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Hi, welcome to another Cold Fusion video.
If you've been a follower of this channel for a
while, it would be obvious to you that artificial intelligence
has made some significant strides in the past couple of
years.
But today, we're going to look at something a little
bit different.
Google has just created an AI that is capable of
creating its own AI that performs better than anything else
made before in its field.
This sounds impossible, but it's just happened in this video.
We'll take a look.
The AI in question is called Automatic Machine Learning, or
Auto ML for short.
It's the work of a team of researchers at Google
Brain.
The researchers at Google used reinforcement learning to create AI
inception, as described by Google CEO Sundar Pichai, designing better
machine learning models.
But today it is really time consuming.
It's a painstaking effort of a few engineers and scientists,
mainly machine learning PhDs.
We want it to be possible for hundreds of thousands
of developers to use machine learning.
So what better way to do this than getting neural
Nets to design better neural Nets?
We call this approach auto MO.
It's learning to learn.
We are already approaching state-of-the-art in standard tasks like SIFAR
image recognition.
So whenever I spend time with the team and think
about neural Nets, building their own neural Nets, it reminds
me of one of my favorite movies, Inception.
And I tell them we must go deeper.
So instead of spending painstaking hours coding to create an
artificial intelligence to do a certain task, Auto ML actually
automates this process.
Here's a quote from Google's research blog.
Quote.
In our approach, which we call Auto ML, a controller
neural net can propose a child model architecture, which can
then be trained and evaluated for quality on a particular
task.
That feedback is then used to inform the controller how
to improve its proposals for the next round.
We repeat this process thousands of times, generating new architectures,
testing them, and giving that feedback to the controller to
learn from.
In a strange way, you can think of it like
a parent giving birth to a child and then teaching
that child how to perform a particular task through giving
it tests and telling it when it passed or failed.
The parent in this case is Auto ML and the
child is another neural network called Nasnet.
Nasnet is an AI capable of recognizing objects such as
people, cars, signs, and most things in real time video.
Now you might be saying that doesn't sound that great,
but the most incredible thing here is that this automated
AI outperforms any other computer vision that was made before
it.
Nasnet can perform with 82.7% accuracy, 1.2% better than all
other efforts while being 4% more efficient.
And AI built a child AI that performed better than
any other artificial intelligence created by humans, and it was
more efficient.
Just let that sink in.
Obvious applications for such a system include self driving vision
support, or enabling object recognition for robots that work in
close quarters with humans, such as healthcare for example.
This all has huge implications, mainly because if an AI
can create systems that are accurate while remaining efficient, it
can really take the hard work out of the field
of machine learning.
Auto ML may be one of the first examples, but
it does set a powerful precedent.
Auto ML also makes AI accessible to a wider set
of people, as Google puts it.
Quote We hope that the larger machine learning community will
be able to build on these models to address multitudes
of computer vision problems we have not yet imagined.
We think that this can inspire new types of neural
Nets and make it possible for non experts to create
neural Nets tailored to their particular needs, allowing machine learning
to have a greater impact to everyone.
End Quote.
There are some questions that naturally need to be asked
with auto machine learning technology.
That is, if it becomes widely used beyond just computer
vision.
Some of the concerns are as follows.
What if a parent AI passes down a mistake or
bias to a child?
And furthermore, what if the pace of learning of the
child AI develops so quickly that us as humans have
trouble keeping up and then we can no longer understand
what it's even doing?
These might seem like odd questions today, but it's possible
that one day these will be concerns of the future.
On a slightly different but related note, NVIDIA recently released
a video of its own AI learning to alter scenes
by changing the weather without human input.
The results are pretty spectacular.
It's really intriguing to think that this is only the
infancy of AI.
So anyway, that was just a quick video outlining some
stuff with computer vision going on at the moment.
I just thought it was interesting.
I thought you guys might want to know about it.
So let me know what you guys think in the
comments section below.
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This has been to go go.
You've been watching Cold Fusion and I'll see you again
soon for the next video.
Cheers guys, have a good one.