybe describe the the institute for people who are

00:03:56.900 not familiar with it yeah so the santa fe institute is in santa fe new mexico as the name would suggest

00:04:02.200 it was founded in the mid-80s by a group of nobel laureates um from physics and economics and others

00:04:09.860 um who were interested in trying to do for the complex world what mathematical physics had done so

00:04:18.520 successfully for the simple world and i should explain that so the simple world would be the solar system

00:04:24.680 um or inorganic chemistry or black holes they're not easy to understand but you can

00:04:31.440 encapsulate their fundamental properties by writing down a system of equations when you get to the

00:04:38.000 complex world which basically means networked adaptive systems so that could be a brain a network of neurons

00:04:45.720 it could be a society it could even be the internet and in those networked adaptive systems complex systems

00:04:52.240 the kinds of formalisms that we had created historically to deal with simple systems

00:04:57.380 failed that's why we don't have maxwell's equations of the brain right we have large textbooks with many

00:05:05.360 anatomical descriptions some schematic representations of function and some very specialized models and the

00:05:13.400 question for us that sfi is are there general principles that span the economy brains the internet

00:05:22.080 and so on and what is the most natural way of articulating them mathematically and computationally

00:05:28.220 and how is sfi different from the the institute for advanced study at princeton where i think you

00:05:34.460 also were if i'm not mistaken yes that's right so um the ias in princeton is a lot older is founded in the

00:05:41.220 30s we were founded in the 80s and um ias is an extraordinary place but the model

00:05:47.160 if you like is much more traditional so ias has tenure it has departments and it has schools

00:05:54.200 we do not have tenure we do not have departments and we do not have schools

00:05:58.520 so they've created in some sense they've replicated i guess um a very successful model that is the

00:06:07.060 university model we decided to start again from a blank slate and we asked the question if you were

00:06:14.480 now reinventing the research institute based on everything that we now know post-scientific

00:06:20.720 revolution post-technological revolution etc what should it look like and so it's a more radical

00:06:27.960 model and uh we so we just decided very early just to discard any mention of disciplines and

00:06:35.280 departments and focus as hard as we could on the common denominators of the complex systems that we

00:06:42.580 were studying and it's it's truly interdisciplinary you have economists and mathematicians and biologists

00:06:48.420 and physicists all throwing their in their two cents on the same problems is that correct

00:06:54.500 absolutely i mean just as an example i mean we you know there's all this debate now about

00:06:59.120 the demise of the humanities and but we from the very beginning decided that that wasn't a worthwhile

00:07:06.020 distinction between the natural sciences and the humanities so we were working on the archaeology of

00:07:11.180 the southwest and using computational and physical models since the 80s and have produced what is by now a

00:07:18.540 very well-known series of theories for why for example some of the native civilizations of the

00:07:24.940 american southwest declined the origin of ancient cities and all of these are based on computational

00:07:32.020 and energetic theories and close collaborations between archaeologists and say physicists so the way

00:07:38.980 we do it i don't like to call it interdisciplinary because that's in some sense genuflecting in the direction

00:07:44.060 of a superstition that i don't want to take seriously right and so what happens when you ignore all of that

00:07:49.780 and say let's certainly use the skills that we've acquired in the disciplines but let's leave them at the door

00:07:57.500 and and just be intelligent about complex problems yeah really what you have as an institutional

00:08:03.300 argument it seems to me for the unity of knowledge or consilience that really the boundaries between

00:08:09.820 disciplines are much more a matter of university architecture and just the kind of bandwidth issues

00:08:16.360 of any individual life where you have it takes a long time to get very good at one thing and so by

00:08:22.340 definition you know someone starts out in one area as opposed to another and spends rather a long time

00:08:28.460 there in order to get competent anyway i think what you're doing there is very exciting thank you so before

00:08:33.500 we get into your talk there's a few things i just want you to enlighten me and our audience about

00:08:39.580 because there's some concepts here that that you are going to use that i think are difficult to get

00:08:44.220 one's head around and the first is the concept of information and i think there are many senses in

00:08:50.060 which we use this term and not all of them are commensurable it seems to me that the there is a root

00:08:56.220 concept however that potentially unites fields like genetics and brain science and computer science and

00:09:02.780 even physics so how do you think about information yeah so i should say we've talked about this

00:09:08.940 before sam and that is it's sometimes what i call the m cubed mayhem uh that is m raised to the power

00:09:17.260 three mayhem and the mayhem comes from not understanding the difference between mathematics

00:09:23.180 the first m mathematical models the second m and metaphors the third and there are terms scientific

00:09:31.580 terms mathematical terms that are also used uh idiomatically or have a colloquial meaning and they

00:09:39.260 very often get us into you know deep water energy fitness utility capacity uh information computation

00:09:48.940 and so we all use them in our daily lives and probably very effectively but they also have a technical

00:09:54.380 meaning and what happens often is that uh arguments uh flare up because one person is using it

00:10:01.020 mathematically and another person metaphorically and they don't realize they're doing this so that's

00:10:06.300 the first point to make um and they're all valuable i don't mean to say that there is only a mathematical

00:10:11.420 definition of information but it's worth bearing in mind that when i talk about it that's what i mean

00:10:16.140 um so that's the first point it has a beautiful scientific storied history um you know starting with

00:10:25.820 essentially the birth of the field that we now call statistical mechanics and this was essentially uh

00:10:33.660 boltsman trying to understand the arrow of time in in in the physical world the origin of irreversibility

00:10:42.620 you know why is it that you can crack and break an egg uh but the reverse almost never happens why

00:10:48.540 is it that you can burn wood into ash and smoke but the reverse almost never happens and he created

00:10:54.540 in the 1870s a theory called the h theorem uh where he essentially had in mind lots of little billiard

00:11:01.340 balls bumping into each other chaotically he called it molecular chaos and through repeated collisions you

00:11:06.860 start with a fairly ordered billiard table but at the end uh they're distributed rather randomly

00:11:12.380 all over the table and that was boltzman and he thought maybe the underlying molecular structure

00:11:18.540 of matter was like lots of little billiard balls and the reason why we observe certain phenomena in

00:11:25.580 nature as irreversible is because of molecular chaos and that was formalized later by a very famous

00:11:31.980 american physicist josiah willard gibbs but many years later uh the baton was picked up by

00:11:39.340 an engineer working at bell labs claude shannon and he realized that there was a connection between

00:11:45.580 physics and irreversibility and the error of time and information it was very deep insight that he had

00:11:53.340 and before explaining how that works it's what did what did claude shannon do he said look here's what

00:11:57.580 information is let's say i want you to navigate from one part of a city to another from a to b

00:12:02.780 in a car i could just drive around randomly it would take an awful long time to get there but i might

00:12:11.260 eventually get there alternatively i could give you a map or driving directions and you'd get there

00:12:19.100 very efficiently and the difference between the time taken to get there randomly

00:12:25.260 and the time taken to get there with directions is a measure of information and shannon mathematized

00:12:32.220 that concept and said that is the reduction of uncertainty you start off not knowing where to go

00:12:39.900 you get information in the form of a map or driving directions and then you get there directly and he

00:12:45.420 said he formalized that and he called that information and um it's the opposite of what

00:12:51.740 boltsman and gives we're talking about it's a system going instead of going from the ordered

00:12:57.420 into the disordered state the billiard balls on the table starting maybe in a lattice and ending up

00:13:03.260 randomly distributed uh it's going from a state of then being random because you don't know where to go

00:13:09.100 to becoming ordered and so it turns out that shannon realized that information is in fact the negative

00:13:17.580 of thermodynamic entropy and it was a beautiful connection that he made between what we now

00:13:23.420 think of the science of information and what was the science of statistical physics well so let's bring

00:13:29.100 this into the domain of biology because i've been hearing now with increasing frequency this idea that

00:13:35.900 biological systems and even brains do not process information and that the the analogy of the brain as a

00:13:44.300 computer is no more valid than the analogy of it as a system of hydraulic pumps or a wheel works powered

00:13:51.740 by springs and gears or a telegraph and these are all old analogies to the the most current technology

00:13:58.700 of the time and there was an article in aeon magazine i think it's just an online journal that

00:14:04.220 probably a dozen people sent to me and i thought it made this case very badly and you and i talked about

00:14:10.060 this briefly when we first met yes now it seems to me i mean no one to my knowledge thinks that the

00:14:15.980 brain is a computer in exactly the way our current computers are computers we're not talking about

00:14:21.740 von neumann architecture in our brains yes but the idea that it doesn't process information at all

00:14:28.060 and the idea that the claim that it does is just as crazy as claiming that it's a mechanism of gears and

00:14:34.620 springs strikes me as fairly delusional and i but i keep meeting people who will argue this and and

00:14:40.140 some of them are very high level in the in the sciences so i was hoping we could talk a little

00:14:45.420 bit about the ways in which biological systems in particular brains encode and and transmit information

00:14:52.860 yes so uh this takes me right back to my m cubed mayhem because that's a beautiful example in that paper

00:15:00.380 of the author not knowing the difference between a mathematical model and a metaphor and so beautiful

00:15:06.220 you gave a beautiful example you talked about springs and levers and their physical artifacts uh

00:15:12.620 right and then there are mathematical models of springs and levers which are actually used in

00:15:17.340 understanding string theory so okay so let's talk a little bit about the the computer in the brain it's

00:15:22.940 very important because you mentioned von neumann um and it spans elegantly that spectrum

00:15:30.060 from mathematics to mathematical models to metaphors um the first real theory of computing

00:15:36.140 that we have is due to alan turing uh in the 1930s and he was a mathematician many of him know many

00:15:43.660 people know him from the movie the imitation game and for his extraordinary work on uh enigma um and

00:15:51.340 decoding german submarine codes in the second world war um but what he's most famous for in our world

00:15:58.380 is answering a really deep mathematical question that was posed by the german mathematician david hilbert

00:16:04.700 in 1928 and um hilbert said could i give a machine a mathematical question or proposition and it would

00:16:15.820 tell me in reasonable amount of time whether it was true or whether it was false right and that's the

00:16:23.420 question he posed could we in some sense automate mathematics and in 1936 turing in answering that

00:16:31.420 question invented a mathematical model that we now know as the turing machine and it's a beautiful thing

00:16:40.140 i'm sure you've talked about it on your show before and turing did something remarkable he said you know you

00:16:44.940 can't answer that question there are certain mathematical statements that are fundamentally

00:16:49.980 uncomputable you could never answer them and it was a really profound breakthrough in mathematics

00:16:56.220 because it said there are certain things in the world that we could never know through computation

00:17:00.700 so years later um turing himself in the 40s realized that in solving a mathematical problem

00:17:07.420 he had actually invented a mathematical model the turing machine and he realized the turing machine

00:17:14.060 was actually not just a model for solving math problems but it was actually the model of problem solving

00:17:21.740 itself and the model of problem solving itself is what we mean by computation and then in the 1950s

00:17:29.260 actually 58 john von neumann who you mentioned wrote a book the famous book called the computer and the

00:17:35.420 brain yeah they said perhaps what alan turing had done in his paper on intelligent machinery is given us

00:17:43.900 the mathematical machinery for understanding the brain itself and at that point it became a metaphor

00:17:51.020 and john von neumann himself realized it was a metaphor but he thought it was a very powerful one

00:17:55.100 as they saw so often are so that's the history and um so now up into the present so as you point out

00:18:03.980 there's a tendency to be a bit you know epistemologically narcissistic we we tend to use whatever current

00:18:10.380 model we use and project that onto the natural world it's almost the best fitting um template for how

00:18:16.540 it operates um here's the value and uh the utility and disutility of the concept the value of what

00:18:25.500 turing and von neumann did was give us a framework for starting to understand how a problem solving

00:18:32.380 machine could operate we didn't really have in our mind's eye an understanding for how that could work

00:18:39.500 and they gave us a model for how it could work for many reasons that some of which you've mentioned

00:18:45.580 the model is highly imperfect computers are not robust if i stick a pencil in your cpu your machine

00:18:54.220 will stop working but i can sever the two hemispheres of the brain and you can still function um you're very

00:19:00.380 efficient um your brain consumes about 20 of the energy of your body which is like 20 watts

00:19:07.100 it's 20 of a light bulb uh your laptop consumes about that and has you know some tiny fraction of

00:19:13.180 your power um and they're highly connected the neurons are densely wired whereas that's not true

00:19:21.340 of computer circuits which are only locally wired and most importantly the brain is constantly rewiring

00:19:27.420 and adapting based on inputs and your in your computer is not so we know the ways in which it's

00:19:32.140 not the same uh but there are but as i say it's useful as a thought experiment um for how the brain might

00:19:39.100 operate so that's the computer term but now let's take the information term that one for me and that

00:19:46.140 magazine article you mentioned is criticizing the information concept not the computer concept

00:19:50.140 which is limited and we all agree but the information concept is not right so so i've we've already

00:19:56.460 determined what information is mathematically it's the reduction of uncertainty and if you think

00:20:01.580 about your visual system when you open your eyes in the morning and you don't know what's out there in

00:20:06.060 the world uh electromagnetic energy which is transduced by photoreceptors in your retina

00:20:14.060 and then transmitted through to visual cortex allows you to know something about the world that

00:20:18.940 you did not know before so it's like going from the billiard balls all over the table to the

00:20:23.260 billiard balls in a particular configuration very formally speaking you have reduced the uncertainty

00:20:29.420 about the world you've increased the information and it turns out you can measure that mathematically

00:20:35.340 and the extent to which that's useful is proved by essentially neuroprosthetics

00:20:42.620 the information theory of the brain allows us to build cochlear implants it allows us to control

00:20:50.220 robotic limbs with our brains so it's not a metaphor it's a deep mathematical principle

00:20:56.940 it's a principle that allows us to understand how the brain is operating and re-engineer it and so it's

00:21:02.620 one of those cases where i think the article is so utterly confused uh that it's almost not worth

00:21:07.500 attending to uh the now that's information information processing if that's synonymous

00:21:14.940 in your vocabulary with computing in the turing sense then you and i have just agreed that it's not

00:21:21.740 right but if information processing is what you do with shannon information for example to transduce

00:21:30.140 electromagnetic impulses into electrical firing patterns in the brain then it's absolutely applicable

00:21:37.420 and then how you store it and then how you combine information sources so when i see an orange

00:21:42.940 it's orange color and it's also a sphere i have tactile uh mechanical impulses um i have visual

00:21:51.260 electromagnetic electromagnetic impulses and in my brain they're combined into a coherent representation

00:21:56.220 of an object in the world and the coherent representation is in the form of an informational

00:22:03.340 language spiking and so uh you know it's extraordinarily useful it's allowed us to engineer uh neuro

00:22:13.180 you know biologically mimetic architectures and it's made a huge difference in the lives of many

00:22:19.500 individuals um who have been born with uh severe disabilities so i think we can take that article

00:22:25.980 and shred it yeah as i was reading the article again this is it was one of those almost not even wrong

00:22:33.260 categories of error but you know i was thinking of things like genes can be on or off right so there's

00:22:38.780 there's a there's a digital component going all the way down into the genome and and the genome itself

00:22:46.540 is a kind of memory right it's it's a it's a memory for structure and physiology and even certain

00:22:53.660 behaviors that have proved adaptive in the past and and and therefore it's a template for producing those in

00:23:00.860 future organisms that's that's exactly right and so that's the great power of mathematical concepts

00:23:06.460 because and again we have to be clear in making distinctions between the metaphor of memory

00:23:12.540 right um and the mathematical model of memory and once and the beautiful thing that's why mathematics

00:23:17.180 is so extraordinarily powerful is that once we move to the mathematical model of memory exactly as you

00:23:22.780 say you can demonstrate that there are memories stored in genes there are memories stored in the brain

00:23:28.380 there are memories stored in culture and they bear an extraordinary family resemblance through the

00:23:35.260 resemblance in the mathematical equations so you described it as consilience in ed wilson's term you

00:23:41.660 could describe it as unification in the language of physics and they're totally legitimate uh where we

00:23:47.980 run into into into trouble is if we don't move to mathematics but we only remain in the world of metaphor

00:23:53.340 and there of course um everyone has a slightly different matrix of associations and you can

00:23:59.020 never fully resolve the the ambiguities right except though even at the level forget about the math for

00:24:05.340 a second let's just talk about something that's perilously close to metaphor we are simply talking about

00:24:12.380 cause and effect relationships that in this case reliably link inputs and outputs right so there's i mean

00:24:20.780 there is just a even in that article he was talking about the nervous system being changed by experience he

00:24:27.900 just didn't want to talk about the resulting changes in terms of memory or information storage or encoding or

00:24:34.540 anything else that that suggested an analogy to a computer but there's just this this fact that change in physical

00:24:42.940 structure can produce reliable change in its capacities going forward yes and you know whether

00:24:50.700 we want to call that memory or not or learning or not biologically physically that's what we're talking about

00:24:56.140 absolutely it's what we're talking about no you're right you see that's the point it has to do with

00:25:00.860 this legitimate fear of anthropomorphism and um and i think that what we do in these sort of more exact

00:25:10.860 sciences is try and pin down our definitions so as to eliminate some of the ambiguities they never go away entirely

00:25:19.660 but my suspicion sam is that the author of that um article will simply find a language

00:25:27.820 that isn't doesn't have its roots if you like in in the world of information and apply these new terms but we would realize if we read it

00:25:36.300 through thoroughly that they were in fact just synonyms right he right he would find himself having to use

00:25:43.900 these terms because they are to the best of our knowledge the best terms we have to explain the

00:25:49.820 regularities we observe right and and yet we don't have to use terms like hydraulic pumps or the four humors or

00:25:58.220 we can grant that there have been bad analogies in the past where the details are not actually

00:26:02.700 conserved in any way going forward well but look at a good you know it's a beautiful example because

00:26:07.500 where we have used that is if you're talking about your uh cardiac system yeah yeah or your urinal genital

00:26:15.660 system it is entirely appropriate to use harvey's uh model which was the pump right so the ones that

00:26:23.420 worked have stuck and um and i think it's just time that will tell us whether or not our use of the

00:26:30.060 informational concept is uh will be an anachronism uh will have enduring value well for those of you

00:26:36.860 who are interested to read this this paper that we are trashing i will put the link on my blog beneath

00:26:42.700 where i embed this podcast so now moving on to your core area of interest we've dealt with information

00:26:50.220 what is complexity yes and so that's a very that's a wonderful example of one of these terms that we

00:26:58.460 use in daily life but also has mathematical uh meaning so um the simplest way to think about complexity

00:27:06.860 is as follows um imagine you had a very regular object like a cube um you could express it just by

00:27:18.540 describing its linear dimensions right and um that would tell you what a cube is

00:27:26.780 and imagine you want to explain something at the other end of the spectrum like a gas in a room

00:27:33.420 you could articulate that very reliably uh by just giving the mean velocities of particles in air you

00:27:41.180 know so these two extremes the um very regular um a crystal um to the very random a gas um permit

00:27:53.340 of a description which is very short and so over the phone or over skype as we're speaking i could

00:27:59.980 describe to you very reliably uh a regular object or a very irregular object but now let's imagine you said

00:28:07.500 can you please describe to me david um a mouse and i said well oh it's this sort of weird

00:28:16.300 tubular thing and it's got hairs at one end it's got this long appendage at the other etc it would

00:28:23.500 take an awfully long time to describe and complexity is essentially proportional to that description

00:28:29.820 so that's a metaphor and it turns out mathematically the complex phenomena

00:28:37.980 live somewhere between the regular and the random and their hallmark signature

00:28:44.300 is that their mathematical descriptions are long and that's what's made complexity science so hard

00:28:50.780 because einstein could write down a beautiful equation like e equals mc squared

00:28:55.980 that captures the equivalence between energy and mass and has all these beautiful implications

00:29:00.300 in special relativity you know less than a line but how would you write down a mouse which seems like

00:29:05.660 a much more boring thing than than energy and matter and you can't and so the that's one way intuitive

00:29:15.180 way of thinking about a complex phenomena which is how long does the description have to be

00:29:21.740 to reliably capture much of what you consider interesting about it and one point to make immediately

00:29:29.100 is that you know if you look at physical phenomena they started off long too right so before kepler

00:29:35.500 revolutionized our understanding of celestial mechanics we had armillary spheres with all these epicycles

00:29:42.380 and deference right explaining in incorrectly the circular motion of celestial mass

00:29:48.540 and um and it took a while uh for us to realize that there was a very compact elegant way of describing

00:29:57.500 them and it could be that for many many complex phenomena there is a very elegant compact way of

00:30:03.900 describing them but many others i don't think that will be the case so complexity are as i said these

00:30:08.940 networked adaptive systems complexity itself as a concept mathematically tries to capture how hard

00:30:16.780 uh it is to describe a phenomenon and they get as they get more complex they get a lot these

00:30:23.900 descriptions get longer and longer and longer and longer right right you said something about randomness

00:30:28.780 there that caught my ear because i thought so if i gave you a a truly random string of digits unless

00:30:36.060 you're talking about there was some some method by which to produce it reliably let's say you know like

00:30:41.420 the decimal expansion of pi yeah that can be compressed but if it's just a truly random series of digits

00:30:48.780 that's not compressible right that's just that's absolutely right and so that that that's a very

00:30:53.580 important distinction and that is i can describe the process of generating heads and tails by describing

00:31:03.020 the dynamics of the dynamics of the coin and so that's very short right uh but if i was trying

00:31:08.780 to describe the thing i observe uh then you're saying it would be incompressible and the description would

00:31:14.220 be as long as the sequence described you in in all of these cases you're always talking about

00:31:21.420 um the underlying causal process that generates the pattern and not the pattern itself and uh and that's a

00:31:30.060 very important distinction so now i think this is the first time i've ever conducted a conversation or

00:31:36.620 interview like this which is just kind of stepping through definitions but i i think it's it's warranted

00:31:41.340 in this case so what is intelligence and and how how is it related to complexity yeah so you know 0.97

00:31:49.660 intelligence is as i say to people um one of the topics about which we have been most stupid 0.84

00:31:55.580 right it's and um and in so many ways and i we probably shouldn't get into it not least that it's 0.74

00:32:02.620 the topic about which we are least evolutionary right because all of our definitions of intelligence

00:32:07.740 are based on measurements that can only be applied to humans and by and large humans that speak english

00:32:13.100 or what have you so it's one of those areas with that's been extremely foolishly pursued um so i don't

00:32:19.820 mean an iq test okay um because the iq test is not interesting if you're trying to calculate the

00:32:26.140 intelligence of an octopus uh which i would like to know because i believe in evolution

00:32:32.780 and i think that we need to understand where these things come from and and just having a definition

00:32:37.980 that applies to one particular species doesn't help us um so what is it and we've talked about

00:32:44.460 entropy and computation and they're going to be the keys to understanding intelligence um and so

00:32:50.140 let's go back to randomness uh the examples i like to give is the rubik's cube because it's a

00:32:56.060 beautiful little uh mental model metaphor um if i gave you a cube and i asked you to solve it

00:33:03.420 and you just randomly manipulated it since it has on the order of 10 quintillion solutions which is a

00:33:11.100 very large number um you basically if you were immortal would eventually solve it um and uh but 0.99

00:33:20.940 it would take the lifetime of several universes to do so that is random performance stupid performance is

00:33:28.860 if you took one face of the cube and you just manipulated that one face and turned it rotated it forever

00:33:35.340 and as everyone knows if you did that you would never solve the cube if you weren't already at the

00:33:41.900 solution right and uh it would be an infinite process uh that would never be resolved that rule is in my 0.98

00:33:50.300 definition stupid it is significantly worse than chance now let's take someone who's learned how to 0.80

00:33:58.060 manipulate a cube and is familiar with various rules and these rules allow you from 0.99

00:34:05.180 any initial configuration to solve the cube in 20 moves or less that is intelligent behavior

00:34:13.180 so significantly better than chance and this sounds a little counterintuitive perhaps until you realize

00:34:19.500 that's how we use the word in our daily lives um you know if i sat down with an extraordinary

00:34:25.340 mathematician and i said i can't solve that equation and they say well no it's easy here this is what

00:34:31.100 you do and you look at it you say oh yes it is easy right you made that look easy that's what we

00:34:37.180 mean when we say someone is smart they make things look easy uh if on the other hand i sat down with

00:34:41.980 someone who was incapable and they just kept you know dividing by two you know for whatever reason 1.00

00:34:48.060 uh i'd say what on earth are you doing what a stupid thing to do you'll never solve the problem you 1.00

00:34:52.460 you know what a what a foolish thing to do what a inefficient thing to do right so that is what we 1.00

00:34:59.180 mean by intelligence uh it's it's the thing that we do that ensures that the problem is very efficiently 0.99

00:35:06.300 solved and done and in a way that makes it appear effortless and stupidity is a set of rules that we 0.67

00:35:15.020 use to ensure that the problem will be solved in longer than chance or never right um and and is

00:35:23.020 nevertheless uh pursued with alacrity and enthusiasm and so now we're getting closer to the the actual

00:35:29.020 substance of of the lecture you gave that i want you to recapitulate part of here because i just found

00:35:36.860 it fascinating and and and i mean you could you can recapitulate as much as you want to of it but

00:35:42.860 i'm in particular interested in the boundary line you drew between biology and culture and the way

00:35:50.540 in which culture is a machine really for increasing our intelligence and then you at some point express

00:35:58.700 some real fear that we are producing culture or or stewarding our institutional intelligence in a way

00:36:08.700 that is actually making us biologically or you know personally less intelligent perhaps to a dangerous

00:36:15.980 degree in certain circumstances so you could just get get us there at this point yeah so this um is a

00:36:22.460 little bit of a lengthy narrative i'm gonna try and compress it i'll make it as least complex as possible

00:36:29.820 um so you know most of us are brainwashed to believe that we're born with a certain innate intelligence

00:36:38.380 and we learn things uh to solve problems but our intelligence goes basically unchanged right and

00:36:48.220 so and you hear this all the time in conversations they'll say you know that person is really smart

00:36:52.460 just because they never worked very hard they didn't learn very much um whereas that person's not very

00:36:56.620 smart but they learned a great deal it makes them look smarter that sort of thing and i think that's

00:37:00.060 absolute rubbish um so i think there's a very real sense in which education and learning makes you

00:37:06.060 smarter uh so that's sort of in some sense my premise and but but just stop there for a second you you

00:37:12.380 wouldn't dispute though that there are differences in what psychologists have have come to call g so

00:37:20.220 it's a general intelligence and that this is somehow not necessarily predicated upon acquiring new

00:37:27.980 information i would i would dispute that so you think that you think the concept of iq is just useless not

00:37:35.180 just in octopi but in people more or less and uh and i should explain why and i think you know a lot of

00:37:42.780 recent research is required to understand why um i mean for let's just take an example there is this

00:37:49.900 canonical examples you know the young mozart right people will say well look wait a minute this is a

00:37:55.420 kid at the age of seven you know had absolute pitch and you know in his teens you could play him a

00:38:01.420 symphony that he could recollect note for note and reproduce on a score and etc right and surely this

00:38:10.060 is an individual who's born and what we now understand of course is that his father was a tyrant um who from

00:38:15.580 an extraordinarily young age drilled him and his sister in in acquiring perfect pitch in in in the subtleties

00:38:24.380 of musical notation and um consequently he was able to acquire very young characteristics that normally

00:38:32.860 you wouldn't acquire later because normally you wouldn't be drilled and so and in fact more and

00:38:37.980 more studies indicating that if you subject individuals to deliberative practice regimes they

00:38:45.820 can acquire skills that seem almost you know extraordinary let's take g and um the iq in general

00:38:54.780 so we now know uh that what it really seems to be measuring is working memory and uh many working

00:39:02.220 memory tasks are correlated and they live on this low dimensional space that we call g and um now one of

00:39:11.340 the classic studies was the number of numbers that you could hold in your head right in other words

00:39:15.820 i uh recite off a number of numbers and i ask you to remember them and 10 minutes later i ask you

00:39:21.900 you're not allowed to write them down uh but what you do is you replay them in your mind and you know

00:39:28.380 people could do 10 maybe they could do 11 and this was considered to be some upper limit on our short-term

00:39:35.020 memory for numbers and yet a series of experiments have now been studied where through very intelligent and

00:39:42.220 uh ingenious um means of encoding numbers we have people now who can remember up to 300 and these

00:39:50.220 are individuals by the way who at no point in their lives ever showed any particular extraordinary

00:39:56.060 memory capacity and so the evidence is on the side of plasticity not on innate aptitudes and to the extent

00:40:05.580 that iq is fundamentally measuring um working memory we now know how to start extending it so that's that's

00:40:14.460 an important point um i wouldn't deny that there are innate variations i mean i am not uh six foot five

00:40:24.540 i'm not even six foot and so i will never be a basketball player and so there are functions in the world

00:40:31.260 uh that um are responsive to variation that looks as if it's somewhat inflexible but in the world of

00:40:39.420 the brain given that it is not a computer and the wiring diagram is not fixed in the factory but

00:40:46.540 actually uh adapts to inputs uh there's much more hope that the variation is and in fact evidence that

00:40:53.500 the variation is much greater than we had thought so the plasticity and trainability can just ride atop

00:41:00.300 variation that exists that is innate so i mean you could have differences in aptitude with and without

00:41:07.340 training but that's exactly right and i think so and that's precisely true and i think the open

00:41:11.740 question for us is how much of that um if you like innate lego material is universal right whereas how

00:41:21.340 many of those pieces had already been pre-assembled into little castles and cars which we then could

00:41:26.300 build upon and i think that are some people arriving on the stage with an advantage is actually not known

00:41:34.780 and i think all i'm reporting is that the current deliberative practice data suggests that that's less

00:41:42.780 true right the than we thought it was that that's the point right well which puts the onus to an even

00:41:50.860 greater degree than than most people would expect on culture and on what you do with your time and on

00:41:58.780 parenting and all of this machinery that is outside any individual brain which is in a very material

00:42:07.660 sense augmenting its intelligence and so take us into that direction yes so that that's a very

00:42:13.180 important point so that that's why that that connection is is important to make um so okay so 0.80

00:42:18.780 now we've basically understood what intelligence is what stupidity is um we understand that we are flexible

00:42:25.180 to an extraordinary degree maybe not infinitely so um and as you point out the inputs then become 0.95

00:42:32.940 much more important than we had thought in the past and so let's now move into uh intelligent or what

00:42:41.100 or what sometimes gets called cognitive artifacts um so here's an example um your ability to do

00:42:51.100 mathematics or perform mathematical reasoning is not something you were born with you did not invent

00:42:56.380 numbers you did not invent geometry or topology or calculus or algebraic geometry or number theory or

00:43:04.780 anything else for that matter they were all given to you if you chose to study mathematics as a class

00:43:09.900 in a class and uh and what those things allow you to do is solve problems that other people cannot solve

00:43:15.900 and for all of us in our lives numbers are the you know in some sense the lowest hanging fruit in our

00:43:22.860 mathematical education and so let's look at numbers there are many number systems in the world

00:43:29.500 um they're very ancient um ancient sumerian guneiform numbers about 5 000 years old ancient egyptian numbers 0.97

00:43:38.940 and here's a good example of stupidity and culture western europe

00:43:45.100 for 1500 years used roman numbers roman numerals from about the second century um bc to about 1500 0.98

00:43:53.580 a.d towards the end of the holy roman empire and roman numbers are good at measuring magnitude the

00:44:03.660 number of objects but terrible for performing calculation so adding to what's x plus v you know what's x

00:44:11.980 x one one multiplied by one v and so on it's just doesn't work and yet for 1500 years the human brain

00:44:22.620 opted to deliberate over arithmetic operations using roman numerals that don't work and the consequence of

00:44:31.020 that is that europeans for much of their history could not divide and multiply and it's it's an 1.00

00:44:37.260 extraordinary thing because it's unbelievably stupid and it's unbelievably stupid when you realize that 0.99

00:44:43.100 in india and arabia they had a number system started in india moved to arabia uh that was available from 0.99

00:44:50.060 about the second century uh that is the one that we use today that would effortlessly be able to

00:44:55.020 multiply and divide numbers and so that's a beautiful example of the interface between culture

00:45:01.340 and our own reasoning and the reason it's so intriguing is because once i've taught you a

00:45:06.380 number system like the indian arabic number system base 10 number system you don't need

00:45:13.100 the world anymore you don't need paper anymore to write it down you can do these operations in your

00:45:19.180 mind's eye and that's what makes them so fascinating and i call that kind of object that was invented

00:45:29.020 over the course of centuries by many many minds complementary cognitive artifacts and their unique

00:45:37.500 characteristic to is not only do they augment your ability to reason in the form for example of

00:45:43.260 multiplying or dividing but when i take them away from you you have in your mind a trace

00:45:50.940 of their attributes that you can deploy and and that it's interesting that's probably what's new in

00:45:57.100 thinking about the evolution of cultural intelligence for a long time um psychologists cognitive scientists

00:46:04.380 archaeologists have understood that there are objects in the world that allow us to do things

00:46:10.620 we couldn't do otherwise right i mean a fork right or a scythe right or a wheel you know it's been

00:46:18.940 understood but there is a special kind of object in the world that not only does what the wheel and the

00:46:24.620 scythe and the fork does but it also changes the wiring of your brain so that you can build in your

00:46:31.100 brain a virtual fork or a virtual side or a virtual wheel of course not those but and and that is i would

00:46:37.900 claim by the way the unique characteristic of human evolution wouldn't you put language itself into

00:46:43.900 this category absolutely i would absolutely i would the reason i separate them by the way is that many

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Making Sense - Sam Harris - July 12, 2016

#40 — Complexity & Stupidity

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