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>> As humans, we run scenarios in our heads all the time,
trying to predict how our actions might play out,
but the only way to test
that theories is to take action in the real world.
What if we could accurately simulate the world?
Try different actions, embattled realities.
Perhaps thousands of times,
see the consequences, and then go back and try again.
If you could do this,
we could learn and improve at an exponential rate,
innovation hinges on our ability to see the world differently,
breaking boundaries and looking between
the lines in an effort to solve
some of the world's greatest problems.
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>> I started my career at Microsoft in the '90s.
It was the dawn of the PC and Internet era.
As a software engineer at the time,
my peers and I couldn't imagine what lay ahead,
but we were driven by this belief that if you
could create a malleable medium for innovation,
human ingenuity would do amazing things with it.
The vast potential of the Internet couldn't be fully
understood or realized until it evolved from an academic,
unattainable piece of tech into
something we could use in our daily lives.
Today, we stand at the dawn of a new era
with artificial intelligence and autonomous systems.
Imagining the utopian future so far has been the realm of
the literary and sure Sentient AI
and flying cars are fun to contemplate.
But the real question isn't what can be envisioned?
It is how do we make it real?
How do we get there?
Autonomous systems connect the world of
bits to the world of atoms.
The intelligence is capable of sensing,
understanding, and making decisions in the real world.
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>> Software is the most malleable medium there is.
With careful design, you can create abstractions with layers upon
layers of capabilities that allow
or created to create any kind of an autonomous system.
Today, if you look at how early autonomous systems
are being attempted,
they are super complex software stacks, typically,
siloed, built by a super team of experts,
building a highly fragile and bespoke system.
We know from experience that siloed systems constrain innovation.
They are expensive in time and effort,
and they don't produce building blocks that can be re-used.
The history of technology is replete with
silos tipped by open-end horizontal systems,
and the software industry is no different.
We're breaking down the silos with open,
reusable, powerful AI building blocks.
These blocks can be used for building
all kinds of highly capable autonomous systems.
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>> Consider the products that power the world around us.
They're designed to be operated by humans, they are basic,
and hard-coded to a rigid function without innate smarts.
We as humans, learn how to operate
them and tolerate their limitations.
I wince every time my car navigation system
guides me loudly through the street my house is on.
What if we could teach these machines to
actually work the way we want them to?
By focusing on machine teaching,
we are creating tools to capture
human taught expertise and to apply to machines.
We humans know this paradigm well.
We teach children, we teach assistance,
interns, we teach ourselves.
What turns out that many of the techniques used
for teaching humans can be used for teaching machines as well.
Things like concepts, curriculum, evaluation.
What if your waste management company
could program its machines to
separate the garbage from
the recycling so that you didn't have to?
They won't need a PhD in AI or data science to do this.
They could simply show the object like
a bottle or a can to program the parameters.
Just like that, any expert with a great idea can
similarly teach the autonomous system in their field.
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>> The autonomous systems operate in the real world.
We must use an AI technique that allows
learning through interaction with the real world.
Reinforcement learning is exactly that.
Very simply, it keeps trying and trying until it masters at task.
Autonomous systems exist in the physical world with us.
Teaching them can be expensive and potentially hazardous.
It often takes millions of data points
to train an AI to perform a task.
How do we solve for this?
The big idea here is to create a digital replica of the world
and a digital agent that learns inside of this environment.
The digital agent can try tasks, fail,
try again repeatedly until it masters the task.
This approach only works if you can
adequately represent all the aspects of the physical world.
Like it's visual representation,
its degrees of freedom, and it's physics.
Because it's only software,
thousands of instances of this synthetic reality can be created
simultaneously and because this digital replica
is not constrained by real time,
we can overclock reality.
Just one second of human time can
be millions of seconds of training time.
Perhaps most importantly in this alternate digital world,
AI can be trained for
rare and potentially dangerous situations,
all without consequence.
Then there are some scenarios that are
impossible to test in the real world.
Robots helping in mine rescue operations
or even finding water on Mars.
What if, before taking an action in the real world,
the autonomous agents took thousands of
actions in a parallel reality,
looked at the outcome of those actions,
and then selected the best action to take in the real world?
Digital replicas allow for this breadth of
training, contra factual reasoning,
and black swan events preparation,
critical before these autonomous systems
can be let loose amongst us.
But breadth of training is not
sufficient if we are to truly trust these systems.
Safety must be built into the teaching platform.
An engineer must be able to assure
that the system is behaving as it was designed to.
Equally important, the designer must be able to
explain everything the agent did and why it did so.
Most AI systems trade-off
this transparency to achieve impressive results.
Millions of numbers adjusting
themselves inside the neural network,
opaquely doing their thing.
Now, that may be sufficient for playing a game,
but for autonomous systems that are operating
around us, that isn't.
Autonomous systems must be built with
safety and explainability in every components.
I believe the world is ready for autonomous systems.
Whether this be autonomous forklifts
shipping products at the time of COVID,
drones inspecting power lines to prevent
forest fires or agents helping assisted living.
By digitizing our reality with this malleable medium of software,
with our full teaching tools and with experientially AI,
we're creating a world where
autonomous systems are ubiquitous and safe,
having a positive impact on our planet,
our economy, and our lives.
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