February 3, 2025 • 27 mins
For a transcript of this episode, click here (https://sites.mit.edu/compass/files/2025/06/Truth-and-Knowledge-Transcript-1.pdf).
This episode explores the question “What are truth and knowledge?” with MIT professors from philosophy, history, mechanical engineering, and brain and cognitive sciences.
Featuring: Alex Byrne, Professor of Philosophy (host); Jim DiCarlo, Professor of Brain and Cognitive Sciences; Anette (Peko) Hosoi, Professor of Mechanical Engineering; and Anne McCants, Professor of History.
This podcast was created as part of the MIT Compass Initiative, "21.01: Love, Death, and Taxes.” For more information about Compass, check out compass.mit.edu.
This podcast was recorded at the MIT AV Studios and produced by Adina Karp.
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Episode Transcript
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Intro Speaker 1 (00:03):
Welcome to MIT Compass
about Being Human, a show aboutexploring fundamental questions,
Professor Emily Richmond P (00:09):
What do we value and why?
Intro Speaker 2 (00:10):
What do we know and how do we know it?
Professor Alex Byrne (00:13):
What do we owe to each other?
Intro Speaker 3 (00:15):
by MIT professors from across the
Humanities, Arts, and SocialSciences, each episode takes on
the moral and ethical questionsof the human experience.
Professor Alex Byrne (00:26):
This week, we'll be talking about truth and
knowledge. I'm Alex Byrne fromthe Department of Linguistics
and Philosophy. With me arethree other MIT faculty members,
first Professor Anne McCants.
Professor Anne McCants (00:37):
I'm Anne McCants, and I'm an economic
historian of the late MiddleAges and early modern Europe.
And I also direct a program hereat MIT called Concourse. It's
for first year students, and ourgoal is to embed the science
(00:59):
General Institute Requirementsin a context of social and
humanistic study.
Professor Alex Byrne (01:06):
Thanks, Anne. Next is Professor Peko
Professor Peko Hosoi (01:09):
Hi. I'm Peko Hosoi in Mechanical Hosoi.
Engineering and Mathematics. Ialso have an appointment in
IDSS, which is the Institute forData, Systems, and Society. My
research focuses on fluidmechanics. I've worked on soft
robotics, and most recently,I've gotten interested in
biomechanics and sports.
Professor Alex Byrne (01:26):
Thanks, Peko and last, Professor Jim
DiCarlo.
Professor Jim DiCarlo (01:29):
Thanks, Alex. I am a professor in the
Department of Brain andCognitive Sciences. My lab works
on vision in non-human primatesand building models of visual
processing that apply to bothmonkeys and humans. And I also
am the director of the MIT Questfor Intelligence, which aims to
discover an engineered levelunderstanding of human
intelligence with impacts onhuman health and in future
(01:51):
artificial intelligence.
Professor Alex Byrne (01:53):
Thanks Jim, and thanks again to all
three of you for making the timeto do this. So we're going to
have a chat about truth andknowledge, and let's start with
truth. So the MIT ValueStatement mentions the pursuit
of truth, or perhaps the questfor truth would have done
equally well as something worthvaluing. So I thought I'd begin
(02:16):
with a deceptively simple andprobably unfair question: what
is truth? Perhaps there's noeasy answer, but if you had to
answer in a sentence or two,what would you say? Anne, do you
want to have a stab?
Professor Anne McCants (02:32):
Sure, Alex. I think that this question
is so fundamental preciselybecause it cannot be answered
once and for all and for allpeople from all perspectives.
But if you come at it from,let's say, a theological
(02:54):
perspective, I think it has, Imean, historically, it has been
answered from that perspectiveas being the transcendental
reality, if you will, that isout there, that is beyond us. So
that's one way that many peoplehave thought about what truth
(03:14):
is, something outside ofourselves that really exists,
even if our ability to access itis limited.
Professor Peko Hosoi (03:25):
Yeah, I'll build on what what Anne said. So
I like this, her framing ofsomething that exists outside of
our ourselves. I think, youknow, putting my engineering hat
on, I think table stakes is thatit has to be something that is
supported by empirical evidence.
And you know, like Anne said,that's not something that is an
absolute, because the way weinterpret empirical evidence
(03:47):
shifts as we learn more or astime progresses. But I like this
idea of something that is insome way absolute, beyond our
perceptions.
Professor Alex Byrne (04:00):
Excellent, Jim?
Professor Jim DiCarlo (04:01):
I agree completely with both with Anne
and Peko about being outside ofus. I would think of it as
something that's in the limitthat we will never actually
achieve, but it's an aspiration,and so I think we'd shift the
conversation again, maybethinking also as engineer from
truth towards utility, and howwe seek truth to actually gain
(04:22):
utility and but truth is theaspiration which we never
actually achieve.
Professor Alex Byrne (04:26):
Okay, excellent, right? So there's a
there is a sort of common themethat that truth is an ideal or
an aspiration, and perhaps we'llwe'll never reach it. You know,
you can pursue a rabbit butnever catch it. And similarly,
the idea seems to be, you canindeed, we should pursue truth,
but that doesn't mean we'll everfind it. But what do you think
(04:48):
of the sort of flat footedresponse that surely we can all
agree that it's sunny today, butthen assuming you agree with
that, you wouldn't disagree withthe addition that it's true that
it's sunny today. I mean, ifAnne said it's sunny today, and
I said, you know, Anne, that'strue. It's not that you Peko and
(05:09):
you Jim, would be saying, "Oh,well, that's a terrible mistake.
Because, you know, yeah, Anne'sassertion was fine, but when
Alex followed it up with that'strue, that was really
overreaching, because truth isthis aspirational thing that we
can only aim to but neveractually reach."
Professor Anne McCants (05:26):
Alex, I I think you're pushing our
linguistic strings a little bithere. The the form in which you
use true, right? The casualconversational "Anne made an
observation. Alex said, 'Yeah, Ithink that's true.' " What
you're really saying is "that'show I see the situation as well.
(05:47):
I look up in the sky, I don'tsee clouds. The sun is out. It's
a sunny day." And I think thatthat kind of true, and the truth
you asked us about at the outsetare, they are semantically
related, but they are not thesame thing.
Professor Alex Byrne (06:05):
So there's kind of truth with it, with a
little T and truth with a with acapital T. And
Professor Anne McCants (06:11):
That's more or less how I operate.
Professor Peko Hosoi (06:13):
Yeah. I mean, so if we think if we were
2000 years ago and somebody wentoutside and said, hey, the sun
goes around the Earth. Somebodyelse would say, yeah, that seems
true. Yeah, that's what it lookslike to me, right? So there
isn't a, there is sort of anacknowledged agreement between
those two people, but it's, it'snot related to what actually
(06:34):
happens between the Sun and theEarth,
Professor Alex Byrne (06:36):
Right. Or I was going to say "that's,
that's true," but maybe Ishouldn't. But of course, in
your envisaged example, thefirst person was was incorrect,
saying that the sun goes aroundthe earth. And so because of
that, when the second personsaid, that's true, that was,
(06:56):
that was just another mistake,
Professor Peko Hosoi (06:58):
Correct, yeah. Actually, I want to, I
want to second what Jim saidearly about the utilitarian
value of getting close to truth.
There's the famous quote thatall models are wrong, but some
are useful, right? And so Ithink when we make those models
we are we are trying to uncoversomething fundamental and
something that gives uspredictive power, and sort of
(07:19):
the more accurate, or the morepredictive those models are, so
they enable us to become betterdecision makers. I think that's
sort of the direction of andputting air quotes now "truth"
in engineering.
Professor Jim DiCarlo (07:34):
That's exactly right. That's the George
Box quote, and as you say, youapproximate it. The models lead
to predictive power. The modelstry to embody our current
statement of truth, but themodels also accept that they're
not yet true. If we take themseriously. And to push on your
point about who's wrong aboutthe earth being we actually
don't know. Right now, theevidence suggest, if you take it
(07:55):
at its limit, right right now,the evidence suggests that the
planets are rotating around thesun, but if you really push the
limit, we don't really can'tprove that right? So again, that
sounds odd, but this is reallypushing against the notion of
you never actually get toabsolute truth, but, but it's
more utilitarian to accept thesecond view for the moment, it
(08:16):
makes better predictions. Sowe're going to go with that for
now.
Professor Alex Byrne (08:19):
Okay, well, shall we segue to to
knowledge. We've been talkingabout truth and reaching the
truth or finding something out,discovering that the butler
committed the murder, seems tobe coming to know that the
butler did it. So justbracketing Jim's skepticism
about whether we can actuallyknow anything for the moment,
(08:40):
let's begin with the MIT missionstatement. Perhaps, yeah, should
be read as aspirational ratherthan as a claim about what MIT
actually does. The mission ofMIT is to advance knowledge and
educate students in science,technology and other areas of
(09:00):
scholarship that will best servethe nation and the world in the
21st Century. So the mission isadvancing knowledge and also
preserving and disseminatingknowledge. And so Jim, I think I
mean consistent with your viewabout about knowledge being
aspirational, you could stillsay that, well, we, we are a
(09:23):
massive improvement overAristotle, as far as getting
close to the truth goes, becauseour theories are much closer to
the truth than Aristotle's. Orjust putting it more, just
putting it entirelypragmatically, our theories are
much more useful thanAristotle's. They enable us to
do things that Aristotle'stheories didn't
Professor Jim DiCarlo (09:46):
I agree and I liked how you put the
word--again, it's not more wethey're more predictive, they're
more useful.
Professor Alex Byrne (09:51):
Yeah, yeah, right.
Professor Jim DiCarlo (09:52):
That doesn't make them right. It also
doesn't make the other oneswrong. Back to Anne's point,
they were just less predictiveand less useful, right? So I
think getting away from theseblack and white notions of right
and wrong is part of thechallenge here, for for students
and for scientists, right, likeyou discovered oxygen. No, we
have a better model thatincludes this thing that we call
(10:12):
oxygen at the moment. Thatsounds odd, but that's actually
at the core what's going on,right? And it's just, we're
taught, of course there'soxygen. Well, you know, for a
while, of course there wasphlogiston, right? That doesn't
make these--you can't accept thepermanence of all of that, and
it's that makes life feelunstable a little bit. But if
you just think of it, this isour current belief system, and
here's how well it can do. Andit will evolve in ways that we
(10:34):
can't yet predict. We'll workwithin that for a while, until
somehow something doesn't workright. Then somebody smart will
see, oh, maybe if we think aboutit this way, we'll shift, and
that will require people andcommunities, as you say, to make
that happen. But it's sort ofthis notion that we don't MIT
students shouldn't show up andsay, everybody that professors
(11:00):
know at MIT, here's all thetruths, and they're going to
teach them to us, and then we'llgo forth and we'll know it. No,
it's the process of discoveringtruth. And how do we
approximate? How do we even knowwe're getting closer? What do we
even choose to measure? Is thissort of social part of that as
well,
Professor Alex Byrne (11:06):
Right, so really you want to, I mean,
maybe it's not completely clearwhat this notion of getting
closer to the truth amounts to,but what, I think, what you
fundamentally, fundamentallywant to say, is that our theory
is just much more predictive andmuch more useful. And that's
what that's what scientificprogress consists in, not in
(11:27):
discovering the existence ofoxygen, or realizing that
there's no such thing asphlogiston,
Professor Jim DiCarlo (11:33):
Building a model that includes one but
not the other has then incontext with other assumptions,
can lead to more predictivepower. That's how I would put
it. I don't think those are justa linguistic difference, I
think.
Professor Alex Byrne (11:45):
Right. Can I just ask you, just to go back
to the claim about about models?
Sorry, what was the famous quotethat models are always wrong?
Professor Peko Hosoi (11:54):
All models are wrong, but some are useful.
Professor Alex Byrne (11:56):
Okay, so someone might say, "Okay, what
if by model you meanmathematical model?" So we could
have a mathematical model of thesolar system, let's say or
mathematical model of a neuron.
Or...
Professor Jim DiCarlo (12:10):
It doesn't have to be mathematical.
Professor Alex Byrne (12:11):
No, it doesn't have to be mathematical.
But just for simplicity, justtake a mathematical model as an
example. So we have amathematical model of the solar
system, and it makes certainsimplifying assumptions to make
the computations tractable.
Maybe it treats the planets aspoint masses or something or
something like that. So it'sit's highly predictive. It
(12:33):
enables you to predict whereNeptune is going to be next
year. But you know, it's not,it's not perfectly accurate. If
we run the model out, likebillions of years or something,
it's going to get somethingwrong. But for the purposes of
flying by Neptune or getting toMars or something, it's it's
good enough. So in that sense,that model is is wrong, and we
(12:54):
understand why it's wrong, it'sjust like almost every
mathematical model, it's just asimplification of the real
situation. But that doesn't meanthat we don't know anything
about the actual situation thatwe are imperfectly modeling. We
still know that there areplanets and that they go around
(13:15):
the sun, and that there's thismassive object that we call
Neptune and so on. So therecould be some kind of mistake in
focusing so much on the on theimperfections of models, because
perhaps that can sort of blindyou to the fact that, well,
actually, we really are justflatly right about some things,
(13:36):
like, you know, the approximatesize of Jupiter, or the cause of
the Great Red Spot, or somethinglike that.
Professor Peko Hosoi (13:46):
Well, I don't think the I think the
argument which Jim is making,which I resonate with, and Jim
tell me if I'm wrong, it's not.
It's not a criticism to say allmodels are wrong. It's an
acknowledgement that part ofmodeling is to understand where
we made those assumptions, andare they good enough for the
predictions that we need tomake, right? So, so, you know,
saying that all models are wrongis perfectly fine. That's That's
(14:09):
not saying science orengineering is bad. They're,
they are. That is our frameworkthat we need to make progress
right now, and we need tounderstand the limits of those
framework, of that framework.
Professor Jim DiCarlo (14:22):
Right, I agree.
Professor Peko Hosoi (14:22):
Related to that. I wanted to go back. Jim
also briefly said that part ofthis is getting comfortable with
uncertainty, right? And I thinkone of the things, especially
when I think about MIT students,these are students who have come
in, who have been terrific inphysics, who've been terrific in
math in high school, where thereare absolute answers, right? You
(14:42):
have a problem set. There's ananswer that people are looking
for. And then you get tocollege, and you know, you
realize, oh, I've learned Fequals ma. And then you learn,
but wait a minute, actually, itdepends on your reference frame.
And you learn about relativity.
And then you read, Oh, but waita minute. Actually, you
shouldn't be thinking aboutthings as particles. I should be
thinking about them asprobability distributions,
because it's because now I'velearned quantum and so we keep
as you go through, as youadvance, you're actually
(15:04):
starting to dismantle some ofthe things that you took to be
absolute truth, even in science,and realizing that there are
subtleties that have not thatthat have not been accounted for
through in the early parts ofthat journey.
Professor Jim DiCarlo (15:19):
Well, I completely agree with that and
getting comfortable withuncertainty.
Professor Alex Byrne (15:22):
So, well, just just touching on that
uncertainty point. I mean, howdoes that manifest in the in the
classroom, if at all? Because,after all, Peko, as you were
saying, I mean, this is a bigdifference between most of the
classes that students at MITtake and and their philosophy
classes. For example, sometimesI have actually given Psets in
(15:45):
in philosophy classes, but thequestions are all about, you
know, the reading or what somephilosopher thought, or whether
some argument is valid. Those doseem to have clear answers, but
they're not about, like, grandphilosophical questions, where
there's where there's a lot ofof disagreement. But of course,
in most of the sciences, you'vegot your Psets and there's,
(16:09):
there's either a right or wronganswer. Grading them is, is
totally straightforward. So whatdo you do, or what, or what
could you do to make studentsaware that there's really a
great deal of uncertainty in thescientific enterprise as a
whole.
Professor Peko Hosoi (16:27):
I think there are a bunch of tools that
we the students need to developbefore they can start to apply
them to these sort of biggerstories, right? So like before
you can build a house, you needto know how to use a hammer and
a screwdriver and a wrench andall those kinds of things. And
so there are tools that we areequipping them with, like, how
do you solve a lineardifferential equation? Is this
thing going to be stable orunstable, right? And those are
(16:48):
things where there's a logicalprogression. And so once you
frame things, put things inthese frameworks, you can get a
sense of how you think, how youthink these systems are going to
evolve. So, but you have to begood at the tools. And the tools
are things where you can justwhere you can assign a problem
set, practice this a bunch oftimes, and you will now have the
tool. As you move through yourcurriculum in I think, in most
(17:12):
departments, things become moreand more open ended. So for
example, right now, I'm teachingthe capstone in mechanical
engineering, and this is aproduct development class, and
one of the things they'rethinking about now is
feasibility studies. So if Ihave a product that I I might
want to design, and there's noright or wrong answer as to
whether or not you should designthis product, but you should
check, okay, will this, can Irun this on a battery, or do I
(17:34):
need to build a nuclear powerstation? Right? And so you can
start to apply those tools tounderstand to kind of scope the
problems that you're thinkingabout. And I think, and again,
there's no right answer to that,but there's a, there's sort of a
skill in how you apply thosetools that you've been
developing early on.
Professor Alex Byrne (17:55):
Right, so just, I suppose, in a way,
keeping the focus on theclassroom. So sometimes people
talk about the scientificmethod, and laud the scientific
method as a particularlyreliable way of finding things
out. And when I was anundergraduate, I studied physics
as an undergraduate, and I don'tremember having a class on on
(18:19):
the scientific method. No oneever taught me what it was. So
do you think that there is sucha thing as the scientific
method? And if so, what? What isit? And do do historians, for
example, we have a historianhere, employ the scientific
method?
Professor Anne McCants (18:37):
So, so I had reason actually, for the
Compass course, to go back and,you know, sort of look it up
officially, right? The, youknow, the five steps, although
it's six now, because they'veadded persuasion and
communication to the end, whichis, which is a good thing,
right? But the first step was,you know, to, sort of, to
collect all your observations. Idon't remember the exact words,
(19:00):
and it really presumes a zerostep, if you will, that's not
listed there. And that is thatthere are somehow unfiltered
observations. And we've already,I think, agreed between the
between us, that that doesn'texist in the world. And so
that's a real problem for thescientific method, right, as a,
(19:24):
as a, as a canned box, you know,if it, if it relies on, you
know, a prior step that can't bedone, then it's a shaky
foundation. You know, that saidit's tremendously useful, I
think, when students arelearning tools, and maybe
(19:46):
younger than college students,right to, you know, just sort of
have a little list that they canfollow, right? So I don't want
to bash the scientific method,even though I don't believe in
it.
Professor Peko Hosoi (19:59):
I mean, it's one framework, but it's not
the only framework that youwould apply to advance
knowledge. I would say, I'mgoing to now circle back, Alex,
to your because you originallycame to the work in the MIT
mission statement, which is toadvance knowledge. I mean, it's
implicit in that, that we're notthat there's no end point here,
like to Jim's point, that toadvance knowledge, that means
(20:20):
there's more things to uncover,right? There's we're always
peeling back more layers whichimprove our understanding or
improve our predictive power ofthe world.
Professor Alex Byrne (20:30):
So there are different ways of knowing.
Professor Peko Hosoi (20:33):
Yeah, yeah.
Professor Alex Byrne (20:34):
Right, right, right. Excellent. Okay,
so, so the mission statementdoes presuppose that all of us
at MIT are really in the samebusiness, at least at a certain
high level of abstraction. We'reall in the truth knowledge
business. We're all pursuing, weshould be pursuing the truth or
advancing knowledge in ourdifferent disciplines
Professor Anne McCants (20:58):
And to different ends.
Professor Alex Byrne (20:59):
Yeah.
Professor Jim DiCarlo (21:01):
Yeah.
Professor Alex Byrne (21:02):
Not totally sure what the takeaway
is.
Professor Peko Hosoi (21:04):
Good luck.
Alex, yeah.
Professor Alex Byrne (21:07):
So it's hard to sum up this very wide
ranging discussion. So instead,I'll end with a completely
different, different question,just to humanize the three of
you, if you're not humanizedenough already. So. So the
question is, what artwork,whether that's a book or a piece
(21:30):
of visual art or a musicalcomposition or a poem or
whatever was your favorite orthe thing that influenced you
most in in college.
Professor Peko Hosoi (21:41):
This is going to be a somewhat long
answer. So the first when I,when I, when I got this
question, the first thing Irealized is that college was a
really long time ago. Prettyhard to figure out what I was
doing in college. The second,once I started thinking about
it, is that this is actually avery personal question, and so,
(22:02):
so I will, if you answerhonestly, you will learn a lot
about about us. So I will answerhonestly. I'm going to give you
a few things. The first is whenI was in college, that was when
this amazing new technology cameout called the musical CD. And
so I had, I I got very excitedabout buying CDs. And it was
also around the time that sortof like, like the alternative
(22:25):
rock seed came out. So I thinkthat, I think the CDs I listened
to most were the was probablyJane's Addiction, if I'm gonna
be honest, and Peter Gabriel, Ihave the whole Peter Gabriel
collection. Then I was thinking,Okay, what about books? Because
I also, I also did a lot ofreading, and there's, there's
two that I remember from thattime. One is a book called "The
(22:48):
River Why" by David Duncan. Andthis was, I'm from Oregon, so
this was recommended to me by myfriends in the Pacific
Northwest. And again, I thinkit, it sort of fits with this
podcast, because it is about,it's a coming of age and and how
to make choices about leading agood life. And the other one
that I remember from college was"A Canticle for Liebowitz" by
(23:11):
Herman Miller, which I randomlywalked into a used bookstore and
just picked it off the shelf,and it blew my mind. So those
are the ones I'll contribute.
Professor Anne McCants (23:21):
I'll start by saying that I grew up
in a evangelical Christianfamily milieu with very
thoughtful parents who were inthe process of asking more
(23:42):
complicated questions than thefamilies that they had come out
of. And when I got to college, Istarted reading theology, sort
of as a side project, and Iactually was having trouble,
sort of thinking of any oneparticular influence, but it
opened up a whole new world forme, in part because I discovered
(24:07):
that people's religious beliefshad these deep histories that
were complicated, right and andI think to some extent, Both my
parents are scientists, as arethree of my four grandparents,
and so, you know, I'm the "whichone of these is not like the
(24:28):
other" in my family. And I thinkin part, I became a historian
because that was a way ofthinking about theology without
having to become a theologian.
And I think my interest in thekinds of questions we've been
talking about today came, youknow, came out of that.
Professor Jim DiCarlo (24:53):
Yeah, so that, if I may, the personal
side of this for me is I wasraised like my parents were both
scientists, also, my dad was aphysicist. And they raised me in
a Catholic family, and Iremember hearing sermons about
how evolution was impossible,and I was revolting at the time
about going to college. And mybook that I remember in college
was the was Richard Dawkins'"Blind Watchmaker," which sort
(25:15):
of explains how complexity couldarise without a creator, right?
And it's very convincing, ifyou're into reading about
evolution and how biologiststhink about that. And so that in
reading that, in parallel with,you know, looking at my other
book I remember in college, isjust this huge textbook, tome of
the, you know, principles ofneuroscience, which is
(25:36):
essentially should be called thecollection of facts about the
brain, which beautiful drawingsof anatomy, and, like, look at
all these complicated wiringsystems, so juxtaposing that
those tomes of, well, here's allthese wires. We don't actually
know what they're doing withthese kind of, hey, some process
has produced this that was, youknow, by selection. And there's
an opportunity here that at somepoint we'll figure out this, who
(25:58):
we are as a machine, and andunderstand what all those wires
are doing, and that that sort ofinspired me to to move on to
sort of do this that I do now.
Professor Alex Byrne (26:08):
I also second what Peko said about
Peter Gabriel, early Genesis.
You can't beat it. Yeah. Ofcourse, no one listening to this
podcast will have any idea whatwe're talking about, none
whatsoever.
Professor Peko Hosoi (26:20):
None.
Zero. Yeah, exactly, yeah.
Professor Anne McCants (26:22):
I was busy listening to Bach and
Palestrina in college,
Professor Alex Byrne (26:25):
Okay, okay.
Professor Anne McCants (26:28):
And I sadly haven't changed at all.
Professor Alex Byrne (26:31):
No. Well, there is, you know, there is
that sort of imprinting effect.
There's Peter Gabriel. And I doremember reading Bertrand
Russell's little introduction tophilosophy called--which still
can't be beat. It's flawed in somany ways, but, but it's a,
certainly a gripping read, Ithink, to an undergraduate
(26:51):
interested in the big questions.
And that's his little bookcalled "The Problems of of
Philosophy" 1912, I think. Thankyou so much. And Peko, Jim, I
really enjoyed the conversation,and I really appreciate you
taking the time.
Professor Peko Hosoi (27:10):
Thanks, Alex.
Professor Anne McCants (27:10):
Thank you.
Professor Jim DiCarlo (27:10):
Thank you, Alex for having us.
Intro Speaker 3 (27:12):
Thanks for listening to MIT Compass:
Thinking and Talking about BeingHuman.
Intro Speaker 1 (27:19):
We hope you'll check out other episodes at
compass.mit.edu
Intro Speaker 2 (27:23):
This podcast was created as part of the MIT
Compass (27:25):
Love, Death and Taxes."
Intro Speaker 1 (27:30):
This podcast was recorded at the MIT AV
studios
Intro Speaker 3 (27:33):
and produced by Adina Karp.
Welcome to MIT Compass
about Being Human, a show aboutexploring fundamental questions,
Professor Emily Richmond P (00:09):
What do we value and why?
Intro Speaker 2 (00:10):
What do we know and how do we know it?
Professor Alex Byrne (00:13):
What do we owe to each other?
Intro Speaker 3 (00:15):
by MIT professors from across the
Humanities, Arts, and SocialSciences, each episode takes on
the moral and ethical questionsof the human experience.
Professor Alex Byrne (00:26):
This week, we'll be talking about truth and
knowledge. I'm Alex Byrne fromthe Department of Linguistics
and Philosophy. With me arethree other MIT faculty members,
first Professor Anne McCants.
Professor Anne McCants (00:37):
I'm Anne McCants, and I'm an economic
historian of the late MiddleAges and early modern Europe.
And I also direct a program hereat MIT called Concourse. It's
for first year students, and ourgoal is to embed the science
(00:59):
General Institute Requirementsin a context of social and
humanistic study.
Professor Alex Byrne (01:06):
Thanks, Anne. Next is Professor Peko
Professor Peko Hosoi (01:09):
Hi. I'm Peko Hosoi in Mechanical Hosoi.
Engineering and Mathematics. Ialso have an appointment in
IDSS, which is the Institute forData, Systems, and Society. My
research focuses on fluidmechanics. I've worked on soft
robotics, and most recently,I've gotten interested in
biomechanics and sports.
Professor Alex Byrne (01:26):
Thanks, Peko and last, Professor Jim
DiCarlo.
Professor Jim DiCarlo (01:29):
Thanks, Alex. I am a professor in the
Department of Brain andCognitive Sciences. My lab works
on vision in non-human primatesand building models of visual
processing that apply to bothmonkeys and humans. And I also
am the director of the MIT Questfor Intelligence, which aims to
discover an engineered levelunderstanding of human
intelligence with impacts onhuman health and in future
(01:51):
artificial intelligence.
Professor Alex Byrne (01:53):
Thanks Jim, and thanks again to all
three of you for making the timeto do this. So we're going to
have a chat about truth andknowledge, and let's start with
truth. So the MIT ValueStatement mentions the pursuit
of truth, or perhaps the questfor truth would have done
equally well as something worthvaluing. So I thought I'd begin
(02:16):
with a deceptively simple andprobably unfair question: what
is truth? Perhaps there's noeasy answer, but if you had to
answer in a sentence or two,what would you say? Anne, do you
want to have a stab?
Professor Anne McCants (02:32):
Sure, Alex. I think that this question
is so fundamental preciselybecause it cannot be answered
once and for all and for allpeople from all perspectives.
But if you come at it from,let's say, a theological
(02:54):
perspective, I think it has, Imean, historically, it has been
answered from that perspectiveas being the transcendental
reality, if you will, that isout there, that is beyond us. So
that's one way that many peoplehave thought about what truth
(03:14):
is, something outside ofourselves that really exists,
even if our ability to access itis limited.
Professor Peko Hosoi (03:25):
Yeah, I'll build on what what Anne said. So
I like this, her framing ofsomething that exists outside of
our ourselves. I think, youknow, putting my engineering hat
on, I think table stakes is thatit has to be something that is
supported by empirical evidence.
And you know, like Anne said,that's not something that is an
absolute, because the way weinterpret empirical evidence
(03:47):
shifts as we learn more or astime progresses. But I like this
idea of something that is insome way absolute, beyond our
perceptions.
Professor Alex Byrne (04:00):
Excellent, Jim?
Professor Jim DiCarlo (04:01):
I agree completely with both with Anne
and Peko about being outside ofus. I would think of it as
something that's in the limitthat we will never actually
achieve, but it's an aspiration,and so I think we'd shift the
conversation again, maybethinking also as engineer from
truth towards utility, and howwe seek truth to actually gain
(04:22):
utility and but truth is theaspiration which we never
actually achieve.
Professor Alex Byrne (04:26):
Okay, excellent, right? So there's a
there is a sort of common themethat that truth is an ideal or
an aspiration, and perhaps we'llwe'll never reach it. You know,
you can pursue a rabbit butnever catch it. And similarly,
the idea seems to be, you canindeed, we should pursue truth,
but that doesn't mean we'll everfind it. But what do you think
(04:48):
of the sort of flat footedresponse that surely we can all
agree that it's sunny today, butthen assuming you agree with
that, you wouldn't disagree withthe addition that it's true that
it's sunny today. I mean, ifAnne said it's sunny today, and
I said, you know, Anne, that'strue. It's not that you Peko and
(05:09):
you Jim, would be saying, "Oh,well, that's a terrible mistake.
Because, you know, yeah, Anne'sassertion was fine, but when
Alex followed it up with that'strue, that was really
overreaching, because truth isthis aspirational thing that we
can only aim to but neveractually reach."
Professor Anne McCants (05:26):
Alex, I I think you're pushing our
linguistic strings a little bithere. The the form in which you
use true, right? The casualconversational "Anne made an
observation. Alex said, 'Yeah, Ithink that's true.' " What
you're really saying is "that'show I see the situation as well.
(05:47):
I look up in the sky, I don'tsee clouds. The sun is out. It's
a sunny day." And I think thatthat kind of true, and the truth
you asked us about at the outsetare, they are semantically
related, but they are not thesame thing.
Professor Alex Byrne (06:05):
So there's kind of truth with it, with a
little T and truth with a with acapital T. And
Professor Anne McCants (06:11):
That's more or less how I operate.
Professor Peko Hosoi (06:13):
Yeah. I mean, so if we think if we were
2000 years ago and somebody wentoutside and said, hey, the sun
goes around the Earth. Somebodyelse would say, yeah, that seems
true. Yeah, that's what it lookslike to me, right? So there
isn't a, there is sort of anacknowledged agreement between
those two people, but it's, it'snot related to what actually
(06:34):
happens between the Sun and theEarth,
Professor Alex Byrne (06:36):
Right. Or I was going to say "that's,
that's true," but maybe Ishouldn't. But of course, in
your envisaged example, thefirst person was was incorrect,
saying that the sun goes aroundthe earth. And so because of
that, when the second personsaid, that's true, that was,
(06:56):
that was just another mistake,
Professor Peko Hosoi (06:58):
Correct, yeah. Actually, I want to, I
want to second what Jim saidearly about the utilitarian
value of getting close to truth.
There's the famous quote thatall models are wrong, but some
are useful, right? And so Ithink when we make those models
we are we are trying to uncoversomething fundamental and
something that gives uspredictive power, and sort of
(07:19):
the more accurate, or the morepredictive those models are, so
they enable us to become betterdecision makers. I think that's
sort of the direction of andputting air quotes now "truth"
in engineering.
Professor Jim DiCarlo (07:34):
That's exactly right. That's the George
Box quote, and as you say, youapproximate it. The models lead
to predictive power. The modelstry to embody our current
statement of truth, but themodels also accept that they're
not yet true. If we take themseriously. And to push on your
point about who's wrong aboutthe earth being we actually
don't know. Right now, theevidence suggest, if you take it
(07:55):
at its limit, right right now,the evidence suggests that the
planets are rotating around thesun, but if you really push the
limit, we don't really can'tprove that right? So again, that
sounds odd, but this is reallypushing against the notion of
you never actually get toabsolute truth, but, but it's
more utilitarian to accept thesecond view for the moment, it
(08:16):
makes better predictions. Sowe're going to go with that for
now.
Professor Alex Byrne (08:19):
Okay, well, shall we segue to to
knowledge. We've been talkingabout truth and reaching the
truth or finding something out,discovering that the butler
committed the murder, seems tobe coming to know that the
butler did it. So justbracketing Jim's skepticism
about whether we can actuallyknow anything for the moment,
(08:40):
let's begin with the MIT missionstatement. Perhaps, yeah, should
be read as aspirational ratherthan as a claim about what MIT
actually does. The mission ofMIT is to advance knowledge and
educate students in science,technology and other areas of
(09:00):
scholarship that will best servethe nation and the world in the
21st Century. So the mission isadvancing knowledge and also
preserving and disseminatingknowledge. And so Jim, I think I
mean consistent with your viewabout about knowledge being
aspirational, you could stillsay that, well, we, we are a
(09:23):
massive improvement overAristotle, as far as getting
close to the truth goes, becauseour theories are much closer to
the truth than Aristotle's. Orjust putting it more, just
putting it entirelypragmatically, our theories are
much more useful thanAristotle's. They enable us to
do things that Aristotle'stheories didn't
Professor Jim DiCarlo (09:46):
I agree and I liked how you put the
word--again, it's not more wethey're more predictive, they're
more useful.
Professor Alex Byrne (09:51):
Yeah, yeah, right.
Professor Jim DiCarlo (09:52):
That doesn't make them right. It also
doesn't make the other oneswrong. Back to Anne's point,
they were just less predictiveand less useful, right? So I
think getting away from theseblack and white notions of right
and wrong is part of thechallenge here, for for students
and for scientists, right, likeyou discovered oxygen. No, we
have a better model thatincludes this thing that we call
(10:12):
oxygen at the moment. Thatsounds odd, but that's actually
at the core what's going on,right? And it's just, we're
taught, of course there'soxygen. Well, you know, for a
while, of course there wasphlogiston, right? That doesn't
make these--you can't accept thepermanence of all of that, and
it's that makes life feelunstable a little bit. But if
you just think of it, this isour current belief system, and
here's how well it can do. Andit will evolve in ways that we
(10:34):
can't yet predict. We'll workwithin that for a while, until
somehow something doesn't workright. Then somebody smart will
see, oh, maybe if we think aboutit this way, we'll shift, and
that will require people andcommunities, as you say, to make
that happen. But it's sort ofthis notion that we don't MIT
students shouldn't show up andsay, everybody that professors
(11:00):
know at MIT, here's all thetruths, and they're going to
teach them to us, and then we'llgo forth and we'll know it. No,
it's the process of discoveringtruth. And how do we
approximate? How do we even knowwe're getting closer? What do we
even choose to measure? Is thissort of social part of that as
well,
Professor Alex Byrne (11:06):
Right, so really you want to, I mean,
maybe it's not completely clearwhat this notion of getting
closer to the truth amounts to,but what, I think, what you
fundamentally, fundamentallywant to say, is that our theory
is just much more predictive andmuch more useful. And that's
what that's what scientificprogress consists in, not in
(11:27):
discovering the existence ofoxygen, or realizing that
there's no such thing asphlogiston,
Professor Jim DiCarlo (11:33):
Building a model that includes one but
not the other has then incontext with other assumptions,
can lead to more predictivepower. That's how I would put
it. I don't think those are justa linguistic difference, I
think.
Professor Alex Byrne (11:45):
Right. Can I just ask you, just to go back
to the claim about about models?
Sorry, what was the famous quotethat models are always wrong?
Professor Peko Hosoi (11:54):
All models are wrong, but some are useful.
Professor Alex Byrne (11:56):
Okay, so someone might say, "Okay, what
if by model you meanmathematical model?" So we could
have a mathematical model of thesolar system, let's say or
mathematical model of a neuron.
Or...
Professor Jim DiCarlo (12:10):
It doesn't have to be mathematical.
Professor Alex Byrne (12:11):
No, it doesn't have to be mathematical.
But just for simplicity, justtake a mathematical model as an
example. So we have amathematical model of the solar
system, and it makes certainsimplifying assumptions to make
the computations tractable.
Maybe it treats the planets aspoint masses or something or
something like that. So it'sit's highly predictive. It
(12:33):
enables you to predict whereNeptune is going to be next
year. But you know, it's not,it's not perfectly accurate. If
we run the model out, likebillions of years or something,
it's going to get somethingwrong. But for the purposes of
flying by Neptune or getting toMars or something, it's it's
good enough. So in that sense,that model is is wrong, and we
(12:54):
understand why it's wrong, it'sjust like almost every
mathematical model, it's just asimplification of the real
situation. But that doesn't meanthat we don't know anything
about the actual situation thatwe are imperfectly modeling. We
still know that there areplanets and that they go around
(13:15):
the sun, and that there's thismassive object that we call
Neptune and so on. So therecould be some kind of mistake in
focusing so much on the on theimperfections of models, because
perhaps that can sort of blindyou to the fact that, well,
actually, we really are justflatly right about some things,
(13:36):
like, you know, the approximatesize of Jupiter, or the cause of
the Great Red Spot, or somethinglike that.
Professor Peko Hosoi (13:46):
Well, I don't think the I think the
argument which Jim is making,which I resonate with, and Jim
tell me if I'm wrong, it's not.
It's not a criticism to say allmodels are wrong. It's an
acknowledgement that part ofmodeling is to understand where
we made those assumptions, andare they good enough for the
predictions that we need tomake, right? So, so, you know,
saying that all models are wrongis perfectly fine. That's That's
(14:09):
not saying science orengineering is bad. They're,
they are. That is our frameworkthat we need to make progress
right now, and we need tounderstand the limits of those
framework, of that framework.
Professor Jim DiCarlo (14:22):
Right, I agree.
Professor Peko Hosoi (14:22):
Related to that. I wanted to go back. Jim
also briefly said that part ofthis is getting comfortable with
uncertainty, right? And I thinkone of the things, especially
when I think about MIT students,these are students who have come
in, who have been terrific inphysics, who've been terrific in
math in high school, where thereare absolute answers, right? You
(14:42):
have a problem set. There's ananswer that people are looking
for. And then you get tocollege, and you know, you
realize, oh, I've learned Fequals ma. And then you learn,
but wait a minute, actually, itdepends on your reference frame.
And you learn about relativity.
And then you read, Oh, but waita minute. Actually, you
shouldn't be thinking aboutthings as particles. I should be
thinking about them asprobability distributions,
because it's because now I'velearned quantum and so we keep
as you go through, as youadvance, you're actually
(15:04):
starting to dismantle some ofthe things that you took to be
absolute truth, even in science,and realizing that there are
subtleties that have not thatthat have not been accounted for
through in the early parts ofthat journey.
Professor Jim DiCarlo (15:19):
Well, I completely agree with that and
getting comfortable withuncertainty.
Professor Alex Byrne (15:22):
So, well, just just touching on that
uncertainty point. I mean, howdoes that manifest in the in the
classroom, if at all? Because,after all, Peko, as you were
saying, I mean, this is a bigdifference between most of the
classes that students at MITtake and and their philosophy
classes. For example, sometimesI have actually given Psets in
(15:45):
in philosophy classes, but thequestions are all about, you
know, the reading or what somephilosopher thought, or whether
some argument is valid. Those doseem to have clear answers, but
they're not about, like, grandphilosophical questions, where
there's where there's a lot ofof disagreement. But of course,
in most of the sciences, you'vegot your Psets and there's,
(16:09):
there's either a right or wronganswer. Grading them is, is
totally straightforward. So whatdo you do, or what, or what
could you do to make studentsaware that there's really a
great deal of uncertainty in thescientific enterprise as a
whole.
Professor Peko Hosoi (16:27):
I think there are a bunch of tools that
we the students need to developbefore they can start to apply
them to these sort of biggerstories, right? So like before
you can build a house, you needto know how to use a hammer and
a screwdriver and a wrench andall those kinds of things. And
so there are tools that we areequipping them with, like, how
do you solve a lineardifferential equation? Is this
thing going to be stable orunstable, right? And those are
(16:48):
things where there's a logicalprogression. And so once you
frame things, put things inthese frameworks, you can get a
sense of how you think, how youthink these systems are going to
evolve. So, but you have to begood at the tools. And the tools
are things where you can justwhere you can assign a problem
set, practice this a bunch oftimes, and you will now have the
tool. As you move through yourcurriculum in I think, in most
(17:12):
departments, things become moreand more open ended. So for
example, right now, I'm teachingthe capstone in mechanical
engineering, and this is aproduct development class, and
one of the things they'rethinking about now is
feasibility studies. So if Ihave a product that I I might
want to design, and there's noright or wrong answer as to
whether or not you should designthis product, but you should
check, okay, will this, can Irun this on a battery, or do I
(17:34):
need to build a nuclear powerstation? Right? And so you can
start to apply those tools tounderstand to kind of scope the
problems that you're thinkingabout. And I think, and again,
there's no right answer to that,but there's a, there's sort of a
skill in how you apply thosetools that you've been
developing early on.
Professor Alex Byrne (17:55):
Right, so just, I suppose, in a way,
keeping the focus on theclassroom. So sometimes people
talk about the scientificmethod, and laud the scientific
method as a particularlyreliable way of finding things
out. And when I was anundergraduate, I studied physics
as an undergraduate, and I don'tremember having a class on on
(18:19):
the scientific method. No oneever taught me what it was. So
do you think that there is sucha thing as the scientific
method? And if so, what? What isit? And do do historians, for
example, we have a historianhere, employ the scientific
method?
Professor Anne McCants (18:37):
So, so I had reason actually, for the
Compass course, to go back and,you know, sort of look it up
officially, right? The, youknow, the five steps, although
it's six now, because they'veadded persuasion and
communication to the end, whichis, which is a good thing,
right? But the first step was,you know, to, sort of, to
collect all your observations. Idon't remember the exact words,
(19:00):
and it really presumes a zerostep, if you will, that's not
listed there. And that is thatthere are somehow unfiltered
observations. And we've already,I think, agreed between the
between us, that that doesn'texist in the world. And so
that's a real problem for thescientific method, right, as a,
(19:24):
as a, as a canned box, you know,if it, if it relies on, you
know, a prior step that can't bedone, then it's a shaky
foundation. You know, that saidit's tremendously useful, I
think, when students arelearning tools, and maybe
(19:46):
younger than college students,right to, you know, just sort of
have a little list that they canfollow, right? So I don't want
to bash the scientific method,even though I don't believe in
it.
Professor Peko Hosoi (19:59):
I mean, it's one framework, but it's not
the only framework that youwould apply to advance
knowledge. I would say, I'mgoing to now circle back, Alex,
to your because you originallycame to the work in the MIT
mission statement, which is toadvance knowledge. I mean, it's
implicit in that, that we're notthat there's no end point here,
like to Jim's point, that toadvance knowledge, that means
(20:20):
there's more things to uncover,right? There's we're always
peeling back more layers whichimprove our understanding or
improve our predictive power ofthe world.
Professor Alex Byrne (20:30):
So there are different ways of knowing.
Professor Peko Hosoi (20:33):
Yeah, yeah.
Professor Alex Byrne (20:34):
Right, right, right. Excellent. Okay,
so, so the mission statementdoes presuppose that all of us
at MIT are really in the samebusiness, at least at a certain
high level of abstraction. We'reall in the truth knowledge
business. We're all pursuing, weshould be pursuing the truth or
advancing knowledge in ourdifferent disciplines
Professor Anne McCants (20:58):
And to different ends.
Professor Alex Byrne (20:59):
Yeah.
Professor Jim DiCarlo (21:01):
Yeah.
Professor Alex Byrne (21:02):
Not totally sure what the takeaway
is.
Professor Peko Hosoi (21:04):
Good luck.
Alex, yeah.
Professor Alex Byrne (21:07):
So it's hard to sum up this very wide
ranging discussion. So instead,I'll end with a completely
different, different question,just to humanize the three of
you, if you're not humanizedenough already. So. So the
question is, what artwork,whether that's a book or a piece
(21:30):
of visual art or a musicalcomposition or a poem or
whatever was your favorite orthe thing that influenced you
most in in college.
Professor Peko Hosoi (21:41):
This is going to be a somewhat long
answer. So the first when I,when I, when I got this
question, the first thing Irealized is that college was a
really long time ago. Prettyhard to figure out what I was
doing in college. The second,once I started thinking about
it, is that this is actually avery personal question, and so,
(22:02):
so I will, if you answerhonestly, you will learn a lot
about about us. So I will answerhonestly. I'm going to give you
a few things. The first is whenI was in college, that was when
this amazing new technology cameout called the musical CD. And
so I had, I I got very excitedabout buying CDs. And it was
also around the time that sortof like, like the alternative
(22:25):
rock seed came out. So I thinkthat, I think the CDs I listened
to most were the was probablyJane's Addiction, if I'm gonna
be honest, and Peter Gabriel, Ihave the whole Peter Gabriel
collection. Then I was thinking,Okay, what about books? Because
I also, I also did a lot ofreading, and there's, there's
two that I remember from thattime. One is a book called "The
(22:48):
River Why" by David Duncan. Andthis was, I'm from Oregon, so
this was recommended to me by myfriends in the Pacific
Northwest. And again, I thinkit, it sort of fits with this
podcast, because it is about,it's a coming of age and and how
to make choices about leading agood life. And the other one
that I remember from college was"A Canticle for Liebowitz" by
(23:11):
Herman Miller, which I randomlywalked into a used bookstore and
just picked it off the shelf,and it blew my mind. So those
are the ones I'll contribute.
Professor Anne McCants (23:21):
I'll start by saying that I grew up
in a evangelical Christianfamily milieu with very
thoughtful parents who were inthe process of asking more
(23:42):
complicated questions than thefamilies that they had come out
of. And when I got to college, Istarted reading theology, sort
of as a side project, and Iactually was having trouble,
sort of thinking of any oneparticular influence, but it
opened up a whole new world forme, in part because I discovered
(24:07):
that people's religious beliefshad these deep histories that
were complicated, right and andI think to some extent, Both my
parents are scientists, as arethree of my four grandparents,
and so, you know, I'm the "whichone of these is not like the
(24:28):
other" in my family. And I thinkin part, I became a historian
because that was a way ofthinking about theology without
having to become a theologian.
And I think my interest in thekinds of questions we've been
talking about today came, youknow, came out of that.
Professor Jim DiCarlo (24:53):
Yeah, so that, if I may, the personal
side of this for me is I wasraised like my parents were both
scientists, also, my dad was aphysicist. And they raised me in
a Catholic family, and Iremember hearing sermons about
how evolution was impossible,and I was revolting at the time
about going to college. And mybook that I remember in college
was the was Richard Dawkins'"Blind Watchmaker," which sort
(25:15):
of explains how complexity couldarise without a creator, right?
And it's very convincing, ifyou're into reading about
evolution and how biologiststhink about that. And so that in
reading that, in parallel with,you know, looking at my other
book I remember in college, isjust this huge textbook, tome of
the, you know, principles ofneuroscience, which is
(25:36):
essentially should be called thecollection of facts about the
brain, which beautiful drawingsof anatomy, and, like, look at
all these complicated wiringsystems, so juxtaposing that
those tomes of, well, here's allthese wires. We don't actually
know what they're doing withthese kind of, hey, some process
has produced this that was, youknow, by selection. And there's
an opportunity here that at somepoint we'll figure out this, who
(25:58):
we are as a machine, and andunderstand what all those wires
are doing, and that that sort ofinspired me to to move on to
sort of do this that I do now.
Professor Alex Byrne (26:08):
I also second what Peko said about
Peter Gabriel, early Genesis.
You can't beat it. Yeah. Ofcourse, no one listening to this
podcast will have any idea whatwe're talking about, none
whatsoever.
Professor Peko Hosoi (26:20):
None.
Zero. Yeah, exactly, yeah.
Professor Anne McCants (26:22):
I was busy listening to Bach and
Palestrina in college,
Professor Alex Byrne (26:25):
Okay, okay.
Professor Anne McCants (26:28):
And I sadly haven't changed at all.
Professor Alex Byrne (26:31):
No. Well, there is, you know, there is
that sort of imprinting effect.
There's Peter Gabriel. And I doremember reading Bertrand
Russell's little introduction tophilosophy called--which still
can't be beat. It's flawed in somany ways, but, but it's a,
certainly a gripping read, Ithink, to an undergraduate
(26:51):
interested in the big questions.
And that's his little bookcalled "The Problems of of
Philosophy" 1912, I think. Thankyou so much. And Peko, Jim, I
really enjoyed the conversation,and I really appreciate you
taking the time.
Professor Peko Hosoi (27:10):
Thanks, Alex.
Professor Anne McCants (27:10):
Thank you.
Professor Jim DiCarlo (27:10):
Thank you, Alex for having us.
Intro Speaker 3 (27:12):
Thanks for listening to MIT Compass:
Thinking and Talking about BeingHuman.
Intro Speaker 1 (27:19):
We hope you'll check out other episodes at
compass.mit.edu
Intro Speaker 2 (27:23):
This podcast was created as part of the MIT
Compass (27:25):
Love, Death and Taxes."
Intro Speaker 1 (27:30):
This podcast was recorded at the MIT AV
studios
Intro Speaker 3 (27:33):
and produced by Adina Karp.
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If you've ever wanted to know about champagne, satanism, the Stonewall Uprising, chaos theory, LSD, El Nino, true crime and Rosa Parks, then look no further. Josh and Chuck have you covered.
Dateline NBC
Current and classic episodes, featuring compelling true-crime mysteries, powerful documentaries and in-depth investigations. Follow now to get the latest episodes of Dateline NBC completely free, or subscribe to Dateline Premium for ad-free listening and exclusive bonus content: DatelinePremium.com
CrimeLess: Hillbilly Heist
It’s 1996 in rural North Carolina, and an oddball crew makes history when they pull off America’s third largest cash heist. But it’s all downhill from there. Join host Johnny Knoxville as he unspools a wild and woolly tale about a group of regular ‘ol folks who risked it all for a chance at a better life. CrimeLess: Hillbilly Heist answers the question: what would you do with 17.3 million dollars? The answer includes diamond rings, mansions, velvet Elvis paintings, plus a run for the border, murder-for-hire-plots, and FBI busts.