0:01
Noël Paasch:
I think it’s super important that we can
0:08
use machine learning to challenge assumptions and to kind of show us new perspectives.
0:15
Fiona Walford:
You’re here hopefully to demystify a lot
0:18
of those conversations that we have around machine learning, and to give us some insight
0:22
into actually what machine learning can do, and what it can bring to the creative process.
0:24
Noël:
Yeah, I think right now we’re at this stage
0:29
where machine learning has gotten a bad rap, in kind of the creative process.
0:33
Everybody’s afraid it’s going to take over their jobs and eat their lunch.
0:38
But in fact, if we kind of take a step back, it’s a new challenge that is presented to
0:41
creativity, and something that we need to learn to embrace.
0:44
Fiona:
Can you explain kind of then why it fundamentally
0:46
can’t replace people in the creative process?
0:48
Noël:
A lot of times, there’s a misconception
0:55
that the machines will have all the answers, that ML will kind of provide the solution,
1:00
and really, the way it should be viewed is as a way to reveal new perspectives, or to
1:06
challenge long-held assumptions of things that we thought would constantly work, especially
1:09
Fiona:
So what are the implications now for creative
1:14
Noël:
I think the opportunity for machine learning
1:16
will really come from kind of finding the sweet spot.
1:21
We come from a TV-driven world where we had a ton of media waste, and no real way to measure it.
1:26
And now we’re kind of on the opposite end of the spectrum where we can sometimes create
1:31
thousands of versions of an ad, sometimes at the click of a button, but if you only
1:36
have twenty to thirty of those thousands of ads that are actually working for you, is
1:38
it worth that production cost?
1:43
So we think that machine learning can really find that sweet spot, in between the two,
1:46
really maximizing your return on ad spend.
