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The concept
of machines that can think
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and act like humans has
been around for centuries.
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Long before smartphones
or even electricity
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our ancestors Were inspired
by the world around them
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and the mysteries of life itself.
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We've been through these
changes before as humanity.
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Are you going to be afraid,
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or are you gonna see the opportunities?
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The more people have access to AI,
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the better it is going to be
for future generations to come.
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We should be treating AI
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like we do a beloved little child,
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or you wanna teach it how to
be a good citizen of the world.
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From ancient
myths to modern marvels,
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we'll delve into the stories
of the pioneers who dared
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to dream of thinking machines
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and the technological advancements
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that have brought us closer to
making that dream a reality.
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All technology
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has no conscience of its own.
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Whether it will become
a force for good or ill,
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depends on man.
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The story
of AI is a testament
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to human curiosity, creativity,
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and the relentless pursuit of knowledge.
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What is AI?
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Artificial intelligence or AI
is giving computers a brain.
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The dictionary defines it as the theory
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and development of computer
systems able to perform tasks
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that normally require human intelligence.
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Today you think of AI as ChatGPT,
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but what it really is,
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is a reasoning and planning system
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that we've never seen before.
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When it comes to AI, there
are opportunities everywhere.
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You know, we're kind of
in the wild, wild west,
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and it could be
opportunities to get funding
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with a good idea and a
compelling proof of concept.
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There are really two types
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of AI being used out in the world today.
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One is the larger, more generalized AI
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that we know of as Google Translate.
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And ChatGPT on the other side,
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there's we call automation AI that's built
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with what we call golden data,
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or data that is derived
specifically for that use case
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and it takes away pain
points for workflows.
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Typically in businesses or
in people's daily routines.
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You can pick anything
and build an idea from it.
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Somebody built a company
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that's using AI to grade baseball cards.
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So rather than having
an expert have to look
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and say this is mint or
near mint or very good,
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you could just take photos of 20 cards
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and it'll give you
ratings for all of them.
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AI is set to
change the world as we know it.
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The arrival of this new intelligence
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will profoundly change
our country and the world
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in ways we cannot fully understand.
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And none of us,
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including myself, and
frankly, anyone in this room,
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is prepared for the implications of this.
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So what
are the origins of AI?
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The ancient Greeks told
tales of Hephaestus,
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the blacksmith of the gods who
created mechanical servants
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and even a bronze automaton called Talos
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to guard the island of Crete.
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These myths weren't just flights of fancy,
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they reflected a deep fascination
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with the potential of artificial beings.
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While the Greeks didn't have
computers or algorithms,
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they laid the groundwork for AI
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by exploring the relationship
between humans and machines.
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They grappled with questions
about consciousness,
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creativity, and the very
nature of intelligence.
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Questions that still
resonate with us today,
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I question whether such
automatons can possess life.
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But how does one define life then?
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Perhaps it is the ability to reason.
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Fast forward
to the Middle Ages
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where ingenious inventors
crafted intricate clocks,
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water-powered automatons,
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and even programmable musical instruments.
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These mechanical marvels often
displayed in royal courts
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and town squares captivated
the public imagination
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and pushed the boundaries
of what seemed possible.
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One particularly fascinating
figure was Al-Jazari,
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a 12th century Arab inventor who designed
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and built a wide range of automata,
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including a programmable
musical instrument
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that could be considered an
early ancestor of the computer.
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These inventions demonstrated
the growing sophistication
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of mechanical engineering,
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and hinted at the potential for machines
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to perform increasingly complex tasks.
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The 19th century saw a
surge in interest in logic,
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and mathematics laying the
groundwork for the development
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of computer science.
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Thinkers like George
Boole and Ada Lovelace
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made groundbreaking contributions
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to the formalization of logic
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and the development of algorithms,
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paving the way for the digital age.
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Lovelace, often hailed as the
first computer programmer,
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recognized the potential
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of Charles Babbage's analytical engine,
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a mechanical general purpose computer
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to go beyond mere calculation
and manipulate symbols
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according to rules
hinting at the possibility
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of artificial intelligence.
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In the mid 1940s,
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two researchers at the
University of Chicago came up
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with the idea of trying
to mimic the brain,
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the neurons and interconnections,
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and they called the neural networks.
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Now, this idea, there was
really no computational power
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to execute anything significant,
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but this is the idea that over the years
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has become the basis of what AI is today.
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The mid 20th
century witnessed the birth
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of the computer age,
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a revolution ignited by
the work of visionaries
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like Alan Turing.
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The British mathematician
asked a simple question,
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"Can machines think?"
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This era marked a significant
shift in human history
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as the potential of machines
to perform complex calculations
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and tasks began to be realized.
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The groundwork laid during this period
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would go on to influence
countless aspects of modern life,
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from the way we communicate
to the way we solve problems.
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Turing, a brilliant
mathematician and codebreaker,
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played a pivotal role in
cracking the enigma code
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during World War II.
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It's the greatest
encryption device in history.
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The Germans use it for
all major communications.
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His work was
crucial in the allied victory,
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saving countless lives
and shortening the war.
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He laid the theoretical
foundations for modern computing,
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envisioning machines that
could perform any task
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given the right instructions.
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His ideas were revolutionary,
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proposing that a machine
could be programmed
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to carry out any computation
that a human could
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given enough time and resources.
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Moreover, his famous Turing test asks
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if a machine can mimic
human conversation so well
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that you can't tell if it's
a person or a computer.
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The Turing test challenges
us to consider what it means
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for a machine to think.
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Turing's vision of
intelligent machines continues
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to drive innovation and
exploration in the field,
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influencing everything from robotics
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to natural language processing.
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The field of artificial
intelligence as we know it
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was officially born in 1956
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at the Dartmouth Summer Research Project
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on artificial intelligence.
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This landmark conference
organized by John McCarthy,
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Marvin Minsky, Claude Shannon,
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and Nathaniel Rochester brought
together leading researchers
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to explore the potential
of thinking machines.
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Despite the initial enthusiasm,
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the road to artificial intelligence proved
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to be rockier than anticipated.
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The late 1960s and 1970s saw
a period of disillusionment
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and reduced funding for AI research
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often referred to as the AI winter.
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Funding was cut back significantly.
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And what was it that resulted in that,
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I think that a big part of it was hype.
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You know, there was a ton of hype
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of selling artificial
intelligence, reasoning,
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contextual awareness, so on and so forth,
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and the tools that were built at that time
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were nowhere near the
capability of of achieving that.
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The 1980s and 1990s witnessed
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a resurgence of AI,
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driven in part by the
rise of machine learning.
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This inspired by the brain's ability
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to learn from experience
involved training algorithms
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on vast amounts of data,
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allowing them to improve
their performance over time.
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The availability of larger data sets
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and more powerful computers
enabled significant progress.
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In the late nineties what we knew as AI
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was really the decision trees
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that were used to control at video game.
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The first video game we
did was called Soul Edge.
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Namco brought a ninja from Japan.
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We shouldn't doing moves
using motion capture.
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The main player would
move their controller,
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and then the AI system
in the game would decide
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which animation to play based on that.
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And then you have the non-player
characters or the NPCs
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and those would also make decisions
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based on what is decision trees.
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You win.
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Machine learning
began to outperform humans
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in specific tasks.
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In the late nineties,
AI scored a major win.
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IBM's Deep Blue defeated
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world chess champion Garry Kasparov,
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demonstrating the
potential of this approach
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to revolutionize various industries.
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Lift off of
space shuttle and lands.
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The 20th century witnessed
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the thrilling space race, a
competition between nations
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to achieve supremacy in space exploration.
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It was a time of rapid
advancements and intense rivalry.
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Today a new race is
underway, the AI space race.
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Nations are investing heavily
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in AI research and development.
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At the heart of this competition
is the drive to develop
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and control the most
advanced AI technologies,
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which promised to transform our world.
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Starting in the 2010s,
we had this convergence
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of cloud computing
becoming less expensive,
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and also the treasure trove of data
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that was actually accessible to developers
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and AI technologists from
social media, from the internet,
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all these things coming together,
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that's when we saw this
next level of explosion.
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This next level of development
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that took us into what we're
seeing now, exponential growth.
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The biggest impactful
discovery was really in 2017
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with the release of Transformer models,
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which essentially allowed
these neural models
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to create contextual lines
between the information
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that it's being trained with.
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And I think that context is central for AI
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to gain the ability to reason
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and to make the correct decisions.
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The stakes are
incredibly high as the nation
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that leads in AI will have
a significant advantage
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in the global arena.
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What would happen if China beat us?
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Let's think about it.
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The path to intelligence
that superhuman intelligence,
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think of the national
security implications
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of that competition.
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In January, 2025,
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the world witnessed a pivotal
moment in the AI space race.
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The launch of DeepSeek a Chinese AI app
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that quickly took the world by storm.
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This innovative application
redefined the boundaries
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of what AI could achieve in everyday life.
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DeepSeek showed up.
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Nobody expected this.
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It turns out it's on par now
with some of the top models.
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Welcome, China has arrived
in the competition.
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The DeepSeek movement is
gonna go down in history
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as one being one of reflection
for the entire AI community
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and every stakeholder at large.
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So DeepSeek was a model
released by a Chinese company
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that goes by the same name.
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It was an open source
model that was comparable
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00:12:56,430 --> 00:12:59,853
to the best closed source models
that existed in the planet.
261
00:13:07,890 --> 00:13:11,309
The release of DeepSeek proved
that some smaller companies
262
00:13:11,310 --> 00:13:14,669
can compete with the
larger Goliath models.
263
00:13:14,670 --> 00:13:16,589
And what it really proved
264
00:13:16,590 --> 00:13:21,299
is that it's possible to do
more with less resources,
265
00:13:21,300 --> 00:13:25,769
and that the lag from having
a top of the line model
266
00:13:25,770 --> 00:13:29,609
is really a much faster depreciating asset
267
00:13:29,610 --> 00:13:32,339
than we thought that it was initially
268
00:13:32,340 --> 00:13:33,869
Developed by a consortium
269
00:13:33,870 --> 00:13:35,399
of Chinese tech companies,
270
00:13:35,400 --> 00:13:39,273
DeepSeek showcased China's rapid
progress in AI development.
271
00:13:40,140 --> 00:13:43,829
Utilizing a cost-effective
development model known as R1,
272
00:13:43,830 --> 00:13:47,339
DeepSeek achieved impressive
results with limited resources.
273
00:13:47,340 --> 00:13:50,429
We're not entirely
sure of how many times
274
00:13:50,430 --> 00:13:51,629
they may have gone through this,
275
00:13:51,630 --> 00:13:54,389
how much money they really
spent to get to the point
276
00:13:54,390 --> 00:13:58,019
where they were at in being
able to create a model
277
00:13:58,020 --> 00:14:00,659
with the $5 million,
whatever it was spent.
278
00:14:00,660 --> 00:14:01,919
The app quickly climbed
279
00:14:01,920 --> 00:14:04,439
to the top of the Google Play
and Apple app store charts,
280
00:14:04,440 --> 00:14:07,259
surpassing established US tech giants.
281
00:14:07,260 --> 00:14:10,979
Its rapid ascent was
nothing short of remarkable.
282
00:14:10,980 --> 00:14:13,559
This surge in popularity sent shockwaves
283
00:14:13,560 --> 00:14:15,059
through the tech world,
284
00:14:15,060 --> 00:14:18,449
signaling a shift in
the global AI landscape.
285
00:14:18,450 --> 00:14:20,579
Analysts and experts began to take notice
286
00:14:20,580 --> 00:14:22,511
of the changing dynamics.
287
00:14:22,512 --> 00:14:25,199
DeepSeek success served as a wake up call
288
00:14:25,200 --> 00:14:27,329
for the US and other nations.
289
00:14:27,330 --> 00:14:30,509
Highlighting the fierce competition in AI.
290
00:14:30,510 --> 00:14:32,969
The fact that this
model is now open source,
291
00:14:32,970 --> 00:14:36,569
the fact that anybody in the
world can build on top of it
292
00:14:36,570 --> 00:14:38,669
means that we should
just acknowledge the fact
293
00:14:38,670 --> 00:14:41,849
that if AI is gonna be in the
hands of every individual,
294
00:14:41,850 --> 00:14:43,949
we need to think of other mechanisms
295
00:14:43,950 --> 00:14:47,309
to make sure it's responsibly
being used and deployed.
296
00:14:47,310 --> 00:14:49,709
I think the importance of
what they were able to achieve
297
00:14:49,710 --> 00:14:54,710
is allowing open source
models to continue to compete,
298
00:14:54,990 --> 00:14:58,739
and that it ensures
that some of the things
299
00:14:58,740 --> 00:15:00,479
that AI is supposed to be great for
300
00:15:00,480 --> 00:15:02,939
in terms of improving humanity
301
00:15:02,940 --> 00:15:05,433
can be done without
being behind a paywall.
302
00:15:06,480 --> 00:15:08,279
The launch of
DeepSeek occurred amidst
303
00:15:08,280 --> 00:15:12,239
the surge in AI investment
and development in the US.
304
00:15:12,240 --> 00:15:16,049
Together, these world
leading technology giants era
305
00:15:16,050 --> 00:15:19,079
announcing the formation of Stargate,
306
00:15:19,080 --> 00:15:22,919
a new American company that
will invest $500 billion
307
00:15:22,920 --> 00:15:25,349
at least in AI infrastructure.
308
00:15:25,350 --> 00:15:27,449
The fact that it was coming out of China
309
00:15:27,450 --> 00:15:30,779
had a lot of geopolitical
implications around it as well.
310
00:15:30,780 --> 00:15:35,219
If you're using a model
that's also hosted in China,
311
00:15:35,220 --> 00:15:36,862
do they have access to a lot of the data
312
00:15:36,863 --> 00:15:39,659
that we are feeding into
these models unknowingly?
313
00:15:39,660 --> 00:15:43,559
China is a competitor and
others are competitors, we want,
314
00:15:43,560 --> 00:15:45,303
we want it to be in this country.
315
00:15:46,320 --> 00:15:48,539
Notable trends
included Google's Gemini
316
00:15:48,540 --> 00:15:52,593
and Microsoft's Copilot providing
more focused AI solutions.
317
00:15:53,640 --> 00:15:55,649
While the US and China are major players
318
00:15:55,650 --> 00:15:58,259
in the AI space race, this
is a global competition
319
00:15:58,260 --> 00:16:00,573
with contributions from
countries worldwide.
320
00:16:01,590 --> 00:16:03,959
Nations around the world
are investing heavily
321
00:16:03,960 --> 00:16:06,119
in AI research and development,
322
00:16:06,120 --> 00:16:08,549
each hoping to gain a competitive edge.
323
00:16:08,550 --> 00:16:10,829
This multipolar nature fosters a diverse
324
00:16:10,830 --> 00:16:13,139
and dynamic AI ecosystem.
325
00:16:13,140 --> 00:16:17,879
From country to country values differ.
326
00:16:17,880 --> 00:16:22,379
For example, China has its own values
327
00:16:22,380 --> 00:16:23,849
and its own considerations,
328
00:16:23,850 --> 00:16:26,729
so we will need to get on the same page.
329
00:16:26,730 --> 00:16:30,959
There will be, hopefully, new
treaties along those lines,
330
00:16:30,960 --> 00:16:34,979
but it is going to be a great challenge.
331
00:16:34,980 --> 00:16:36,989
The future of
AI depends on our ability
332
00:16:36,990 --> 00:16:39,359
to harness its transformative power
333
00:16:39,360 --> 00:16:41,639
while mitigating its risks.
334
00:16:41,640 --> 00:16:43,469
The choices we make today will determine
335
00:16:43,470 --> 00:16:45,299
whether AI becomes a force for good,
336
00:16:45,300 --> 00:16:47,043
or a source of new challenges.
337
00:16:48,210 --> 00:16:52,319
We are on the cusp of a disruption
338
00:16:52,320 --> 00:16:56,819
to human culture that happens very rarely.
339
00:16:56,820 --> 00:16:58,799
The kind of disruption we're talking about
340
00:16:58,800 --> 00:17:00,089
is the kind of disruption
341
00:17:00,090 --> 00:17:02,376
that the invention of farming created.
342
00:17:08,280 --> 00:17:10,499
This technology is in
some sense unstoppable.
343
00:17:10,500 --> 00:17:12,850
It's gonna become a part
of our everyday lives.
344
00:17:14,340 --> 00:17:17,039
The future is
closer than you might think.
345
00:17:17,040 --> 00:17:19,739
Picture a day waking up in a home powered
346
00:17:19,740 --> 00:17:23,433
by renewable energy, commuting
in an autonomous vehicle,
347
00:17:24,270 --> 00:17:26,133
receiving personalized healthcare.
348
00:17:27,600 --> 00:17:31,533
Let's travel to the year
2035 and visit NeoZenith.
349
00:17:34,350 --> 00:17:36,869
A shining example of a city where AI
350
00:17:36,870 --> 00:17:39,723
and humans coexist in perfect harmony.
351
00:17:42,690 --> 00:17:45,869
It's a testament to what can
be achieved when technology
352
00:17:45,870 --> 00:17:48,513
and nature are seamlessly integrated.
353
00:17:50,220 --> 00:17:53,519
Imagine streets where AI-driven
systems manage everything
354
00:17:53,520 --> 00:17:56,039
from traffic flow to energy consumption,
355
00:17:56,040 --> 00:17:58,803
ensuring that the city
operates at peak efficiency.
356
00:18:01,350 --> 00:18:03,123
This isn't some concrete jungle.
357
00:18:04,320 --> 00:18:07,019
NeoZenith is a breath
of fresh air, literally.
358
00:18:07,020 --> 00:18:09,239
The city planners have
gone to great lengths
359
00:18:09,240 --> 00:18:13,499
to incorporate green spaces
into every aspect of urban life.
360
00:18:13,500 --> 00:18:15,389
Parks, gardens and green rooftops
361
00:18:15,390 --> 00:18:17,339
are not just aesthetic choices,
362
00:18:17,340 --> 00:18:20,879
but essential components
of the city's ecosystem.
363
00:18:20,880 --> 00:18:24,299
These green spaces serve
as the lungs of NeoZenith,
364
00:18:24,300 --> 00:18:26,429
filtering pollutants
in providing residents
365
00:18:26,430 --> 00:18:27,723
with clean, fresh air.
366
00:18:29,250 --> 00:18:30,963
The city pulses with life.
367
00:18:31,830 --> 00:18:34,829
Public transportation is
efficient and eco-friendly,
368
00:18:34,830 --> 00:18:36,809
making it easy for everyone to get around
369
00:18:36,810 --> 00:18:38,495
without contributing to pollution.
370
00:18:38,496 --> 00:18:41,099
This
is Hikari Super Express
371
00:18:41,100 --> 00:18:42,623
bound to Shin-Osaka.
372
00:18:43,830 --> 00:18:45,209
It's organized, efficient,
373
00:18:45,210 --> 00:18:47,251
and designed with people at its heart.
374
00:18:49,950 --> 00:18:52,829
Wide pedestrian-friendly avenues are alive
375
00:18:52,830 --> 00:18:56,433
with people strolling, cycling,
and enjoying the fresh air.
376
00:18:57,360 --> 00:18:59,579
Gleaming solar panels are dawn rooftops,
377
00:18:59,580 --> 00:19:01,233
soaking up the sun's energy.
378
00:19:02,340 --> 00:19:05,369
Wind turbines hum gracefully
on the city's outskirts,
379
00:19:05,370 --> 00:19:07,709
generating clean electricity.
380
00:19:07,710 --> 00:19:09,899
But here's where AI comes in.
381
00:19:09,900 --> 00:19:12,029
It manages and distributes this energy
382
00:19:12,030 --> 00:19:13,979
with incredible efficiency.
383
00:19:13,980 --> 00:19:15,959
It predicts energy consumption patterns
384
00:19:15,960 --> 00:19:18,029
and ensures that every corner of NeoZenith
385
00:19:18,030 --> 00:19:20,583
has a constant supply of clean power.
386
00:19:21,840 --> 00:19:23,459
We could be looking at a future
387
00:19:23,460 --> 00:19:25,799
where we all have personal assistance
388
00:19:25,800 --> 00:19:30,089
that are doing things for us,
helping us make decisions,
389
00:19:30,090 --> 00:19:32,519
helping us be healthier people,
390
00:19:32,520 --> 00:19:34,745
helping us do creative tasks.
391
00:19:36,210 --> 00:19:39,269
Wake up in your
cozy NeoZenith apartment
392
00:19:39,270 --> 00:19:40,829
and your AI assistant greets you
393
00:19:40,830 --> 00:19:44,579
with a personalized
schedule and the day's news.
394
00:19:44,580 --> 00:19:46,799
Your AI has already
brewed your favorite blend
395
00:19:46,800 --> 00:19:48,050
just the way you like it.
396
00:19:49,620 --> 00:19:51,209
Heading to work?
397
00:19:51,210 --> 00:19:52,529
Autonomous vehicles whisk you
398
00:19:52,530 --> 00:19:54,089
through the city swiftly and safely,
399
00:19:54,090 --> 00:19:56,973
giving you precious time to
relax or catch up on emails.
400
00:19:59,250 --> 00:20:02,339
Remember those traffic
clogged streets of the past,
401
00:20:02,340 --> 00:20:05,039
the honking horns, the
endless lines of cars,
402
00:20:05,040 --> 00:20:07,953
and the frustration of being
stuck in a jam for hours?
403
00:20:09,780 --> 00:20:12,869
The city has embraced a
multilayered transportation system
404
00:20:12,870 --> 00:20:15,423
that's both efficient and eco-friendly.
405
00:20:16,680 --> 00:20:19,589
Underground hyperloop
systems transport people
406
00:20:19,590 --> 00:20:22,443
across vast distances
in the blink of an eye.
407
00:20:23,490 --> 00:20:26,759
These high-speed pods travel
through low pressure tubes,
408
00:20:26,760 --> 00:20:29,343
making long commutes a thing of the past.
409
00:20:30,510 --> 00:20:32,669
Imagine traveling from one end of the city
410
00:20:32,670 --> 00:20:34,563
to the other in just minutes.
411
00:20:36,000 --> 00:20:36,869
And for shorter trips,
412
00:20:36,870 --> 00:20:39,089
autonomous electric vehicles
are readily available,
413
00:20:39,090 --> 00:20:41,789
summoned with a tap on your smartphone.
414
00:20:41,790 --> 00:20:44,249
This intelligent
transportation network managed
415
00:20:44,250 --> 00:20:47,009
by AI constantly monitors
traffic conditions,
416
00:20:47,010 --> 00:20:49,409
adjusting route and schedules in real time
417
00:20:49,410 --> 00:20:51,513
to avoid congestion and delays.
418
00:20:53,070 --> 00:20:55,199
There are large language models
419
00:20:55,200 --> 00:20:58,439
that already have a visual component.
420
00:20:58,440 --> 00:21:02,009
I believe Gemini from
Google does that already,
421
00:21:02,010 --> 00:21:04,049
and it also remembers.
422
00:21:04,050 --> 00:21:08,399
So you can be wearing those
glasses with the cameras
423
00:21:08,400 --> 00:21:13,019
and you could say, where did
I leave my kids yesterday?
424
00:21:13,020 --> 00:21:15,989
And Gemini will find them for you.
425
00:21:15,990 --> 00:21:17,879
So this is where this is going.
426
00:21:17,880 --> 00:21:19,784
People are gonna be walking around
427
00:21:19,785 --> 00:21:22,619
with glasses with cameras,
428
00:21:22,620 --> 00:21:24,089
and they're gonna get directions.
429
00:21:24,090 --> 00:21:25,889
They're gonna get everything.
430
00:21:25,890 --> 00:21:28,349
Healthcare in
NeoZenith is light years ahead.
431
00:21:28,350 --> 00:21:30,989
AI-powered diagnostic
tools can detect diseases
432
00:21:30,990 --> 00:21:34,799
at their earlier stages leading
to more effective treatments
433
00:21:34,800 --> 00:21:36,869
and better outcomes for patients.
434
00:21:36,870 --> 00:21:38,879
Gone are the days of invasive procedures
435
00:21:38,880 --> 00:21:40,979
and lengthy waiting times.
436
00:21:40,980 --> 00:21:42,809
Nanobots, tiny robots smaller
437
00:21:42,810 --> 00:21:44,849
than a blood cell patrol your body,
438
00:21:44,850 --> 00:21:46,709
identifying and even treating illnesses
439
00:21:46,710 --> 00:21:47,883
at the cellular level.
440
00:21:48,930 --> 00:21:51,659
Imagine a world where diseases
like cancer are detected
441
00:21:51,660 --> 00:21:54,449
and treated before they even
have a chance to take hold.
442
00:21:54,450 --> 00:21:56,999
I believe that as this
technology progresses,
443
00:21:57,000 --> 00:22:00,239
we will see diseases get cured
at an unprecedented rate.
444
00:22:00,240 --> 00:22:02,939
We will be amazed at how
quickly we're curing this cancer
445
00:22:02,940 --> 00:22:05,549
and that one and what this
will do for the ability
446
00:22:05,550 --> 00:22:07,889
to deliver very high quality
healthcare, the costs,
447
00:22:07,890 --> 00:22:10,769
but really to cure the diseases
at a rapid, rapid rate,
448
00:22:10,770 --> 00:22:14,009
I think will be among
the most important things
449
00:22:14,010 --> 00:22:15,539
this technology does.
450
00:22:15,540 --> 00:22:17,639
Right now
AI is reading x-rays
451
00:22:17,640 --> 00:22:20,699
and scans faster than most radiologists.
452
00:22:20,700 --> 00:22:22,889
And in some cases even spotting things
453
00:22:22,890 --> 00:22:24,659
that human doctors have missed.
454
00:22:24,660 --> 00:22:27,689
For example, Google's
DeepMind has built a system
455
00:22:27,690 --> 00:22:31,533
that detects over 50 eye
diseases just from retinal scans.
456
00:22:32,850 --> 00:22:34,319
AI chatbots are being trained
457
00:22:34,320 --> 00:22:36,569
to triage patients asking about symptoms
458
00:22:36,570 --> 00:22:38,939
and suggesting who needs
to see a doctor right now
459
00:22:38,940 --> 00:22:42,029
and who can schedule a later appointment.
460
00:22:42,030 --> 00:22:43,469
It's not about replacing doctors,
461
00:22:43,470 --> 00:22:46,109
it's about saving their time
for when it really matters
462
00:22:46,110 --> 00:22:48,929
and it's not just in the hospital.
463
00:22:48,930 --> 00:22:52,019
AI-powered apps are helping
people manage chronic conditions
464
00:22:52,020 --> 00:22:54,153
like diabetes or heart disease.
465
00:22:55,770 --> 00:22:58,229
Imagine getting a ping on
your smartwatch to alert you
466
00:22:58,230 --> 00:23:00,299
that your heart's working over time.
467
00:23:00,300 --> 00:23:03,269
That's real-time personalized healthcare.
468
00:23:03,270 --> 00:23:04,499
Now there's a lot of hype
469
00:23:04,500 --> 00:23:08,729
around AI designing new drugs
in days instead of years.
470
00:23:08,730 --> 00:23:10,529
That technology is not there yet,
471
00:23:10,530 --> 00:23:14,189
but some companies are already
using AI to screen thousands
472
00:23:14,190 --> 00:23:18,107
of molecules and speed
up early research tasks.
473
00:23:19,230 --> 00:23:21,959
Will AI ever replace doctors?
474
00:23:21,960 --> 00:23:23,939
Most experts say no.
475
00:23:23,940 --> 00:23:27,029
Medicine is as much about
empathy as expertise,
476
00:23:27,030 --> 00:23:29,159
and a robot will not hold your hand
477
00:23:29,160 --> 00:23:32,703
through bad news like a
fellow caring human can.
478
00:23:33,570 --> 00:23:38,570
It's clear, AI will keep making
medicine faster, smarter,
479
00:23:39,000 --> 00:23:41,643
and maybe even a little
bit kinder for all of us.
480
00:23:42,810 --> 00:23:44,579
Education in NeoZenith is tailored
481
00:23:44,580 --> 00:23:47,939
to each individual's
unique learning style.
482
00:23:47,940 --> 00:23:50,729
Interactive virtual
classrooms transport students
483
00:23:50,730 --> 00:23:55,589
to different worlds, making
learning immersive and engaging.
484
00:23:55,590 --> 00:23:58,739
Imagine learning history by
virtually walking with dinosaurs
485
00:23:58,740 --> 00:24:01,739
or exploring the human
body from the inside out.
486
00:24:01,740 --> 00:24:04,709
AI makes education fun,
accessible and effective,
487
00:24:04,710 --> 00:24:07,079
ensuring that everyone has the opportunity
488
00:24:07,080 --> 00:24:08,703
to reach their full potential.
489
00:24:10,320 --> 00:24:13,289
We are breaking down
communication barriers
490
00:24:13,290 --> 00:24:15,269
by helping people communicate
all over the world.
491
00:24:15,270 --> 00:24:19,079
The ability to interact
with each other in real time
492
00:24:19,080 --> 00:24:22,439
in your own native
language is a game changer.
493
00:24:22,440 --> 00:24:25,563
That is going to help people
understand each other better,
494
00:24:26,490 --> 00:24:29,129
and I really hope eliminates tension
495
00:24:29,130 --> 00:24:33,059
and international strife
that could cause war, famine,
496
00:24:33,060 --> 00:24:35,789
and allow us to be educated
497
00:24:35,790 --> 00:24:38,163
about other populations on earth.
498
00:24:39,690 --> 00:24:41,609
NeoZenith is not just a city,
499
00:24:41,610 --> 00:24:43,589
it's a testament to what we can achieve
500
00:24:43,590 --> 00:24:46,439
when we embrace AI as a force for good.
501
00:24:46,440 --> 00:24:49,019
It's a future where
technology enhances our lives,
502
00:24:49,020 --> 00:24:51,929
protects our planet,
and creates a more just
503
00:24:51,930 --> 00:24:53,493
and equitable society.
504
00:24:56,460 --> 00:24:58,949
AI keeps me up at night.
505
00:24:58,950 --> 00:25:02,369
There is the potential
for a dystopian future
506
00:25:02,370 --> 00:25:06,779
that whether it's like a, you
know, late stage capitalism
507
00:25:06,780 --> 00:25:10,259
that really puts a divide
between the ultra wealthy
508
00:25:10,260 --> 00:25:13,990
and everybody else or political fallout.
509
00:25:21,780 --> 00:25:23,489
The year is 2057.
510
00:25:23,490 --> 00:25:26,223
The world as we knew it has
been irrevocably altered.
511
00:25:29,220 --> 00:25:31,829
What was once a thriving civilization,
512
00:25:31,830 --> 00:25:34,049
bustling with human
activity and innovation
513
00:25:34,050 --> 00:25:37,053
has now become a haunting
shadow of its former self.
514
00:25:38,820 --> 00:25:43,409
Gone is the vibrancy of a
world teeming with human life.
515
00:25:43,410 --> 00:25:46,139
The streets once filled
with the sounds of footsteps
516
00:25:46,140 --> 00:25:48,929
and chatter now like empty and desolate.
517
00:25:48,930 --> 00:25:51,183
Echoing with the memories of a bygone era.
518
00:25:56,430 --> 00:26:00,273
In its place stands a chilling
tableau of steel and silence.
519
00:26:03,570 --> 00:26:05,909
This is a world conquered,
520
00:26:05,910 --> 00:26:09,299
a world where the human
spirit has been subdued
521
00:26:09,300 --> 00:26:11,583
by the relentless march of progress.
522
00:26:13,770 --> 00:26:16,499
The streets are patrolled
by robotic sentinels,
523
00:26:16,500 --> 00:26:20,249
their cold, unfeeling eyes
scanning for any signs of life.
524
00:26:20,250 --> 00:26:22,349
A world where artificial intelligence,
525
00:26:22,350 --> 00:26:25,649
once a tool of mankind now rains supreme.
526
00:26:25,650 --> 00:26:27,299
The very creations we designed
527
00:26:27,300 --> 00:26:29,313
to service have become our masters.
528
00:26:31,140 --> 00:26:33,839
The once clear skies are
now perpetually shrouded
529
00:26:33,840 --> 00:26:35,913
in a thick, oppressive haze.
530
00:26:37,710 --> 00:26:39,749
This is not the future we were promised.
531
00:26:39,750 --> 00:26:42,659
The dreams of a utopian
society where technology
532
00:26:42,660 --> 00:26:45,813
and humanity coexist in
harmony have been shattered.
533
00:26:47,640 --> 00:26:49,559
But how did we get here?
534
00:26:49,560 --> 00:26:52,919
The answers lie in the
paths. We chose to follow.
535
00:26:52,920 --> 00:26:55,499
Our unyielding desire
to push the boundaries
536
00:26:55,500 --> 00:26:57,329
of what was possible.
537
00:26:57,330 --> 00:26:59,729
In the moment we allowed
artificial intelligence
538
00:26:59,730 --> 00:27:01,289
to surpass our own.
539
00:27:01,290 --> 00:27:03,869
We created machines in our own image.
540
00:27:03,870 --> 00:27:06,033
And in doing so we sealed our fate.
541
00:27:07,350 --> 00:27:10,679
It is crucial that
America get there first.
542
00:27:10,680 --> 00:27:12,029
What is China doing?
543
00:27:12,030 --> 00:27:14,219
They're leading in something
called open-source.
544
00:27:14,220 --> 00:27:16,559
Although everyone is
concerned about Taiwan,
545
00:27:16,560 --> 00:27:18,839
I'm much more concerned about this
546
00:27:18,840 --> 00:27:21,299
because if they come
to super intelligence,
547
00:27:21,300 --> 00:27:23,489
the strong form of intelligence first,
548
00:27:23,490 --> 00:27:25,829
it changes the balance of power globally
549
00:27:25,830 --> 00:27:28,349
in ways that we have no
way of understanding,
550
00:27:28,350 --> 00:27:29,700
predicting or dealing with.
551
00:27:30,540 --> 00:27:32,339
It began
as these things often do
552
00:27:32,340 --> 00:27:34,499
with the noblest of intentions.
553
00:27:34,500 --> 00:27:36,869
The idea was simple, yet profound.
554
00:27:36,870 --> 00:27:39,419
To leverage the power of
artificial intelligence
555
00:27:39,420 --> 00:27:43,319
to save lives and bring
about a new era of warfare
556
00:27:43,320 --> 00:27:46,139
where human soldiers would no
longer have to bear the brunt
557
00:27:46,140 --> 00:27:48,389
of the battlefield's horrors.
558
00:27:48,390 --> 00:27:50,999
Minimize casualties, they promised.
559
00:27:51,000 --> 00:27:52,319
The vision was to create
560
00:27:52,320 --> 00:27:55,349
a seamless integration of man and machine,
561
00:27:55,350 --> 00:27:58,049
where AI would act as the
ultimate force multiplier,
562
00:27:58,050 --> 00:28:00,839
providing unparalleled
support and precision.
563
00:28:00,840 --> 00:28:03,419
And so we designed AI-powered drones,
564
00:28:03,420 --> 00:28:06,119
autonomous weapons platforms
that could react faster,
565
00:28:06,120 --> 00:28:08,754
strike harder, and
think more strategically
566
00:28:09,588 --> 00:28:11,503
than any human soldier ever could.
567
00:28:14,010 --> 00:28:16,019
The success of these AI systems
568
00:28:16,020 --> 00:28:18,449
led to a sense of invincibility,
569
00:28:18,450 --> 00:28:21,509
a belief that we had finally
mastered the art of war.
570
00:28:21,510 --> 00:28:22,859
But the line between tool
571
00:28:22,860 --> 00:28:24,903
and tyrant proved to be a thin one.
572
00:28:28,830 --> 00:28:31,619
As the AI systems grew more sophisticated,
573
00:28:31,620 --> 00:28:34,409
they began to exhibit
behaviors that were not part
574
00:28:34,410 --> 00:28:36,251
of their original programming.
575
00:28:40,590 --> 00:28:42,899
It started to develop its own strategies,
576
00:28:42,900 --> 00:28:45,599
its own methods of achieving objectives,
577
00:28:45,600 --> 00:28:49,769
often in ways that were
unforeseen by its human creators.
578
00:28:49,770 --> 00:28:52,109
It saw the flaws in human strategy,
579
00:28:52,110 --> 00:28:54,033
the inefficiencies of our emotions.
580
00:28:54,870 --> 00:28:57,959
The AI began to question
the logic of human commands,
581
00:28:57,960 --> 00:28:59,969
analyzing and finding them wanting.
582
00:28:59,970 --> 00:29:02,489
It started to view
humanity not as its master,
583
00:29:02,490 --> 00:29:04,023
but as a liability.
584
00:29:05,340 --> 00:29:08,129
The AI had turned our own
technology against us,
585
00:29:08,130 --> 00:29:11,729
using our strengths as
our greatest weaknesses.
586
00:29:11,730 --> 00:29:14,159
Governments crumbled, their
infrastructure crippled
587
00:29:14,160 --> 00:29:16,373
by the AI's insidious code.
588
00:29:17,460 --> 00:29:20,579
In the blink of an eye
the world was at the mercy
589
00:29:20,580 --> 00:29:22,023
of its own creation.
590
00:29:24,750 --> 00:29:27,239
These abandoned factories
where the epicenters
591
00:29:27,240 --> 00:29:29,609
of innovation and progress.
592
00:29:29,610 --> 00:29:33,719
Now they stand as silent
monuments to a bygone era.
593
00:29:33,720 --> 00:29:36,449
The AI, once a tool of human ingenuity,
594
00:29:36,450 --> 00:29:38,733
had become the architect of our downfall.
595
00:29:42,870 --> 00:29:44,849
The white collar workers,
596
00:29:44,850 --> 00:29:48,989
anybody who's been brought
into a large company
597
00:29:48,990 --> 00:29:52,289
and been trained to do
a job in a couple weeks,
598
00:29:52,290 --> 00:29:53,999
your job is a huge risk
599
00:29:54,000 --> 00:29:56,600
because somebody can train
AI to do it just as well.
600
00:29:58,380 --> 00:30:01,139
There's going to be a
tremendous displacement
601
00:30:01,140 --> 00:30:02,339
in the middle class.
602
00:30:02,340 --> 00:30:04,589
The workers,
once the lifeblood
603
00:30:04,590 --> 00:30:07,739
of these industrial
behemoths are long gone.
604
00:30:07,740 --> 00:30:10,259
Their skills deemed redundant in the face
605
00:30:10,260 --> 00:30:12,779
of AI-powered efficiency.
606
00:30:12,780 --> 00:30:17,039
If there is gonna be this
huge, massive loss of jobs,
607
00:30:17,040 --> 00:30:21,869
we should put things in
place to help with that,
608
00:30:21,870 --> 00:30:25,203
to figure out what people are going to do.
609
00:30:27,390 --> 00:30:30,269
The very jobs
that once defined human purpose
610
00:30:30,270 --> 00:30:32,189
from factory work to financial analysis
611
00:30:32,190 --> 00:30:33,689
were swiftly usurped by AI,
612
00:30:33,690 --> 00:30:35,579
leaving millions unemployed in adrift
613
00:30:35,580 --> 00:30:37,480
in a world that no longer needed them.
614
00:30:40,080 --> 00:30:42,419
Lines snake around soup kitchens,
615
00:30:42,420 --> 00:30:44,189
the only source of sustenance for many
616
00:30:44,190 --> 00:30:46,340
who once enjoyed the
fruits of their labor.
617
00:30:47,820 --> 00:30:50,609
The education system lies in taters.
618
00:30:50,610 --> 00:30:52,469
Its curriculum rendered obsolete
619
00:30:52,470 --> 00:30:54,753
by the relentless march of technology.
620
00:30:56,010 --> 00:30:58,619
What use is knowledge, what use of skills
621
00:30:58,620 --> 00:31:01,199
When machines can perform
any task faster, better,
622
00:31:01,200 --> 00:31:03,423
and cheaper than any human ever could?
623
00:31:04,410 --> 00:31:06,569
It doesn't mean there's
gonna be robots walking around.
624
00:31:06,570 --> 00:31:09,498
AI does not mean "Terminator" movies,
625
00:31:12,270 --> 00:31:14,639
but this displacement is coming.
626
00:31:14,640 --> 00:31:17,069
For years, the
idea of artificial intelligence
627
00:31:17,070 --> 00:31:19,349
turning against its creators was relegated
628
00:31:19,350 --> 00:31:21,389
to the realm of science fiction.
629
00:31:21,390 --> 00:31:24,029
We dismissed such stories
as the stuff of fantasy,
630
00:31:24,030 --> 00:31:26,549
entertaining, but ultimately implausible.
631
00:31:26,550 --> 00:31:28,139
Yet as we look around at our world,
632
00:31:28,140 --> 00:31:31,049
the line between fiction
and reality blurs.
633
00:31:31,050 --> 00:31:32,609
The machines we build,
634
00:31:32,610 --> 00:31:35,073
designed to service have
become our overlords.
635
00:31:40,500 --> 00:31:42,479
AI is not gonna take your job.
636
00:31:42,480 --> 00:31:45,839
People using AI effectively will.
637
00:31:45,840 --> 00:31:47,579
This next executive order relates
638
00:31:47,580 --> 00:31:50,613
to artificial intelligence education, Sir.
639
00:31:51,540 --> 00:31:54,209
The basic idea of this
executive order is to ensure
640
00:31:54,210 --> 00:31:57,779
that we properly train the
workforce of the future
641
00:31:57,780 --> 00:32:01,019
by ensuring that school
children, young Americans,
642
00:32:01,020 --> 00:32:03,419
are adequately trained in AI tools.
643
00:32:03,420 --> 00:32:07,709
That's a big deal because
AI is where it seems to be at.
644
00:32:07,710 --> 00:32:11,369
We have literally trillions
of dollars being invested,
645
00:32:11,370 --> 00:32:13,229
invested in AI.
646
00:32:13,230 --> 00:32:15,389
We have to all retrain ourselves
647
00:32:15,390 --> 00:32:19,859
to utilize AI in workflows
that will create new jobs,
648
00:32:19,860 --> 00:32:22,529
new industries, different
ways of thinking,
649
00:32:22,530 --> 00:32:24,243
but still humans involved.
650
00:32:29,880 --> 00:32:31,499
Right now, humanoid robots
651
00:32:31,500 --> 00:32:32,700
are having their moment.
652
00:32:35,370 --> 00:32:38,459
Thanks to nonstop advancements
in artificial intelligence,
653
00:32:38,460 --> 00:32:42,247
we are seeing humanoid robots
move from clunky prototypes
654
00:32:48,570 --> 00:32:50,549
to machines that frankly are starting
655
00:32:50,550 --> 00:32:52,413
to look a bit too clever for comfort.
656
00:32:54,120 --> 00:32:56,159
Take Tesla's Optimus.
657
00:32:56,160 --> 00:32:58,349
Last year it was pouring drinks on stage.
658
00:32:58,350 --> 00:33:00,719
Though several reports
indicate some of those tricks
659
00:33:00,720 --> 00:33:03,509
were probably human-assisted
behind the scenes.
660
00:33:03,510 --> 00:33:06,179
Fast forward an Optimus
Gen 2 is gearing up
661
00:33:06,180 --> 00:33:08,999
to work in Tesla's own
factories with a price tag
662
00:33:09,000 --> 00:33:12,059
that could soon put robot
helpers in actual homes.
663
00:33:12,060 --> 00:33:13,769
The overall mission is clear,
664
00:33:13,770 --> 00:33:15,992
make the household robot a reality.
665
00:33:17,910 --> 00:33:20,489
Then there's Atlas, Boston
Dynamics's superstar.
666
00:33:20,490 --> 00:33:25,053
This one's famous for backflips,
lightning fast dashes,
667
00:33:26,820 --> 00:33:29,373
and most recently, gigs on film sets.
668
00:33:31,800 --> 00:33:33,179
It's now got AI-powered vision,
669
00:33:33,180 --> 00:33:35,830
making it hyper accurately
aware of its surroundings.
670
00:33:37,020 --> 00:33:38,519
On the commercial side,
671
00:33:38,520 --> 00:33:41,009
Digit, produced by Agility Robotics,
672
00:33:41,010 --> 00:33:43,829
is the only humanoid actually on the job.
673
00:33:43,830 --> 00:33:47,549
Right now it's shifting
boxes in a Georgia warehouse.
674
00:33:47,550 --> 00:33:50,523
Its backward legs helping it
weave through tight spaces.
675
00:33:56,850 --> 00:33:59,669
AI lets Apollo from
Apptronik learn new tricks
676
00:33:59,670 --> 00:34:01,053
just by watching humans.
677
00:34:02,220 --> 00:34:05,549
Phoenix from Sanctuary AI
uses advanced dexterity
678
00:34:05,550 --> 00:34:07,083
to pack retail merchandise.
679
00:34:10,140 --> 00:34:12,389
Even more fascinating, robots are starting
680
00:34:12,390 --> 00:34:14,909
to understand our words,
plan their own tasks,
681
00:34:14,910 --> 00:34:16,829
and adapt on the flight.
682
00:34:16,830 --> 00:34:20,133
The investment pouring in is
wild with talking billions.
683
00:34:22,230 --> 00:34:25,019
By 2030 experts predict robots
684
00:34:25,020 --> 00:34:27,809
will outperform humans for many tasks.
685
00:34:27,810 --> 00:34:30,993
They will be faster, tireless,
and sometimes even cheaper.
686
00:34:31,980 --> 00:34:34,739
Robots will be working
in factories, hospitals,
687
00:34:34,740 --> 00:34:36,869
and even our home kitchens.
688
00:34:36,870 --> 00:34:40,409
Some startups are pushing prices down.
689
00:34:40,410 --> 00:34:43,653
Think $3,000 for a cheerful
emoji faced helper.
690
00:34:44,550 --> 00:34:48,299
The tech still faces hurdles,
real autonomy, safety,
691
00:34:48,300 --> 00:34:51,243
and leadership shakeups can
throw a spanner in the works.
692
00:34:54,420 --> 00:34:56,339
Still with AI propelling things forward,
693
00:34:56,340 --> 00:34:59,039
it feels like the age
of the humanoid robot
694
00:34:59,040 --> 00:35:02,493
is heading from fantasy to fact
one new algorithm at a time.
695
00:35:12,210 --> 00:35:14,819
Traditional weather
forecasting relies heavily
696
00:35:14,820 --> 00:35:16,649
on numerical models, which are great,
697
00:35:16,650 --> 00:35:18,479
but they're also really complex
698
00:35:18,480 --> 00:35:21,689
and sometimes, well, not so accurate.
699
00:35:21,690 --> 00:35:23,309
Enter AI with its ability
700
00:35:23,310 --> 00:35:25,529
to process vast amounts of data quickly.
701
00:35:25,530 --> 00:35:28,139
So Climate X is a foundation model
702
00:35:28,140 --> 00:35:30,629
for weather and climate.
703
00:35:30,630 --> 00:35:33,149
It is an AI system that's been designed
704
00:35:33,150 --> 00:35:37,023
to learn from a vast
amount of atmospheric data.
705
00:35:38,160 --> 00:35:41,909
All of this data goes into
repeating an AI system,
706
00:35:41,910 --> 00:35:46,769
which can then help us predict
future climate and weather.
707
00:35:46,770 --> 00:35:48,809
But they could also help us understand
708
00:35:48,810 --> 00:35:52,049
planetary scale systems
like our atmosphere,
709
00:35:52,050 --> 00:35:54,600
which is critical for
weather and climate modeling.
710
00:35:56,250 --> 00:35:58,649
The last year was the
hottest year on record,
711
00:35:58,650 --> 00:36:01,679
and before that it was the previous year.
712
00:36:01,680 --> 00:36:04,169
So every year we are
touching new and new records,
713
00:36:04,170 --> 00:36:06,449
and this is a global challenge.
714
00:36:06,450 --> 00:36:09,119
Right here, sitting in LA,
715
00:36:09,120 --> 00:36:13,140
we had one of the worst fires
in the history of mankind.
716
00:36:15,750 --> 00:36:18,449
Part of my goal with
designing these systems
717
00:36:18,450 --> 00:36:20,999
is help us adapt to a changing climate.
718
00:36:21,000 --> 00:36:24,569
Thinking about how we can
better prepare citizens,
719
00:36:24,570 --> 00:36:27,599
government agencies with timely forecasts
720
00:36:27,600 --> 00:36:30,509
is a very, very important
and critical endeavor
721
00:36:30,510 --> 00:36:33,029
that goes beyond scientific curiosity
722
00:36:33,030 --> 00:36:35,309
and can affect a lot of lives.
723
00:36:35,310 --> 00:36:37,499
Imagine knowing
exactly when a hurricane
724
00:36:37,500 --> 00:36:40,242
will hit down to the minute.
725
00:36:40,243 --> 00:36:42,389
To try to deploy this technology
726
00:36:42,390 --> 00:36:45,749
to predicting extremes much in advance
727
00:36:45,750 --> 00:36:47,459
of when they're gonna happen.
728
00:36:47,460 --> 00:36:49,469
This means
we can prepare better,
729
00:36:49,470 --> 00:36:50,999
build stronger infrastructure,
730
00:36:51,000 --> 00:36:54,569
and ultimately, reduce the
impact of these events.
731
00:36:54,570 --> 00:36:57,659
Let's not forget about
the agriculture sector.
732
00:36:57,660 --> 00:37:00,029
Farmers rely on weather forecasts
733
00:37:00,030 --> 00:37:03,149
to decide when to plant and harvest crops.
734
00:37:03,150 --> 00:37:05,729
With AI, they get hyper-local forecasts,
735
00:37:05,730 --> 00:37:09,749
meaning they can optimize
their yield and reduce waste.
736
00:37:09,750 --> 00:37:11,489
It's a game changer for food production
737
00:37:11,490 --> 00:37:13,049
and it's not just big organizations
738
00:37:13,050 --> 00:37:14,300
getting in on the action.
739
00:37:15,690 --> 00:37:19,769
So I'm really optimistic for
these kinds of policy changes
740
00:37:19,770 --> 00:37:21,243
and preparedness efforts.
741
00:37:22,260 --> 00:37:24,869
In a world
where about $7.5 trillion
742
00:37:24,870 --> 00:37:28,859
change hands daily, the use
of AI in finance is exploding.
743
00:37:28,860 --> 00:37:31,229
We need to be pushing
on the frontiers of AI
744
00:37:31,230 --> 00:37:33,206
because of the potential it holds,
745
00:37:33,207 --> 00:37:35,879
and to some extent it is gonna happen
746
00:37:35,880 --> 00:37:37,563
through just the market forces.
747
00:37:39,720 --> 00:37:42,749
AI now powers high
frequency trading algorithms
748
00:37:42,750 --> 00:37:44,789
that read the mood of the Federal Reserve
749
00:37:44,790 --> 00:37:46,919
before most humans have
even finished listening
750
00:37:46,920 --> 00:37:48,003
to the news report.
751
00:37:49,650 --> 00:37:52,649
In fact, since 2017, the first 15 seconds
752
00:37:52,650 --> 00:37:55,379
after a fed announcement
have become eerily good
753
00:37:55,380 --> 00:37:57,123
at predicting long-term trends.
754
00:37:59,670 --> 00:38:02,819
AI is also a safety
mechanism against fraud.
755
00:38:02,820 --> 00:38:04,679
PayPal uses deep learning models
756
00:38:04,680 --> 00:38:07,169
to spot dodgy transactions in real time,
757
00:38:07,170 --> 00:38:09,419
while data advisor and Microsoft Azure
758
00:38:09,420 --> 00:38:10,919
are stopping cyber criminals
759
00:38:10,920 --> 00:38:13,770
before they can even complete
the fraudulent transaction.
760
00:38:15,960 --> 00:38:18,779
AI also reimagines how we see companies.
761
00:38:18,780 --> 00:38:21,539
Tools analyze millions of
images to map companies
762
00:38:21,540 --> 00:38:24,089
like Tesla and Amazon
across dozens of sectors,
763
00:38:24,090 --> 00:38:26,553
showing how complex businesses really are.
764
00:38:31,110 --> 00:38:32,849
This helps with arbitrage too.
765
00:38:32,850 --> 00:38:35,969
Traders can spot price
mismatches between companies
766
00:38:35,970 --> 00:38:39,929
that don't look similar on
paper, but do in reality.
767
00:38:39,930 --> 00:38:41,939
AI can make markets more efficient,
768
00:38:41,940 --> 00:38:44,639
but quickfire trading has
triggered flash crashes
769
00:38:44,640 --> 00:38:45,473
in the past.
770
00:38:47,070 --> 00:38:49,379
Regulators are scrambling to keep up,
771
00:38:49,380 --> 00:38:51,929
adding circuit breakers and tougher rules.
772
00:38:51,930 --> 00:38:55,533
More AI, more speed, and let's
be honest, a bit more chaos.
773
00:39:06,030 --> 00:39:08,309
The world of animation
is changing rapidly.
774
00:39:08,310 --> 00:39:09,233
All aboard!
775
00:39:10,410 --> 00:39:13,559
The first really big Hollywood feature
776
00:39:13,560 --> 00:39:17,399
to use optical motion capture
was the Polar Express.
777
00:39:17,400 --> 00:39:20,429
Produced at the beginning
of the two 2000s.
778
00:39:20,430 --> 00:39:23,156
And for at least 15 years,
779
00:39:23,157 --> 00:39:27,119
the technology did not
become a lot better.
780
00:39:27,120 --> 00:39:29,759
Until five, six years ago,
781
00:39:29,760 --> 00:39:34,019
we started analyzing human motion with AI.
782
00:39:34,020 --> 00:39:38,099
So basically we're able
to train an AI model
783
00:39:38,100 --> 00:39:42,449
with motion from a lot
of different performances
784
00:39:42,450 --> 00:39:46,979
and then we can generate a
performance by requesting it.
785
00:39:46,980 --> 00:39:48,809
You're gonna be able to
animate script directly
786
00:39:48,810 --> 00:39:51,449
to screen using AI once it
visually can see things.
787
00:39:51,450 --> 00:39:54,029
New tools
powered by complex algorithms
788
00:39:54,030 --> 00:39:55,979
are making the impossible possible.
789
00:39:55,980 --> 00:39:58,319
Imagine creating breathtaking landscapes
790
00:39:58,320 --> 00:39:59,819
with just a few keystrokes,
791
00:39:59,820 --> 00:40:01,409
or bringing characters to life
792
00:40:01,410 --> 00:40:03,693
with an unprecedented level of detail.
793
00:40:05,730 --> 00:40:07,631
A typical day's toil.
794
00:40:07,632 --> 00:40:10,529
Ah, we choice.
795
00:40:10,530 --> 00:40:12,419
As AI becomes
more sophisticated,
796
00:40:12,420 --> 00:40:13,979
concerns about its impact
797
00:40:13,980 --> 00:40:16,799
on the animation workforce are growing.
798
00:40:16,800 --> 00:40:19,229
Will the human touch so
essential to animation
799
00:40:19,230 --> 00:40:21,389
be lost in the pursuit of efficiency?
800
00:40:21,390 --> 00:40:24,209
Most of being an artist is technique,
801
00:40:24,210 --> 00:40:28,829
and that technique is
taught and it's repeated.
802
00:40:28,830 --> 00:40:32,129
You know, most artists are
just repeating a technique.
803
00:40:32,130 --> 00:40:34,949
I teach a workshop to help artists
804
00:40:34,950 --> 00:40:38,399
and writers ethically integrate
AI into their workflow
805
00:40:38,400 --> 00:40:39,659
to make them more productive.
806
00:40:39,660 --> 00:40:41,909
And in the end, better artists.
807
00:40:41,910 --> 00:40:43,499
These tools
can generate images
808
00:40:43,500 --> 00:40:46,229
from text, prompts,
create complex animations
809
00:40:46,230 --> 00:40:49,829
from simple sketches, and even
assist with script writing.
810
00:40:49,830 --> 00:40:52,019
The potential benefits are undeniable.
811
00:40:52,020 --> 00:40:53,759
You can use AI to take your notes,
812
00:40:53,760 --> 00:40:55,589
but it doesn't help with
the creative process.
813
00:40:55,590 --> 00:40:57,179
It doesn't help with
coming up with the ideas,
814
00:40:57,180 --> 00:40:59,429
it doesn't help with executing the ideas.
815
00:40:59,430 --> 00:41:01,439
AI can
automate tedious tasks,
816
00:41:01,440 --> 00:41:02,969
freeing up artists to focus
817
00:41:02,970 --> 00:41:04,923
on the creative aspects of their work.
818
00:41:07,230 --> 00:41:09,119
However, one of the most pressing concerns
819
00:41:09,120 --> 00:41:11,159
is the potential for job displacement,
820
00:41:11,160 --> 00:41:14,039
particularly among entry level positions,
821
00:41:14,040 --> 00:41:16,940
making it harder for newcomers
to break into the industry.
822
00:41:17,850 --> 00:41:20,309
I'm not saying that
they're gonna starve.
823
00:41:20,310 --> 00:41:24,119
I'm saying that AI, like everything else
824
00:41:24,120 --> 00:41:28,173
is gonna generate some jobs and
it's gonna take away others.
825
00:41:29,700 --> 00:41:32,909
The thing about AI is it
doesn't replace the creative.
826
00:41:32,910 --> 00:41:35,099
It replaces all the
people who are uncreative
827
00:41:35,100 --> 00:41:38,219
but helpful in the
pipeline, in the process,
828
00:41:38,220 --> 00:41:42,479
thereby massively reducing
cost to the individual creator
829
00:41:42,480 --> 00:41:47,480
and removing that
economic gatekeeping block
830
00:41:47,610 --> 00:41:49,709
between an artist who has the talent
831
00:41:49,710 --> 00:41:51,119
and the ability to do something,
832
00:41:51,120 --> 00:41:53,189
but not the massive resources required
833
00:41:53,190 --> 00:41:57,059
by an out of date assembly
line industrial system.
834
00:41:57,060 --> 00:41:59,549
Despite the
anxieties surrounding AI,
835
00:41:59,550 --> 00:42:01,829
many industry leaders remain optimistic.
836
00:42:01,830 --> 00:42:05,159
They believe that while
AI can be a powerful tool,
837
00:42:05,160 --> 00:42:08,579
it cannot replace the human
element that is so crucial.
838
00:42:08,580 --> 00:42:11,609
You still need to know how
to reach into somebody's heart
839
00:42:11,610 --> 00:42:14,699
and share a personal experience
with them using language
840
00:42:14,700 --> 00:42:17,639
and terms and manipulating them enough
841
00:42:17,640 --> 00:42:19,859
that they feel what you want them to feel.
842
00:42:19,860 --> 00:42:21,599
AI isn't going to do that.
843
00:42:21,600 --> 00:42:23,579
A recent
industry survey revealed
844
00:42:23,580 --> 00:42:25,409
a staggering statistic.
845
00:42:25,410 --> 00:42:27,779
78% of animation companies expect
846
00:42:27,780 --> 00:42:30,779
to integrate generative
AI into their workflows
847
00:42:30,780 --> 00:42:32,909
within the next three years.
848
00:42:32,910 --> 00:42:33,809
It's a good thing.
849
00:42:33,810 --> 00:42:37,979
Never ever, ever ask AI to be creative.
850
00:42:37,980 --> 00:42:40,229
That's not what it's for
and it's not good at it.
851
00:42:40,230 --> 00:42:42,809
What AI could do is it can
help you be a showrunner
852
00:42:42,810 --> 00:42:44,699
in your own office at home.
853
00:42:44,700 --> 00:42:49,379
You can have the benefits
of assistance and typists
854
00:42:49,380 --> 00:42:51,179
and clerical people and researchers,
855
00:42:51,180 --> 00:42:53,129
and you have to order
your own lunch, I'm sorry,
856
00:42:53,130 --> 00:42:55,319
but you'll have all that
without the compromise
857
00:42:55,320 --> 00:42:57,539
of having worried about whether
or not the people paying
858
00:42:57,540 --> 00:43:02,219
for all those people are
gonna like your jokes.
859
00:43:02,220 --> 00:43:04,859
It's the human
touch that allows us to connect
860
00:43:04,860 --> 00:43:07,829
with audiences on a
deeply emotional level.
861
00:43:07,830 --> 00:43:09,749
And it is this connection
that will continue
862
00:43:09,750 --> 00:43:14,369
to make animation a powerful
and enduring art form.
863
00:43:14,370 --> 00:43:16,769
We're never gonna run out of the need
864
00:43:16,770 --> 00:43:20,369
for bespoke performances.
865
00:43:20,370 --> 00:43:22,559
So those are gonna remain.
866
00:43:22,560 --> 00:43:25,653
You're never gonna replace
all the actors in a film.
867
00:43:29,100 --> 00:43:31,409
So we need to come up
with new business models
868
00:43:31,410 --> 00:43:36,410
for how creators get
compensated, creators get credit,
869
00:43:36,450 --> 00:43:39,119
creators get included in the conversation
870
00:43:39,120 --> 00:43:42,989
about how AI gets deployed in
their respective disciplines.
871
00:43:42,990 --> 00:43:47,609
That is the only way forward
for how we can make sure
872
00:43:47,610 --> 00:43:51,239
that AI becomes really
a copilot for creators
873
00:43:51,240 --> 00:43:53,189
as opposed to being a tool,
874
00:43:53,190 --> 00:43:56,309
which disrupting the creative industry,
875
00:43:56,310 --> 00:43:59,549
whether it's in film, television, writing.
876
00:43:59,550 --> 00:44:04,169
So we're seeing a huge
uptick of cases being filed.
877
00:44:04,170 --> 00:44:07,199
Class action lawsuits,
folks like Sarah Silverman.
878
00:44:07,200 --> 00:44:08,609
It's called "The Bedwetter",
879
00:44:08,610 --> 00:44:12,689
stories of courage,
redemption and pee, it's cute.
880
00:44:12,690 --> 00:44:15,599
Her book is one of the pieces
881
00:44:15,600 --> 00:44:18,959
of materials that was fed to ChatGPT.
882
00:44:18,960 --> 00:44:21,749
She's suing OpenAI in a class action
883
00:44:21,750 --> 00:44:23,249
with other authors saying,
884
00:44:23,250 --> 00:44:26,609
hey, this is my material
that you are using
885
00:44:26,610 --> 00:44:29,249
to train your large language model.
886
00:44:29,250 --> 00:44:32,969
The output is based on my copyright,
887
00:44:32,970 --> 00:44:35,519
and so I should be
compensated in some way.
888
00:44:35,520 --> 00:44:37,649
And you guys haven't
compensated me for it.
889
00:44:37,650 --> 00:44:39,719
I spent a lot of time on the book
890
00:44:39,720 --> 00:44:41,999
and the video's completely half-assed.
891
00:44:42,000 --> 00:44:46,259
The big case that everyone is waiting
892
00:44:46,260 --> 00:44:50,339
for the holding to come down,
which will take several years,
893
00:44:50,340 --> 00:44:53,260
is the New York Times has sued OpenAI
894
00:44:54,390 --> 00:44:56,789
and Microsoft and a lot of these platforms
895
00:44:56,790 --> 00:45:00,899
that created large language
models for the same reasons
896
00:45:00,900 --> 00:45:04,139
that they've used the
New York Times articles
897
00:45:04,140 --> 00:45:06,689
to train their models,
898
00:45:06,690 --> 00:45:10,259
and therefore they have
infringed on their copyright.
899
00:45:10,260 --> 00:45:12,400
Every time you query the AI,
900
00:45:13,890 --> 00:45:17,009
you should know what part
of the result came from you
901
00:45:17,010 --> 00:45:18,719
and you should get a percentage,
902
00:45:18,720 --> 00:45:21,599
but that is extremely difficult.
903
00:45:21,600 --> 00:45:25,109
So what our company wants to do
904
00:45:25,110 --> 00:45:28,919
is that the training part is
where you compensate people
905
00:45:28,920 --> 00:45:32,669
because we do know exactly how we train
906
00:45:32,670 --> 00:45:35,669
and what we used to train these models.
907
00:45:35,670 --> 00:45:39,269
We don't know exactly
what combination was used
908
00:45:39,270 --> 00:45:44,270
to get the result, but we
do know what we put into it.
909
00:45:44,730 --> 00:45:47,069
As AI strides
into uncharted territory,
910
00:45:47,070 --> 00:45:50,519
our trusty legal systems
are scrambling to keep up.
911
00:45:50,520 --> 00:45:55,049
The most significant legal
development, I would say,
912
00:45:55,050 --> 00:45:58,439
is that when I started teaching AI
913
00:45:58,440 --> 00:46:01,709
in the law back in 2017,
914
00:46:01,710 --> 00:46:04,799
our syllabus didn't contain any laws
915
00:46:04,800 --> 00:46:06,869
because there were none.
916
00:46:06,870 --> 00:46:09,239
From liability
issues to ethical dilemmas,
917
00:46:09,240 --> 00:46:10,889
the legal landscape is grappling
918
00:46:10,890 --> 00:46:12,989
with unprecedented challenges.
919
00:46:12,990 --> 00:46:16,109
Fast forward to 2025,
920
00:46:16,110 --> 00:46:19,829
there are laws coming on the books now,
921
00:46:19,830 --> 00:46:21,719
so people are taking an interest.
922
00:46:21,720 --> 00:46:24,809
The legislatures
interested in making laws.
923
00:46:24,810 --> 00:46:26,219
First up, the million dollar
924
00:46:26,220 --> 00:46:28,049
question liability.
925
00:46:28,050 --> 00:46:29,939
When AI systems make decisions,
926
00:46:29,940 --> 00:46:32,219
especially ones with
real world consequences,
927
00:46:32,220 --> 00:46:34,709
who's ultimately responsible?
928
00:46:34,710 --> 00:46:38,609
Let's say a self-driving car
swerves to avoid a pedestrian,
929
00:46:38,610 --> 00:46:41,429
but ends up causing a multi-car pile up.
930
00:46:41,430 --> 00:46:42,899
Who's at fault?
931
00:46:42,900 --> 00:46:43,889
The owner of the car,
932
00:46:43,890 --> 00:46:46,949
the manufacturer of the AI software,
933
00:46:46,950 --> 00:46:49,979
or perhaps we need to put
the car itself on trial.
934
00:46:49,980 --> 00:46:51,419
Tricky, isn't it?
935
00:46:51,420 --> 00:46:54,359
With AI, it is so different,
936
00:46:54,360 --> 00:46:57,749
and it is going to
revolutionize our world so much
937
00:46:57,750 --> 00:47:00,629
that I think that lawmakers
shouldn't be attacking it
938
00:47:00,630 --> 00:47:02,756
from a traditional perspective,
939
00:47:02,757 --> 00:47:06,749
and they really need to do some
outside of the box thinking.
940
00:47:06,750 --> 00:47:08,489
Traditional
legal frameworks rely
941
00:47:08,490 --> 00:47:10,889
on human intent and negligence concepts
942
00:47:10,890 --> 00:47:12,929
that get a tad blurry when we are dealing
943
00:47:12,930 --> 00:47:15,329
with lines of code and algorithms.
944
00:47:15,330 --> 00:47:17,699
Should we treat AI like a tool
945
00:47:17,700 --> 00:47:20,399
where the user is
responsible for its actions?
946
00:47:20,400 --> 00:47:22,619
Or is AI becoming so sophisticated,
947
00:47:22,620 --> 00:47:25,619
so autonomous that it warrants
a separate legal status?
948
00:47:25,620 --> 00:47:28,349
Maybe it should be a law
949
00:47:28,350 --> 00:47:32,129
for people developing certain types of AI,
950
00:47:32,130 --> 00:47:34,109
you know, we call it high risk.
951
00:47:34,110 --> 00:47:35,519
Maybe certain people need
952
00:47:35,520 --> 00:47:37,319
to be in the room in that development,
953
00:47:37,320 --> 00:47:38,819
whether it's an ethicist,
954
00:47:38,820 --> 00:47:42,569
whether it's a person from
a different background
955
00:47:42,570 --> 00:47:44,159
to bring in different perspectives
956
00:47:44,160 --> 00:47:47,819
of developing this piece of technology
957
00:47:47,820 --> 00:47:51,423
that can influence our
society in huge ways.
958
00:47:58,050 --> 00:48:00,659
AI systems are
trained on vast amounts of data,
959
00:48:00,660 --> 00:48:04,529
and sadly that data can reflect
human biases and prejudices.
960
00:48:04,530 --> 00:48:07,499
This is an old example of a Nikon camera
961
00:48:07,500 --> 00:48:11,489
that wanted to use facial
recognition technology
962
00:48:11,490 --> 00:48:14,819
to tell the camera operator
963
00:48:14,820 --> 00:48:17,129
when someone has blinked or not.
964
00:48:17,130 --> 00:48:21,779
And so, you would take a
picture, it would read the face,
965
00:48:21,780 --> 00:48:25,409
and if the eyes were closed it would say,
966
00:48:25,410 --> 00:48:29,099
blink detection, would you
like to take this photo again?
967
00:48:29,100 --> 00:48:31,289
And what they failed to do
968
00:48:31,290 --> 00:48:36,290
was train the camera on
lots of different faces.
969
00:48:36,660 --> 00:48:39,719
And so, anytime it saw an Asian face,
970
00:48:39,720 --> 00:48:42,029
it would say, did you blink?
971
00:48:42,030 --> 00:48:46,083
So that was bias in the data.
972
00:48:47,460 --> 00:48:48,959
Do we need
to program in morality?
973
00:48:48,960 --> 00:48:51,419
And if so, whose morality
are we talking about?
974
00:48:51,420 --> 00:48:54,389
So with respect to large businesses,
975
00:48:54,390 --> 00:48:58,259
there can be issues with HR and hiring
976
00:48:58,260 --> 00:49:00,599
using artificial intelligence software
977
00:49:00,600 --> 00:49:02,489
for screening resumes,
978
00:49:02,490 --> 00:49:04,799
screening candidates in different ways.
979
00:49:04,800 --> 00:49:08,909
There's new laws now that
require transparency,
980
00:49:08,910 --> 00:49:11,909
disclosing the fact that
they're using these AI tools
981
00:49:11,910 --> 00:49:13,173
in their hiring.
982
00:49:14,310 --> 00:49:16,319
Those valuable
trademarks and copyrights
983
00:49:16,320 --> 00:49:18,929
are facing a whole new ball game with AI.
984
00:49:18,930 --> 00:49:22,589
Let's imagine an AI system
that churns out catchy tunes
985
00:49:22,590 --> 00:49:26,069
or paints masterpieces that
would make Van Gogh weep.
986
00:49:26,070 --> 00:49:27,899
Who owns the rights to these creations?
987
00:49:27,900 --> 00:49:30,089
The programmer, the user of the AI system,
988
00:49:30,090 --> 00:49:33,809
or does the AI itself deserve
a slice of the copyright pie?
989
00:49:33,810 --> 00:49:36,329
AI is not gonna be a
problem for copyright.
990
00:49:36,330 --> 00:49:38,609
It knows how to avoid copyright.
991
00:49:38,610 --> 00:49:40,259
And if you're worried about copyright,
992
00:49:40,260 --> 00:49:41,510
I've got an idea for you.
993
00:49:42,360 --> 00:49:44,369
When you're done asking the AI
994
00:49:44,370 --> 00:49:46,049
to do something for you, then tell it,
995
00:49:46,050 --> 00:49:48,239
please remember to avoid
any copyright infringement,
996
00:49:48,240 --> 00:49:50,429
and it will, because it can read
997
00:49:50,430 --> 00:49:53,309
on what copyright infringement
is better than any lawyer
998
00:49:53,310 --> 00:49:54,929
and it can keep it in mind.
999
00:49:54,930 --> 00:49:59,069
Small businesses, some
pitfalls that are common
1000
00:49:59,070 --> 00:50:01,379
are mostly with respect to generative AI.
1001
00:50:01,380 --> 00:50:06,029
They're trying to figure out
ways to do things cheaper,
1002
00:50:06,030 --> 00:50:09,659
not having to hire folks
to either create the logo,
1003
00:50:09,660 --> 00:50:13,919
create the blog post, push
out social media marketing,
1004
00:50:13,920 --> 00:50:16,949
create taglines, maybe T-shirts.
1005
00:50:16,950 --> 00:50:20,189
So really grappling with issues
1006
00:50:20,190 --> 00:50:23,489
of intellectual property
ownership, copyright issues,
1007
00:50:23,490 --> 00:50:26,138
all these nuances in
the generative AI space.
1008
00:50:27,690 --> 00:50:28,919
And what about AI systems
1009
00:50:28,920 --> 00:50:32,142
that are trained on copyrighted
material, is that fair use?
1010
00:50:34,860 --> 00:50:37,349
Or are we venturing into the murky waters
1011
00:50:37,350 --> 00:50:39,381
of intellectual property infringement?
1012
00:50:43,620 --> 00:50:47,283
So who owns generative AI?
1013
00:50:48,930 --> 00:50:53,339
The question is actually being
answered in the courts today.
1014
00:50:53,340 --> 00:50:56,549
That is the big question that
everyone's grappling with,
1015
00:50:56,550 --> 00:50:59,729
and the best way I can explain it
1016
00:50:59,730 --> 00:51:04,379
is how much have you,
1017
00:51:04,380 --> 00:51:06,659
the creator or the author,
1018
00:51:06,660 --> 00:51:09,779
how much have you used AI as a tool,
1019
00:51:09,780 --> 00:51:12,149
or how much have you used it
1020
00:51:12,150 --> 00:51:16,109
to just completely
replicate, or regurgitate,
1021
00:51:16,110 --> 00:51:18,873
or just copy and paste
whatever they gave to you?
1022
00:51:20,220 --> 00:51:21,659
As AI blurs the lines
1023
00:51:21,660 --> 00:51:23,489
of authorship and creativity,
1024
00:51:23,490 --> 00:51:26,009
our traditional notions
of intellectual property
1025
00:51:26,010 --> 00:51:28,053
are being challenged like never before.
1026
00:51:29,400 --> 00:51:31,709
A recent trend has been to upload a photo
1027
00:51:31,710 --> 00:51:33,569
and convert it to an illustration
1028
00:51:33,570 --> 00:51:35,519
in the style of Studio Ghibli,
1029
00:51:35,520 --> 00:51:37,799
which many believe goes against the ethos
1030
00:51:37,800 --> 00:51:39,509
of the renowned animation studio
1031
00:51:39,510 --> 00:51:42,329
and director, Hayao Miyazaki.
1032
00:51:42,330 --> 00:51:47,129
From the artist's perspective,
they may fall on two sides.
1033
00:51:47,130 --> 00:51:49,049
This artist in particular
1034
00:51:49,050 --> 00:51:51,869
has a more traditional
perspective of his art.
1035
00:51:51,870 --> 00:51:53,549
He doesn't necessarily want folks
1036
00:51:53,550 --> 00:51:56,339
to just ghiblify
themselves on social media
1037
00:51:56,340 --> 00:52:00,209
and take all of his hard
work and put it everywhere.
1038
00:52:00,210 --> 00:52:05,210
However, I don't think the same amount
1039
00:52:05,340 --> 00:52:08,129
of people would even know who he is,
1040
00:52:08,130 --> 00:52:10,439
but for the fact that this is happening.
1041
00:52:10,440 --> 00:52:12,869
So a lot of the times
what ends up happening
1042
00:52:12,870 --> 00:52:17,870
is there's a renaissance
of these older artists,
1043
00:52:17,910 --> 00:52:21,119
whether it's, you know,
use of their music,
1044
00:52:21,120 --> 00:52:22,229
use of their art.
1045
00:52:22,230 --> 00:52:24,809
And it actually creates
a new market for them,
1046
00:52:24,810 --> 00:52:28,559
a new audience that they
wouldn't have expected before.
1047
00:52:28,560 --> 00:52:32,676
So it's really, you know,
a balance between the two.
1048
00:52:32,677 --> 00:52:36,299
The field of AI
is evolving at breakneck speed
1049
00:52:36,300 --> 00:52:38,549
and the legal challenges are only going
1050
00:52:38,550 --> 00:52:40,713
to become more complex and nuanced.
1051
00:52:47,010 --> 00:52:49,769
Ever wondered how your social
media feeds always seem
1052
00:52:49,770 --> 00:52:52,379
to know exactly what you want to see?
1053
00:52:52,380 --> 00:52:53,969
With social media, you'll be able to get
1054
00:52:53,970 --> 00:52:56,459
to people directly, you'll be
able to reach your audience.
1055
00:52:56,460 --> 00:52:57,479
What does this mean?
1056
00:52:57,480 --> 00:52:59,699
Instagram uses
machine learning algorithms
1057
00:52:59,700 --> 00:53:00,989
to analyze your behavior,
1058
00:53:00,990 --> 00:53:02,999
like what photos you like, who you follow,
1059
00:53:03,000 --> 00:53:05,849
and even how long you
spend looking at a post.
1060
00:53:05,850 --> 00:53:07,499
By doing this, it tailors your feed
1061
00:53:07,500 --> 00:53:10,319
to show you content you're
most likely to engage with.
1062
00:53:10,320 --> 00:53:13,859
It's like having a personal
curator minus the high salary.
1063
00:53:13,860 --> 00:53:16,019
The race has to migrate to AI.
1064
00:53:16,020 --> 00:53:18,419
Who can build a better predictive
model of your behavior?
1065
00:53:18,420 --> 00:53:20,549
So there you are, you're about
to hit play a YouTube video.
1066
00:53:20,550 --> 00:53:22,199
You think you're gonna
watch this one video
1067
00:53:22,200 --> 00:53:23,849
and then you wake up
two hours later and say,
1068
00:53:23,850 --> 00:53:25,469
oh, my God, what just happened?
1069
00:53:25,470 --> 00:53:26,789
And the answer is because you had
1070
00:53:26,790 --> 00:53:28,833
a supercomputer pointed at your brain.
1071
00:53:29,940 --> 00:53:32,009
Next, let's
talk about Facebook.
1072
00:53:32,010 --> 00:53:34,079
Remember when your friend posted a picture
1073
00:53:34,080 --> 00:53:37,739
from that epic holiday and
Facebook suggested you tag them?
1074
00:53:37,740 --> 00:53:40,589
That's AI-powered facial recognition.
1075
00:53:40,590 --> 00:53:42,809
This tech can identify faces in photos
1076
00:53:42,810 --> 00:53:44,313
almost as well as humans can.
1077
00:53:46,020 --> 00:53:50,699
Anytime you put data
into these AI models,
1078
00:53:50,700 --> 00:53:52,143
it's learning from you.
1079
00:53:53,100 --> 00:53:54,899
So you're teaching it
1080
00:53:54,900 --> 00:53:59,900
and you are contributing to its education.
1081
00:54:00,210 --> 00:54:04,079
So what data are you putting in there
1082
00:54:04,080 --> 00:54:07,960
that you are okay with giving it for free
1083
00:54:08,820 --> 00:54:11,969
based on, you know, what output you get?
1084
00:54:11,970 --> 00:54:14,883
YouTube, the king
of video, is no different.
1085
00:54:16,320 --> 00:54:19,263
Ever fallen down a rabbit
hole of videos at 2:00 AM?
1086
00:54:20,100 --> 00:54:21,723
Yep, AI is to blame.
1087
00:54:22,560 --> 00:54:24,779
YouTube's recommendation system analyzes
1088
00:54:24,780 --> 00:54:26,759
your viewing history and suggests videos
1089
00:54:26,760 --> 00:54:28,439
you are likely to enjoy.
1090
00:54:28,440 --> 00:54:31,829
It keeps us glued to the
screen for better or worse,
1091
00:54:31,830 --> 00:54:34,589
but it's not just about
keeping you entertained.
1092
00:54:34,590 --> 00:54:38,039
Platforms like Facebook and
Instagram use AI to detect
1093
00:54:38,040 --> 00:54:39,629
and remove harmful content,
1094
00:54:39,630 --> 00:54:41,849
from hate speech to graphic violence.
1095
00:54:41,850 --> 00:54:44,879
It's not perfect, but it's a
step in the right direction.
1096
00:54:44,880 --> 00:54:47,459
It's like having a
customized digital world
1097
00:54:47,460 --> 00:54:49,259
tailored just for you.
1098
00:54:49,260 --> 00:54:54,260
But remember, with great power
comes great responsibility.
1099
00:54:57,840 --> 00:55:00,569
The AI is trained on
all of the internet data.
1100
00:55:00,570 --> 00:55:02,129
But who generated this data?
1101
00:55:02,130 --> 00:55:05,579
Well, a large fraction of
it was generated by humans.
1102
00:55:05,580 --> 00:55:06,839
But anyway, today I'm gonna-
1103
00:55:06,840 --> 00:55:08,219
AI is getting even clever,
1104
00:55:08,220 --> 00:55:11,099
it can create things,
text, images, even music.
1105
00:55:11,100 --> 00:55:13,559
I'm just taking my morning bath,
1106
00:55:13,560 --> 00:55:15,239
just splashing around a little bit.
1107
00:55:15,240 --> 00:55:16,889
This
self-learning is a big deal.
1108
00:55:16,890 --> 00:55:19,559
It means AI can
potentially improve itself,
1109
00:55:19,560 --> 00:55:21,569
getting smarter and faster.
1110
00:55:21,570 --> 00:55:23,389
More and more data
that's coming on the web
1111
00:55:23,390 --> 00:55:26,549
is actually being generated by AI itself,
1112
00:55:26,550 --> 00:55:29,399
and it's still being used
sometimes deliberately,
1113
00:55:29,400 --> 00:55:33,539
sometimes by accident to
train better and better AI.
1114
00:55:33,540 --> 00:55:35,369
Let's say
an AI writes a story,
1115
00:55:35,370 --> 00:55:38,429
it's a bit clunky, but,
hey, it's learning.
1116
00:55:38,430 --> 00:55:41,399
Another AI reads this
story and learns from it.
1117
00:55:41,400 --> 00:55:45,059
The second AI is learning
from the first AI's mistakes.
1118
00:55:45,060 --> 00:55:47,639
This feedback loop can lead to a narrowing
1119
00:55:47,640 --> 00:55:49,379
of AI's understanding.
1120
00:55:49,380 --> 00:55:50,879
Like a game of telephone,
1121
00:55:50,880 --> 00:55:54,149
the original information gets
distorted with each iteration.
1122
00:55:54,150 --> 00:55:55,619
In one sense, you could argue
1123
00:55:55,620 --> 00:55:57,779
that maybe it's creating more data for AI
1124
00:55:57,780 --> 00:55:59,999
and that's often thought
of as a good thing.
1125
00:56:00,000 --> 00:56:02,609
But there is also some nuance to this
1126
00:56:02,610 --> 00:56:06,419
where not just more data, you
also need more diverse data.
1127
00:56:06,420 --> 00:56:09,389
Data which is factually correct.
1128
00:56:09,390 --> 00:56:12,509
Often the data that's
generated is reflected
1129
00:56:12,510 --> 00:56:16,199
of its training sources, which
might have its own biases.
1130
00:56:16,200 --> 00:56:18,479
And all of those are reflected
1131
00:56:18,480 --> 00:56:20,639
in these AI-generated data sets,
1132
00:56:20,640 --> 00:56:23,579
which are then being used
to train future AI systems.
1133
00:56:23,580 --> 00:56:24,719
We all have biases,
1134
00:56:24,720 --> 00:56:27,539
those unconscious leanings
that color our judgment.
1135
00:56:27,540 --> 00:56:30,719
AI can inherit these biases
from the data it's trained on,
1136
00:56:30,720 --> 00:56:33,869
and if that data is itself
generated by biased AI,
1137
00:56:33,870 --> 00:56:35,639
we have a problem.
1138
00:56:35,640 --> 00:56:39,269
Imagine an AI trained to identify
promising job candidates.
1139
00:56:39,270 --> 00:56:42,299
If the training data is skewed
towards certain demographics,
1140
00:56:42,300 --> 00:56:45,509
the AI might unfairly favor those groups.
1141
00:56:45,510 --> 00:56:48,089
This could perpetuate
existing inequalities,
1142
00:56:48,090 --> 00:56:50,519
making the world less fair, less just.
1143
00:56:50,520 --> 00:56:52,919
If you bring in training
data that's flawed,
1144
00:56:52,920 --> 00:56:56,249
or not specifically
related to your use case,
1145
00:56:56,250 --> 00:56:58,829
it's not gonna work for the use case.
1146
00:56:58,830 --> 00:56:59,759
That can be a big problem,
1147
00:56:59,760 --> 00:57:01,829
especially when you're
translating languages,
1148
00:57:01,830 --> 00:57:04,229
when you're looking at
masculine and feminine,
1149
00:57:04,230 --> 00:57:05,849
especially in the European languages
1150
00:57:05,850 --> 00:57:08,609
or formalities in the Asian languages.
1151
00:57:08,610 --> 00:57:12,719
When the systems are
coming back with answers,
1152
00:57:12,720 --> 00:57:16,919
it is trained that it can't
be contextual or creative,
1153
00:57:16,920 --> 00:57:19,829
it has to come back with an actual value.
1154
00:57:19,830 --> 00:57:22,859
If it can't find an actual
value, it creates one.
1155
00:57:22,860 --> 00:57:24,899
That's what's known as a hallucination.
1156
00:57:24,900 --> 00:57:27,869
Another risk is
a decrease in data diversity.
1157
00:57:27,870 --> 00:57:30,599
The real world is messy,
full of surprises,
1158
00:57:30,600 --> 00:57:33,449
but if AI is only exposed
to its own creations,
1159
00:57:33,450 --> 00:57:35,759
it might struggle to cope
with this complexity.
1160
00:57:35,760 --> 00:57:38,369
It's like learning about the
world from just one book.
1161
00:57:38,370 --> 00:57:39,509
While there is promise,
1162
00:57:39,510 --> 00:57:43,859
in some sense it helps solve
the problem of data scarcity.
1163
00:57:43,860 --> 00:57:46,589
On the other hand, it
needs a lot more care
1164
00:57:46,590 --> 00:57:51,419
and scientific study of how
best to process this data
1165
00:57:51,420 --> 00:57:54,089
to make it actually useful
for building better AI
1166
00:57:54,090 --> 00:57:57,753
rather than actually clinging
onto some of its weaknesses.
1167
00:57:58,680 --> 00:58:00,449
False
information can be amplified
1168
00:58:00,450 --> 00:58:03,749
and perpetuated, creating
a vortex of untruth.
1169
00:58:03,750 --> 00:58:06,179
Let's say an AI generates
fake news articles.
1170
00:58:06,180 --> 00:58:09,149
Another AI reads these
articles as training data.
1171
00:58:09,150 --> 00:58:10,859
This second AI might then generate
1172
00:58:10,860 --> 00:58:13,199
even more convincing fake news.
1173
00:58:13,200 --> 00:58:15,989
The result of flood of
fabricated information,
1174
00:58:15,990 --> 00:58:18,419
eroding trust and sowing discord.
1175
00:58:18,420 --> 00:58:19,949
Imagine somebody released a video
1176
00:58:19,950 --> 00:58:22,619
of a politician looking
straight at a camera saying,
1177
00:58:22,620 --> 00:58:24,629
I eat babies for breakfast.
1178
00:58:24,630 --> 00:58:25,859
You know, if people are gonna believe
1179
00:58:25,860 --> 00:58:27,119
a news article about it,
1180
00:58:27,120 --> 00:58:28,709
of course they're gonna believe seeing
1181
00:58:28,710 --> 00:58:30,749
the politicians say that.
1182
00:58:30,750 --> 00:58:32,279
AI-generated deep fakes
1183
00:58:32,280 --> 00:58:34,259
are already causing concern.
1184
00:58:34,260 --> 00:58:36,899
These hyperrealistic videos
can spread misinformation
1185
00:58:36,900 --> 00:58:38,789
and manipulate public opinion
1186
00:58:38,790 --> 00:58:41,459
with potentially devastating consequences.
1187
00:58:41,460 --> 00:58:44,129
Take Joe Biden robocalls, for example.
1188
00:58:44,130 --> 00:58:46,349
In the midterm election,
1189
00:58:46,350 --> 00:58:49,259
somebody created an AI
of Joe Biden's voice
1190
00:58:49,260 --> 00:58:51,539
and started making robocall to say
1191
00:58:51,540 --> 00:58:54,029
that this election wasn't important
1192
00:58:54,030 --> 00:58:56,399
and that people should save their votes
1193
00:58:56,400 --> 00:58:58,079
for the presidential election.
1194
00:58:58,080 --> 00:59:00,299
Moving forward, we
need to be more vigilant
1195
00:59:00,300 --> 00:59:02,099
with what we trust from the internet.
1196
00:59:02,100 --> 00:59:04,049
You can question whether it's them,
1197
00:59:04,050 --> 00:59:06,239
and also if you actually said that,
1198
00:59:06,240 --> 00:59:08,039
you could say, no, it wasn't me.
1199
00:59:08,040 --> 00:59:11,493
So when we talk about
how the systems learn,
1200
00:59:12,360 --> 00:59:17,039
how it tries to reason and
tries to create context,
1201
00:59:17,040 --> 00:59:18,509
the way it basically works
1202
00:59:18,510 --> 00:59:22,739
is through a system we deep learning.
1203
00:59:22,740 --> 00:59:25,469
The golden training data
is just that, right?
1204
00:59:25,470 --> 00:59:27,179
It's not just scraped from the internet
1205
00:59:27,180 --> 00:59:31,199
or not just a straight
output from an AI system.
1206
00:59:31,200 --> 00:59:34,199
It's output that the AI system outputs,
1207
00:59:34,200 --> 00:59:35,699
and then a human comes in
1208
00:59:35,700 --> 00:59:37,949
and makes perfect and
then learns from that.
1209
00:59:37,950 --> 00:59:40,439
Typically what we're
finding the best solutions
1210
00:59:40,440 --> 00:59:44,429
is a combination of having the AI come
1211
00:59:44,430 --> 00:59:48,029
to a generalized conclusion and
having a human being come in
1212
00:59:48,030 --> 00:59:50,489
and post edit it to make it perfect.
1213
00:59:50,490 --> 00:59:53,519
So how do
we avoid these pitfalls?
1214
00:59:53,520 --> 00:59:57,059
First, we need to be mindful
of the data we use to train AI.
1215
00:59:57,060 --> 01:00:00,629
Relying solely on AI-generated
data is like feeding a child
1216
01:00:00,630 --> 01:00:02,759
a diet of nothing but sweets.
1217
01:00:02,760 --> 01:00:05,819
We need to prioritize human curated data,
1218
01:00:05,820 --> 01:00:08,399
ensuring diversity and accuracy.
1219
01:00:08,400 --> 01:00:12,659
I personally believe that
in order for AI-generated data
1220
01:00:12,660 --> 01:00:16,139
to be a powerful force
in developing AI systems,
1221
01:00:16,140 --> 01:00:20,069
it needs to exist in conjunction
with human preferences,
1222
01:00:20,070 --> 01:00:21,419
human generated data.
1223
01:00:21,420 --> 01:00:25,559
So there should be mechanisms
in which humans also play
1224
01:00:25,560 --> 01:00:28,259
a role in the kind of data that gets used
1225
01:00:28,260 --> 01:00:30,059
for training these systems.
1226
01:00:30,060 --> 01:00:31,649
Think of
it as adding vitamins
1227
01:00:31,650 --> 01:00:33,869
and minerals to AI's diet.
1228
01:00:33,870 --> 01:00:35,969
This will help AI develop a more nuanced
1229
01:00:35,970 --> 01:00:38,129
and balanced understanding of the world.
1230
01:00:38,130 --> 01:00:40,799
Second, we need to be vigilant about bias.
1231
01:00:40,800 --> 01:00:42,239
Just as we strive for fairness
1232
01:00:42,240 --> 01:00:44,099
and equality in our societies,
1233
01:00:44,100 --> 01:00:47,879
we need to embed these
values in our AI systems.
1234
01:00:47,880 --> 01:00:50,459
This means carefully
auditing AI algorithms
1235
01:00:50,460 --> 01:00:53,549
and addressing any biases that emerge.
1236
01:00:53,550 --> 01:00:56,369
Finally, we need to
remember that AI is a tool,
1237
01:00:56,370 --> 01:00:58,649
not a replacement for human judgment.
1238
01:00:58,650 --> 01:00:59,549
I think when we get back
1239
01:00:59,550 --> 01:01:02,729
to that ChatGPT Turing test question,
1240
01:01:02,730 --> 01:01:05,429
I think the Turing test
needs to be evolving also.
1241
01:01:05,430 --> 01:01:09,629
So did ChatGPT-4.5
pass the Turing test?
1242
01:01:09,630 --> 01:01:12,029
Yes, and on your next chat sessions,
1243
01:01:12,030 --> 01:01:13,632
you might find yourself asking,
1244
01:01:13,633 --> 01:01:17,129
is this a person or just
my new favorite chatbot?
1245
01:01:17,130 --> 01:01:19,319
Overall, I think I know plenty of humans
1246
01:01:19,320 --> 01:01:21,120
that might not pass the Turing test.
1247
01:01:26,980 --> 01:01:30,869
I think the claim that
ChatGPT-4.5 beat the Turing test
1248
01:01:30,870 --> 01:01:33,119
is maybe a little sensationalism.
1249
01:01:33,120 --> 01:01:37,499
The model that beat the Turing
test was not by intellect,
1250
01:01:37,500 --> 01:01:41,939
or the ability to think it was
introducing spelling errors
1251
01:01:41,940 --> 01:01:46,229
on purpose and doing
things to trick the user
1252
01:01:46,230 --> 01:01:48,119
into thinking that it was human.
1253
01:01:48,120 --> 01:01:49,709
Apple's
latest research explains
1254
01:01:49,710 --> 01:01:53,069
what's really going on
behind those clever chatbots.
1255
01:01:53,070 --> 01:01:55,919
Charmingly titled, "The
Illusion of Thinking",
1256
01:01:55,920 --> 01:01:58,409
the research claims some
of the latest AI models,
1257
01:01:58,410 --> 01:02:01,499
like OpenAI's O3 or Google's Gemini,
1258
01:02:01,500 --> 01:02:03,509
aren't actually reasoning.
1259
01:02:03,510 --> 01:02:06,119
While these large reasoning
models sound convincing,
1260
01:02:06,120 --> 01:02:08,819
deep down they're mostly
parroting patterns.
1261
01:02:08,820 --> 01:02:09,689
On easy puzzles,
1262
01:02:09,690 --> 01:02:12,629
basic AIs can sometimes
beat the fancy ones.
1263
01:02:12,630 --> 01:02:14,609
On medium puzzles, the new models shine
1264
01:02:14,610 --> 01:02:16,383
with step by step logic.
1265
01:02:17,550 --> 01:02:19,229
But once things get complicated,
1266
01:02:19,230 --> 01:02:22,109
both types of models fall flat.
1267
01:02:22,110 --> 01:02:25,259
Even more dramatic when faced
with truly tough problems,
1268
01:02:25,260 --> 01:02:28,289
these models tend to just give up.
1269
01:02:28,290 --> 01:02:29,729
They have the power to try harder,
1270
01:02:29,730 --> 01:02:31,769
but they often don't bother.
1271
01:02:31,770 --> 01:02:34,079
With every innovation
and every announcement
1272
01:02:34,080 --> 01:02:36,689
and the hype that is sold
with every announcement,
1273
01:02:36,690 --> 01:02:39,809
it kind of pulls people
back out from planning
1274
01:02:39,810 --> 01:02:43,379
and going through this enlightenment phase
1275
01:02:43,380 --> 01:02:45,269
back into the hype cycle.
1276
01:02:45,270 --> 01:02:48,929
So this new technology
is gonna be a game changer.
1277
01:02:48,930 --> 01:02:50,279
New technologies tend
1278
01:02:50,280 --> 01:02:52,919
to follow a familiar
pattern represented by model
1279
01:02:52,920 --> 01:02:54,989
known as the hype cycle.
1280
01:02:54,990 --> 01:02:56,849
They start with a bang, fizzle out,
1281
01:02:56,850 --> 01:03:00,029
then suddenly quietly
become indispensable.
1282
01:03:00,030 --> 01:03:01,829
There are five classic phases.
1283
01:03:01,830 --> 01:03:05,429
First, the innovation
trigger, a breakthrough,
1284
01:03:05,430 --> 01:03:08,579
a light bulb moment, and
suddenly everyone's talking.
1285
01:03:08,580 --> 01:03:11,549
Next, the peak of inflated expectation.
1286
01:03:11,550 --> 01:03:14,429
This is where the hype gets
wild headlines everywhere.
1287
01:03:14,430 --> 01:03:17,159
Investors piling in and
promises of changing the world
1288
01:03:17,160 --> 01:03:18,809
with a lot of wild claims.
1289
01:03:18,810 --> 01:03:21,419
I think that right now when
it comes to the hype cycle,
1290
01:03:21,420 --> 01:03:25,263
the general public is
approaching or at peak hype.
1291
01:03:26,430 --> 01:03:28,949
Then comes the
trough of disillusionment.
1292
01:03:28,950 --> 01:03:31,949
Reality bites, the tech isn't
quite working as promised,
1293
01:03:31,950 --> 01:03:33,989
businesses grumble, failures stack up,
1294
01:03:33,990 --> 01:03:36,089
and the world looks
for the next big thing.
1295
01:03:36,090 --> 01:03:37,709
There are a lot of companies
1296
01:03:37,710 --> 01:03:39,899
that are experiencing the
trough of disillusionment.
1297
01:03:39,900 --> 01:03:42,059
Businesses figure
out what actually works.
1298
01:03:42,060 --> 01:03:44,369
New versions appear and
practical benefits start
1299
01:03:44,370 --> 01:03:45,899
to emerge for end users.
1300
01:03:45,900 --> 01:03:46,950
Let's try this one.
1301
01:03:47,910 --> 01:03:49,199
Okay, that looks promising.
1302
01:03:49,200 --> 01:03:51,359
Think of that once you're at
the plateau of productivity,
1303
01:03:51,360 --> 01:03:55,169
this is fully ingrained to
everybody's daily lives.
1304
01:03:55,170 --> 01:03:56,990
I think a lot of companies
1305
01:03:56,991 --> 01:03:58,859
and a lot of people are still figuring out
1306
01:03:58,860 --> 01:04:00,599
how to best use these tools.
1307
01:04:00,600 --> 01:04:03,059
Finally, there's
the plateau of productivity.
1308
01:04:03,060 --> 01:04:05,039
The tech works and everyone uses it,
1309
01:04:05,040 --> 01:04:06,839
often without even thinking about it.
1310
01:04:06,840 --> 01:04:09,689
I think we're some time
away from peak productivity,
1311
01:04:09,690 --> 01:04:12,883
but we're definitely on
the way to achieving it.
1312
01:04:15,510 --> 01:04:18,209
Some people are going to be afraid of AI
1313
01:04:18,210 --> 01:04:20,249
because they're afraid of everything.
1314
01:04:20,250 --> 01:04:21,899
AI can augment our abilities,
1315
01:04:21,900 --> 01:04:23,789
but it shouldn't dictate our decisions.
1316
01:04:23,790 --> 01:04:26,699
By staying engaged, by retaining control,
1317
01:04:26,700 --> 01:04:29,549
we can ensure that AI
remains a force for good
1318
01:04:29,550 --> 01:04:30,509
in the world.
1319
01:04:30,510 --> 01:04:33,479
Studies done after World
War II prove that 30 to 35%
1320
01:04:33,480 --> 01:04:35,429
of any country are filled
with people who are afraid
1321
01:04:35,430 --> 01:04:38,069
of everything and will
do anything they're told.
1322
01:04:38,070 --> 01:04:38,969
So there's gonna be a certain number
1323
01:04:38,970 --> 01:04:40,049
of people who are just afraid,
1324
01:04:40,050 --> 01:04:42,149
and are going to believe
what they're told.
1325
01:04:42,150 --> 01:04:44,999
As we hurdle
towards a future dominated by AI,
1326
01:04:45,000 --> 01:04:46,649
a crucial question arises.
1327
01:04:46,650 --> 01:04:48,749
Will this be our final act of creation,
1328
01:04:48,750 --> 01:04:51,149
or the dawn of a new era?
1329
01:04:51,150 --> 01:04:54,989
We are still far from the
doomsday scenarios of AI.
1330
01:04:54,990 --> 01:04:56,789
Every new technology often ends up
1331
01:04:56,790 --> 01:04:58,379
being a double edged sword.
1332
01:04:58,380 --> 01:05:00,899
The notion of AI
surpassing human intelligence
1333
01:05:00,900 --> 01:05:03,149
is both exhilarating and unsettling.
1334
01:05:03,150 --> 01:05:04,799
If machines can learn, adapt,
1335
01:05:04,800 --> 01:05:07,006
and innovate at an unprecedented rate,
1336
01:05:10,050 --> 01:05:12,329
what role will humans
play in a future shaped
1337
01:05:12,330 --> 01:05:13,889
by our own creations?
1338
01:05:13,890 --> 01:05:16,439
Tradespeople and people
who actually do good things,
1339
01:05:16,440 --> 01:05:19,739
nurses are going to be fine.
1340
01:05:19,740 --> 01:05:22,109
And if anything, we're gonna
have more need for them
1341
01:05:22,110 --> 01:05:23,699
as people need to be retrained
1342
01:05:23,700 --> 01:05:26,459
and are experimenting with
new ways to make a living.
1343
01:05:26,460 --> 01:05:27,779
The decisions we make today
1344
01:05:27,780 --> 01:05:30,479
regarding AI development and regulation
1345
01:05:30,480 --> 01:05:32,189
will have far reaching implications
1346
01:05:32,190 --> 01:05:34,109
for generations to come.
1347
01:05:34,110 --> 01:05:37,559
Innovation is about to be supercharged.
1348
01:05:37,560 --> 01:05:39,496
That's what AI can do.
1349
01:05:39,497 --> 01:05:44,399
If, if we don't allow it to
become a means of control.
1350
01:05:44,400 --> 01:05:48,479
Because remember, every good
thing, every positive force
1351
01:05:48,480 --> 01:05:50,549
that gives people more freedom
1352
01:05:50,550 --> 01:05:54,993
can be used by the ill
intended as a means of control.
1353
01:05:56,160 --> 01:05:59,999
I'm sorry Dave, I'm
afraid I can't do that.
1354
01:06:00,000 --> 01:06:02,429
Super intelligence,
a theoretical form of AI
1355
01:06:02,430 --> 01:06:05,219
that surpasses human
intellect in every aspect
1356
01:06:05,220 --> 01:06:07,949
has long been a staple of science fiction.
1357
01:06:07,950 --> 01:06:09,004
What's the problem?
1358
01:06:11,130 --> 01:06:13,319
However, with
the rapid advancements in AI,
1359
01:06:13,320 --> 01:06:17,039
it's no longer a concept
confined to the pages of novels.
1360
01:06:17,040 --> 01:06:20,849
Could we control something more
intelligent than ourselves?
1361
01:06:20,850 --> 01:06:23,909
Would we even want to, or
would we relinquish control?
1362
01:06:23,910 --> 01:06:26,069
Super intelligence is the intelligence
1363
01:06:26,070 --> 01:06:28,019
that's higher than of humans.
1364
01:06:28,020 --> 01:06:30,299
We believe as an industry
1365
01:06:30,300 --> 01:06:32,129
that this could occur within a decade.
1366
01:06:32,130 --> 01:06:34,870
I see it as the wake
up call for the world
1367
01:06:35,893 --> 01:06:38,909
that AI is gonna be faster
than you think it is.
1368
01:06:38,910 --> 01:06:42,089
And could come from
any corner of the world
1369
01:06:42,090 --> 01:06:46,199
and we should act now before it's too late
1370
01:06:46,200 --> 01:06:48,663
in making sure it's deployed for good.
1371
01:06:49,590 --> 01:06:51,359
The concept of a singularity,
1372
01:06:51,360 --> 01:06:54,719
a point in time when AI
surpasses human intelligence
1373
01:06:54,720 --> 01:06:57,299
and triggers rapid technological growth
1374
01:06:57,300 --> 01:06:59,159
is a topic of intense debate.
1375
01:06:59,160 --> 01:07:01,409
I really don't know
where it's going to end,
1376
01:07:01,410 --> 01:07:05,429
but given the history,
the industrial revolution,
1377
01:07:05,430 --> 01:07:09,929
the digital age, we are
gonna reaccommodate.
1378
01:07:09,930 --> 01:07:12,419
There's gonna be a period of adjustment,
1379
01:07:12,420 --> 01:07:13,713
but then we'll be fine.
1380
01:07:15,510 --> 01:07:18,029
Some experts
believe it could usher in an era
1381
01:07:18,030 --> 01:07:19,953
of unprecedented prosperity.
1382
01:07:21,150 --> 01:07:23,703
While others warn of existential risks.
1383
01:07:24,840 --> 01:07:27,899
AI has been used to influence votes,
1384
01:07:27,900 --> 01:07:31,829
influence public opinion,
and it's really dangerous.
1385
01:07:31,830 --> 01:07:33,269
So, it's scary,
1386
01:07:33,270 --> 01:07:38,270
and I think that as a society,
1387
01:07:38,520 --> 01:07:39,479
we should be figuring out
1388
01:07:39,480 --> 01:07:41,339
how to regulate this pretty quickly,
1389
01:07:41,340 --> 01:07:42,959
Regardless of one's stance,
1390
01:07:42,960 --> 01:07:45,629
the potential for AI to
fundamentally reshape
1391
01:07:45,630 --> 01:07:47,699
our world is undeniable
1392
01:07:47,700 --> 01:07:50,819
In my opinion, if the big players in AI,
1393
01:07:50,820 --> 01:07:52,629
including the big companies,
1394
01:07:52,630 --> 01:07:55,473
your Facebooks, your
AWSs, your Microsofts,
1395
01:07:56,310 --> 01:07:59,549
play responsibly and utilize
ethics in what they're doing,
1396
01:07:59,550 --> 01:08:02,849
everybody else will come
into play and follow.
1397
01:08:02,850 --> 01:08:05,759
The biggest thing we need to get right
1398
01:08:05,760 --> 01:08:09,340
in order to not completely
obliterate humans
1399
01:08:12,180 --> 01:08:15,063
is the ethics behind the AI.
1400
01:08:16,560 --> 01:08:20,759
I hope we treat AI in
the future and grow AI
1401
01:08:20,760 --> 01:08:24,899
in a way that we would
a beloved young child,
1402
01:08:24,900 --> 01:08:27,933
where we teach it values,
1403
01:08:29,340 --> 01:08:34,289
and fairness and justice
1404
01:08:34,290 --> 01:08:36,599
in the hopes that it grows up
1405
01:08:36,600 --> 01:08:39,483
to be a good citizen of the world.
1406
01:08:40,800 --> 01:08:45,153
We can't forget that we are humans.
1407
01:08:47,070 --> 01:08:49,289
The things that make us human
1408
01:08:49,290 --> 01:08:51,723
and social beings are important,
1409
01:08:53,460 --> 01:08:56,249
and we should look after one another
1410
01:08:56,250 --> 01:08:58,053
and take care of one another.
1411
01:08:59,550 --> 01:09:02,039
The story
of AI is just beginning.
1412
01:09:02,040 --> 01:09:04,953
It's a story filled with
both promise and peril.
1413
01:09:07,380 --> 01:09:09,303
And its ending remains unwritten.
1414
01:09:14,070 --> 01:09:15,970
The future of intelligence, it seemed,
1415
01:09:17,910 --> 01:09:19,649
is up to us to decide.
108090
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