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<i>preroll music</i>

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Herald: Our next talk is going to be about AI and
it's going to be about proper AI.

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It's not going to be about
deep learning or buzz word bingo.

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It's going to be about actual psychology.
It's going to be about computational metapsychology.

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And now please welcome Joscha!

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<i>applause</i>

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Joscha: Thank you.

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I'm interested in understanding
how the mind works,

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and I believe that the most foolproof perspective
at looking ... of looking at minds is to understand

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that they are systems that if you saw patterns
at them you find meaning.

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And you find meaning in those in very particular
ways and this is what makes us who we are.

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So they way to study and understand who we
are in my understanding is

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to build models of information processing
that constitutes our minds.

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Last year about the same time, I've answered
the four big questions of philosophy:

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"Whats the nature of reality?", "What can
be known?", "Who are we?",

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"What should we do?"
So now, how can I top this?

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<i>applause</i>

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I'm going to give you the drama
that divided a planet.

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Some of a very, very big events,
that happened in the course of last year,

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so I couldn't tell you about it before.

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What color is the dress
<i>laughs</i><i>applause</i>

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I mean ahmm... If you have.. do not have any
mental defects you can clearly see it's white

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and gold. Right?

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[voices from audience]

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Turns out, ehmm.. most people seem to have
mental defects and say it is blue and black.

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I have no idea why. Well Ok, I have an idea,
why that is the case.

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Ehmm, I guess that you got too, it has to
do with color renormalization

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and color renormalization happens differently
apparently in different people.

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So we have different wireing to renormalize
the white balance.

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And it seems to work in real world
situations in pretty much the same way,

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but not necessarily for photographs.
Which have only very small fringe around them,

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which gives you hint about the lighting situation.

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And that's why you get this huge divergencies,
which is amazing!

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So what we see that our minds can not know

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objective truths in any way. Outside of mathematics.

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They can generate meaning though.

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How does this work?

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I did robotic soccer for a while,
and there you have the situation,

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that you have a bunch of robots, that are
situated on a playing field.

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And they have a model of what goes on
in the playing field.

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Physics generates data for their sensors.
They read the bits of the sensors.

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And then they use them to.. erghmm update
the world model.

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And sometimes we didn't want
to take the whole playing field along,

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and the physical robots, because they are
expensive and heavy and so on.

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Instead if you just want to improve the learning
and the game play of the robots

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you can use the simulations.

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So we've wrote a computer simulation of the
playing field and the physics, and so on,

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that generates pretty some the same data,
and put the robot mind into the simulator

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robot body, and it works just as well.

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That is, if you the robot, because you can
not know the difference if you are the robot.

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You can not know what's out there. The only
thing that you get to see is what is the structure

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of the data at you system bit interface.

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And then you can derive model from this.

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And this is pretty much the situation
that we are in.

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That is, we are minds that are somehow computational,

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they are able to find regularity in patterns,

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and they are... we.. seem to have access to
something that is full of regularity,

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so we can make sense out of it.

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[ghulp, ghulp]

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Now, if you discover that you are in the same
situation as these robots,

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basically you discover that you are some kind
of apparently biological robot,

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that doesn't have direct access
to the world of concepts.

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That has never actually seen matter
and energy and other people.

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All it got to see was little bits of information,

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that were transmitted through the nerves,

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and the brain had to make sense of them,

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by counting them in elaborate ways.

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What's the best model of the world
that you can have with this?

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What will the state of affairs,
what's the system that you are in?

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And what are the best algorithms that you
should be using, to fix your world model.

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And this question is pretty old.

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And I think that has been answered for the
first time by Ray Solomonoff in the 1960.

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He has discovered an algorithm,
that you can apply when you discover

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that you are an robot,
and all you have is data.

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What is the world like?

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And this algorithm is basically
a combination of induction and Occam's razor.

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And we can mathematically prove that we can
not do better than Solomonoff induction.

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Unfortunately, Solomonoff induction
is not quite computable.

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But everything that we are going to do is
some... is going to be some approximation

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of Salomonoff induction.

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So our concepts can not really refer
to the facts in the world out there.

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We do not get the truth by referring
to stuff out there, in the world.

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We get meaning by suitably encoding
the patterns at our systemic interface.

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And AI has recently made a huge progress in
encoding data at perceptual interfaces.

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Deep learning is about using a stacked hierarchy
of feature detectors.

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That is, we use pattern detectors and we build
them into a networks that are arranged in

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hundreds of layers.

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And then we adjust the links
between these layers.

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Usually some kind of... using
some kind of gradient descent.

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And we can use this to classify
for instance images and parts of speech.

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So, we get to features that are more and more
complex, they started as very, very simple patterns.

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And then get more and more complex,
until we get to object categories.

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And now this systems are able
in image recognition task,

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to approach performance that is very similar
to human performance.

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Also what is nice is that it seems to be somewhat
similar to what the brain seems to be doing

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in visual processing.

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And if you take the activation in different
levels of these networks and you

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erghm... improve the... that... erghmm...
enhance this activation a little bit, what

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you get is stuff that look very psychedelic.

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Which may be similar to what happens, if you
put certain illegal substances into people,

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and enhance the activity on certain layers
of their visual processing.

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[BROKEN AUDIO]If you want to classify the
differences what we do if we want quantify

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this you filter out all the invariences in
the data.

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The pose that she has, the lighting,
the dress that she is on.. has on,

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her facial expression and so on.

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And then we go to only to this things that
is left after we've removed all the nuance data.

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But what if we... erghmm
want to get to something else,

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for instance if we want to understand poses.

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Could be for instance that we have several
dancers and we want to understand what they

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have in common.

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So our best bet is not just to have a single
classification based filtering,

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but instead what we want to have is to take
the low level input

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and get a whole universe of features,
that is interrelated.

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So we have different levels of interrelations.

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At the lowest levels we have percepts.

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On the slightly higher level we have simulations.

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And on even higher level we have concept landscape.

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How does this representation
by simulation work?

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Now imagine you want to understand sound.

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[Ghulp]

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If you are a brain and you want to understand
sound you need to model it.

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Unfortunatly we can not really model sound
with neurons, because sound goes up to 20kHz,

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or if you are old like me maybe to 12 kHz.
20 kHz is what babies could do.

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And... neurons do not want to do 20 kHz.
That's way too fast for them.

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They like something like 20 Hz.

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So what do you do? You need
to make a Fourier transform.

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The Fourier transform measures the amount
of energy at different frequencies.

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And because you can not do it with neurons,
you need to do it in hardware.

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And turns out this is exactly
what we are doing.

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We have this cochlea which is this snail like
thing in our ears,

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and what it does, it transforms energy of
sound in different frequency intervals into

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energy measurments.

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And then gives you something
like what you see here.

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And this is something that the brain can model,

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so we can get a neurosimulator that tries
to recreate this patterns.

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And we can predict the next input from the
cochlea that then understand the sound.

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Of course if you want to understand music,

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we have to go beyond understanding sound.

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We have to understand the transformations
that sound can have if you play it at different pitch.

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We have to arrange the sound in the sequence
that give you rhythms and so on.

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And then we want to identify
some kind of musical grammar

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that we can use to again control the sequencer.

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So we have stucked structures.
That simulate the world.

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And once you've learned this model of music,

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once you've learned the musical grammar,
the sequencer and the sounds.

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You can get to the structure
of the individual piece of music.

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So, if you want to model the world of music.

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You need to have the lowest level of percepts
then we have the higher level of mental simulations.

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And... which give the sequences of the music
and the grammars of music.

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And beyond this you have the conceptual landscape
that you can use

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to describe different styles of music.

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And if you go up in the hierarchy,
you get to more and more abstract models.

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More and more conceptual models.

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And more and more analytic models.

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And this are causal models at some point.

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This causal models can be weakly deterministic,

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basically associative models, which tell you

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if this state happens, it's quite probable
that this one comes afterwords.

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Or you can get to a strongly determined model.

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Strongly determined model is one which tells
you, if you are in this state

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and this condition is met,

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You are are going to go exactly in this state.

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If this condition is not met, or a different
condition is met, you are going to this state.

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And this is what we call an alghorithm.

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it's.. now we are on the domain of computation.

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Computation is slightly different from mathematics.

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It's important to understand this.

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For a long time people have thought that the
universe is written in mathematics.

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Or that.. minds are mathematical,
or anything is mathematical.

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In fact nothing is mathematical.

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Mathematics is just the domain
of formal languages. It doesn't exist.

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Mathematics starts with a void.

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You throw in a few axioms, and if you've chosen
a nice axioms, then you get infinite complexity.

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Most of which is not computable.

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In mathematics you can express arbitrary statements,

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because it's all about formal languages.

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Many of this statements will not make sense.

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Many of these statements will make sense
in some way,

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but you can not test whether they make sense,

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because they're not computable.

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Computation is different.
Computation can exist.

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It's starts with an initial state.

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And then you have a transition function.
You do the work.

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You apply the transition function,
and you get into the next state.

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Computation is always finite.

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Mathematics is the kingdom of specification.

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And computation is the kingdom of implementation.

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It's very important to understand this difference.

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All our access to mathematics of course is
because we do computation.

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We can understand mathematics,

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because our brain can compute
some parts of mathematics.

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Very, very little of it, and to
very constrained complexity.

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But enough, so we can map
some of the infinite complexity

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and noncomputability of mathematics
into computational patterns,

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that we can explore.

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So computation is about doing the work,

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it's about executing the transition function.

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Now we've seen that mental representation
is about concepts,

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mental simulations, conceptual representations

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and this conceptual representations
give us concept spaces.

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And the nice thing
about this concept spaces is

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that they give us an interface
to our mental representations,

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We can use to address and manipulate them.

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And we can share them in cultures.

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And this concepts are compositional.

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We can put them together, to create new concepts.

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And they can be described using
higher dimensional vector spaces.

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They don't do simulation
and prediction and so on,

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but we can capture regularity
in our concept wisdom.

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With this vector space
you can do amazing things.

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For instance, if you take the vector from
"King" to "Queen"

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is pretty much the same vector
as to.. between "Man" and "Woman"

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And because of this properties, because it's
really a high dimentional manifold

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this concepts faces, we can do interesting
things, like machine translation

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without understanding what it means.

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That is without doing any proper mental representation,
that predicts the world.

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So this is a type of meta representation,
that is somewhat incomplete,

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but it captures the landscape that we share
in a culture.

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And then there is another type of meta representation,
that is linguistic protocols.

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Which is basically a formal grammar and vocabulary.

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And we need this linguistic protocols

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to transfer mental representations
between people.

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And we do this by basically
scanning our mental representation,

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disassembling them in some way
or disambiguating them.

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And then we use it as discrete string of symbols
to get it to somebody else,

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and he trains an assembler,
that reverses this process,

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and build something that is pretty similar
to what we intended to convey.

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And if you look at the progression of AI models,

229
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it pretty much went the opposite direction.

230
00:12:55,600 --> 00:13:00,279
So AI started with linguistic protocols, which
were expressed in formal grammars.

231
00:13:00,279 --> 00:13:05,209
And then it got to concepts spaces, and now
it's about to address percepts.

232
00:13:05,209 --> 00:13:09,689
And at some point in near future it's going
to get better at mental simulations.

233
00:13:09,689 --> 00:13:11,730
And at some point after that we get to

234
00:13:11,730 --> 00:13:14,769
attention directed and
motivationally connected systems,

235
00:13:14,769 --> 00:13:16,600
that make sense of the world.

236
00:13:16,600 --> 00:13:20,290
that are in some sense able to address meaning.

237
00:13:20,290 --> 00:13:23,489
This is the hardware that we have can do.

238
00:13:23,489 --> 00:13:25,629
What kind of hardware do we have?

239
00:13:25,629 --> 00:13:28,480
That's a very interesting question.

240
00:13:28,480 --> 00:13:32,230
It could start out with a question:
How difficult is it to define a brain?

241
00:13:32,230 --> 00:13:35,439
We know that the brain must be
somewhere hidden in the genome.

242
00:13:35,439 --> 00:13:38,290
The genome fits on a CD ROM.
It's not that complicated.

243
00:13:38,290 --> 00:13:40,399
It's easier than Microsoft Windows. <i>laughter</i>

244
00:13:40,399 --> 00:13:45,549
And we also know, that about 2%
of the genome is coding for proteins.

245
00:13:45,549 --> 00:13:48,429
And maybe about 10% of the genome
has some kind of stuff

246
00:13:48,429 --> 00:13:51,239
that tells you when to switch protein.

247
00:13:51,239 --> 00:13:52,829
And the remainder is mostly garbage.

248
00:13:52,829 --> 00:13:57,170
It's old viruses that are left over and has
never been properly deleted and so on.

249
00:13:57,170 --> 00:14:01,420
Because there are no real
code revisions in the genome.

250
00:14:01,420 --> 00:14:08,119
So how much of this 10%
that is 75 MB code for the brain.

251
00:14:08,119 --> 00:14:09,469
We don't really know.

252
00:14:09,469 --> 00:14:13,399
What we do know is we share
almost all of this with mice.

253
00:14:13,399 --> 00:14:15,769
Genetically speaking human
is a pretty big mouse.

254
00:14:15,769 --> 00:14:21,049
With a few bits changed, so.. to fix some
of the genetic expressions

255
00:14:21,049 --> 00:14:25,879
And that is most of the stuff there is going
to code for cells and metabolism

256
00:14:25,879 --> 00:14:27,999
and how your body looks like and so on.

257
00:14:27,999 --> 00:14:33,679
But if you look at erghmm... how much is expressed
in the brain and only in the brain,

258
00:14:33,679 --> 00:14:35,170
in terms of proteins and so on.

259
00:14:35,170 --> 00:14:45,639
We find it's about... well of the 2% it's
about 5%. That is only the 5% of the 2% that

260
00:14:45,639 --> 00:14:46,799
is only in the brain.

261
00:14:46,799 --> 00:14:50,199
And another 5% of the 2% is predominantly
in the brain.

262
00:14:50,199 --> 00:14:52,069
That is more in the brain than anywhere else.

263
00:14:52,069 --> 00:14:54,249
Which gives you some kind of thing
like a lower bound.

264
00:14:54,249 --> 00:14:59,379
Which means to encode a brain genetically
base on the hardware that we are using.

265
00:14:59,379 --> 00:15:03,539
We need something like
at least 500 kB of code.

266
00:15:03,539 --> 00:15:06,670
Actually ehmm.. this... we very conservative
lower bound.

267
00:15:06,670 --> 00:15:08,720
It's going to be a little more I guess.

268
00:15:08,720 --> 00:15:11,449
But it sounds surprisingly little, right?

269
00:15:11,449 --> 00:15:13,709
But in terms of scientific theories
this is a lot.

270
00:15:13,709 --> 00:15:16,519
I mean the universe,
according to the core theory

271
00:15:16,519 --> 00:15:19,420
of the quantum mechanics and so on
is like so much of code.

272
00:15:19,420 --> 00:15:20,569
It's like half a page of code.

273
00:15:20,569 --> 00:15:23,100
That's it. That's all you need
to generate the universe.

274
00:15:23,100 --> 00:15:25,489
And if you want to understand evolution
it's like a paragraph.

275
00:15:25,489 --> 00:15:29,609
It's couple lines you need to understand
evolutionary process.

276
00:15:29,609 --> 00:15:32,199
And there is a lots, lots of details, that's
you get afterwards.

277
00:15:32,199 --> 00:15:34,220
Because this process itself doesn't define

278
00:15:34,220 --> 00:15:37,259
how the animals are going to look like,
and in similar way is..

279
00:15:37,259 --> 00:15:41,269
the code of the universe doesn't tell you
what this planet is going to look like.

280
00:15:41,269 --> 00:15:43,279
And what you guys are going to look like.

281
00:15:43,279 --> 00:15:45,949
It's just defining the rulebook.

282
00:15:45,949 --> 00:15:49,209
And in the same sense genome defines the rulebook,

283
00:15:49,209 --> 00:15:51,569
by which our brain is build.

284
00:15:51,569 --> 00:15:56,399
erghmmm,.. The brain boots itself
into developer process,

285
00:15:56,399 --> 00:15:58,119
and this booting takes some time.

286
00:15:58,119 --> 00:16:01,069
So subliminal learning in which
initial connections are forged

287
00:16:01,069 --> 00:16:04,910
And basic models are build of the world,
so we can operate in it.

288
00:16:04,910 --> 00:16:06,999
And how long does this booting take?

289
00:16:06,999 --> 00:16:09,669
I thing it's about 80 mega seconds.

290
00:16:09,669 --> 00:16:14,319
That's the time that a child is awake until
it's 2.5 years old.

291
00:16:14,319 --> 00:16:16,449
By this age you understand Star Wars.

292
00:16:16,449 --> 00:16:20,029
And I think that everything after
understanding Star Wars is cosmetics.

293
00:16:20,029 --> 00:16:26,799
<i>laughter</i><i>applause</i>

294
00:16:26,799 --> 00:16:32,820
You are going to be online, if you get to
arrive old age for about 1.5 giga seconds.

295
00:16:32,820 --> 00:16:37,929
And in this time I think you are going to
get not to watch more than 5 milion concepts.

296
00:16:37,929 --> 00:16:41,600
Why? I don't know real...
If you look at this child.

297
00:16:41,600 --> 00:16:45,480
If a child would be able to form a concept
let say every 5 minutes,

298
00:16:45,480 --> 00:16:48,529
then by the time it's about 4 years old,
it's going to have

299
00:16:48,529 --> 00:16:51,549
something like 250 thousands concepts.

300
00:16:51,549 --> 00:16:54,119
And... so... a quarter million.

301
00:16:54,119 --> 00:16:56,809
And if we extrapolate this into our lifetime,

302
00:16:56,809 --> 00:16:59,799
at some point it slows down,
because we have enough concepts,

303
00:16:59,799 --> 00:17:01,230
to describe the world.

304
00:17:01,230 --> 00:17:04,410
Maybe it's something... It's I think it's
less that 5 million.

305
00:17:04,410 --> 00:17:07,140
How much storage capacity does the brain has?

306
00:17:07,140 --> 00:17:12,319
I think that the... the estimates
are pretty divergent,

307
00:17:12,319 --> 00:17:14,930
The lower bound is something like a 100 GB,

308
00:17:14,930 --> 00:17:18,569
And the upper bound
is something like 2.5 PB.

309
00:17:18,569 --> 00:17:21,890
There is even...
even some higher outliers this..

310
00:17:21,890 --> 00:17:25,630
If you for instance think that we need all
those synaptic vesicle to store information,

311
00:17:25,630 --> 00:17:27,530
maybe even more fits into this.

312
00:17:27,530 --> 00:17:31,740
But the 2.5 PB is usually based
on what you need

313
00:17:31,740 --> 00:17:34,760
to code the information
that is in all the neurons.

314
00:17:34,760 --> 00:17:36,770
But maybe the neurons
do not really matter so much,

315
00:17:36,770 --> 00:17:39,930
because if the neuron dies it's not like the
word is changing dramatically.

316
00:17:39,930 --> 00:17:44,270
The brain is very resilient
against individual neurons failing.

317
00:17:44,270 --> 00:17:48,930
So the 100 GB capacity is much more
what you actually store in the neurons.

318
00:17:48,930 --> 00:17:51,380
If you look at all the redundancy
that you need.

319
00:17:51,380 --> 00:17:54,230
And I think this is much closer to the actual
Ballpark figure.

320
00:17:54,230 --> 00:17:58,130
Also if you want to store 5 hundred...
5 million concepts,

321
00:17:58,130 --> 00:18:02,330
and maybe 10 times or 100 times the number
of percepts, on top of this,

322
00:18:02,330 --> 00:18:05,490
this is roughly the Ballpark figure
that you are going to need.

323
00:18:05,490 --> 00:18:07,110
So our brain

324
00:18:07,110 --> 00:18:08,320
is a prediction machine.

325
00:18:08,320 --> 00:18:11,490
It... What it does is it reduces the entropy
of the environment,

326
00:18:11,490 --> 00:18:14,610
to solve whatever problems you are encountering,

327
00:18:14,610 --> 00:18:17,790
if you don't have a... feedback loop, to fix
them.

328
00:18:17,790 --> 00:18:20,240
So normally if something happens, we have
some kind of feedback loop,

329
00:18:20,240 --> 00:18:23,440
that regulates our temperature or that makes
problems go away.

330
00:18:23,440 --> 00:18:26,050
And only when this is not working
we employ recognition.

331
00:18:26,050 --> 00:18:29,250
And then we start this arbitrary
computational processes,

332
00:18:29,250 --> 00:18:31,830
that is facilitated by the neural cortex.

333
00:18:31,830 --> 00:18:34,940
And this.. arhmm.. neural cortex has really
do arbitrary programs.

334
00:18:34,940 --> 00:18:37,870
But it can do so
with only with very limited complexity,

335
00:18:37,870 --> 00:18:42,070
because really you just saw,
it's not that complex.

336
00:18:42,070 --> 00:18:43,900
The modeling of the world is very slow.

337
00:18:43,900 --> 00:18:46,570
And it's something
that we see in our eye models.

338
00:18:46,570 --> 00:18:48,150
To learn the basic structure of the world

339
00:18:48,150 --> 00:18:49,330
takes a very long time.

340
00:18:49,330 --> 00:18:52,650
To learn basically that we are moving in 3D
and objects are moving,

341
00:18:52,650 --> 00:18:54,030
and what they look like.

342
00:18:54,030 --> 00:18:55,130
Once we have this basic model,

343
00:18:55,130 --> 00:18:59,300
we can get to very, very quick
understanding within this model.

344
00:18:59,300 --> 00:19:02,110
Basically encoding based
on the structure of the world,

345
00:19:02,110 --> 00:19:03,610
that we've learned.

346
00:19:03,610 --> 00:19:07,100
And this is some kind of
data compression, that we are doing.

347
00:19:07,100 --> 00:19:09,740
We use this model, this grammar of the world,

348
00:19:09,740 --> 00:19:12,150
this simulation structures that we've learned,

349
00:19:12,150 --> 00:19:15,190
to encode the world very, very efficently.

350
00:19:15,190 --> 00:19:17,740
How much data compression do we get?

351
00:19:17,740 --> 00:19:19,860
Well... if you look at the retina.

352
00:19:19,860 --> 00:19:24,610
The retina get's data
in the order of about 10Gb/s.

353
00:19:24,610 --> 00:19:27,500
And the retina already compresses these data,

354
00:19:27,500 --> 00:19:31,120
and puts them into optic nerve
at the rate of about 1Mb/s

355
00:19:31,120 --> 00:19:34,030
This is what you get fed into visual cortex.

356
00:19:34,030 --> 00:19:36,370
And the visual cortex
does some additional compression,

357
00:19:36,370 --> 00:19:42,110
and by the time it gets to layer four of the
first layer of vision, to V1.

358
00:19:42,110 --> 00:19:46,880
We are down to something like 1Kb/s.

359
00:19:46,880 --> 00:19:50,720
So if we extrapolate this, and you get live
to the age of 80 years,

360
00:19:50,720 --> 00:19:54,140
and you are awake for 2/3 of your lifetime.

361
00:19:54,140 --> 00:19:56,930
That is you have your eyes open for 2/3 of
your lifetime.

362
00:19:56,930 --> 00:19:59,040
The stuff that you get into your brain,

363
00:19:59,040 --> 00:20:03,700
via your visual perception
is going to be only 2TB.

364
00:20:03,700 --> 00:20:05,370
Only 2TB of visual data.

365
00:20:05,370 --> 00:20:06,680
Throughout all your lifetime.

366
00:20:06,680 --> 00:20:09,430
That's all you are going to get ever to see.

367
00:20:09,430 --> 00:20:11,160
Isn't this depressing?

368
00:20:11,160 --> 00:20:12,790
<i>laughter</i>

369
00:20:12,790 --> 00:20:16,540
So I would really like to eghmm..
to tell you,

370
00:20:16,540 --> 00:20:22,750
choose wisely what you
are going to look at. <i>laughter</i>

371
00:20:22,750 --> 00:20:26,940
Ok. Let's look at this problem of neural compositionality.

372
00:20:26,940 --> 00:20:29,250
Our brains has this amazing thing
that they can put

373
00:20:29,250 --> 00:20:31,510
meta representation together very, very quickly.

374
00:20:31,510 --> 00:20:33,150
For instance you read a page of code,

375
00:20:33,150 --> 00:20:35,190
you compile it in you mind
into some kind of program

376
00:20:35,190 --> 00:20:37,700
it tells you what this page is going to do.

377
00:20:37,700 --> 00:20:39,110
Isn't that amazing?

378
00:20:39,110 --> 00:20:40,810
And then you can forget about this,

379
00:20:40,810 --> 00:20:43,910
disassemble it all, and use the
building blocks for something else.

380
00:20:43,910 --> 00:20:45,230
It's like legos.

381
00:20:45,230 --> 00:20:48,000
How you can do this with neurons?

382
00:20:48,000 --> 00:20:50,160
Legos can do this, because they have
a well defined interface.

383
00:20:50,160 --> 00:20:52,180
They have all this slots, you know,
that fit together

384
00:20:52,180 --> 00:20:53,600
in well defined ways.

385
00:20:53,600 --> 00:20:54,530
How can neurons do this?

386
00:20:54,530 --> 00:20:57,280
Well, neurons can maybe learn
the interface of other neurons.

387
00:20:57,280 --> 00:20:59,780
But that's difficult, because every neuron
looks slightly different,

388
00:20:59,780 --> 00:21:04,830
after all this... some kind of biologically
grown natural stuff.

389
00:21:04,830 --> 00:21:06,610
<i>laughter</i>

390
00:21:06,610 --> 00:21:10,620
So what you want to do is,
you want to encapsulate this erhmm...

391
00:21:10,620 --> 00:21:13,020
diversity of the neurons to make the predictable.

392
00:21:13,020 --> 00:21:14,820
To give them well defined interface.

393
00:21:14,820 --> 00:21:16,410
And I think that nature solution to this

394
00:21:16,410 --> 00:21:19,770
is cortical columns.

395
00:21:19,770 --> 00:21:24,250
Cortical column is a circuit of
between 100 and 400 neurons.

396
00:21:24,250 --> 00:21:26,860
And this circuit has some kind of neural network,

397
00:21:26,860 --> 00:21:28,650
that can learn stuff.

398
00:21:28,650 --> 00:21:31,070
And after it has learned particular function,

399
00:21:31,070 --> 00:21:35,320
and in between, it's able to link up these
other cortical columns.

400
00:21:35,320 --> 00:21:37,120
And we have about 100 million of those.

401
00:21:37,120 --> 00:21:39,770
Depending on how many neurons
you assume is in there,

402
00:21:39,770 --> 00:21:41,490
it's... erghmm we guess it's something,

403
00:21:41,490 --> 00:21:46,500
at least 20 million and maybe
something like a 100 million.

404
00:21:46,500 --> 00:21:48,330
And this cortical columns, what they can do,

405
00:21:48,330 --> 00:21:50,280
is they can link up like lego bricks,

406
00:21:50,280 --> 00:21:54,130
and then perform,
by transmitting information between them,

407
00:21:54,130 --> 00:21:55,990
pretty much arbitrary computations.

408
00:21:55,990 --> 00:21:57,540
What kind of computation?

409
00:21:57,540 --> 00:22:00,130
Well... Solomonoff induction.

410
00:22:00,130 --> 00:22:03,820
And... they have some short range links,
to their neighbors.

411
00:22:03,820 --> 00:22:05,690
Which comes almost for free, because erghmm..

412
00:22:05,690 --> 00:22:08,490
well, they are connected to them,
they are direct neighborhood.

413
00:22:08,490 --> 00:22:10,050
And they have some long range connectivity,

414
00:22:10,050 --> 00:22:13,000
so you can combine everything
in your cortex with everything.

415
00:22:13,000 --> 00:22:14,900
So you need some kind of global switchboard.

416
00:22:14,900 --> 00:22:17,630
Some grid like architecture
of long range connections.

417
00:22:17,630 --> 00:22:18,900
They are going to be more expensive,

418
00:22:18,900 --> 00:22:20,640
they are going to be slower,

419
00:22:20,640 --> 00:22:23,590
but they are going to be there.

420
00:22:23,590 --> 00:22:26,070
So how can we optimize
what these guys are doing?

421
00:22:26,070 --> 00:22:28,270
In some sense it's like an economy.

422
00:22:28,270 --> 00:22:31,460
It's not enduring based system,
as we often use in machine learning.

423
00:22:31,460 --> 00:22:32,780
It's really an economy. You have...

424
00:22:32,780 --> 00:22:35,560
The question is, you have a fixed number of
elements,

425
00:22:35,560 --> 00:22:37,970
how can you do the most valuable stuff with
them.

426
00:22:37,970 --> 00:22:41,030
Fixed resources, most valuable stuff, the
problem is economy.

427
00:22:41,030 --> 00:22:43,320
So you have an economy of information brokers.

428
00:22:43,320 --> 00:22:45,830
Every one of these guys,
this little cortical columns,

429
00:22:45,830 --> 00:22:48,150
is very simplistic information broker.

430
00:22:48,150 --> 00:22:50,950
And they trade rewards against neg entropy,

431
00:22:50,950 --> 00:22:54,140
Against reducing entropy in the...
in the world.

432
00:22:54,140 --> 00:22:55,790
And to do this, as we just saw

433
00:22:55,790 --> 00:22:58,890
that they need some kind of standardized interface.

434
00:22:58,890 --> 00:23:02,090
And internally, to use this interface
they are going to

435
00:23:02,090 --> 00:23:03,880
have some kind of state machine.

436
00:23:03,880 --> 00:23:05,660
And then they are going to pass messages

437
00:23:05,660 --> 00:23:07,400
between each other.

438
00:23:07,400 --> 00:23:08,630
And what are these messages?

439
00:23:08,630 --> 00:23:11,100
Well, it's going to be hard
to discover these messages,

440
00:23:11,100 --> 00:23:12,800
by looking at brains.

441
00:23:12,800 --> 00:23:14,800
Because it's very difficult to see in brains,

442
00:23:14,800 --> 00:23:15,450
what the are actually doing.

443
00:23:15,450 --> 00:23:17,250
you just see all these neurons.

444
00:23:17,250 --> 00:23:18,790
And if you would be waiting for neuroscience,

445
00:23:18,790 --> 00:23:20,970
to discover anything, we wouldn't even have

446
00:23:20,970 --> 00:23:22,590
gradient descent or anything else.

447
00:23:22,590 --> 00:23:23,720
We wouldn't have neuron learning.

448
00:23:23,720 --> 00:23:25,420
We wouldn't have all this advances in AI.

449
00:23:25,420 --> 00:23:28,230
Jürgen Schmidhuber said that the biggest,

450
00:23:28,230 --> 00:23:30,010
the last contribution of neuroscience to

451
00:23:30,010 --> 00:23:32,220
artificial intelligence
was about 50 years ago.

452
00:23:32,220 --> 00:23:34,280
That's depressing, and it might be

453
00:23:34,280 --> 00:23:37,870
overemphasizing the unimportance of neuroscience,

454
00:23:37,870 --> 00:23:39,490
because neuroscience is very important,

455
00:23:39,490 --> 00:23:41,090
once you know what are you looking for.

456
00:23:41,090 --> 00:23:42,510
You can actually often find this,

457
00:23:42,510 --> 00:23:44,320
and see whether you are on the right track.

458
00:23:44,320 --> 00:23:45,860
But it's very difficult to take neuroscience

459
00:23:45,860 --> 00:23:47,940
to understand how the brain is working.

460
00:23:47,940 --> 00:23:49,290
Because it's really like understanding

461
00:23:49,290 --> 00:23:53,230
flight by looking at birds through a microscope.

462
00:23:53,230 --> 00:23:55,150
So, what are these messages?

463
00:23:55,150 --> 00:23:57,850
You are going to need messages,
that tell these cortical columns

464
00:23:57,850 --> 00:24:00,160
to join themselves into a structure.

465
00:24:00,160 --> 00:24:01,990
And to unlink again once they're done.

466
00:24:01,990 --> 00:24:03,690
You need ways that they can request each other

467
00:24:03,690 --> 00:24:06,040
to perform computations for them.

468
00:24:06,040 --> 00:24:07,510
You need ways they can inhibit each other

469
00:24:07,510 --> 00:24:08,320
when they are linked up.

470
00:24:08,320 --> 00:24:10,990
So they don't do conflicting computations.

471
00:24:10,990 --> 00:24:12,940
Then they need to tell you whether the computation,

472
00:24:12,940 --> 00:24:14,110
the result of the computation

473
00:24:14,110 --> 00:24:16,730
that the are asked to do is probably false.

474
00:24:16,730 --> 00:24:19,340
Or whether it's probably true,
but you still need to wait for others,

475
00:24:19,340 --> 00:24:21,990
to tell you whether the details worked out.

476
00:24:21,990 --> 00:24:24,240
Or whether it's confirmed true that the concepts

477
00:24:24,240 --> 00:24:26,730
that they stand for is actually the case.

478
00:24:26,730 --> 00:24:28,150
And then you want to have learning,

479
00:24:28,150 --> 00:24:29,630
to tell you how well this worked.

480
00:24:29,630 --> 00:24:31,390
So you will have to announce a bounty,

481
00:24:31,390 --> 00:24:34,380
that tells them to link up
and kind of reward signal

482
00:24:34,380 --> 00:24:36,740
that makes do computation in the first place.

483
00:24:36,740 --> 00:24:38,680
And then you want to have
some kind of reward signal

484
00:24:38,680 --> 00:24:40,550
once you got the result as an organism.

485
00:24:40,550 --> 00:24:42,280
But you reach your goal if you made

486
00:24:42,280 --> 00:24:45,810
the disturbance go away
or what ever you consume the cake.

487
00:24:45,810 --> 00:24:47,710
And then you will have
some kind of reward signal

488
00:24:47,710 --> 00:24:49,250
that's you give everybody.

489
00:24:49,250 --> 00:24:50,650
That was involved in this.

490
00:24:50,650 --> 00:24:52,720
And this reward signal facilitates learning,

491
00:24:52,720 --> 00:24:55,230
so the.. difference between the announce reward

492
00:24:55,230 --> 00:24:57,530
and consumption reward is the learning signal

493
00:24:57,530 --> 00:24:58,740
for these guys.

494
00:24:58,740 --> 00:25:00,210
So they can learn how to play together,

495
00:25:00,210 --> 00:25:02,700
and how to do the Solomonoff induction.

496
00:25:02,700 --> 00:25:04,660
Now, I've told you that Solomonoff induction

497
00:25:04,660 --> 00:25:05,280
is not computable.

498
00:25:05,280 --> 00:25:07,630
And it's mostly because of two things,

499
00:25:07,630 --> 00:25:09,280
First of all it's needs infinite resources

500
00:25:09,280 --> 00:25:11,200
to compare all the possible models.

501
00:25:11,200 --> 00:25:13,530
And the other one is that we do not know

502
00:25:13,530 --> 00:25:15,440
the priori probability for our Bayesian model.

503
00:25:15,440 --> 00:25:19,280
If we do not know
how likely unknown stuff is in the world.

504
00:25:19,280 --> 00:25:22,520
So what we do instead is,
we set some kind of hyperparameter,

505
00:25:22,520 --> 00:25:25,050
Some kind of default
priori probability for concepts,

506
00:25:25,050 --> 00:25:28,110
that are encoded by cortical columns.

507
00:25:28,110 --> 00:25:30,580
And if we set these parameters very low,

508
00:25:30,580 --> 00:25:32,140
then we are going to end up with inferences

509
00:25:32,140 --> 00:25:35,250
that are quite probable.

510
00:25:35,250 --> 00:25:36,480
For unknown things.

511
00:25:36,480 --> 00:25:37,690
And then we can test for those.

512
00:25:37,690 --> 00:25:41,350
If we set this parameter higher, we are going
to be very, very creative.

513
00:25:41,350 --> 00:25:43,670
But we end up with many many theories,

514
00:25:43,670 --> 00:25:45,140
that are difficult to test.

515
00:25:45,140 --> 00:25:48,470
Because maybe there are
too many theories to test.

516
00:25:48,470 --> 00:25:50,650
Basically every of these cortical columns
will now tell you,

517
00:25:50,650 --> 00:25:52,240
when you ask them if they are true:

518
00:25:52,240 --> 00:25:54,960
"Yes I'm probably true,
but i still need to ask others,

519
00:25:54,960 --> 00:25:56,980
to work on the details"

520
00:25:56,980 --> 00:25:58,670
So these others are going to be get active,

521
00:25:58,670 --> 00:26:00,640
and they are being asked by the asking element:

522
00:26:00,640 --> 00:26:01,730
"Are you going to be true?",

523
00:26:01,730 --> 00:26:04,380
and they say "Yeah, probably yes,
I just have to work on the details"

524
00:26:04,380 --> 00:26:05,930
and they are going to ask even more.

525
00:26:05,930 --> 00:26:07,980
So your brain is going to light up like a
christmas tree,

526
00:26:07,980 --> 00:26:10,240
and do all these amazing computations,

527
00:26:10,240 --> 00:26:12,450
and you see connections everywhere,
most of them are wrong.

528
00:26:12,450 --> 00:26:16,310
You are basically in psychotic state
if your hyperparameter is too high.

529
00:26:16,310 --> 00:26:20,790
You're brain invents more theories
that it can disproof.

530
00:26:20,790 --> 00:26:24,550
Would it actually sometimes be good
to be in this state?

531
00:26:24,550 --> 00:26:27,850
You bet. So i think every night our brain
goes in this state.

532
00:26:27,850 --> 00:26:31,720
We turn up this hyperparameter.
We dream. We get all kinds

533
00:26:31,720 --> 00:26:34,100
weird connections, and we get to see connections,

534
00:26:34,100 --> 00:26:36,140
that otherwise we couldn't be seeing.

535
00:26:36,140 --> 00:26:38,080
Even though... because they are highly improbable.

536
00:26:38,080 --> 00:26:42,750
But sometimes they hold, and we see... "Oh
my God, DNA is organized in double helix".

537
00:26:42,750 --> 00:26:44,640
And this is what we remember in the morning.

538
00:26:44,640 --> 00:26:46,870
All the other stuff is deleted.

539
00:26:46,870 --> 00:26:48,440
So we usually don't form long term memories

540
00:26:48,440 --> 00:26:51,480
in dreams, if everything goes well.

541
00:26:51,480 --> 00:26:56,670
If you accidentally trip this up.. your modulators,

542
00:26:56,670 --> 00:26:59,100
for instance by consuming illegal substances,

543
00:26:59,100 --> 00:27:01,690
or because you just gone randomly psychotic

544
00:27:01,690 --> 00:27:04,600
you was basically entering
a dreaming state I guess.

545
00:27:04,600 --> 00:27:06,990
You get to a state
when the brain starts inventing more

546
00:27:06,990 --> 00:27:10,860
concepts that it can disproof.

547
00:27:10,860 --> 00:27:13,600
So you want to have a state
where this is well balanced.

548
00:27:13,600 --> 00:27:16,180
And the difference between
highly creative people,

549
00:27:16,180 --> 00:27:20,070
and very religious people is probably
a different setting of this hyperparameter.

550
00:27:20,070 --> 00:27:21,890
So I suspect that people that people
that are genius,

551
00:27:21,890 --> 00:27:23,880
like people like Einstein and so on,

552
00:27:23,880 --> 00:27:26,600
do not simply have better neurons than others.

553
00:27:26,600 --> 00:27:29,130
What they mostly have is a slightly hyperparameter,

554
00:27:29,130 --> 00:27:33,860
that is very finely tuned, so they can get
better balance than other people

555
00:27:33,860 --> 00:27:43,850
in finding theories that might be true,
but can still be disprooven.

556
00:27:43,850 --> 00:27:49,480
So inventiveness could be
a hyperparameter in the brain.

557
00:27:49,480 --> 00:27:54,169
If you want to measure
the quality of belief that we have

558
00:27:54,169 --> 00:27:56,370
we are going to have to have
some kind of some cost function

559
00:27:56,370 --> 00:27:58,710
which is based on motivational system.

560
00:27:58,710 --> 00:28:02,400
And to identify if belief
is good or not we can abstract criteria,

561
00:28:02,400 --> 00:28:06,440
for instance how well does it predict the
wourld, or how about does it reduce uncertainty

562
00:28:06,440 --> 00:28:07,590
in the world,

563
00:28:07,590 --> 00:28:10,020
or is it consistency and sparse.

564
00:28:10,020 --> 00:28:14,080
And then of course utility, how about does
it help me to satisfy my needs.

565
00:28:14,080 --> 00:28:18,920
And the motivational system is going
to evaluate all this things by giving a signal.

566
00:28:18,920 --> 00:28:24,200
And the first signal.. kind of signal
is the possible rewards if we are able to compute

567
00:28:24,200 --> 00:28:25,020
the task.

568
00:28:25,020 --> 00:28:27,430
And this is probably done by dopamine.

569
00:28:27,430 --> 00:28:30,350
So we have a very small area in the brain,
substantia nigra,

570
00:28:30,350 --> 00:28:33,610
and the ventral tegmental area,
and they produce dopamine.

571
00:28:33,610 --> 00:28:38,180
And this get fed into lateral frontal cortext
and the frontal lobe,

572
00:28:38,180 --> 00:28:41,920
which control attention,
and tell you what things to do.

573
00:28:41,920 --> 00:28:46,020
And if we have successfully done
what you wanted to do,

574
00:28:46,020 --> 00:28:49,300
we consume the rewards.

575
00:28:49,300 --> 00:28:51,940
And we do this with another signal
which is serotonine.

576
00:28:51,940 --> 00:28:53,480
It's also announce to motivational system,

577
00:28:53,480 --> 00:28:55,870
to this very small are the Raphe nuclei.

578
00:28:55,870 --> 00:28:58,690
And it feeds into all the areas of the brain
where learning is necessary.

579
00:28:58,690 --> 00:29:02,160
A connection is strengthen
once you get to result.

580
00:29:02,160 --> 00:29:07,559
These two substances are emitted
by the motivational system.

581
00:29:07,559 --> 00:29:09,710
The motivational system is a bunch of needs,

582
00:29:09,710 --> 00:29:11,510
essentially you regulate it below the cortext.

583
00:29:11,510 --> 00:29:14,490
They are not part of your mental representations.

584
00:29:14,490 --> 00:29:16,930
They are part of something
that is more primary than this.

585
00:29:16,930 --> 00:29:19,360
This is what makes us go,
this is what makes us human.

586
00:29:19,360 --> 00:29:22,290
This is not our rationality, this is what we want.

587
00:29:22,290 --> 00:29:27,000
And the needs are physiological,
they are social, they are cognitive.

588
00:29:27,000 --> 00:29:28,960
And you pretty much born with them.

589
00:29:28,960 --> 00:29:30,470
They can not be totally adaptive,

590
00:29:30,470 --> 00:29:33,340
because if we were adaptive,
we wouldn't be doing anything.

591
00:29:33,340 --> 00:29:35,390
The needs are resistive.

592
00:29:35,390 --> 00:29:38,290
They are pushing us against the world.

593
00:29:38,290 --> 00:29:40,170
If you wouldn't have all this needs,

594
00:29:40,170 --> 00:29:41,740
If you wouldn't have this motivational system,

595
00:29:41,740 --> 00:29:43,630
you would just be doing what best for you.

596
00:29:43,630 --> 00:29:45,150
Which means collapse on the ground,

597
00:29:45,150 --> 00:29:49,010
be a vegetable, rod, give into gravity.

598
00:29:49,010 --> 00:29:50,270
Instead you do all this unpleasant things,

599
00:29:50,270 --> 00:29:52,690
to get up in the morning,
you eat, you have sex,

600
00:29:52,690 --> 00:29:54,120
you do all this crazy things.

601
00:29:54,120 --> 00:29:58,809
And it's only because the
motivational system forces you to.

602
00:29:58,809 --> 00:30:00,850
The motivational system
takes this bunch of matter,

603
00:30:00,850 --> 00:30:02,890
and makes us to do all these strange things,

604
00:30:02,890 --> 00:30:05,940
just so genomes get replicated and so on.

605
00:30:05,940 --> 00:30:10,470
And... so to do this, we are going to build
resistance against the world.

606
00:30:10,470 --> 00:30:13,360
And the motivational system
is in a sense forcing us,

607
00:30:13,360 --> 00:30:15,470
to do all this things by giving us needs,

608
00:30:15,470 --> 00:30:18,330
and the need have some kind
of target value and current value.

609
00:30:18,330 --> 00:30:21,850
If we have a differential
between the target value and current value,

610
00:30:21,850 --> 00:30:24,590
we perceive some urgency
to do something about the need.

611
00:30:24,590 --> 00:30:26,680
And when the target value
approaches the current value

612
00:30:26,680 --> 00:30:28,660
we get the pleasure, which is a learning signal.

613
00:30:28,660 --> 00:30:30,540
If it gets away from it
we get a displeasure signal,

614
00:30:30,540 --> 00:30:31,870
which is also a learning signal.

615
00:30:31,870 --> 00:30:35,370
And we can use this to structure
our understanding of the world.

616
00:30:35,370 --> 00:30:36,870
To understand what goals are and so on.

617
00:30:36,870 --> 00:30:40,020
Goals are learned. Needs are not.

618
00:30:40,020 --> 00:30:42,780
To learn we need success
and failure in the world.

619
00:30:42,780 --> 00:30:45,940
But to do things we need anticipated reward.

620
00:30:45,940 --> 00:30:48,120
So it's dopamine that's makes brain go round.

621
00:30:48,120 --> 00:30:50,560
Dopamine makes you do things.

622
00:30:50,560 --> 00:30:52,750
But in order to do this in the right way,

623
00:30:52,750 --> 00:30:54,610
you have to make sure,
that the cells can not

624
00:30:54,610 --> 00:30:55,880
produce dopamine themselves.

625
00:30:55,880 --> 00:30:59,100
If they do this they can start
to drive others to work for them.

626
00:30:59,100 --> 00:31:01,870
You are going to get something like
bureaucracy in your neural cortext,

627
00:31:01,870 --> 00:31:05,650
where different bosses try
to set up others to they own bidding

628
00:31:05,650 --> 00:31:07,910
and pitch against other groups in nerual cortext.

629
00:31:07,910 --> 00:31:09,730
It's going to be horrible.

630
00:31:09,730 --> 00:31:12,210
So you want to have some kind of central authority,

631
00:31:12,210 --> 00:31:16,290
that make sure that the cells
do not produce dopamine themselves.

632
00:31:16,290 --> 00:31:19,679
It's only been produce in
very small area and then given out,

633
00:31:19,679 --> 00:31:21,059
and pass through the system.

634
00:31:21,059 --> 00:31:23,350
And after you're done with it's going to be gone,

635
00:31:23,350 --> 00:31:26,070
so there is no hoarding of the dopamine.

636
00:31:26,070 --> 00:31:29,770
And in our society the role of dopamine
is played by money.

637
00:31:29,770 --> 00:31:32,150
Money is not reward in itself.

638
00:31:32,150 --> 00:31:35,570
It's in some sense way
that you can trade against the reward.

639
00:31:35,570 --> 00:31:36,850
You can not eat money.

640
00:31:36,850 --> 00:31:40,500
You can take it later and take
a arbitrary reward for it.

641
00:31:40,500 --> 00:31:45,400
And in some sense money is the dopamine
that makes organizations

642
00:31:45,400 --> 00:31:48,410
and society, companies
and many individuals do things.

643
00:31:48,410 --> 00:31:50,500
They do stuff because of money.

644
00:31:50,500 --> 00:31:53,309
But money if you compare to dopamine
is pretty broken,

645
00:31:53,309 --> 00:31:54,850
because you can hoard it.

646
00:31:54,850 --> 00:31:57,400
So you are going to have this
cortical columns in the real world,

647
00:31:57,400 --> 00:31:59,670
which are individual people
or individual corporations.

648
00:31:59,670 --> 00:32:03,250
They are hoarding the dopamine,
they sit on this very big pile of dopamine.

649
00:32:03,250 --> 00:32:07,890
They are starving the rest
of the society of the dopamine.

650
00:32:07,890 --> 00:32:10,630
They don't give it away,
and they can make it do it's bidding.

651
00:32:10,630 --> 00:32:13,970
So for instance they can pitch
substantial part of society

652
00:32:13,970 --> 00:32:16,130
against understanding of global warming.

653
00:32:16,130 --> 00:32:20,110
because they profit of global warming
or of technology that leads to global warming,

654
00:32:20,110 --> 00:32:22,850
which is very bad for all of us. <i>applause</i>

655
00:32:22,850 --> 00:32:28,850
So our society is a nervous system
that lies to itself.

656
00:32:28,850 --> 00:32:30,429
How can we overcome this?

657
00:32:30,429 --> 00:32:32,480
Actually, we don't know.

658
00:32:32,480 --> 00:32:34,639
To do this we would need
to have some kind of centrialized,

659
00:32:34,639 --> 00:32:36,660
top-down reward motivational system.

660
00:32:36,660 --> 00:32:39,010
We have this for instance in the military,

661
00:32:39,010 --> 00:32:42,520
you have this system of
military rewards that you get.

662
00:32:42,520 --> 00:32:44,950
And this are completely
controlled from the top.

663
00:32:44,950 --> 00:32:47,260
Also within working organizations
you have this.

664
00:32:47,260 --> 00:32:49,600
In corporations you have centralized rewards,

665
00:32:49,600 --> 00:32:51,850
it's not like rewards flow bottom-up,

666
00:32:51,850 --> 00:32:55,120
they always flown top-down.

667
00:32:55,120 --> 00:32:57,850
And there was an attempt
to model society in such a way.

668
00:32:57,850 --> 00:33:03,380
That was in Chile in the early 1970,
the Allende government had the idea

669
00:33:03,380 --> 00:33:07,320
to redesign society or economy
in society using cybernetics.

670
00:33:07,320 --> 00:33:12,590
So Allende invited a bunch of cyberneticians
to redesign the Chilean economy.

671
00:33:12,590 --> 00:33:14,550
And this was meant to be the control room,

672
00:33:14,550 --> 00:33:17,460
where Allende and his chief economists
would be sitting,

673
00:33:17,460 --> 00:33:19,709
to look at what the economy is doing.

674
00:33:19,709 --> 00:33:23,880
We don't know how this would work out,
because we know how it ended.

675
00:33:23,880 --> 00:33:27,260
In 1973 there was this big putsch in Chile,

676
00:33:27,260 --> 00:33:30,290
and this experiment ended among other things.

677
00:33:30,290 --> 00:33:34,170
Maybe it would have worked, who knows?
Nobody tried it.

678
00:33:34,170 --> 00:33:38,370
So, there is something else
what is going on in people,

679
00:33:38,370 --> 00:33:40,030
beyond the motivational system.

680
00:33:40,030 --> 00:33:43,610
That is: we have social criteria, for learning.

681
00:33:43,610 --> 00:33:47,670
We also check if our ideas
are normativly acceptable.

682
00:33:47,670 --> 00:33:50,510
And this is actually a good thing,
because individual may shortcut

683
00:33:50,510 --> 00:33:52,590
the learning through communication.

684
00:33:52,590 --> 00:33:55,260
Other people have learned stuff
that we don't need to learn ourselves.

685
00:33:55,260 --> 00:33:59,800
We can build on this, so we can accelerate
learning by many order of magnitutde,

686
00:33:59,800 --> 00:34:00,970
which makes culture possible.

687
00:34:00,970 --> 00:34:04,190
And which makes many anything possible,
because if you were on your own

688
00:34:04,190 --> 00:34:06,860
you would not be going to find out
very much in your lifetime.

689
00:34:08,520 --> 00:34:11,270
You know how they say?
Everything that you do,

690
00:34:11,270 --> 00:34:14,250
you do by standing on the shoulders of giants.

691
00:34:14,250 --> 00:34:17,779
Or on a big pile of dwarfs
it works either way.

692
00:34:17,779 --> 00:34:27,089
<i>laughter</i><i>applause</i>

693
00:34:27,089 --> 00:34:30,379
Social learning usually outperforms
individual learning. You can test this.

694
00:34:30,379 --> 00:34:33,949
But in the case of conflict
between different social truths,

695
00:34:33,949 --> 00:34:36,659
you need some way to decide who to believe.

696
00:34:36,659 --> 00:34:39,498
So you have some kind of reputation
estimate for different authority,

697
00:34:39,498 --> 00:34:42,399
and you use this to check whom you believe.

698
00:34:42,399 --> 00:34:45,748
And the problem of course is this
in existing society, in real society,

699
00:34:45,748 --> 00:34:48,389
this reputation system is going
to reflect power structure,

700
00:34:48,389 --> 00:34:51,699
which may distort your belief systematically.

701
00:34:51,699 --> 00:34:54,759
Social learning therefore leads groups
to synchronize their opinions.

702
00:34:54,759 --> 00:34:57,220
And the opinions become ...get another role.

703
00:34:57,220 --> 00:35:02,180
They become important part
of signalling which group you belong to.

704
00:35:02,180 --> 00:35:06,630
So opinions start to signal
group loyalty in societies.

705
00:35:06,630 --> 00:35:11,170
And people in this, and that's the actual world,
they should optimize not for getting the best possible

706
00:35:11,170 --> 00:35:12,619
opinions in terms of truth.

707
00:35:12,619 --> 00:35:17,289
They should guess... they should optimize
for doing... having the best possible opinion,

708
00:35:17,289 --> 00:35:19,799
with respect to agreement with their peers.

709
00:35:19,799 --> 00:35:22,029
If you have the same opinion
as your peers, you can signal them

710
00:35:22,029 --> 00:35:24,299
that you are the part of their ingroup,
they are going to like you.

711
00:35:24,299 --> 00:35:28,160
If you don't do this, chances are
they are not going to like you.

712
00:35:28,160 --> 00:35:34,049
There is rarely any benefit in life to be
in disagreement with your boss. Right?

713
00:35:34,049 --> 00:35:39,230
So, if you evolve an opinion forming system
in these curcumstances,

714
00:35:39,230 --> 00:35:41,220
you should be ending up
with an opinion forming system,

715
00:35:41,220 --> 00:35:42,980
that leaves you with the most usefull opinion,

716
00:35:42,980 --> 00:35:45,400
which is the opinion in your environment.

717
00:35:45,400 --> 00:35:48,400
And it turns out, most people are able
to do this effortlessly.

718
00:35:48,400 --> 00:35:50,969
<i>laughter</i>

719
00:35:50,969 --> 00:35:55,529
They have an instinct, that makes them adapt
the dominant opinion in their social environment.

720
00:35:55,529 --> 00:35:56,599
It's amazing, right?

721
00:35:56,599 --> 00:36:01,040
And if you are nerd like me,
you don't get this.

722
00:36:01,040 --> 00:36:08,999
<i>lauging</i><i>applause</i>

723
00:36:08,999 --> 00:36:12,999
So in the world out there,
explanations piggyback on you group allegiance.

724
00:36:12,999 --> 00:36:15,900
For instance you will find that there is a
substantial group of people that believes

725
00:36:15,900 --> 00:36:18,380
the minimum wage is good
for the economy and for you

726
00:36:18,380 --> 00:36:20,549
and another one believes that its bad.

727
00:36:20,549 --> 00:36:23,470
And its pretty much aligned
with political parties.

728
00:36:23,470 --> 00:36:25,970
Its not aligned with different
understandings of economy,

729
00:36:25,970 --> 00:36:30,740
because nobody understands
how the economy works.

730
00:36:30,740 --> 00:36:36,330
And if you are a nerd you try to understand
the world in terms of what is true and false.

731
00:36:36,330 --> 00:36:40,680
You try to prove everything by putting it
in some kind of true and false level

732
00:36:40,680 --> 00:36:43,589
and if you are not a nerd
you try to get to right and wrong

733
00:36:43,589 --> 00:36:45,609
you try to understand
whether you are in alignment

734
00:36:45,609 --> 00:36:49,559
with what's objectively right
in your society, right?

735
00:36:49,559 --> 00:36:55,680
So I guess that nerds are people that have
a defect in there opinion forming system.

736
00:36:55,680 --> 00:36:57,069
<i>laughing</i>

737
00:36:57,069 --> 00:37:00,609
And usually that's maladaptive
and under normal circumstances

738
00:37:00,609 --> 00:37:03,099
nerds would mostly be filtered
from the world,

739
00:37:03,099 --> 00:37:06,529
because they don't reproduce so well,
because people don't like them so much.

740
00:37:06,529 --> 00:37:07,960
<i>laughing</i>

741
00:37:07,960 --> 00:37:11,119
And then something very strange happened.
The computer revolution came along and

742
00:37:11,119 --> 00:37:14,170
suddenly if you argue with the computer
it doesn't help you if you have the

743
00:37:14,170 --> 00:37:17,849
normatively correct opinion you need to
be able to understand things in terms of

744
00:37:17,849 --> 00:37:26,029
true and false, right? <i>applause</i>

745
00:37:26,029 --> 00:37:29,779
So now we have this strange situation that
the weird people that have this offensive,

746
00:37:29,779 --> 00:37:33,410
strange opinions and that really don't
mix well with the real normal people

747
00:37:33,410 --> 00:37:38,119
get all this high paying jobs
and we don't understand how is that happening.

748
00:37:38,119 --> 00:37:42,599
And it's because suddenly
our maladapting is a benefit.

749
00:37:42,599 --> 00:37:47,300
But out there there is this world of the
social norms and it's made of paperwalls.

750
00:37:47,300 --> 00:37:50,349
There are all this things that are true
and false in a society that make

751
00:37:50,349 --> 00:37:51,549
people behave.

752
00:37:51,549 --> 00:37:57,390
It's like this japanese wall, there.
They made palaces out of paper basically.

753
00:37:57,390 --> 00:38:00,339
And these are walls by convention.

754
00:38:00,339 --> 00:38:04,009
They exist because people agree
that this is a wall.

755
00:38:04,009 --> 00:38:06,630
And if you are a hypnotist
like Donald Trump

756
00:38:06,630 --> 00:38:11,109
you can see that these are paper walls
and you can shift them.

757
00:38:11,109 --> 00:38:14,079
And if you are a nerd like me
you can not see these paperwalls.

758
00:38:14,079 --> 00:38:20,230
If you pay closely attention you see that
people move and then suddenly middair

759
00:38:20,230 --> 00:38:22,869
they make a turn. Why would they do this?

760
00:38:22,869 --> 00:38:24,360
There must be something
that they see there

761
00:38:24,360 --> 00:38:26,549
and this is basically a normative agreement.

762
00:38:26,549 --> 00:38:29,690
And you can infer what this is
and then you can manipulate it and understand it.

763
00:38:29,690 --> 00:38:32,640
Of course you can't fix this, you can
debug yourself in this regard,

764
00:38:32,640 --> 00:38:34,690
but it's something that is hard
to see for nerds.

765
00:38:34,690 --> 00:38:38,109
So in some sense they have a superpower:
they can think straight in the presence

766
00:38:38,109 --> 00:38:39,079
of others.

767
00:38:39,079 --> 00:38:42,590
But often they end up in their living room
and people are upset.

768
00:38:42,590 --> 00:38:45,810
<i>laughter</i>

769
00:38:45,810 --> 00:38:49,789
Learning in a complex domain can not
guarantee that you find the global maximum.

770
00:38:49,789 --> 00:38:53,970
We know that we can not find truth
because we can not recognize whether we live

771
00:38:53,970 --> 00:38:57,059
on a plain field or on a
simulated plain field.

772
00:38:57,059 --> 00:39:00,579
But what we can do is, we can try to
approach a global maximum.

773
00:39:00,579 --> 00:39:02,339
But we don't know if that
is the global maximum.

774
00:39:02,339 --> 00:39:05,509
We will always move along
some kind of belief gradient.

775
00:39:05,509 --> 00:39:09,110
We will take certain elements of
our belief and then give them up

776
00:39:09,110 --> 00:39:12,650
for new elements of a belief based on
thinking, that this new element

777
00:39:12,650 --> 00:39:15,049
of belief is better than the one
we give up.

778
00:39:15,049 --> 00:39:17,079
So we always move along
some kind of gradient.

779
00:39:17,079 --> 00:39:19,789
and the truth does not matter,
the gradient matters.

780
00:39:19,789 --> 00:39:23,650
If you think about teaching for a moment,
when I started teaching I often thought:

781
00:39:23,650 --> 00:39:27,489
Okay, I understand the truth of the
subject, the students don't, so I have to

782
00:39:27,489 --> 00:39:30,069
give this to them
and at some point I realized:

783
00:39:30,069 --> 00:39:33,450
Oh, I changed my mind so many times
in the past and I'm probably not going to

784
00:39:33,450 --> 00:39:35,769
stop changing it in the future.

785
00:39:35,769 --> 00:39:38,710
I'm always moving along a gradient
and I keep moving along a gradient.

786
00:39:38,710 --> 00:39:43,099
So I'm not moving to truth,
I'm moving forward.

787
00:39:43,099 --> 00:39:45,230
And when we teach our kids
we should probably not think about

788
00:39:45,230 --> 00:39:46,390
how to give them truth.

789
00:39:46,390 --> 00:39:51,039
We should think about how to put them onto
an interesting gradient, that makes them

790
00:39:51,039 --> 00:39:55,079
explore the world,
world of possible beliefs.

791
00:39:55,079 --> 00:40:03,150
<i>applause</i>

792
00:40:03,150 --> 00:40:05,359
And this possible beliefs
lead us into local minima.

793
00:40:05,359 --> 00:40:08,150
This is inevitable. This are like valleys
and sometimes this valleys are

794
00:40:08,150 --> 00:40:11,210
neighbouring and we don't understand
what the people in the neighbouring

795
00:40:11,210 --> 00:40:15,700
valley are doing unless we are willing to
retrace the steps they have been taken.

796
00:40:15,700 --> 00:40:19,569
And if you want to get from one valley
into the next, we will have to have some kind

797
00:40:19,569 --> 00:40:21,789
of energy that moves us over the hill.

798
00:40:21,789 --> 00:40:27,739
We have to have a trajectory were every
step works by finding reason to give up

799
00:40:27,739 --> 00:40:30,380
bit of our current belief and adopt a
new belief, because it's somehow

800
00:40:30,380 --> 00:40:34,739
more useful, more relevant,
more consistent and so on.

801
00:40:34,739 --> 00:40:38,349
Now the problem is that this is not
monotonous we can not guarantee that

802
00:40:38,349 --> 00:40:40,499
we're always climbing,
because the problem is, that

803
00:40:40,499 --> 00:40:44,599
the beliefs themselfs can change
our evaluation of the belief.

804
00:40:44,599 --> 00:40:50,390
It could be for instance that you start
believing in a religion and this religion

805
00:40:50,390 --> 00:40:54,299
could tell you: If you give up the belief
in the religion, you're going to face

806
00:40:54,299 --> 00:40:56,500
eternal damnation in hell.

807
00:40:56,500 --> 00:40:59,489
As long as you believe in the religion,
it's going to be very expensive for you

808
00:40:59,489 --> 00:41:02,430
to give up the religion, right?
If you truly belief in it.

809
00:41:02,430 --> 00:41:05,109
You're now caught
in some kind of attractor.

810
00:41:05,109 --> 00:41:08,680
Before you believe the religion it is not
very dangerous but once you've gotten

811
00:41:08,680 --> 00:41:13,019
into the attractor it's very,
very hard to get out.

812
00:41:13,019 --> 00:41:16,309
So these belief attractors
are actually quite dangerous.

813
00:41:16,309 --> 00:41:19,920
You can get not only to chaotic behaviour,
where you can not guarantee that your

814
00:41:19,920 --> 00:41:23,470
current belief is better than the last one
but you can also get into beliefs that are

815
00:41:23,470 --> 00:41:26,849
almost impossible to change.

816
00:41:26,849 --> 00:41:33,739
And that makes it possible to program
people to work in societies.

817
00:41:33,739 --> 00:41:37,529
Social domains are structured by values.
Basically a preference is what makes you

818
00:41:37,529 --> 00:41:40,769
do things, because you anticipate
pleasure or displeasure,

819
00:41:40,769 --> 00:41:45,339
and values make you do things
even if you don't anticipate any pleasure.

820
00:41:45,339 --> 00:41:49,809
These are virtual rewards.
They make us do things, because we believe

821
00:41:49,809 --> 00:41:51,799
that is stuff
that is more important then us.

822
00:41:51,799 --> 00:41:55,109
This is what values are about.

823
00:41:55,109 --> 00:42:00,690
And these values are the source
of what we would call true meaning, deeper meaning.

824
00:42:00,690 --> 00:42:05,220
There is something that is more important
than us, something that we can serve.

825
00:42:05,220 --> 00:42:08,769
This is what we usually perceive as
meaningful life, it is one which

826
00:42:08,769 --> 00:42:12,759
is in the serves of values that are more
important than I myself,

827
00:42:12,759 --> 00:42:15,749
because after all I'm not that important.
I'm just this machine that runs around

828
00:42:15,749 --> 00:42:20,789
and tries to optimize its pleasure and
pain, which is kinda boring.

829
00:42:20,789 --> 00:42:26,329
So my PI has puzzled me, my principle
investigator in the Havard department,

830
00:42:26,329 --> 00:42:29,349
where I have my desk, Martin Nowak.

831
00:42:29,349 --> 00:42:33,970
He said, that meaning can not exist without
god; you are either religious,

832
00:42:33,970 --> 00:42:36,950
or you are a nihilist.

833
00:42:36,950 --> 00:42:42,789
And this guy is the head of the
department for evolutionary dynamics.

834
00:42:42,789 --> 00:42:45,769
Also he is a catholic.. <i>chuckling</i>

835
00:42:45,769 --> 00:42:49,729
So this really puzzled me and I tried
to understand what he meant by this.

836
00:42:49,729 --> 00:42:53,200
Typically if you are a good atheist
like me,

837
00:42:53,200 --> 00:42:57,920
you tend to attack gods that are
structured like this, religious gods,

838
00:42:57,920 --> 00:43:02,940
that are institutional, they are personal,
they are some kind of person.

839
00:43:02,940 --> 00:43:08,239
They do care about you, they prescribe
norms, for instance don't mastrubate

840
00:43:08,239 --> 00:43:10,060
it's bad for you.

841
00:43:10,060 --> 00:43:14,759
Many of this norms are very much aligned
with societal institutions, for instance

842
00:43:14,759 --> 00:43:20,799
don't questions the authorities,
god wants them to be ruling above you

843
00:43:20,799 --> 00:43:23,839
and be monogamous and so on and so on.

844
00:43:23,839 --> 00:43:28,979
So they prescribe norms that do not make
a lot of sense in terms of beings that

845
00:43:28,979 --> 00:43:31,200
creates world every now and then,

846
00:43:31,200 --> 00:43:34,619
but they make sense in terms of
what you should be doing to be a

847
00:43:34,619 --> 00:43:36,730
functioning member of society.

848
00:43:36,730 --> 00:43:40,799
And this god also does things like it
creates world, they like to manifest as

849
00:43:40,799 --> 00:43:43,660
burning shrubbery and so on. There are
many books that describe stories that

850
00:43:43,660 --> 00:43:45,700
these gods have allegedly done.

851
00:43:45,700 --> 00:43:48,819
And it's very hard to test for all these
features which makes this gods very

852
00:43:48,819 --> 00:43:54,280
improbable for us. And makes Atheist
very dissatisfied with these gods.

853
00:43:54,280 --> 00:43:56,569
But then there is a different kind of god.

854
00:43:56,569 --> 00:43:58,599
This is what we call the spiritual god.

855
00:43:58,599 --> 00:44:02,410
This spiritual god is independent of
institutions, it still does care about you.

856
00:44:02,410 --> 00:44:06,489
It's probably conscious. It might not be a
person. There are not that many stories,

857
00:44:06,489 --> 00:44:10,579
that you can consistently tell about it,
but you might be able to connect to it

858
00:44:10,579 --> 00:44:15,259
spiritually.

859
00:44:15,259 --> 00:44:19,470
Then there is a god that is even less
expensive. That is god as a transcendental

860
00:44:19,470 --> 00:44:23,489
principle and this god is simply the reason
why there is something rather then

861
00:44:23,489 --> 00:44:28,150
nothing. This god is the question the
universe is the answer to, this is the

862
00:44:28,150 --> 00:44:29,600
thing that gives meaning.

863
00:44:29,600 --> 00:44:31,489
Everything else about it is unknowable.

864
00:44:31,489 --> 00:44:34,190
This is the god of Thomas of Aquinus.

865
00:44:34,190 --> 00:44:38,089
The God that Thomas of Aquinus discovered
is not the god of Abraham this is not the

866
00:44:38,089 --> 00:44:39,180
religious god.

867
00:44:39,180 --> 00:44:43,559
It's a god that is basically a principle
that us ... the universe into existence.

868
00:44:43,559 --> 00:44:47,140
It's the one that gives
the universe it's purpose.

869
00:44:47,140 --> 00:44:50,200
And because every other property
is unknowable about this,

870
00:44:50,200 --> 00:44:52,010
this god is not that expensive.

871
00:44:52,010 --> 00:44:55,960
Unfortunately it doesn't really work.
I mean Thomas of Aquinus tried to prove

872
00:44:55,960 --> 00:45:00,049
god. He tried to prove an necessary god,
a god that has to be existing and

873
00:45:00,049 --> 00:45:02,779
I think we can only prove a possible god.

874
00:45:02,779 --> 00:45:05,339
So if you try to prove a necessary god,
this god can not exist.

875
00:45:05,339 --> 00:45:11,650
Which means your god prove is going to
fail. You can only prove possible gods.

876
00:45:11,650 --> 00:45:13,259
And then there is an even more improper god.

877
00:45:13,259 --> 00:45:15,890
And that's the god of Aristotle and he said:

878
00:45:15,890 --> 00:45:20,069
"If there is change in the universe,
something in going to have to change it."

879
00:45:20,069 --> 00:45:23,640
There must be something that moves it
along from one state to the next.

880
00:45:23,640 --> 00:45:26,289
So I would say that is the primary
computational transition function

881
00:45:26,289 --> 00:45:35,079
of the universe.
<i>laughing</i><i>applause</i>

882
00:45:35,079 --> 00:45:38,439
And Aristotle discovered it.
It's amazing isn't it?

883
00:45:38,439 --> 00:45:41,509
We have to have this because we
can not be conscious in a single state.

884
00:45:41,509 --> 00:45:43,279
We need to move between states
to be conscious.

885
00:45:43,279 --> 00:45:45,979
We need to be processes.

886
00:45:45,979 --> 00:45:50,859
So we can take our gods and sort them by
their metaphysical cost.

887
00:45:50,859 --> 00:45:53,290
The 1st degree god would be the first mover.

888
00:45:53,290 --> 00:45:56,069
The 2nd degree god is the god of purpose and meaning.

889
00:45:56,069 --> 00:45:59,089
3rd degree god is the spiritual god.
And the 4th degree god is this bound to

890
00:45:59,089 --> 00:46:01,229
religious institutions, right?

891
00:46:01,229 --> 00:46:03,720
So if you take this statement
from Martin Nowak,

892
00:46:03,720 --> 00:46:07,759
"You can not have meaning without god!"
I would say: yes! You need at least

893
00:46:07,759 --> 00:46:14,990
a 2nd degree god to have meaning.
So objective meaning can only exist

894
00:46:14,990 --> 00:46:19,119
with a 2nd degree god. <i>chuckling</i>

895
00:46:19,119 --> 00:46:22,269
And subjective meaning can exist as a
function in a cognitive system of course.

896
00:46:22,269 --> 00:46:24,180
We don't need objective meaning.

897
00:46:24,180 --> 00:46:27,410
So we can subjectively feel that there is
something more important to us

898
00:46:27,410 --> 00:46:30,509
and this makes us work in society and
makes us perceive that we have values

899
00:46:30,509 --> 00:46:34,329
and so on, but we don't need to believe
that there is something outside of the

900
00:46:34,329 --> 00:46:36,869
universe to have this.

901
00:46:36,869 --> 00:46:40,650
So the 4th degree god is the one
that is bound to religious institutions,

902
00:46:40,650 --> 00:46:45,400
it requires a belief attractor and it
enables complex norm prescriptions.

903
00:46:45,400 --> 00:46:48,430
It my theory is right then it should be
much harder for nerds to believe in

904
00:46:48,430 --> 00:46:52,039
a 4th degree god then for normal people.

905
00:46:52,039 --> 00:46:56,489
And what this god does it allows you to
have state building mind viruses.

906
00:46:56,489 --> 00:47:00,269
Basically religion is a mind virus. And
the amazing thing about these mind viruses

907
00:47:00,269 --> 00:47:02,489
is that they structure behaviour
in large groups.

908
00:47:02,489 --> 00:47:06,130
We have evolved to live in small groups
of a few 100 individuals, maybe somthing

909
00:47:06,130 --> 00:47:07,249
like a 150.

910
00:47:07,249 --> 00:47:10,059
This is roughly the level
to which reputation works.

911
00:47:10,059 --> 00:47:15,369
We can keep track of about 150 people and
after this it gets much much worse.

912
00:47:15,369 --> 00:47:18,290
So in this system where you have
reputation people feel responsible

913
00:47:18,290 --> 00:47:21,349
for each other and they can
keep track of their doings

914
00:47:21,349 --> 00:47:23,049
and society kind of sort of works.

915
00:47:23,049 --> 00:47:27,789
If you want to go beyond this, you have
to right a software that controls people.

916
00:47:27,789 --> 00:47:32,420
And religions were the first software,
that did this on a very large scale.

917
00:47:32,420 --> 00:47:35,319
And in order to keep stable they had to be
designed like operating systems

918
00:47:35,319 --> 00:47:36,039
in some sense.

919
00:47:36,039 --> 00:47:39,930
They give people different roles
like insects in a hive.

920
00:47:39,930 --> 00:47:44,529
And they have even as part of this roles is
to update this religion but it has to be

921
00:47:44,529 --> 00:47:48,380
done very carefully and centrally
because otherwise the religion will split apart

922
00:47:48,380 --> 00:47:51,719
and fall together into new religions
or be overcome by new ones.

923
00:47:51,719 --> 00:47:54,259
So there is some kind of
evolutionary dynamics that goes on

924
00:47:54,259 --> 00:47:55,930
with respect to religion.

925
00:47:55,930 --> 00:47:58,519
And if you look the religions,
there is actually a veritable evolution

926
00:47:58,519 --> 00:47:59,739
of religions.

927
00:47:59,739 --> 00:48:04,789
So we have this Israelic tradition and
the Mesoputanic mythology that gave rise

928
00:48:04,789 --> 00:48:13,019
to Judaism. <i>applause</i>

929
00:48:13,019 --> 00:48:16,299
It's kind of cool, right? <i>laughing</i>

930
00:48:16,299 --> 00:48:36,289
Also history totally repeats itself.
<i>roaring laughter</i><i>applause</i>

931
00:48:36,289 --> 00:48:41,889
Yeah, it totally blew my mind when
I discovered this. <i>laughter</i>

932
00:48:41,889 --> 00:48:45,039
Of course the real tree of programming
languages is slightly more complicated,

933
00:48:45,039 --> 00:48:48,599
And the real tree of religion is slightly
more complicated.

934
00:48:48,599 --> 00:48:51,229
But still its neat.

935
00:48:51,229 --> 00:48:54,289
So if you want to immunize yourself
against mind viruses,

936
00:48:54,289 --> 00:48:58,570
first of all you want to check yourself
whether you are infected.

937
00:48:58,570 --> 00:49:02,809
You should check: Can I let go of my
current beliefs without feeling that

938
00:49:02,809 --> 00:49:07,670
meaning departures me and I feel very
terrible, when I let go of my beliefs.

939
00:49:07,670 --> 00:49:11,279
Also you should check: All the other
people around there that don't

940
00:49:11,279 --> 00:49:17,019
share my belief, are they either stupid,
or crazy, or evil?

941
00:49:17,019 --> 00:49:19,890
If you think this chances are you are
infected by some kind of mind virus,

942
00:49:19,890 --> 00:49:23,710
because they are just part
of the out group.

943
00:49:23,710 --> 00:49:28,059
And does your god have properties that
you know but you did not observe.

944
00:49:28,059 --> 00:49:32,490
So basically you have a god
of 2nd or 3rd degree or higher.

945
00:49:32,490 --> 00:49:34,589
In this case you also probably got a mind virus.

946
00:49:34,589 --> 00:49:37,259
There is nothing wrong
with having a mind virus,

947
00:49:37,259 --> 00:49:39,920
but if you want to immunize yourself
against this people have invented

948
00:49:39,920 --> 00:49:44,059
rationalism and enlightenment,
basically to act as immunization against

949
00:49:44,059 --> 00:49:50,660
mind viruses.
<i>loud applause</i>

950
00:49:50,660 --> 00:49:53,869
And in some sense its what the mind does
by itself because, if you want to

951
00:49:53,869 --> 00:49:56,949
understand how you go wrong,
you need to have a mechanism

952
00:49:56,949 --> 00:49:58,839
that discovers who you are.

953
00:49:58,839 --> 00:50:03,109
Some kind of auto debugging mechanism,
that makes the mind aware of itself.

954
00:50:03,109 --> 00:50:04,779
And this is actually the self.

955
00:50:04,779 --> 00:50:08,339
So according to Robert Kegan:
"The development of ourself is a process,

956
00:50:08,339 --> 00:50:13,400
in which we learn who we are by making
thing explicit", by making processes that

957
00:50:13,400 --> 00:50:17,249
are automatic visible to us and by
conceptualize them so we no longer

958
00:50:17,249 --> 00:50:18,859
identify with them.

959
00:50:18,859 --> 00:50:22,019
And it starts out with understanding
that there is only pleasure and pain.

960
00:50:22,019 --> 00:50:25,180
If you are a baby, you have only
pleasure and pain you identify with this.

961
00:50:25,180 --> 00:50:27,869
And then you turn into a toddler and the
toddler understands that they are not

962
00:50:27,869 --> 00:50:31,059
their pleasure and pain
but they are their impulses.

963
00:50:31,059 --> 00:50:34,259
And in the next level if you grow beyond
the toddler age you actually know that

964
00:50:34,259 --> 00:50:38,880
you have goals and that your needs and
impulses are there to serve goals, but its

965
00:50:38,880 --> 00:50:40,210
very difficult to let go of the goals,

966
00:50:40,210 --> 00:50:42,789
if you are a very young child.

967
00:50:42,789 --> 00:50:46,329
And at some point you realize: Oh, the
goals don't really matter, because

968
00:50:46,329 --> 00:50:49,509
sometimes you can not reach them, but
we have preferences, we have thing that we

969
00:50:49,509 --> 00:50:52,950
want to happen and thing that we do not
want to happen. And then at some point

970
00:50:52,950 --> 00:50:55,869
we realize that other people have
preferences, too.

971
00:50:55,869 --> 00:50:58,979
And then we start to model the world
as a system where different people have

972
00:50:58,979 --> 00:51:01,940
different preferences and we have
to navigate this landscape.

973
00:51:01,940 --> 00:51:06,420
And then we realize that this preferences
also relate to values and we start

974
00:51:06,420 --> 00:51:09,700
to identify with this values as members of
society.

975
00:51:09,700 --> 00:51:13,469
And this is basically the stage if you
are an adult being, that you get into.

976
00:51:13,469 --> 00:51:16,910
And you can get to a stage beyond that,
especially if you have people this, which

977
00:51:16,910 --> 00:51:20,059
have already done this. And this means
that you understand that people have

978
00:51:20,059 --> 00:51:23,660
different values and what they do
naturally flows out of them.

979
00:51:23,660 --> 00:51:26,849
And this values are not necessarily worse
than yours they are just different.

980
00:51:26,849 --> 00:51:29,450
And you learn that you can hold different
sets of values in your mind at

981
00:51:29,450 --> 00:51:33,019
the same time, isn't that amazing?
and understand other people, even if

982
00:51:33,019 --> 00:51:36,660
they are not part of your group.
If you get that, this is really good.

983
00:51:36,660 --> 00:51:39,269
But I don't think it stops there.

984
00:51:39,269 --> 00:51:43,019
You can also learn that the stuff that
you perceive is kind of incidental,

985
00:51:43,019 --> 00:51:45,339
that you can turn it of and you can
manipulate it.

986
00:51:45,339 --> 00:51:49,940
And at some point you also can realize
that yourself is only incidental that you

987
00:51:49,940 --> 00:51:52,559
can manipulate it or turn it of.
And that your basically some kind of

988
00:51:52,559 --> 00:51:57,420
consciousness that happens to run a brain
of some kind of person, that navigates

989
00:51:57,420 --> 00:52:04,279
the world in terms to get rewards or avoid
displeasure and serve values and so on,

990
00:52:04,279 --> 00:52:05,130
but it doesn't really matter.

991
00:52:05,130 --> 00:52:08,119
There is just this consciousness which
understands the world.

992
00:52:08,119 --> 00:52:11,009
And this is the stage that we typically
call enlightenment.

993
00:52:11,009 --> 00:52:14,549
In this stage you realize that you are not
your brain, but you are a story that

994
00:52:14,549 --> 00:52:25,640
your brain tells itself.
<i>applause</i>

995
00:52:25,640 --> 00:52:29,630
So becoming self aware is a process of
reverse engineering your mind.

996
00:52:29,630 --> 00:52:32,890
Its a different set of stages in which
to realize what goes on.

997
00:52:32,890 --> 00:52:33,799
So isn't that amazing.

998
00:52:33,799 --> 00:52:38,930
AI is a way to get to more self awareness?

999
00:52:38,930 --> 00:52:41,319
I think that is a good point to stop here.

1000
00:52:41,319 --> 00:52:44,499
The first talk that I gave in this series
was 2 years ago. It was about

1001
00:52:44,499 --> 00:52:45,979
how to build a mind.

1002
00:52:45,979 --> 00:52:49,670
Last year I talked about how to get from
basic computation to consciousness.

1003
00:52:49,670 --> 00:52:53,709
And this year we have talked about
finding meaning using AI.

1004
00:52:53,709 --> 00:52:57,470
I wonder where it goes next.
<i>laughter</i>

1005
00:52:57,470 --> 00:53:22,769
<i>applause</i>

1006
00:53:22,769 --> 00:53:26,489
Herald: Thank you for this amazing talk!
We now have some minutes for Q&A.

1007
00:53:26,489 --> 00:53:31,190
So please line up at the microphones as
always. If you are unable to stand up

1008
00:53:31,190 --> 00:53:36,430
for some reason please very very visibly
rise your hand, we should be able to dispatch

1009
00:53:36,430 --> 00:53:40,099
an audio angle to your location
so you can have a question too.

1010
00:53:40,099 --> 00:53:44,030
And also if you are locationally
disabled, you are not actually in the room

1011
00:53:44,030 --> 00:53:49,069
if you are on the stream, you can use IRC
or twitter to also ask questions.

1012
00:53:49,069 --> 00:53:50,989
We also have a person for that.

1013
00:53:50,989 --> 00:53:53,779
We will start at microphone number 2.

1014
00:53:53,779 --> 00:53:59,940
Q: Wow that's me. Just a guess! What
would you guess, when can you discuss

1015
00:53:59,940 --> 00:54:04,559
your talk with a machine,
in how many years?

1016
00:54:04,559 --> 00:54:07,400
Joscha: I don't know! As a software
engineer I know if I don't have the

1017
00:54:07,400 --> 00:54:12,619
specification all bets are off, until I
have the implementation. <i>laughter</i>

1018
00:54:12,619 --> 00:54:14,509
So it can be of any order of magnitude.

1019
00:54:14,509 --> 00:54:18,249
I have a gut feeling but I also know as a
software engineer that my gut feeling is

1020
00:54:18,249 --> 00:54:23,450
usually wrong, <i>laughter</i>
until I have the specification.

1021
00:54:23,450 --> 00:54:28,200
So the question is if there are silver
bullets? Right now there are some things

1022
00:54:28,200 --> 00:54:30,569
that are not solved yet and it could be
that they are easier to solve

1023
00:54:30,569 --> 00:54:33,469
than we think, but it could be that
they're harder to solve than we think.

1024
00:54:33,469 --> 00:54:36,710
Before I stumbled on this cortical
self organization thing,

1025
00:54:36,710 --> 00:54:40,719
I thought it's going to be something like
maybe 60, 80 years and now I think it's

1026
00:54:40,719 --> 00:54:47,289
way less, but again this is a very
subjective perspective. I don't know.

1027
00:54:47,289 --> 00:54:49,240
Herald: Number 1, please!

1028
00:54:49,240 --> 00:54:55,589
Q: Yes, I wanted to ask a little bit about
metacognition. It seems that you kind of

1029
00:54:55,589 --> 00:55:01,329
end your story saying that it's still
reflecting on input that you get and

1030
00:55:01,329 --> 00:55:04,900
kind of working with your social norms
and this and that, but Colberg

1031
00:55:04,900 --> 00:55:11,839
for instance talks about what he calls a
postconventional universal morality

1032
00:55:11,839 --> 00:55:17,420
for instance, which is thinking about
moral laws without context, basically

1033
00:55:17,420 --> 00:55:23,069
stating that there is something beyond the
relative norm that we have to each other,

1034
00:55:23,069 --> 00:55:29,579
which would only be possible if you can do
kind of, you know, meta cognition,

1035
00:55:29,579 --> 00:55:32,599
thinking about your own thinking
and then modifying that thinking.

1036
00:55:32,599 --> 00:55:37,229
So kind of feeding back your own ideas
into your own mind and coming up with

1037
00:55:37,229 --> 00:55:43,779
stuff that actually can't get ...
well processing external inputs.

1038
00:55:43,779 --> 00:55:48,469
Joscha: Mhm! I think it's very tricky.
This project of defining morality without

1039
00:55:48,469 --> 00:55:53,119
societies exists longer than Kant of
course. And Kant tried to give this

1040
00:55:53,119 --> 00:55:56,869
internal rules and others tried to.
I find this very difficult.

1041
00:55:56,869 --> 00:56:01,069
From my perspective we are just moving
bits of rocks. And this bits of rocks they

1042
00:56:01,069 --> 00:56:07,589
are on some kind of dust mode in a galaxy
out of trillions of galaxies and how can

1043
00:56:07,589 --> 00:56:08,609
there be meaning?

1044
00:56:08,609 --> 00:56:11,180
It's very hard for me to say:

1045
00:56:11,180 --> 00:56:13,969
One chimpanzee species is better than
another chimpanzee species or

1046
00:56:13,969 --> 00:56:16,559
a particular monkey
is better than another monkey.

1047
00:56:16,559 --> 00:56:18,539
This only happens
within a certain framework

1048
00:56:18,539 --> 00:56:20,160
and we have to set this framework.

1049
00:56:20,160 --> 00:56:23,700
And I don't think that we can define this
framework outside of a context of

1050
00:56:23,700 --> 00:56:26,420
social norms, that we have to agree on.

1051
00:56:26,420 --> 00:56:29,650
So objectively I'm not sure
if we can get to ethics.

1052
00:56:29,650 --> 00:56:33,769
I only think that is possible based on
some kind of framework that people

1053
00:56:33,769 --> 00:56:38,339
have to agree on implicitly or explicitly.

1054
00:56:38,339 --> 00:56:40,630
Herald: Microphone number 4, please.

1055
00:56:40,630 --> 00:56:46,559
Q: Hi, thank you, it was a fascinating talk.
I have 2 thought that went through my mind.

1056
00:56:46,559 --> 00:56:51,589
And the first one is that it's so
convincing the models that you present,

1057
00:56:51,589 --> 00:56:56,709
but it's kind of like you present
another metaphor of understanding the

1058
00:56:56,709 --> 00:57:01,670
brain which is still something that we try
to grasp on different levels of science

1059
00:57:01,670 --> 00:57:07,469
basically. And the 2nd one is that your
definition of the nerd who walks

1060
00:57:07,469 --> 00:57:10,950
and doesn't see the walls is kind of
definition... or reminds me

1061
00:57:10,950 --> 00:57:15,229
Richard Rortys definition of the ironist
which is a person who knows that their

1062
00:57:15,229 --> 00:57:20,799
vocabulary is finite and that other people
have also a finite vocabulary and

1063
00:57:20,799 --> 00:57:24,599
then that obviously opens up the whole question
of meaning making which has been

1064
00:57:24,599 --> 00:57:28,979
discussed in so many
other disciplines and fields.

1065
00:57:28,979 --> 00:57:32,930
And I thought about Darridas
deconstruction of ideas and thoughts and

1066
00:57:32,930 --> 00:57:36,300
Butler and then down the rabbit hole to
Nietzsche and I was just wondering,

1067
00:57:36,300 --> 00:57:39,009
if you could maybe
map out other connections

1068
00:57:39,009 --> 00:57:44,430
where basically not AI helping us to
understand the mind, but where

1069
00:57:44,430 --> 00:57:49,819
already existing huge, huge fields of
science, like cognitive process

1070
00:57:49,819 --> 00:57:53,359
coming from the other end could help us
to understand AI.

1071
00:57:53,359 --> 00:57:59,680
Joscha: Thank you, the tradition that you
mentioned Rorty and Butler and so on

1072
00:57:59,680 --> 00:58:02,989
are part of a completely different belief
attractor in my current perspective.

1073
00:58:02,989 --> 00:58:06,209
That is they are mostly
social constructionists.

1074
00:58:06,209 --> 00:58:10,880
They believe that reality at least in the
domains of the mind and sociality

1075
00:58:10,880 --> 00:58:15,359
are social constructs they are part
of social agreement.

1076
00:58:15,359 --> 00:58:17,190
Personally I don't think that
this is the case.

1077
00:58:17,190 --> 00:58:19,630
I think that patterns that we refer to

1078
00:58:19,630 --> 00:58:23,890
are mostly independent of your mind.
The norms are part of social constructs,

1079
00:58:23,890 --> 00:58:28,099
but for instance our motivational
preferences that make us adapt or

1080
00:58:28,099 --> 00:58:32,719
reject norms, are something that builds up
resistance to the environment.

1081
00:58:32,719 --> 00:58:35,660
So they are probably not part
of social agreement.

1082
00:58:35,660 --> 00:58:41,569
And the only thing I can invite you to is
try to retrace both of the different

1083
00:58:41,569 --> 00:58:45,640
belief attractors, try to retrace the
different paths on the landscape.

1084
00:58:45,640 --> 00:58:48,529
All this thing that I tell you, all of
this is of course very speculative.

1085
00:58:48,529 --> 00:58:52,390
These are that seem to be logical
to me at this point in my life.

1086
00:58:52,390 --> 00:58:55,400
And I try to give you the arguments
why I think that is plausible, but don't

1087
00:58:55,400 --> 00:58:59,109
believe in them, question them, challenge
them, see if they work for you!

1088
00:58:59,109 --> 00:59:00,559
I'm not giving you any truth.

1089
00:59:00,559 --> 00:59:05,720
I'm just going to give you suitable encodings
according to my current perspective.

1090
00:59:05,720 --> 00:59:11,739
Q:Thank you!
<i>applause</i>

1091
00:59:11,739 --> 00:59:15,099
Herald: The internet, please!

1092
00:59:19,179 --> 00:59:26,029
Signal angel: So, someone is asking
if in this belief space you're talking about

1093
00:59:26,029 --> 00:59:30,109
how is it possible
to get out of local minima?


1094
00:59:30,109 --> 00:59:33,959
And very related question as well:

1095
00:59:33,959 --> 00:59:38,530
Should we teach some momentum method
to our children,

1096
00:59:38,530 --> 00:59:41,599
so we don't get stuck in a local minima.

1097
00:59:41,599 --> 00:59:44,829
Joscha: I believe at some level it's not
possible to get out of a local minima.

1098
00:59:44,829 --> 00:59:50,329
In an absolute sense, because you only get
to get into some kind of meta minimum,

1099
00:59:50,329 --> 00:59:56,769
but what you can do is to retrace the
path that you took whenever you discover

1100
00:59:56,769 --> 00:59:59,989
that somebody else has a fundamentally
different set of beliefs.

1101
00:59:59,989 --> 01:00:02,769
And if you realize that this person is
basically a smart person that is not

1102
01:00:02,769 --> 01:00:07,359
completely insane but has reasons to
believe in their beliefs and they seem to

1103
01:00:07,359 --> 01:00:10,579
be internally consistent it's usually
worth to retrace what they

1104
01:00:10,579 --> 01:00:12,180
have been thinking and why.

1105
01:00:12,180 --> 01:00:15,930
And this means you have to understand
where their starting point was and

1106
01:00:15,930 --> 01:00:18,279
how they moved from their current point
to their starting point.

1107
01:00:18,279 --> 01:00:22,219
You might not be able to do this
accurately and the important thing is

1108
01:00:22,219 --> 01:00:25,369
also afterwards you discover a second
valley, you haven't discovered

1109
01:00:25,369 --> 01:00:27,059
the landscape inbetween.

1110
01:00:27,059 --> 01:00:30,839
But the only way that we can get an idea
of the lay of the land is that we try to

1111
01:00:30,839 --> 01:00:33,200
retrace as many paths as possible.

1112
01:00:33,200 --> 01:00:36,339
And if we try to teach our children, what
I think what we should be doing is:

1113
01:00:36,339 --> 01:00:38,650
To tell them how to explore
this world on there own.

1114
01:00:38,650 --> 01:00:43,900
It's not that we tell them this is the
valley, basically it's given, it's

1115
01:00:43,900 --> 01:00:47,599
the truth, but instead we have to tell
them: This is the path that we took.

1116
01:00:47,599 --> 01:00:51,239
And these are the things that we saw
inbetween and it is important to be not

1117
01:00:51,239 --> 01:00:54,390
completely naive when we go into this
landscape, but we also have to understand

1118
01:00:54,390 --> 01:00:58,170
that it's always an exploration that
never stops and that might change

1119
01:00:58,170 --> 01:01:01,140
everything that you believe now
at a later point.

1120
01:01:01,140 --> 01:01:05,700
So for me it's about teaching my own
children how to be explorers,

1121
01:01:05,700 --> 01:01:10,950
how to understand that knowledge is always
changing and it's always a moving frontier.

1122
01:01:10,950 --> 01:01:17,230
<i>applause</i>

1123
01:01:17,230 --> 01:01:22,259
Herald: We are unfortunately out of time.
So, please once again thank Joscha!

1124
01:01:22,259 --> 01:01:24,069
<i>applause</i>
Joscha: Thank you!

1125
01:01:24,069 --> 01:01:28,239
<i>applause</i>

1126
01:01:28,239 --> 01:01:32,719
<i>postroll music</i>

1127
01:01:32,719 --> 01:01:40,000
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