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Welcome to Midwest 2023.

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I'm Rob Richardson and I'm your host.
It's an honor to be here.

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Honored to also work with BSL Group
who is teaming up with us

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to do this live podcast.

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As many of you know,
if you watch the podcast Disruption, now

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we have conversations
that talk about disrupting common areas

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and constructs that really challenge
how we think about things.

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And there is not going to be any different
With me as my guest is Javier Viana.

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If you believe
we can change the narrative,

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if you believe
we can change our communities,

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if you believe we can change the outcomes,
then we can change the world.

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I'm Rob Richardson.

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Welcome to Disruption Now.

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But yeah is no different
from from the data that we have.

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It's just a matter of advancing it in
a certain way and providing right outputs

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for that for a particular task.

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Right?

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But it would be, if I can explain it,
this is a very common, humble, right.

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It's like a box.

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It thinks inside,

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process them and it

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and then I'll just get an output.

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Okay. Out of it was cooking.

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Look who's cooking. That's good.

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That's the question. Yes.

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So it's usually well,
you can host your A.I.

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in cloud,
you can have it in your local piece.

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It's usually a computer.

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Write something, a program
that an executable program, an algorithm.

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Right.

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That is that is running through all
the different steps that you have there.

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But it's just following the recipe,

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like all these little things

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that you do inside that that final rate.

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And it's what you put
your passion is the pursuit of knowledge.

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Make sure it sounds like knowledge

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is shared emanated among humankind,
which I share that

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and I believe we share the passion
for innovation hearing of that knowledge.

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So it's I know for myself

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and for you, there's a lot of excitement

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about this new intrigue,
about artificial intelligence,

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but there's also some concern.

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So I'd like to talk to you
about what you are doing with reasons.

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What problem is reason trying to solve.

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Yeah, So before I get to that,
perhaps I can just give a brief overview.

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Yeah.

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What is reason

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or even before reason I would say what,
what is the current state of it.

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Yeah. What are we facing.

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Because that would down
locate a little bit

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for like right now we are, we are,
we're facing a global emergency.

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I said this is a global emergency.

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Yes. In terms of a yeah I think we are
we are reaching a point

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where we are abusing of black box A.I..

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We we call it it's opaque A.I.

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that we don't have logs.

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Yeah, it's like that.

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Definitely. It's not transparent.

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Like, it's it's basically

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something that we

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don't understand the way a certain

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like is becoming a bigger problem
because the models we are using,

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the example you used earlier cooking
we don't know

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would be what's cooking or what
the materials are used to cook.

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Exactly.

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And like how these ingredients
are combined.

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It's a great dish
at the end and it's beautiful.

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It tastes as you want and everything,
but the way you reach that output,

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slightly concerning because there is a lot
of mathematical operations

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behind the scenes
and there's not a clear path to that.

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And humans,
we are having a hard time understanding

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or, you know, verify in these outputs
because when you start implementing A.I.

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into important capabilities
and you start deciding whether what

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what is the amount of oxygen
you are administering to a certain patient

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in a hospital, in a
in an intensive care unit using on a Yeah.

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And that patient dies for whatever reason.

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Are you you want to know why?

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You want to know why we're the inputs
that triggered this result and how right

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and those things.

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I think we are not taking care of them
right now.

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Everybody's focused on performance,
everyone's focus on

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how can you get the greatest, the most,
the most output in the quickest manner.

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Yes, the most efficient manner. Yes.

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Not the most ethical manner.

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Not the most transparent manner.

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That's true. Right. And

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why do people I'm with.

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You mean interrupt you?

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No, no worries.

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How do we get people to care about that?

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Right.

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We we are in a we're in a society.

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And I'm not disagreeing with you
that it's important to I I'm with you.

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Right. We're having this conversation.

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But of course, we're in a society
that believes in fast results,

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quick returns and more returns.

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Yeah.

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So how are we making the case that this is

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this is something that you should pay
attention to?

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Like, how are we making that case

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given we know that balance,
we know we know the place.

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What you said is absolutely true. Yeah.

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It's essential to understand
what's going on.

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But one way of well,
when when you are using A.I.

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and you're starting
to do inference with the A.I., you can

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you can have biased decision making a lot
and not just decision making,

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but like the inference itself
could be biased in a certain way.

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And what I'm trying to say
is that if we have explainability

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in a system, we can detect bias.

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For example, on things like that, right?

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If we are not aware of what's happening
and there's implicit,

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you know, bad things going on. Yes,

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those are going to happen.

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You know, the
the end user is going to suffer them.

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And we might not see that suffering
right now in the short term.

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But as we implement it more and more
and we started integrating

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the AI in every single

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day to the task,
I see we're going to see ourselves.

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We were starting to see it,
you know, in and some time, you know,

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social media was about to say,
I think we've see I mean to you

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I was getting right to my
you must be must be on my way.

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Oh here life
we have seen it like it's not we

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we have seen the destructive nature
of what's an algorithm

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that we don't understand, that
we don't know.

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Yeah. Can affect us.

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You know what?

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What's obviously new is that it's now

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exponentially able
to produce things at a level.

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So, you know, with social media alone,
I've said this on my past

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podcast, it literally

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exacerbates the worst conditions
of human nature sometime, right?

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Yeah, Yeah.

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The vanity of ourselves, the

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the the ability to be jealous,
to divide amongst one another.

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Because what the algorithm is doing
is very simple.

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With social media, their goal is not
their goal isn't enlightenment.

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Their goal isn't to encourage
us to get together

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in society, is not to inform
is it's it's it is

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to get you engaged.

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And so

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the goal is to get clicks
and the goal is to trigger you.

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And the goal is to keep you coming.

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And they're going to repeat
what you want to see.

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Which is it necessarily

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which was doesn't necessarily align
with the truth, doesn't necessarily

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align with what's even best for you,
but it keeps you entertained.

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Yeah.
And people like that until they don't.

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And we've seen what what's happened
with elections and things like that.

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So you're right.

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So what other problems can you foresee

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not having transparent explain away.

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I well it's many of them right
but especially when you start

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connecting together and like
do you allow them to operate

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based on the outputs of the previews
and keep building this chain?

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Right.

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This is called multipolarity.

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And you can

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you can phase an output
that has been processed in a lot of

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different ways, but you have no absolutely
no idea how these output like.

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It's literally like a being
we can think of a as a as a type

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of living creature that is is not
it doesn't need oxygen like us.

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Right.

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It's like it's something that does
a lot of researchers that believes

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that there's that level of consciousness
already achieved in many models.

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But but the problem is that
what I'm back up with.

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Back up.
You said a lot there if I can. Yeah.

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And make sure we can explain this
to a way.

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The people that I need to understand
like you've you've

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because I know you're
deep into this research

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and I want make sure
the audience understand.

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So I like to use analogies
to make sure I understand.

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You talked about multiple morality,

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and I think that how I'm understanding
it is basically you have a lot

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it's like raising a child, let's say,
and you put a lot into that child.

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And you we don't know if there's
you're teaching that child bad habits

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and it's compounding on itself
to create a new entity

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that can cause a lot of damage
in the world. Right. And

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it seems like that can be a good analogy
for what you're saying.

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Like we don't know how the data
what's happening,

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and that's going to create this entity
that's

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going to create more problems for us
because we didn't

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look look at it, look at it
on the front end to be transparent. Yes.

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Yes, that's correct.

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It kind of Yeah, yeah, yeah.

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I I'm sure it's not a perfect analogy.
No, no, no.

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But it explains it
very well. So. So no, no.

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That we, we,
we can agree that there's this big issue.

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Right.

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And we can see it more and more every day.

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Now, how do we solve it?

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That's a question, right?
That's the question.

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How can we you ask the question.
I mean, I'm interested

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there's not clear not a clear answer.

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And the reasons are various.

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One of them is like, you know, it's
a very complex mathematical problem.

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Sometimes the air is advancing very fast.

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You always need to keep up to date
with the current technologies.

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But one way to solve it
and the one we are working on at

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reason is trying to study

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what's going on inside a model of,
you know, you have your air.

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We have think of it as the box.

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It has been trained.

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There's other people
that are trying to create new boxes

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that are transparent from the ground up.

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But that's difficult to scale
because everybody is using already.

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Neural networks are very complex.

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So there's the one, there's
the one solution.

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So it's like it's
so important to understand the problem.

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You're trying to solve the problem.

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You're trying to solve is
how do you make a more transparent?

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And you say the one way is
people are trying to create new models

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with new models that are crazy.
Yeah, not crazy, but whatever.

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Very challenging.

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It's challenge
given that the infrastructure

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is already there,
it's like trying to build a new nation.

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That's what I did for my page.

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For example, a lot of Academy Academy
people are working on that end as well,

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but it takes a lot.

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And industry
hasn't adopted any of these books, right?

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Sometimes it takes a whole year to develop
something. Yeah, it's like building it.

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I tell people like,
could I do a blockchain?

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But as I said, there's similar stuff
in that

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if you want to build a new blockchain,
you got to build a whole new highway

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or a whole new nation.

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And that's that's a lot to start from.

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But you're saying integrating
with the current infrastructure.

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I think whatever it's already Pre-Trained
Yeah, okay.

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And try to make it more transparent.

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Your technology would take

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current technology
that's out there in A.I.

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and make it transparent. So let's say

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everyone's talking to that.

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Getty Of course, as if that's
the only generative model there ever was.

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It's that's, that's this way It is,
because that was the first

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to get the consciousness of the public
and made it in a user friendly way

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that theoretically would would

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reason be able to pierce into that

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the algorithm, if you will,

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to be able to tell the public

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this is actually what's going on
behind the black box theoretically.

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Is that is that
is that what you're looking at?

242
00:11:46,224 --> 00:11:47,890
That's the that's the ideal scenario.

243
00:11:47,890 --> 00:11:52,557
We're going towards that
where we work right now is on the smaller

244
00:11:52,557 --> 00:11:56,957
problems, the classical neural networks
that have been used in a variety

245
00:11:56,957 --> 00:11:59,423
of applications
that, you know, the medical use case

246
00:11:59,423 --> 00:12:02,423
that I was telling you about,
prediction of oxygen, for example,

247
00:12:02,490 --> 00:12:04,657
and trying to explain what were the inputs

248
00:12:04,657 --> 00:12:07,723
that triggered this result
and how each of the inputs contributed.

249
00:12:07,957 --> 00:12:09,090
And from there, you can tell a lot.

250
00:12:09,090 --> 00:12:13,357
You can tell whether, you know, one of
the inputs was contributed too much.

251
00:12:13,590 --> 00:12:16,290
Perhaps you were biasing
the entire reasoning.

252
00:12:16,290 --> 00:12:19,190
You can tell whether these combination

253
00:12:19,190 --> 00:12:23,990
of inputs
in this particular way was leading to

254
00:12:24,090 --> 00:12:28,156
a wrong
outcome or a less confident outcome

255
00:12:28,256 --> 00:12:32,990
if that you can trust more the the
the output if you understand that logic.

256
00:12:32,990 --> 00:12:33,256
Right.

257
00:12:33,256 --> 00:12:36,490
And and we well we go towards that

258
00:12:36,556 --> 00:12:39,956
direction of applying it
to do better and a general way.

259
00:12:40,056 --> 00:12:42,323
But right now we are working on
like more classical problems.

260
00:12:42,323 --> 00:12:45,356
You have a big neural network, huge,
sometimes millions of parameters

261
00:12:45,590 --> 00:12:49,756
that's doing a great job
predicting for whatever you want right.

262
00:12:49,856 --> 00:12:53,723
But but you don't understand why those
predictions are being made like that.

263
00:12:53,723 --> 00:12:56,456
And and you want to have
that level of transparency.

264
00:12:56,456 --> 00:12:57,256
And that's becoming

265
00:12:57,256 --> 00:13:01,156
now a requirement in many
in many places in Europe, for example.

266
00:13:01,223 --> 00:13:04,856
Well, the GDPR and the ACT are explain
what that is.

267
00:13:04,956 --> 00:13:06,489
General data protection regulation.

268
00:13:06,489 --> 00:13:10,789
It's a very popular regulatory
framework in Europe

269
00:13:10,889 --> 00:13:15,989
for for practitioners because it's kind of

270
00:13:16,056 --> 00:13:17,522
making us

271
00:13:17,522 --> 00:13:21,056
design a Yeah, in a different way
if that makes it more transparent.

272
00:13:21,156 --> 00:13:22,989
Whereas the US is like,
do whatever you want, right?

273
00:13:22,989 --> 00:13:25,656
It is still

274
00:13:25,656 --> 00:13:27,689
it's taken a while,
but I think there's going to be

275
00:13:27,689 --> 00:13:31,756
more regulations coming into play over
the next year and we're going to see them.

276
00:13:31,822 --> 00:13:36,122
I think they are necessary, right,
Especially when we start,

277
00:13:36,189 --> 00:13:37,222
you know, using A.I.

278
00:13:37,222 --> 00:13:40,222
for for taking decisions
that may affect human lives.

279
00:13:40,256 --> 00:13:42,722
That's absolutely right. So so yes.

280
00:13:42,722 --> 00:13:43,622
So that's the reason.

281
00:13:43,622 --> 00:13:47,622
Just hold that thought, because I want to
just Georgia, I think when you think

282
00:13:47,622 --> 00:13:52,255
about why this is so important,
this is not just entertainment.

283
00:13:52,355 --> 00:13:55,722
There's also reasons to not want to do
false entertainment and things like that,

284
00:13:55,722 --> 00:13:57,022
because entertainment can lead to things.

285
00:13:57,022 --> 00:13:58,722
But you're talking
we're talking serious decisions.

286
00:13:58,722 --> 00:14:00,455
You know, we're we're going to use

287
00:14:00,455 --> 00:14:04,122
I know we we're going to have someone
from Tallahassee on to talk about that.

288
00:14:04,189 --> 00:14:05,922
We use A.I. within the battlefield.

289
00:14:05,922 --> 00:14:07,288
We already do.

290
00:14:07,288 --> 00:14:12,222
And how are the how are we evaluating
those decisions that A.I.

291
00:14:12,222 --> 00:14:14,855
is making? Is it just about
what's the most efficient?

292
00:14:14,855 --> 00:14:16,955
It's not necessarily
what's the most humane. Yeah.

293
00:14:16,955 --> 00:14:18,488
So these are things we have to grapple
with.

294
00:14:18,488 --> 00:14:21,355
You're talking about medical uses
we already use.

295
00:14:21,355 --> 00:14:22,222
People don't know we are.

296
00:14:22,222 --> 00:14:24,455
We're already using
we're using A.I.. A lot of people.

297
00:14:24,455 --> 00:14:26,722
We already have been much more than just

298
00:14:26,722 --> 00:14:29,555
be deciding whether you have credit
or you get credit or not.

299
00:14:29,555 --> 00:14:30,955
For example, who gets credit or not?

300
00:14:30,955 --> 00:14:33,988
I'm based on
what were the inputs, you know. Yes.

301
00:14:34,155 --> 00:14:35,888
How those inputs played.

302
00:14:35,888 --> 00:14:37,588
Like you said, sometimes they're biased.

303
00:14:37,588 --> 00:14:39,088
They are they are bias off the right.

304
00:14:39,088 --> 00:14:41,721
And I want to make a point here,
if you don't mind. Sorry for interject.

305
00:14:41,721 --> 00:14:44,488
Oh, there's several types of bias.

306
00:14:44,488 --> 00:14:47,021
You can have bias in the data, right?

307
00:14:47,021 --> 00:14:51,055
Like the data is biased
in many ways in the world.

308
00:14:51,155 --> 00:14:56,621
And you can have great software
engineers, future engineers working hard

309
00:14:56,688 --> 00:15:01,421
to make sure that it's clean and balanced
and like perfectly leveled

310
00:15:01,488 --> 00:15:05,121
so that when you put the AI in place
and you train the algorithm,

311
00:15:05,221 --> 00:15:07,921
it's the best quality data
that you can have.

312
00:15:07,921 --> 00:15:09,421
You can do so much always,

313
00:15:09,421 --> 00:15:13,588
but then the learning of the itself
because the learns, you have to train it.

314
00:15:13,688 --> 00:15:16,454
At the very beginning, it's
everything initialized randomly, but

315
00:15:16,454 --> 00:15:19,821
you trained all the time at the adult
learning stage.

316
00:15:19,921 --> 00:15:25,887
You can implicitly extract bias
or even if you have done a great job

317
00:15:25,887 --> 00:15:30,687
in the future engineering under cleaning,
you can still generate biased reasonings

318
00:15:30,787 --> 00:15:34,254
because the learning happens very randomly
and right now there there's

319
00:15:34,354 --> 00:15:37,087
really no way for us to understand

320
00:15:37,087 --> 00:15:40,087
whether your inference
is being biased or not.

321
00:15:40,221 --> 00:15:43,221
You have done a great job in the future
engineering your data is clean,

322
00:15:43,287 --> 00:15:44,221
you know that.

323
00:15:44,221 --> 00:15:49,154
But now when you train your
AI and you're making inference,

324
00:15:49,220 --> 00:15:52,154
you might not actually get it.

325
00:15:52,154 --> 00:15:54,620
You might get to a bias results.
All right.

326
00:15:54,620 --> 00:15:57,854
Let me you're saying because I think often

327
00:15:57,920 --> 00:16:01,787
I think the case
is that the data is actually

328
00:16:01,887 --> 00:16:02,720
is biased

329
00:16:02,720 --> 00:16:06,254
because people that enter the end
or the data are biased.

330
00:16:06,254 --> 00:16:06,520
Right.

331
00:16:06,520 --> 00:16:10,520
So there's you know, one of the well,
the hardware as well, the sensors.

332
00:16:10,520 --> 00:16:14,420
I mean, I'm not talking about human bias,
but that's what I'm trying to understand.

333
00:16:14,420 --> 00:16:15,087
So that's my question.

334
00:16:15,087 --> 00:16:19,020
I'm getting to You're saying
you're assuming that

335
00:16:19,087 --> 00:16:21,187
the input has the people that they're

336
00:16:21,187 --> 00:16:24,953
actually entering the data and playing
the data have taken precautions

337
00:16:24,953 --> 00:16:28,987
to make sure that it's not biased and
and done things intentionally that way?

338
00:16:29,053 --> 00:16:33,187
You're saying
even then after that's happened,

339
00:16:33,253 --> 00:16:34,887
you can still come up with a bias
decision?

340
00:16:34,887 --> 00:16:36,820
Yes. Yes, exactly.

341
00:16:36,820 --> 00:16:40,320
After the taking of the data,
after the processing of the data

342
00:16:40,320 --> 00:16:44,253
and having a clean,
you can still get bias in the AI.

343
00:16:44,320 --> 00:16:47,586
And that's because the learning,
it's very random, very stochastic.

344
00:16:47,586 --> 00:16:48,753
If you train another A.I.

345
00:16:48,753 --> 00:16:49,786
later on the same data,

346
00:16:49,786 --> 00:16:53,053
you're going to have a different
AI and it's going to have some difference.

347
00:16:53,053 --> 00:16:59,086
That difference is bias, you know, I mean,
not directly, but but in a way you can

348
00:16:59,120 --> 00:17:03,453
you can start getting different outputs
in different ways.

349
00:17:03,520 --> 00:17:08,286
And one, I might be considering
some things more another one,

350
00:17:08,353 --> 00:17:12,619
some other things differently, that
different process is just like you and me.

351
00:17:12,619 --> 00:17:17,153
We are understanding whatever problem
we are talking about in different ways.

352
00:17:17,253 --> 00:17:19,453
Sure, we can both give a similar answer.

353
00:17:19,453 --> 00:17:20,353
It's not going to be the same.

354
00:17:20,353 --> 00:17:22,986
We are taking into account
different things in our brains.

355
00:17:22,986 --> 00:17:25,453
So that difference,
that's the one I'm talking about.

356
00:17:25,453 --> 00:17:28,453
There's no way right now, no way

357
00:17:28,586 --> 00:17:32,952
for us to really see what side reasoning
and that's what we do.

358
00:17:33,052 --> 00:17:36,386
Reason So how do you how do you it's

359
00:17:36,452 --> 00:17:39,286
first of all, it's admirable work
and I appreciate it.

360
00:17:39,286 --> 00:17:41,152
We are trying I don't know if we are.

361
00:17:41,152 --> 00:17:44,752
Yeah, well, I mean, there's
not a lot of people working on this.

362
00:17:44,752 --> 00:17:45,752
No, there's not. People.

363
00:17:45,752 --> 00:17:48,286
We have to educate the people.
That's what I say, right?

364
00:17:48,286 --> 00:17:49,519
So it's not a market like.

365
00:17:49,519 --> 00:17:52,919
Yeah, we were predicting, you know, this
or that not we are trying to do that,

366
00:17:52,919 --> 00:17:56,919
but nobody is now it's
it's important work.

367
00:17:57,019 --> 00:17:59,652
But this is what
I'm getting to right. It's

368
00:17:59,752 --> 00:18:02,752
you're going up against

369
00:18:02,752 --> 00:18:05,185
the current the current trajectory of

370
00:18:05,185 --> 00:18:08,185
how to commercialize this one on hormones

371
00:18:08,385 --> 00:18:11,485
like let's worry about
let's worry about ethics.

372
00:18:11,485 --> 00:18:14,619
Let's worry about transparency
after we made $10 billion.

373
00:18:14,619 --> 00:18:16,119
Right. Yeah.

374
00:18:16,119 --> 00:18:17,285
How do you balance against that?

375
00:18:17,285 --> 00:18:19,719
Like, really like
what is your message against that?

376
00:18:19,719 --> 00:18:22,718
Yeah, I think, you know,

377
00:18:22,785 --> 00:18:25,485
it's it's all part of

378
00:18:25,485 --> 00:18:28,418
what we want to leave
to the next generation of people

379
00:18:28,418 --> 00:18:31,852
because when they are facing huge
infrastructures

380
00:18:31,852 --> 00:18:35,852
that are already in big companies
functioning very well

381
00:18:35,852 --> 00:18:40,452
and then you have the problem
of transparency, bias or whatever

382
00:18:40,552 --> 00:18:45,152
and really replacing all that
and like leaving them with that burden.

383
00:18:45,218 --> 00:18:47,185
I think it's a it's a big issue.

384
00:18:47,185 --> 00:18:52,951
So, you know, yeah, it's going to stay for
I mean, yeah, we're allies here like it

385
00:18:53,018 --> 00:18:57,018
and it's going to be in every single thing
you can think of that has an electronic

386
00:18:57,018 --> 00:19:01,018
sort of like even labels these,
these room is going to be controlled.

387
00:19:01,085 --> 00:19:02,818
The light intensity
is going to be controlled

388
00:19:02,818 --> 00:19:07,185
by the conversation and measuring how
well the conversation is going to

389
00:19:07,185 --> 00:19:10,918
maybe go lower
when you know those kind of things.

390
00:19:10,985 --> 00:19:12,551
We're going to see it everywhere.

391
00:19:12,551 --> 00:19:13,585
It's just a matter of time.

392
00:19:13,585 --> 00:19:17,118
So like being able to really trace

393
00:19:17,351 --> 00:19:20,218
that reasoning is essential.

394
00:19:20,218 --> 00:19:24,218
It can make a difference, like in
because the way I like to think about it

395
00:19:24,218 --> 00:19:27,718
is if we are taking decisions
based on an output

396
00:19:27,718 --> 00:19:31,418
that we don't understand
how that processing happened,

397
00:19:31,518 --> 00:19:34,418
who's really making the decision,
the human or the machine?

398
00:19:34,418 --> 00:19:37,418
That's so a couple of things on this.

399
00:19:37,451 --> 00:19:40,351
Like one, I've talked about this in
the past, talked about it two years ago.

400
00:19:40,351 --> 00:19:43,617
I think it's important
for us to continue to innovate.

401
00:19:43,684 --> 00:19:47,584
But I think your your your your best case

402
00:19:47,684 --> 00:19:53,317
that you put forward in terms of people
that are focused on the return is this

403
00:19:53,417 --> 00:19:56,651
if we don't build trust within AI,

404
00:19:56,751 --> 00:19:59,284
you can get a reaction
in the opposite direction.

405
00:19:59,284 --> 00:20:02,884
And we're right where people
there's a mass just

406
00:20:02,984 --> 00:20:05,884
backlash against innovation in

407
00:20:05,884 --> 00:20:09,384
AI that really puts us behind
because we we live in a democracy.

408
00:20:09,450 --> 00:20:12,417
And at the end of the day,
if people decide

409
00:20:12,417 --> 00:20:15,417
that they want to elect leaders
that say no

410
00:20:15,517 --> 00:20:18,950
at all, because we don't trust it,
that can cause greater damage.

411
00:20:18,950 --> 00:20:22,317
So I would say building trust right now

412
00:20:22,317 --> 00:20:26,217
is is paramount to make sure
that we can continue to align.

413
00:20:26,250 --> 00:20:29,284
And sometimes it's
hard to tell people that you're building

414
00:20:29,284 --> 00:20:32,850
could be on fire until it gets on fire,
which is the hardest challenge, right?

415
00:20:32,983 --> 00:20:37,550
People, people like is not sexy to put out
put out fires,

416
00:20:37,617 --> 00:20:41,450
but it's it's better people only

417
00:20:41,517 --> 00:20:45,083
only focus on putting out fires, as you
say, versus actually preventing them.

418
00:20:45,083 --> 00:20:47,350
Yeah. And what you're trying to do
is prevent it.

419
00:20:47,350 --> 00:20:51,217
And I guess we got a lot of people
that really understand how big of a issue

420
00:20:51,217 --> 00:20:52,083
this could be.

421
00:20:52,083 --> 00:20:54,217
So I have another question with this.

422
00:20:54,217 --> 00:20:58,983
What does happen when the algorithms know
us better than we know ourselves?

423
00:20:59,083 --> 00:21:01,516
Is that a good thing
or is that a bad thing?

424
00:21:01,516 --> 00:21:06,416
Well, I mean, it's it's just like
any other technology, I guess it depends.

425
00:21:06,483 --> 00:21:08,250
If I may clarify a little more.

426
00:21:08,250 --> 00:21:11,850
The question, are you asking
whether the algorithm is learning

427
00:21:12,083 --> 00:21:16,283
information about us that we don't know
or or what what do you

428
00:21:16,350 --> 00:21:18,016
what do you question?

429
00:21:18,016 --> 00:21:21,283
I would just say
if the algorithms actually know us

430
00:21:21,283 --> 00:21:24,949
better than we know ourselves,
like like telling us like,

431
00:21:25,049 --> 00:21:28,349
you should go for a weekend in the
I don't know, in a lake?

432
00:21:28,516 --> 00:21:30,716
Sure. Or like,
advising us to do something.

433
00:21:30,716 --> 00:21:34,049
We never thought of that maybe
it's what we want, but have you seen.

434
00:21:34,049 --> 00:21:34,783
I'll give you examples.

435
00:21:34,783 --> 00:21:36,049
So let me give you a concrete example

436
00:21:36,049 --> 00:21:39,783
since we're going to go into a weird
alternative Black Mirror universe.

437
00:21:39,783 --> 00:21:41,083
Okay, So

438
00:21:41,149 --> 00:21:43,083
there was this

439
00:21:43,083 --> 00:21:45,049
show on Netflix X

440
00:21:45,049 --> 00:21:48,049
and it was called
I think it's called the One.

441
00:21:48,116 --> 00:21:48,816
I'm nervous.

442
00:21:48,816 --> 00:21:49,616
Okay, it's fine.

443
00:21:49,616 --> 00:21:53,849
I was last on a weekend doing something
I don't know,

444
00:21:53,916 --> 00:21:58,949
somehow found extra time that I never had
and decided to go into what it was about

445
00:21:58,949 --> 00:22:03,316
is using essentially
artificial intelligence

446
00:22:03,382 --> 00:22:07,616
and some type of data algorithm
to say to help you find who

447
00:22:07,649 --> 00:22:12,016
your actual quote unquote mathematical
one was.

448
00:22:12,082 --> 00:22:12,382
Right.

449
00:22:12,382 --> 00:22:16,215
And so you have people that are already
with significant others in other things.

450
00:22:16,215 --> 00:22:21,882
And they got curious, they got on this
and then they got them to leave the stuff.

451
00:22:21,949 --> 00:22:23,482
That's a wild thing.

452
00:22:23,482 --> 00:22:25,449
But some of them were content before that.

453
00:22:25,449 --> 00:22:29,882
But when they went into this kind of like
social media, people looked at like, Oh,

454
00:22:29,982 --> 00:22:30,882
I can look on here.

455
00:22:30,882 --> 00:22:33,682
And, you know, I look there's now
there's now 10,000 options.

456
00:22:33,682 --> 00:22:36,615
It's not really 10,000 options,
but you feels that way.

457
00:22:36,615 --> 00:22:37,549
That's what I'm saying.

458
00:22:37,549 --> 00:22:38,882
Is that a good thing for us,

459
00:22:38,882 --> 00:22:43,315
that the algorithm might know
what motivates you, what triggers you,

460
00:22:43,382 --> 00:22:46,815
and it might do what social media done
in an amplified way.

461
00:22:46,882 --> 00:22:48,682
Yeah, I think I understand the question.

462
00:22:48,682 --> 00:22:50,782
Okay. So sorry. I was alone. No, no, no.

463
00:22:50,782 --> 00:22:54,482
It was. It was. It was right on point. But

464
00:22:54,582 --> 00:22:57,582
yeah, I'd like to clarify because
I have a different name for this one.

465
00:22:57,582 --> 00:23:01,248
And so you're a recent searcher, I guess.

466
00:23:01,248 --> 00:23:02,448
Yeah.

467
00:23:02,448 --> 00:23:05,081
You figure out a way to go.

468
00:23:05,081 --> 00:23:05,348
Okay.

469
00:23:05,348 --> 00:23:08,348
No, seriously, I think it's,

470
00:23:08,448 --> 00:23:12,181
you know,
it helps to have some sort of, like,

471
00:23:12,281 --> 00:23:15,048
automated decision
making in your life, for sure.

472
00:23:15,048 --> 00:23:18,081
But I'm a very, very classical person, so

473
00:23:18,081 --> 00:23:21,581
I like to have some, you know,
uncertainties every now and then.

474
00:23:21,581 --> 00:23:25,981
And there are some things that you
I don't know, I don't like automated,

475
00:23:26,081 --> 00:23:30,914
but but yeah, I guess it really depends
on where you want to use the.

476
00:23:31,014 --> 00:23:35,214
Yeah, but I guess we are more concerned
about A.I.

477
00:23:35,214 --> 00:23:40,548
being used for our own things,
but it really is is in many other

478
00:23:40,548 --> 00:23:46,114
like processes that doesn't directly
impact our lives maybe

479
00:23:46,214 --> 00:23:46,881
right away,

480
00:23:46,881 --> 00:23:50,681
like maybe on the guidance, navigation
and control of a nerve.

481
00:23:50,781 --> 00:23:53,748
I don't know, like an airplane
perhaps, you know, but.

482
00:23:53,748 --> 00:23:56,347
But like, you have a lot of like,

483
00:23:56,347 --> 00:23:59,647
like maybe remaining just for life
prediction of like different components

484
00:23:59,647 --> 00:24:03,381
that you have in your aircraft
or like there's software

485
00:24:03,447 --> 00:24:06,214
out there,
All the systems are starting to integrate

486
00:24:06,214 --> 00:24:10,347
AI And really the ones that you're
talking about, I guess it's it's

487
00:24:10,381 --> 00:24:14,214
more like on, on, on our

488
00:24:14,281 --> 00:24:16,881
they might change over the course
of our life I guess, right.

489
00:24:16,881 --> 00:24:19,514
Yeah I think they could.
I mean small business. Yeah.

490
00:24:19,514 --> 00:24:20,881
They are small ones now

491
00:24:20,881 --> 00:24:24,647
you know I say that dramatic example is,
you know, they're like

492
00:24:24,714 --> 00:24:28,547
fining or whatever
because I worry less about,

493
00:24:28,614 --> 00:24:32,714
you know, terminate people
like a Terminator and stuff and Matrix.

494
00:24:32,780 --> 00:24:35,880
I don't think per se
that's what it's going to turn into

495
00:24:35,980 --> 00:24:38,347
every time
I have a conversation to explain why

496
00:24:38,347 --> 00:24:40,980
it always it's, yeah, of course
it always does that.

497
00:24:40,980 --> 00:24:42,047
But I know better than to go there.

498
00:24:42,047 --> 00:24:44,014
I'm not going to have I'm not going
to have a Terminator conversation.

499
00:24:44,014 --> 00:24:46,947
I promise.
Sorry. It's a I don't think that's

500
00:24:47,014 --> 00:24:48,847
the likely future.

501
00:24:48,847 --> 00:24:52,013
What is possible,
though, is that because I'm

502
00:24:52,013 --> 00:24:55,013
seeing how social media has already got
people used to it.

503
00:24:55,013 --> 00:24:56,713
I'm not anti social media.

504
00:24:56,713 --> 00:24:58,247
I'm not I'm pro social media,

505
00:24:58,247 --> 00:25:01,947
but I'm pro what I like to say
I'm pro informed consent.

506
00:25:02,013 --> 00:25:05,047
What I believe is happens
with a lot of social media

507
00:25:05,047 --> 00:25:08,480
is that we did not have informed consent,
at least not for the part.

508
00:25:08,480 --> 00:25:13,513
No, not for the we do not know
if that algorithm was which information.

509
00:25:13,580 --> 00:25:14,480
And Yeah.

510
00:25:14,480 --> 00:25:16,780
So that that's, that's what we were trying
to. Exactly.

511
00:25:16,780 --> 00:25:18,380
That's what I was trying to get to. Yes.

512
00:25:18,380 --> 00:25:23,646
I hopefully I mean that's also perhaps
one of the main reasons why we are doing

513
00:25:23,646 --> 00:25:27,513
this is to raise awareness
about like these technology and there's

514
00:25:27,513 --> 00:25:31,380
other people that are building
other technologies that I believe can help

515
00:25:31,446 --> 00:25:33,413
advance transparency in.

516
00:25:33,413 --> 00:25:34,246
A Yeah, but,

517
00:25:34,246 --> 00:25:38,679
but really what we are building on reason,
I think is it's a step forward on that,

518
00:25:38,779 --> 00:25:44,246
like really giving the power to the user,
to the final user, onto the, you know,

519
00:25:44,313 --> 00:25:49,746
and customer that they can understand
how certain inference

520
00:25:49,846 --> 00:25:51,179
or predictions are being made.

521
00:25:51,179 --> 00:25:52,046
Right? Yeah.

522
00:25:52,046 --> 00:25:55,046
And I think that's very valuable
that that's the value of Right.

523
00:25:55,046 --> 00:25:59,513
At the end of the day, you want
to make sure how everything happened.

524
00:25:59,679 --> 00:26:03,646
It's about, again, going back to informed
consent, like I believe

525
00:26:03,713 --> 00:26:07,212
when we got on social media
and everything else, it was

526
00:26:07,279 --> 00:26:10,612
originally to connect with our friends
and to know

527
00:26:10,612 --> 00:26:12,679
what's going on
with our friends and others.

528
00:26:12,679 --> 00:26:16,512
And it evolved into
I mean, I guess it evolved in the media.

529
00:26:16,712 --> 00:26:19,312
I like to say, you know,
even though I'm a media person,

530
00:26:19,312 --> 00:26:23,812
I try not to be a quick bait person.

531
00:26:23,879 --> 00:26:25,446
Maybe it's
why I don't have 10 million followers.

532
00:26:25,446 --> 00:26:27,379
But it is it is what it is.

533
00:26:27,379 --> 00:26:34,079
But I'll say that often media
is to the mind what sugar is to the body.

534
00:26:34,079 --> 00:26:37,079
Oftentimes, particularly news. Right.

535
00:26:37,079 --> 00:26:40,812
And how we consume in news is to the mind
what sugar is to the body.

536
00:26:40,879 --> 00:26:43,679
It's you get it,
you get a hit form and it you know,

537
00:26:43,679 --> 00:26:47,779
it feels good emotionally for a second,
but then it then it really drags you down.

538
00:26:47,845 --> 00:26:49,845
And that's what social media

539
00:26:49,845 --> 00:26:53,212
a lot has done with people
and they don't realize it.

540
00:26:53,279 --> 00:26:53,479
Right.

541
00:26:53,479 --> 00:26:57,378
And and I think the challenge is
how do we have informed consent?

542
00:26:57,445 --> 00:26:59,212
I'm not saying get rid of social media.

543
00:26:59,212 --> 00:27:02,878
I'm not saying get rid of
I we're saying together,

544
00:27:02,945 --> 00:27:07,078
how do we create a more transparent world
where people understand

545
00:27:07,078 --> 00:27:10,912
the decisions that they're making
and are truly given the autonomy?

546
00:27:10,912 --> 00:27:15,212
Because I feel
as if you're saying, if we don't do this,

547
00:27:15,312 --> 00:27:16,378
we're going to have

548
00:27:16,378 --> 00:27:20,178
less autonomy and less understanding
of the world that's happening around us.

549
00:27:20,178 --> 00:27:20,978
That is correct.

550
00:27:20,978 --> 00:27:22,711
I think. I think so, too.

551
00:27:22,711 --> 00:27:25,511
And it's it's a social right.

552
00:27:25,511 --> 00:27:26,478
We'll see where it goes.

553
00:27:26,478 --> 00:27:30,745
I mean, with with all these integration
of generative AI,

554
00:27:30,811 --> 00:27:35,145
awareness has has increased
significantly and

555
00:27:35,211 --> 00:27:37,845
I think it's going to be going in
that direction.

556
00:27:37,845 --> 00:27:38,611
Right.

557
00:27:38,611 --> 00:27:39,678
Everybody's going to request it.

558
00:27:39,678 --> 00:27:41,745
I guess a couple of rapid fire questions.

559
00:27:41,745 --> 00:27:45,245
Are you ready to wrap up? Yeah.

560
00:27:45,345 --> 00:27:48,444
What does the legacy of Harvey look like?

561
00:27:48,444 --> 00:27:49,811
Oh, my legacy. Oh, my. Yes.

562
00:27:49,811 --> 00:27:50,578
I don't know.

563
00:27:50,578 --> 00:27:54,411
I mean, I'm I mean, my late twenties.

564
00:27:54,478 --> 00:27:55,044
I don't know yet.

565
00:27:55,044 --> 00:27:55,544
My legacy.

566
00:27:55,544 --> 00:27:59,178
Well,
that's why I want you to stop asking him,

567
00:27:59,244 --> 00:28:02,344
I guess. I mean, my

568
00:28:02,444 --> 00:28:03,611
my biggest

569
00:28:03,611 --> 00:28:06,511
academic life has been focused
on explainable A.I.

570
00:28:06,511 --> 00:28:07,444
working with Dr.

571
00:28:07,444 --> 00:28:11,878
Kelly Cohen and other people as well. But

572
00:28:11,944 --> 00:28:14,144
if if I can do anything

573
00:28:14,144 --> 00:28:17,611
that is meaningful, I believe it might be
within the field of explainable.

574
00:28:17,777 --> 00:28:22,677
That's also why I'm trying to do reason
and and all this.

575
00:28:22,744 --> 00:28:26,177
It just makes sense.

576
00:28:26,244 --> 00:28:27,644
Yeah. Okay.

577
00:28:27,644 --> 00:28:29,544
All right. Next question.

578
00:28:29,544 --> 00:28:32,411
If you had a theme,
I don't know if I answered very well.

579
00:28:32,411 --> 00:28:33,711
No, it's fine.

580
00:28:33,711 --> 00:28:35,677
It's good. You've answered honestly.

581
00:28:35,677 --> 00:28:38,910
If you had a theme that was the it's

582
00:28:39,010 --> 00:28:43,544
the story of your life or the saying,
what would that scene or theme be?

583
00:28:43,610 --> 00:28:45,944
Do you

584
00:28:45,944 --> 00:28:47,344
have one that I really like?

585
00:28:47,344 --> 00:28:52,444
It's hard from it's of mine,
but the world is like it's like a book.

586
00:28:52,644 --> 00:28:55,644
And those who do not travel
only read one page.

587
00:28:55,777 --> 00:28:58,310
Oh, that's good. Yeah, that's good.

588
00:28:58,310 --> 00:28:59,410
So it's like a book.

589
00:28:59,410 --> 00:29:02,144
And those who don't travel is like reading
one page.

590
00:29:02,144 --> 00:29:03,710
Yeah. Yeah, that's awesome.

591
00:29:03,710 --> 00:29:06,310
I think it's really important to travel
and really to get to know

592
00:29:06,310 --> 00:29:09,743
the different cultures
and understanding how they work,

593
00:29:09,743 --> 00:29:12,743
how they how they socialize,
how they do everything.

594
00:29:12,943 --> 00:29:17,177
And from there
you can learn so much and get new ideas.

595
00:29:17,177 --> 00:29:21,543
Every time I travel,
I get inspired on many of my

596
00:29:21,743 --> 00:29:25,510
because ideas have been a consequence
of of a great trip.

597
00:29:25,610 --> 00:29:27,677
Oh, I completely agree.

598
00:29:27,677 --> 00:29:32,910
You know, I tell people
I spend a lot of my twenties traveling and

599
00:29:33,010 --> 00:29:33,510
and if I could

600
00:29:33,510 --> 00:29:37,410
go back, I wouldn't
I wouldn't have split it any differently.

601
00:29:37,410 --> 00:29:40,310
Like, there's
nothing more valuable than experiences.

602
00:29:40,310 --> 00:29:44,876
Like if you if you have $10 billion
and you haven't traveled,

603
00:29:44,943 --> 00:29:48,110
you don't have any wealth,
You just in fact,

604
00:29:48,110 --> 00:29:52,143
you are a prison of what you of your
of your resources, because traveling

605
00:29:52,310 --> 00:29:53,876
is the most transformative thing.

606
00:29:53,876 --> 00:29:56,509
I agree. It is connecting with cultures.

607
00:29:56,509 --> 00:29:58,876
It's really
what is why we do disrupt our two.

608
00:29:58,876 --> 00:30:01,509
And it's because we want to connect
with creators all across the world.

609
00:30:01,509 --> 00:30:05,809
Is this to me understanding
learning cultures is just interesting.

610
00:30:06,009 --> 00:30:07,343
But to your point,

611
00:30:07,343 --> 00:30:11,509
when you're in a foreign environment,
it forces your brain into another.

612
00:30:11,543 --> 00:30:12,576
Yeah, the brain is like.

613
00:30:12,576 --> 00:30:13,676
It's like it's the spine.

614
00:30:13,676 --> 00:30:16,076
It absorbs everything. Yeah.

615
00:30:16,076 --> 00:30:19,343
And if you don't
get used to that plasticity, it's

616
00:30:19,409 --> 00:30:21,176
it starts to be more rigid.

617
00:30:21,176 --> 00:30:21,976
That's exactly right.

618
00:30:21,976 --> 00:30:26,376
And it's funny because the people
that I've met, at least in my in my life,

619
00:30:26,476 --> 00:30:30,542
that are very, you know, not flexible
usually they haven't traveled that much.

620
00:30:30,776 --> 00:30:31,776
Yeah.

621
00:30:31,776 --> 00:30:34,776
And what you have to guard against and,
you know,

622
00:30:34,776 --> 00:30:42,042
as we advance in age
that we don't become so rigid, you know

623
00:30:42,109 --> 00:30:43,909
I guess to my podcast Rodney Williams,

624
00:30:43,909 --> 00:30:48,175
that this is the most important skill to
his success was learning how to learn.

625
00:30:48,242 --> 00:30:51,575
And you know, learning
is a constant journey and the challenge

626
00:30:51,575 --> 00:30:53,175
when people gather

627
00:30:53,175 --> 00:30:55,609
a little bit of knowledge
because none of us have a ton, even

628
00:30:55,609 --> 00:30:59,975
the smartest of people
have a very small corner of knowledge.

629
00:31:00,075 --> 00:31:03,975
But when you get
when you have success, you

630
00:31:03,975 --> 00:31:05,709
then become hardened to actually

631
00:31:05,709 --> 00:31:10,375
understanding new perspectives, which is
why going to a new country humbles you.

632
00:31:10,375 --> 00:31:12,208
Because you realize this is something
you don't know any of this.

633
00:31:12,208 --> 00:31:14,408
Yeah, you don't know any of the culture.

634
00:31:14,408 --> 00:31:18,308
You don't understand any of the language,
the history for all of your knowledge.

635
00:31:18,308 --> 00:31:21,808
You are a baby here
and it's that humbling reminder

636
00:31:21,808 --> 00:31:24,808
when you stand in front of the ocean
of how small you really are.

637
00:31:24,808 --> 00:31:25,708
Yeah.

638
00:31:25,708 --> 00:31:28,542
And when people lose that perspective,
they lose their way.

639
00:31:28,542 --> 00:31:30,575
Even, you know, Albert Einstein, one of my

640
00:31:30,575 --> 00:31:34,508
one of my favorite people in terms
learn from and obviously a genius.

641
00:31:34,508 --> 00:31:37,975
And he was humanitarian, but

642
00:31:38,041 --> 00:31:40,308
he didn't see
the next evolution of physics.

643
00:31:40,308 --> 00:31:43,041
He actually rejected it. Right.

644
00:31:43,041 --> 00:31:45,141
Even though he was the founder of it.

645
00:31:45,141 --> 00:31:47,741
He rejected that that was coming
because, again,

646
00:31:47,741 --> 00:31:49,841
rigidity can go in the mind
once you have something.

647
00:31:49,841 --> 00:31:54,475
So I think that's the challenge to
everyone is to is to keep that perspective

648
00:31:54,541 --> 00:31:58,041
and and in a humble perspective
when it comes to knowledge.

649
00:31:58,108 --> 00:31:59,508
All right.

650
00:31:59,508 --> 00:32:00,941
Yes. So like.

651
00:32:00,941 --> 00:32:03,641
All right. So a final question. Yeah.
All right.

652
00:32:03,641 --> 00:32:08,574
You have three advisors
for business in life.

653
00:32:08,641 --> 00:32:10,074
Who are these advisors and why?

654
00:32:10,074 --> 00:32:11,508
Paul McCartney, The first one.

655
00:32:11,508 --> 00:32:11,941
Who's that?

656
00:32:11,941 --> 00:32:13,708
Paul McCartney. Okay.

657
00:32:13,708 --> 00:32:16,474
Why he is

658
00:32:16,474 --> 00:32:20,041
he's been so creative
and really creativity to me.

659
00:32:20,241 --> 00:32:24,341
I think you build that ability
to be creative.

660
00:32:24,441 --> 00:32:30,041
You can be, you know, born with, with more
or less capabilities to be creative,

661
00:32:30,141 --> 00:32:34,341
but really to be humans are are excellent

662
00:32:34,341 --> 00:32:38,207
in creativity and it's really
what's driving us and everything.

663
00:32:38,274 --> 00:32:43,641
So I think Paul has a huge amount
of creativity and I,

664
00:32:43,707 --> 00:32:47,041
I, I really admire him for that.

665
00:32:47,107 --> 00:32:50,841
So yeah,
I also like writing songs and music, so.

666
00:32:50,941 --> 00:32:53,641
Oh, you do that. Okay, everybody music.
But you know.

667
00:32:53,641 --> 00:32:54,907
All right, all right.

668
00:32:54,907 --> 00:32:57,440
We're going to we're going to do some
music on this trip down to the platform.

669
00:32:57,440 --> 00:32:59,874
All right. We're going to have to
one of your creative side.

670
00:32:59,874 --> 00:33:01,540
I'm going to get you to drop some music.
All right?

671
00:33:01,540 --> 00:33:03,340
Now that I know this. Okay?

672
00:33:03,340 --> 00:33:04,840
I'll and yourself and everyone.

673
00:33:04,840 --> 00:33:08,407
But don't play
it. So play the life of this.

674
00:33:08,507 --> 00:33:09,340
But. And then.

675
00:33:09,340 --> 00:33:11,040
I don't know.

676
00:33:11,040 --> 00:33:13,940
Yeah, my mother for sure.

677
00:33:13,940 --> 00:33:15,740
Okay. Yeah,

678
00:33:15,740 --> 00:33:16,974
My mother would be on my list, too.

679
00:33:16,974 --> 00:33:18,674
Yeah. Yes.

680
00:33:18,674 --> 00:33:21,807
And well, well, well, that's three.

681
00:33:21,807 --> 00:33:23,040
That's three. You know, you don't have to.

682
00:33:23,040 --> 00:33:24,973
Doesn't mean
you don't love the other people, you know?

683
00:33:24,973 --> 00:33:26,640
I mean, I'm sure it is. They're married.
It's.

684
00:33:26,640 --> 00:33:27,873
It's their doctor call. It'd be on a list.

685
00:33:27,873 --> 00:33:31,473
There's lots of other people
that have been helpful in

686
00:33:31,540 --> 00:33:34,540
and both of our lives like, yeah,
if I started out as a harpist,

687
00:33:34,640 --> 00:33:36,107
you'll just you'll be there forever.

688
00:33:36,107 --> 00:33:38,273
I want you just to go with the
with the top three.

689
00:33:38,273 --> 00:33:38,540
Okay.

690
00:33:38,540 --> 00:33:41,407
Well, obviously, we all live
in their apple, so you're good.

691
00:33:41,407 --> 00:33:43,173
You're good out
here. It's been a pleasure.

692
00:33:43,173 --> 00:33:45,540
Pleasure is glad you come on.
Thank you very much.

693
00:33:45,540 --> 00:33:48,473
Yeah, We're here at Midwest Con 2023.

694
00:33:48,473 --> 00:33:52,506
Rob Richardson with Disrupt Art You we're
taping at the Digital futures building.

695
00:33:52,540 --> 00:33:55,540
It's been an honor to have Harvey
Air Vienna

696
00:33:55,673 --> 00:33:58,240
his company is reason
Make sure you check it out.

697
00:33:58,240 --> 00:34:01,240
Make sure you understand
explain the way I and what it means.

698
00:34:01,473 --> 00:34:03,606
But until then, we'll see you
next time. Thank you so much