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Transcriber: Reihaneh Iranmanesh
Reviewer: Elisabeth Buffard
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When most people think about AI,
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they picture a sci-fi dystopian future,
with man versus machine.
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Terminator,
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Skynet, Black Mirror,
Blade Runner, Westworld.
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But as someone who is working
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on the most ambitious
AI projects in the world,
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every day, I can tell you
that is far from reality.
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To me, it’s the contrary of that.
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AI enhances
and even supercharges humanity.
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Let me explain why.
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There are many reasons
why AI will never replace humans.
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AI always has,
and always will, rely on humans.
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That’s one of the reasons
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that I was actually inspired
to start an AI company.
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That and my background
have had a huge impact on me
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and why I started Scale.
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My parents were brilliant scientists
of Los Alamos,
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who accomplished a lot
in advancing their field.
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That inspired me to use
science and technology
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to have a real impact on the world.
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My dad was a physicist,
and my mom was an astrophysicist,
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both at the top of their field,
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who made meaningful contributions
to plasma fluid dynamics
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and the beginnings of the universe.
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Their scientific work will have
meaningful impact
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on how we understand
and perceive our world.
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And I wanted to work
on something as impactful
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or even more impactful than that.
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That’s why I decide
to become a programmer,
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so I can make a difference in the world.
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Growing up as a programmer,
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despite how powerful computers are,
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you quickly realize
how limited they are.
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In particular, they lack
judgment and intelligence.
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Programming is the art
of giving clear robotic instructions
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to computers to accomplish
simple objectives.
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It’s all black and white,
and there’s no gray area.
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As I learned about AI,
it was clearly transformational.
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It changed the game.
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It equipped computers with intelligence,
and I knew I wanted to be deeply involved.
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I was studying AI at MIT and slowly
became more and more excited
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about all the potential applications of AI
for solving more nuanced problems.
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For example, there was one class project
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where I worked on applying AI
to human emotions.
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The goal was to take picture
of human expressions
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and ultimately identify and understand
the emotion through very subtle signals
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in facial expressions.
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Using AI, we built an algorithm
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that was able to detect intent
with 80% accuracy and efficacy.
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We were extremely proud of that.
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It was the start of using AI to do
entirely new things using computers.
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That’s when I realize
the implications of AI
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and how it could tackle the gray areas
that involve judgment or intelligence.
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You see, AI needs humans
to teach it individual values,
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nudge it to find thoughtful outcomes,
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and ensure that human intentions
and values are aligned with the AI.
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It was a revelation.
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Before, coding was like
a black-and-white film
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versus watching in technicolor.
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What’s more, AI has the potential
to take away the repetition in our lives,
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meaning that new and fresh ideas
will matter more
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and ultimately enable us to be more human.
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But, to power AI,
you need powerful data,
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which was especially hard to come by
at that time, in 2016, while I was at MIT.
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I realized that nobody was building
anything with AI outside of school.
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It’s unusual for MIT students
to not be building something.
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Mechanical engineering majors
are building catapults in the lawn,
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electrical engineering majors
are building robots,
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and computer science majors are building
apps for their friends to use.
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But nobody was building
anything using AI.
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That’s when I discovered what
a bottleneck data can be
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to building meaningful
and powerful AI systems.
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You can't treat data as an afterthought.
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Bad data or lack of data
results in bad AI.
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I even realize this in my personal life.
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I put a camera inside my fridge
to gather data,
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to tell me when to refill my groceries
and what I needed to buy.
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That’s when I realized
just how much data I needed
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to actually be able
to successfully predict what to purchase.
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There’s no way I could create enough data
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to be successful
with the algorithms on my own.
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But it did help me discover
that my roommate was stealing my food.
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(Laughter)
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At that point, I realized
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that this was going to be
a pivotal problem for AI.
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Building large-scale,
high-quality datasets
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to power every single application.
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This was the impetus
behind starting Scale:
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quality data, to create
reliable AI outcomes,
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requires human insight and guidance.
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If you think about the core setup of AI,
the algorithms need data,
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and data needs humans.
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To ensure data is accurate,
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an expert human is often required.
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Only humans can understand
the context and nuance
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to properly annotate
the data to be fed to algorithms.
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Humans are the one who teach
the algorithms what to do.
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They’re the ones making the decisions,
they guide them.
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If something happens,
here’s what you should do.
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And AI learns from that
and replicates it.
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We are teaching the AI
our individual values
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and nudging the AI
to find thoughtful outcomes.
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Machines make mistakes.
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We have to teach them and
incentivize them to tell the truth.
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This is why teaching the AI human
intentions and values is so important.
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It’s through this process
that we will ensure
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that AI will have fair, ethical outcomes
in line with human values.
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It’s this alignment
that we must solve for.
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The constant alignment of AI to human
intentions will always require humans.
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and human ideas and creativity
can actually matter much more,
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with the power of AI behind them.
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The long tale of real-world problems,
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and the fact that there’s always
unknown unknowns
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means that humans
will never be fully removed
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from the AI development lifecycle.
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For example, I remember back in 2016
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when chatbots were first starting
to become a big thing.
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It was right when we were starting Scale.
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We were all thinking there's no way
to build a fully automated system.
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There’re so many different conversations
that can have so many different pathways.
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It’s hard to build AI systems that can
properly handle all these possibilities.
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For chatbots to work, there’re humans
behind it who make the decisions once,
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and from there, the chatbots
can replicate that over and over again.
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That’s again why it’s impossible
for AI to improve
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or change without human input.
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Let’s take you to the front lines of AI.
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The things that AI automates first
are not what you might expect.
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An unintuitive example is the weather.
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Humans have tried for many millennia
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to crack the code
of how to predict the weather.
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It’s especially hard for meteorologists
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because there are
so many different small things
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that can cause massive impacts
on the weather.
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It's the butterfly effect.
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Different elements react to one another
in unexpected ways.
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There’re so many inputs
that affect the weather,
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way more data than any person
would be able to comprehend on their own.
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That’s why we need AI
to analyze the vast oceans of data
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and provide more accurate, nuanced,
and comprehensive analysis.
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At the moment,
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AI can already provide extremely accurate
short-term predictions,
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including for critical storms and floods.
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So, it’s not what humans perceive
to be the simplest task
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that AI will automate first,
but rather where we have the most data.
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The use cases the brightest minds
are focusing on
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are much more positive
than what you might think.
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Much more so than Terminator or Westworld.
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That’s again why I think AI
will be a supercharger for humanity.
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Unlike the movies, AI developers
aren’t focusing their attention
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on building replacements for humans.
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They’re building tools
to help free up our time and energy
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to focus on what human
can uniquely solve.
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A good example about how AI
can be used in practice is health care.
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According to the Association
of American Medical Colleges,
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the United States could see
an estimated shortage
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of between 38,000
and 124,000 physicians by 2034.
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AI could save doctors’ time
with rogue tasks
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and ultimately enable them to serve
more patients and help more people.
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Health care is full of repetitive tasks
which are right for AI.
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When a patient is sick,
they go through all kinds of tests
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which produce all sorts of data:
blood tests, imagery,
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lab results, X-rays, etc.
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Doctors then analyze all this data
to make decisions about a case.
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AI can analyze all this data proactively
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and go through a list of possibilities
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by cross-referencing
against all prior data in cases.
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It can identify when something isn’t right
long before a physician can
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and flag it to a physician,
if it requires more attention.
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With AI,
doctors are still integral to the process,
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but it takes less time to get a diagnosis.
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You have to wait several weeks
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for your case to go
from one doctor to another.
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The AI will supercharge,
finding a diagnosis faster.
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Similarly, in the field of drug discovery,
it’s all about using complex data:
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experiment data, patient data,
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protein simulations and far more
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to guide a more efficient process
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of solving diseas
through new drugs and compounds.
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Recent advancements in AI
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have dramatically sped up
the scientific process
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by allowing us to process and make us
of more data than ever before.
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Another good example, and potentially
more concrete, is the Russia-Ukraine war.
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We've all seen the images of tanks
lining up ready to enter Kiev.
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AI can help assess satellite imagery
with superhuman speed and precision,
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so Ukrainian forces can respond faster.
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At Scale,
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we’re using our platform
to do damage assessment
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in key areas affected by the war.
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We’ve rapidly analyzed
over 2,000 square kilometers of Ukraine,
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identifying over 370,000 structures,
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including thousands not previously
available via other datasets.
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We focused on Kiev, Kharkiv and Dnipro,
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in which we provided some data
directly to government and users.
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We also made the data publicly available
to the broader AI community via Scale.
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We can also use this data
to maximize allocations of resources,
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people or commodities.
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It’s clear satellite data
can be extremely useful
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in these types of situations.
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Thanks to satellite data,
AI can analyze if planes or tanks
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have been moved from one place to another.
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This is called change detection.
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Algorithms can constantly be monitoring
for this kind of data,
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and if it notices a change or movement,
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it will alert a human
to further investigate,
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otherwise known as predictive modeling.
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AI can also help us understand
the economic impacts of war.
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We can use AI to track
farmland in Ukraine
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and measure the agricultural damage
in real time.
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Ukraine is a major food supplier
for much of the world.
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Understanding these impacts
is absolutely critical.
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In conclusion, AI is not
something to be feared,
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but it’s a tool that can be used
to better understand…
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that needs to be better understood,
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and has the potential
to transform our lives for the better.
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AI enables us to make use and sense
of massive amounts of data
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that has historically been
beyond human capacity.
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It allows us to add
intelligence and nuance
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to automated systems
that will dramatically improve humanity.
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Areas like health care and agriculture.
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This then allows humans
to do what they do best.
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Take this information,
put it into context with sensitivity,
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to strategize and act in a timely manner.
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AI is a supercharger for humanity.
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When AI is better than humans,
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it makes humans better.
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AI will automate repetitive tasks
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that don’t require
constant human judgment or creativity,
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which frees us up to explore
and focus on fresher, newer ideas.
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AI will enable us to be
even more creative and more idea-driven,
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which I personally find
incredibly exciting.
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It allows us to embrace
the generative aspects of human nature,
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so we can run faster with ideas
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and build better
and more powerful solutions
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to the world’s biggest problems.
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That’s why I believe
that human-led AI is the path forward,
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and I’m proud to usher all of us
into a future with human-led AI.
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Thank you.
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(Applause)