Name: Blending new tech and standard methodologies
Uploaded: 2021-06-22T08:41:42.669Z
Duration: 313.48
Description: Ujwal Arkalgud goes through theories and findings of blending new technologies with standard methodologies.

SIZZLE Blending New Tech with standard MethodologiesV3.mp4

Blending New Tech with standard MethodologiesV3.mp4.vtt

English

{{00:00:06}}
So I'm a cultural anthropologist and the co-founder and CEO of
motive base and our organization uses artificial intelligence and

{{00:00:29}}
anthropology to study the implicit or the hidden meanings behind
human interactions.Oftentimes, when we tell, other anthropologists,

{{00:00:40}}
other researchers about the work we do the innate assumption.

{{00:00:44}}
I find, I mean, talk about implicit meanings, right?

{{00:00:46}}
When you say AI to a researcher, the researcher assumes,
oh, you're trying to replace, right and that's actually

{{00:00:53}}
never been the, the focus for us as an organization or our
organization from day one has really looked at the role of

{{00:01:00}}
artificial intelligence and big data as a way to improve.

{{00:01:04}}
And in fact, you know, give the human being certain super
powers if you will, that they would just can't otherwise have.

{{00:01:11}}
So I'll give you a simple example if I were to do an
observational ethnography, and that's where, you know, I cut

{{00:01:17}}
my teeth in an observational ethnography, back in the day.

{{00:01:21}}
And you know, I remember I did a project for six months long.

{{00:01:25}}
I was hanging out with wildlife photographers, traveling with
them, pretending to be one, taking copious amounts of notes.

{{00:01:32}}
I had recordings, audio recordings.

{{00:01:35}}
Imagine the, the, how can you rely on a human being's ability?

{{00:01:39}}
And yes, I was working with a couple of other researchers.

{{00:01:42}}
How can you rely on a human being's ability to, you
know, go through hundreds of hours of recordings?

{{00:01:48}}
I was about to say tapes, but recordings of interactions,
Colate them structure them, it's impossible right?

{{00:01:56}}
The human mind is going to remember certain things that we
are predisposed to remember and the exciting part for us

{{00:02:02}}
with the technology was that we could actually remove some
of these, I'm going to use the word bias, but some of these

{{00:02:09}}
innate cognitive biases that it's unavoidable, we could
actually remove some of them and talk about super powers.

{{00:02:16}}
We could have the machine do a lot of the grunt work
that anyway, as an anthropologist, I hated doing that.

{{00:02:22}}
That was annoying.

{{00:02:22}}
The best part about the job was the analysis part.

{{00:02:25}}
The part where I could figure out the relationships in these innate
connections and, and identify, you know, implicit meanings that I

{{00:02:33}}
knew would be major ahas, uh, when presented in front of a client.

{{00:02:38}}
So that's what we focused on.

{{00:02:40}}
So for us, AI does the purpose of it collecting the data,
understanding the meanings, the constellation of meanings

{{00:02:49}}
and I kind of use the energy of the solar system, you know,
so if you're searching, let's say you're searching for

{{00:02:54}}
sustainable apparel, and you want to do an ethnography on it.

{{00:02:58}}
What our AI engine does is instantly.

{{00:03:01}}
Tells me what the constellation of meaning is
around sustainable apparel, kind of as if these

{{00:03:08}}
meanings are the planets revolving around the sun.

{{00:03:10}}
And it does it quantitatively by looking
at these millions of data points.

{{00:03:15}}
So then as a researcher, our job becomes to now play a language
game to, you know, in sort of a,Wittgenstein and sense play

{{00:03:24}}
a language game where we're now exploring the meanings that.

{{00:03:28}}
Consumers have inadvertently associated with sustainable
apparel, and the machine's already done the grunt work.

{{00:03:36}}
So that's the exciting part for us in terms of the application of AI.

{{00:03:40}}
It's, it's getting anthropologist superpowers to be able to do
the kind of work that we just couldn't imagine doing before.

{{00:03:46}}
The example I gave you of collecting the data, organizing
and structuring it, quantifying it as a human being.

{{00:03:53}}
It would take me months and months
to wait through this amount of data.

{{00:03:56}}
So 100% the machine can outperform me there.

{{00:04:00}}
A similar.

{{00:04:00}}
We use machine intelligence to figure out and quantify
the size of the culture so we can size a prize.

{{00:04:07}}
So if you say, you know, sustainable apparel, what does it
really mean to people in how many people is it relevant to,

{{00:04:13}}
or how mature is the consumer's understanding of this topic?

{{00:04:16}}
These are quantifications that our
machine can do the human being cannot do.

{{00:04:20}}
At least not in seven seconds, right?

{{00:04:23}}
The human being may be able to do this, I mean we've
built the algorithms of course, so clearly the human being

{{00:04:28}}
built the machine, but the machine can do things with a
speed and agility that I can't do as a human researcher.

{{00:04:35}}
So those are the areas where the machine outperforms, the
human being, but the areas where the machine can't is to make

{{00:04:44}}
sense of some of the thematic elements that of meaning, right.

{{00:04:48}}
If I can see certain thematic elements of
meaning, then I can group them together.

{{00:04:52}}
I can build a narrative around it, I can
understand how that relates to somebody's business.

{{00:04:56}}
That's where the human anthropologist comes into play and can use
their super powers to do better work and faster in more agile manner.

So I'm a cultural anthropologist and the co-founder and CEO of
motive base and our organization uses artificial intelligence and

anthropology to study the implicit or the hidden meanings behind
human interactions.Oftentimes, when we tell, other anthropologists,

other researchers about the work we do the innate assumption.

I find, I mean, talk about implicit meanings, right?

When you say AI to a researcher, the researcher assumes,
oh, you're trying to replace, right and that's actually

never been the, the focus for us as an organization or our
organization from day one has really looked at the role of

artificial intelligence and big data as a way to improve.

And in fact, you know, give the human being certain super
powers if you will, that they would just can't otherwise have.

So I'll give you a simple example if I were to do an
observational ethnography, and that's where, you know, I cut

my teeth in an observational ethnography, back in the day.

And you know, I remember I did a project for six months long.

I was hanging out with wildlife photographers, traveling with
them, pretending to be one, taking copious amounts of notes.

Imagine the, the, how can you rely on a human being's ability?

And yes, I was working with a couple of other researchers.

How can you rely on a human being's ability to, you
know, go through hundreds of hours of recordings?

I was about to say tapes, but recordings of interactions,
Colate them structure them, it's impossible right?

The human mind is going to remember certain things that we
are predisposed to remember and the exciting part for us

with the technology was that we could actually remove some
of these, I'm going to use the word bias, but some of these

innate cognitive biases that it's unavoidable, we could
actually remove some of them and talk about super powers.

We could have the machine do a lot of the grunt work
that anyway, as an anthropologist, I hated doing that.

The best part about the job was the analysis part.

The part where I could figure out the relationships in these innate
connections and, and identify, you know, implicit meanings that I

knew would be major ahas, uh, when presented in front of a client.

So for us, AI does the purpose of it collecting the data,
understanding the meanings, the constellation of meanings

and I kind of use the energy of the solar system, you know,
so if you're searching, let's say you're searching for

sustainable apparel, and you want to do an ethnography on it.

Tells me what the constellation of meaning is
around sustainable apparel, kind of as if these

meanings are the planets revolving around the sun.

And it does it quantitatively by looking
at these millions of data points.

So then as a researcher, our job becomes to now play a language
game to, you know, in sort of a,Wittgenstein and sense play

a language game where we're now exploring the meanings that.

Consumers have inadvertently associated with sustainable
apparel, and the machine's already done the grunt work.

So that's the exciting part for us in terms of the application of AI.

It's, it's getting anthropologist superpowers to be able to do
the kind of work that we just couldn't imagine doing before.

The example I gave you of collecting the data, organizing
and structuring it, quantifying it as a human being.

It would take me months and months
to wait through this amount of data.

So 100% the machine can outperform me there.

We use machine intelligence to figure out and quantify
the size of the culture so we can size a prize.

So if you say, you know, sustainable apparel, what does it
really mean to people in how many people is it relevant to,

or how mature is the consumer's understanding of this topic?

These are quantifications that our
machine can do the human being cannot do.

The human being may be able to do this, I mean we've
built the algorithms of course, so clearly the human being

built the machine, but the machine can do things with a
speed and agility that I can't do as a human researcher.

So those are the areas where the machine outperforms, the
human being, but the areas where the machine can't is to make

sense of some of the thematic elements that of meaning, right.

If I can see certain thematic elements of
meaning, then I can group them together.

I can build a narrative around it, I can
understand how that relates to somebody's business.

That's where the human anthropologist comes into play and can use
their super powers to do better work and faster in more agile manner.

Ujwal Arkalgud goes through theories and findings of blending new technologies with standard methodologies.  

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thumbnail_Ujwal-ff0924244a89dfbe9f12476e3397486e.jpg

Ujwal-ff0924244a89dfbe9f12476e3397486e.jpg

Ujwal Arkalgud

Co-founder and CEO 

Ujwal Arkalgud, Co-founder and CEO of Motivbase discusses key theories and how AI will inevitably blend with human practices. He also discusses how we could remove cognitive biases with AI-machine learning. 
