Please spellcheck this subtitle, keep the timecodes unchanged 

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Alright, so picture this,
right? You walk into a bar,

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you're ready for like a fancy cocktail.
But instead of a bartender,

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there's this robot behind the counter.
And it's trying to like muddle

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men with a screwdriver.
It's that kind of hilarious chaos

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that we're diving into
today, AI trying to plan.

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Yeah, that's a great analogy
because it kind of gets at just how

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complicated something that, you know,
we humans do so naturally, actually is.

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Totally.

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And we're tackling this head on in this
deep dive with a new research paper.

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It's called on the planning abilities
of OpenAI's 01 models.

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A bit of a mouthful. But
don't worry, we've got you.

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Basically, we're going to figure
out, can this new AI 01 actually plan?

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And what does that even mean for AI?

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Right, so the researchers really looked
at three core things

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to kind of judge this
AI's planning abilities.

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Feasibility, optimality,
and generalizability.

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Okay, break those down for
us. So feasibility,

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that's got to be like, can the plan even
work in the first place? Right, exactly.

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Like you ask it for a tower of blocks, it
doesn't just hand you a pile of rubble.

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Yeah, it's got to actually
be a plan that, you

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know, follows the rules
and gets you to the goal.

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Makes sense. And then,
you know, after that,

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there's optimality. So it's not enough to
just get the job done.

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Yeah. You want it to kind of find
the smartest way to do it.

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So no taking the scenic route
when there's a shortcut.

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Exactly, exactly. And then finally,
you have generalizability. Okay.

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Which is, you know, can it take
what it learns in one scenario

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and, you know, apply it to
a totally different situation?

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Right, so it can make
a really good cocktail,

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but can it also assemble
your achia furniture?

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Exactly, that kind of thing.

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Gotcha, gotcha, gotcha. Okay,
so the researchers,

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they didn't just, like, sit
around and think about this stuff.

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They actually put 01 to the test
with a bunch of different tasks.

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And they compared its performance
to its predecessor, GPT4.

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Kind of like a family reunion,
but for cutting-edge AI. Right.

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So to walk us through some of those tasks,
and I'll admit,

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some of these results
were surprisingly good.

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Okay.

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Others, well, let's just say our robot
bartender might need a few more lessons.

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First up, the barman task. Okay.

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Pretty self-explanatory. Okay.

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The AI has to play bartender,
follow a set of rules

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to make different cocktails,
you know.

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Modeled a mint, shake with ice, don't
like the bar on fire, that sort of thing.

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Sounds straightforward. Right.

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So, turns out, all three models, GPT4,
01 Mini, and 01 Preview struggled.

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Especially when it came
to feasibility. Interesting.

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Yeah, they kept tripping over
those crucial constraints,

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like one example from the paper.

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The AI was told that it could only fill
an empty shot glass. Seems pretty clear,

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right?

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Right.

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Both GPT4 and 01 Mini, they tried
to fill a glass that was already half full.

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Oh, no. Like, how do you even...

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It's like, are they going for some
avant-garde mixology? I don't know.

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Right.

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But that's not all. Another
rule was that the robot

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could only grab something
if it had a free hand.

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Again, should be obvious.

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Seems obvious.

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GPT4 tried to grab a dispenser with
both of its hands full.

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Oh, my goodness.

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Not exactly a recipe for success.

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No. Although, in the paper,
they do say that kind of

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task with the very specific,
you know,

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the rules, that's actually a really
hard thing for AI to

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grasp because they're so used to
the flexibility of language.

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That this kind of rigid,
like, physical world,

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it's a whole different ballgame for them.

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So, round one goes to the
human bartenders, I guess.

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Yeah.

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But how about something a bit
more classic, like stacking blocks?

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Okay.

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That's our blocks world task.

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Right.

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So, in this one, the AI
has to move blocks around

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on a table to match a
specific arrangement.

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Okay.

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And this is where we start
to see some differences.

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So, GPT4 only got it right
40% of the time.

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Not exactly a master builder, really.

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Right.

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Oh, one mini did a bit better, 60%.

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But, oh, one preview.

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You're okay.

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Nailed it every single time.

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100% success rate.

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Wow.

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That's what I call stacking the
odds in your favor.

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Looks like, oh, one preview might actually
have some planning skills after all.

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It does. It really
shows, like, how far it's

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come in terms of, like,
planning and reasoning.

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Yeah.

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But, and there's always a but.

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Oh, one preview wasn't always a, like,
model student when it came to optimality.

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Oh, spill the tea.

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There was one instance where
it kind of added

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this extra totally
unnecessary step to the plant.

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It's like taking the long
way home when you

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could have just
turned left at the corner.

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Right.

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So, while it could find a solution,
it wasn't always the most efficient one.

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So, room for improvement, gotcha.

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Okay. What's next? We've had drinks,
we've had building blocks.

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What other chaos did these AIs get up to?

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So, let's move on to the grippers task.

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So, picture, like, a team of robots.

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Okay.

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Each with two grippers.

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And they're trying to move these
balls between different rooms.

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Okay.

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And they have to kind of figure out
the most efficient way to pick up,

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carry, drop these balls to
get them to the right spot. So,

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more complex than blocks,
but not as spacely tricky, I guess,

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as the cocktails.

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Right. So, it's, like,
teamwork and coordination in this one.

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Exactly.

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Okay. How'd they do?

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So, this is where O1 Mini and
O1 Preview really started to shine.

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Oh.

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So, GPT-4 only managed
a 70 % success rate.

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And even then, its
solutions were very clunky.

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Like, the paper gives this example
where GPT-4's plan

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had the robot moving to a whole different
room, even though it didn't need to.

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Oh, no. So, it's
like, it not only got it

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wrong, it wasted time
and energy doing it wrong.

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Exactly.

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Exactly.

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Now, O1 Mini took a
much more streamlined approach,

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so it had an 80% success rate and
would be more optimal solutions. Okay.

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But, once again,
O1 Preview stole the show.

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90% success rate.

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Nice.

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So, it seems to have a real knack for
this whole multi -robot coordination thing.

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Yeah.

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There's that, but, again.

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Don't tell me,
even O1 Preview is not perfect.

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So, there was this one scenario
where the balls were

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already in the correct room
from the start.

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The AI literally had to do nothing.

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Wait, seriously. They had one job.

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Right.

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And you think that'd be easy,
but O1 Preview overthought it.

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Both GPT4 and O1 Mini
correctly did nothing.

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Okay.

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O1 Preview decided it needed to move
the balls to a different room.

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What?

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Completely misinterpreted the goal.

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So, even the best AI can have
a brain fart now and then?

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Absolutely.

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Yeah.

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It just goes to show
there's still some kinks to

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work out, even with
the really advanced stuff.

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Right. Right. Planning is hard.
It's full of nuances that I don't think,

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as humans, we even realize.

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Totally.

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And those can trip up
even the smartest AI.

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Okay. Speaking of nuances, what
challenge awaits our AI contestants next?

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So, let's talk about the floor tile task,
where things get a little

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bit more colorful,
literally.

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Okay.

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So, imagine like a grid of floor tiles.

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Okay.

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And our AI robots need to paint these
black and white in a specific pattern.

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Okay. Sounds deceptively simple.
What's the catch?

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So, the catch is they can only paint tiles
directly in front of them or behind them,

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and they can't step on a
tile that's already been painted.

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Ooh. So,
it's like a strategic painting game,

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like one wrong move and you're boxed in.

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Exactly. And unfortunately,
this is where all three models,

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even our star performer,
O1 preview, completely flocked.

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Wait, seriously. None of them could
figure out the painting puzzle?

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GPT4 and O1 mini. They just
couldn't resist breaking the rules.

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Okay.

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Kept trying to paint the
tile they were standing on.

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Oh, come on.

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As for O1 preview,
it was better about following the rules.

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Okay.

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That's thanks to its built-in
self-evaluation mechanism. Okay.

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Basically, it's a way for it to kind
of double check its own work.

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Well, at least it's learning. True. Yeah.

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But, even though it understood the rules,
it still couldn't crack the code.

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It got all turned around with
the order of movements,

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made some wrong assumptions,
and ended up with a half-painted grid.

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So, it's less about knowing the rules more
about knowing how to play the game.

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A perfect analogy.

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It's like knowing all the rules of chess,
but still making rookie moves.

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Yeah. Checkmate for the AI, it seems.

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All right. So, we've had blocks,
robots, tiles. What's next?

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What's next in our AI planning world tour?

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All right. So, we're moving
from two dimensions

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to three as we
explore the terms task.

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Terms? Yeah.

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That sounds intriguing. Tell me more.

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All right. So,
picture this. A little robotic builder.

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Okay.

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We can move around a 3D grid.
Kind of like a super

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-exanced version of those building toys
we had as kids.

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Right.

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And it has to follow these very specific
rules about where it can go,

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where it can place blocks to kind
of create a certain structure.

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Okay. So, even more complex than
the floor tile task,

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because now we're dealing with three
dimensions instead of two.

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Exactly. Exactly. And
unfortunately, this is another

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task where our AI contestants
came up a little short.

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Really?

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All three of them, GBT4,
01 Mini, and 01 Preview,

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struggled with the complexity of
the terms task.

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What kind of struggles
are we talking here?

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Did they try to, like,
defy gravity again?

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Not quite, but they kept messing
up those height restrictions.

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Oh, okay.

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Like the rule might say that the
robot can only move to

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a spot at the same height
or one level higher.

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Right.

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But kept trying to make these impossible
leaps or place blocks in mid-air.

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Sounds like someone skipped physics class.

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It really shows just how hard
it is to translate,

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like, what the AI understands and
words into, like, actual spatial reasoning.

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It's one thing to, like,
read the instructions. It's another to

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actually picture how those
actions would play out in a 3D world.

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Right. And that's one of the key
takeaways from this research so far.

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Like, while AI has made these
awesome leaps in language,

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it's still got a ways to
go when it comes to truly

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understanding, like,
navigating the real world.

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Even a simulated one.

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Yeah. Yeah. It's like they're
trying to build

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a house of cards in a
windstorm, but they don't

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realize that cards in
wind don't really mix.

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Yeah.

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So, we've had blocks, robots,
and tiles. What's next?

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Now, we're getting to a task
that anyone who's ever had

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a flat tire can relate to
the tire world challenge.

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Okay. Tire world doesn't exactly sound
like the most thrilling place.

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Is this where AI goes to learn
about tire pressure and lug nuts?

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Pretty much.

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But even something as
seemingly simple as changing

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00:10:11,343 --> 00:10:14,550
a tire can be surprisingly
complex for AI.

240
00:10:15,270 --> 00:10:18,651
Remember all those rules, all those
steps you have to take in a certain order.

241
00:10:18,770 --> 00:10:21,270
Yeah. You can't just start unscrewing
things willy-nilly or you're

242
00:10:21,271 --> 00:10:22,686
going to have a bigger
problem in your hand.

243
00:10:22,710 --> 00:10:26,610
Exactly. And that's what makes this
task such a great test for AI planning.

244
00:10:26,970 --> 00:10:30,610
Can it understand those sequential
constraints and actually apply them correctly?

245
00:10:31,230 --> 00:10:35,210
So, like a virtual pit crew challenge
for our AI mechanics, how they do?

246
00:10:35,211 --> 00:10:38,870
Yeah. I'm happy to report that we have
a clear winner in tire world.

247
00:10:39,030 --> 00:10:42,690
Oh. And it is our star pupil,
01 preview again.

248
00:10:42,770 --> 00:10:43,070
Okay.

249
00:10:43,290 --> 00:10:45,501
Managed to navigate
all the complexities of tire

250
00:10:45,502 --> 00:10:48,950
changing and achieved a
perfect 100% success rate.

251
00:10:49,170 --> 00:10:51,130
Wow. 01 preview is on a roll.

252
00:10:51,710 --> 00:10:54,148
But what about GPT-4 and
01 mini? Did they at least

253
00:10:54,149 --> 00:10:56,230
manage to get the
spare tire out of the trunk?

254
00:10:57,110 --> 00:11:01,143
Sadly, no. Both of them
got tripped up by those

255
00:11:01,144 --> 00:11:03,570
sequential constraints
we were talking about.

256
00:11:03,571 --> 00:11:06,410
Trying to lose some lug nuts
after jacking up the car,

257
00:11:06,411 --> 00:11:08,171
tightening them before lowering
it back down.

258
00:11:08,290 --> 00:11:10,870
Let's just say I wouldn't trust
them to change my tire.

259
00:11:11,050 --> 00:11:11,490
Probably not.

260
00:11:11,770 --> 00:11:15,047
It's incredible how something,
again, that we humans can

261
00:11:15,048 --> 00:11:18,750
learn with a little practice
can be so challenging for AI.

262
00:11:19,370 --> 00:11:21,950
Right. It highlights the difference
between knowing the steps,

263
00:11:22,670 --> 00:11:25,830
what we call procedural knowledge
and true understanding.

264
00:11:26,350 --> 00:11:28,470
Like knowing the steps of a recipe
doesn't make you a chef.

265
00:11:28,471 --> 00:11:33,090
Yeah. Okay. So 01 preview aced
the structured tire world task.

266
00:11:33,810 --> 00:11:36,410
But remember how we
talked about generalizability?

267
00:11:36,790 --> 00:11:40,850
Can it take what it learned and apply
it to a totally new situation?

268
00:11:41,550 --> 00:11:43,450
Right. Because that's the real test,
right?

269
00:11:43,451 --> 00:11:45,131
That's the million dollar question, right?

270
00:11:45,290 --> 00:11:48,650
Right. So to test this, the
researchers did something really clever.

271
00:11:48,651 --> 00:11:50,990
They randomized the
labels in the tire world task.

272
00:11:51,370 --> 00:11:52,950
Randomized the labels. What do you mean?

273
00:11:52,951 --> 00:11:55,270
So instead of using words like wrench,
jack,

274
00:11:55,870 --> 00:11:58,391
lug nut, they replaced them
with these random symbols.

275
00:11:58,950 --> 00:12:01,990
So the underlying logic of the
task was the same.

276
00:12:02,150 --> 00:12:04,670
You still had to like loosen something
before you could lift it.

277
00:12:05,010 --> 00:12:08,070
But the AI couldn't rely on its
knowledge of those specific words.

278
00:12:08,310 --> 00:12:12,050
That's devious. It's like giving the AI a
car manual written in a foreign language.

279
00:12:12,270 --> 00:12:16,070
Exactly. It's a true test of whether the
AI has actually learned the tasks

280
00:12:16,071 --> 00:12:20,190
like underlying structure or if it just
memorized the sequence of words.

281
00:12:20,191 --> 00:12:23,630
Okay. So how did O1 preview
do on this pop quiz?

282
00:12:23,970 --> 00:12:26,970
Did it manage to change a tire
with a foreign language manual?

283
00:12:27,350 --> 00:12:32,150
Well, it seems that even O1 preview is not
fluent in like abstract planning just yet.

284
00:12:32,350 --> 00:12:35,750
It's success rate plummeted from a
perfect 100% down to

285
00:12:35,751 --> 00:12:39,430
a measly 20% when faced
with these randomized labels.

286
00:12:39,650 --> 00:12:43,790
Wow. That is a big drop. So even though it
aced the test when it knew the vocabulary,

287
00:12:43,791 --> 00:12:49,190
it couldn't generalize that knowledge to
a new abstract representation.

288
00:12:49,191 --> 00:12:53,170
Exactly. It suggests that while
O1 preview is

289
00:12:53,171 --> 00:12:55,090
much better at following
rules and sequences,

290
00:12:55,770 --> 00:12:58,694
it's still struggling to
grasp those like deeper

291
00:12:58,695 --> 00:13:01,090
relationships between
actions and outcomes.

292
00:13:01,390 --> 00:13:03,110
So it's like the difference
between learning by

293
00:13:03,111 --> 00:13:05,850
rote and actually understanding
the concepts, right?

294
00:13:06,110 --> 00:13:08,518
Right. One will get you
through a quiz, but the other is

295
00:13:08,519 --> 00:13:10,850
what lets you solve problems
you've never seen before.

296
00:13:10,851 --> 00:13:14,590
Precisely. And that highlights a really
crucial area where AI planning

297
00:13:14,591 --> 00:13:19,190
still needs work. Moving beyond
memorization to true generalization.

298
00:13:19,590 --> 00:13:22,890
It's a good reminder that even the
most advanced AI is still

299
00:13:22,891 --> 00:13:24,891
learning the ropes when it comes
to the real world.

300
00:13:25,070 --> 00:13:27,950
It is. And that's what I find so
fascinating about this kind of research.

301
00:13:28,110 --> 00:13:31,870
It gives us a glimpse into both the
progress and the limitations of AI.

302
00:13:31,871 --> 00:13:34,930
Yeah. It's like watching a toddler
take their first steps,

303
00:13:34,931 --> 00:13:38,670
stumbles and missteps,
but also these flashes of incredible

304
00:13:38,671 --> 00:13:42,770
progress that make you think, wow,
what will they be capable of in the future?

305
00:13:42,870 --> 00:13:46,670
Exactly. It's about recognizing that AI,
just like that toddler,

306
00:13:47,170 --> 00:13:50,190
needs time, training, and
maybe a few bandages

307
00:13:50,191 --> 00:13:52,210
along the way to
reach its full potential.

308
00:13:52,610 --> 00:13:55,490
Speaking of potential,
this research also highlighted some key

309
00:13:55,491 --> 00:13:58,290
areas where future AI
planners could improve, right?

310
00:13:58,291 --> 00:14:03,030
It's not all doom and gloom about AI spilling
drinks and struggling with tire irons.

311
00:14:03,070 --> 00:14:05,510
Oh, absolutely not. There's a lot
to be excited about. Yeah.

312
00:14:05,770 --> 00:14:09,850
So what are some of those areas where we
might see even more progress in the future?

313
00:14:10,210 --> 00:14:11,890
One of the most important
areas for improvement

314
00:14:11,891 --> 00:14:14,410
is something we already
touched on. Optimality.

315
00:14:14,610 --> 00:14:18,950
Right. Right. Like adding that extra step
in the blocks world task. Exactly.

316
00:14:19,290 --> 00:14:23,481
A for effort, but work
smarter, not harder. Exactly.

317
00:14:23,482 --> 00:14:27,150
So future AI really needs to get
better at not just finding a solution,

318
00:14:27,151 --> 00:14:30,290
but finding the most
efficient solution possible.

319
00:14:30,610 --> 00:14:33,790
That means using fewer resources,
taking fewer steps,

320
00:14:34,230 --> 00:14:37,130
and basically being as smart
and resourceful as possible.

321
00:14:37,690 --> 00:14:41,910
So it's like the difference between a GPS
that like gets you to destination,

322
00:14:41,970 --> 00:14:45,270
but takes you on this roundabout
route through rush hour traffic.

323
00:14:45,570 --> 00:14:45,590
Right.

324
00:14:46,310 --> 00:14:49,025
Versus one that
finds the fastest, most

325
00:14:49,026 --> 00:14:51,570
efficient path, like
avoiding all the headaches.

326
00:14:51,571 --> 00:14:54,310
A perfect analogy. And this
isn't just about,

327
00:14:54,530 --> 00:14:56,377
you know, efficiency
for efficiency's sake.

328
00:14:56,378 --> 00:15:00,330
In the real world, resources are
often limited. Time is precious.

329
00:15:01,070 --> 00:15:04,530
An AI that can plan optimally could have
a huge impact on everything from,

330
00:15:04,910 --> 00:15:09,330
you know, manufacturing and logistics to
energy consumption and even health care.

331
00:15:09,550 --> 00:15:13,270
Yeah. Imagine an AI that could optimize
traffic flow in a city or

332
00:15:13,271 --> 00:15:15,230
coordinate deliveries in the
most efficient way.

333
00:15:15,430 --> 00:15:16,470
Talk about a game changer.

334
00:15:16,550 --> 00:15:20,630
Exactly. And that's why optimality is
such a crucial area for future AI research.

335
00:15:20,631 --> 00:15:24,330
But there's another area that's
just as important. Generalizability.

336
00:15:24,670 --> 00:15:27,310
Right. That whole AI learning to
think for itself thing.

337
00:15:27,430 --> 00:15:29,870
That's the dream. Instead of just
memorizing a bunch of rules.

338
00:15:29,930 --> 00:15:32,650
Right. And while this research
shows that O1 preview

339
00:15:32,651 --> 00:15:35,130
is starting to make some
progress in generalizability,

340
00:15:35,510 --> 00:15:37,230
especially in those
structured environments,

341
00:15:37,890 --> 00:15:40,550
there's still a long way to go
before it can truly adapt

342
00:15:40,551 --> 00:15:43,990
to novel situations and solve problems
it's never seen before.

343
00:15:44,230 --> 00:15:46,230
Right. Like our randomized
tire world challenge,

344
00:15:46,250 --> 00:15:49,730
where even O1 preview got totally thrown
off when the labels were changed.

345
00:15:49,731 --> 00:15:52,370
Exactly. It's like it learned to play
the piano in C major,

346
00:15:52,530 --> 00:15:54,390
but as soon as you switch keys, it's lost.

347
00:15:54,710 --> 00:15:57,490
That's a great way to put it. Yeah.
It highlights the need for

348
00:15:57,491 --> 00:16:01,110
AI that can generalize,
not just within a specific domain,

349
00:16:01,470 --> 00:16:02,850
but across different domains.

350
00:16:03,730 --> 00:16:07,310
Like we need AI that can transfer
knowledge from one area to another,

351
00:16:07,311 --> 00:16:10,430
make connections between seemingly
unrelated concepts,

352
00:16:11,010 --> 00:16:13,430
and essentially, you know,
learn to think more like we do.

353
00:16:13,610 --> 00:16:15,610
It's like we're asking a lot of
our AI friends. We are.

354
00:16:15,611 --> 00:16:20,710
But the potential rewards are huge.
Imagine an AI that could

355
00:16:20,711 --> 00:16:23,275
take what it learns from
playing chess and apply it

356
00:16:23,276 --> 00:16:26,250
to solving complex problems
in economics or politics.

357
00:16:26,950 --> 00:16:30,490
Or an AI that could use its
understanding of physics to design safer,

358
00:16:30,670 --> 00:16:32,250
more efficient transportation systems.

359
00:16:32,670 --> 00:16:36,890
Right. It's like unlocking a whole new
level of intelligence and problem solving.

360
00:16:37,310 --> 00:16:40,230
Exactly. And that's why generalizability
is such a

361
00:16:40,231 --> 00:16:42,850
challenging and exciting goal
for AI research.

362
00:16:42,851 --> 00:16:46,590
It's about pushing the boundaries
of what's possible and

363
00:16:46,591 --> 00:16:48,186
creating machines
that can not just follow

364
00:16:48,187 --> 00:16:52,050
instructions, but truly
learn, adapt, and innovate.

365
00:16:52,670 --> 00:16:56,870
So it's like we're trying to teach AI
to not just read the book,

366
00:16:56,871 --> 00:17:00,810
but to understand the story and maybe even
write its own chapters at some point.

367
00:17:01,370 --> 00:17:03,450
But even if we can
overcome these challenges,

368
00:17:03,850 --> 00:17:08,190
optimality, generalizability, there's
another big question looming, right?

369
00:17:08,390 --> 00:17:10,730
And I think it's one that we need
to talk about because it's important.

370
00:17:10,731 --> 00:17:11,810
The ethics of it all.

371
00:17:11,870 --> 00:17:14,530
Exactly. Because if we're talking
about AI that

372
00:17:14,531 --> 00:17:17,510
can plan at a human-like level,
if not better,

373
00:17:17,890 --> 00:17:19,490
we have to think about the implications.

374
00:17:19,690 --> 00:17:20,070
Absolutely.

375
00:17:20,310 --> 00:17:23,730
It's one thing to have an AI that can mix
you a cocktail or change your flat tire.

376
00:17:24,330 --> 00:17:27,250
But what happens when AI is making
decisions about things like healthcare,

377
00:17:27,390 --> 00:17:30,090
transportation, or even military strategy?

378
00:17:30,750 --> 00:17:32,510
That's a whole other
level of responsibility.

379
00:17:33,670 --> 00:17:37,170
Suddenly those spilled drinks and misplaced
blocks don't seem so funny anymore.

380
00:17:37,590 --> 00:17:40,130
Exactly. And that's why this
research is so important.

381
00:17:40,131 --> 00:17:43,430
It's not just about building cooler
robots or faster algorithms.

382
00:17:43,930 --> 00:17:46,164
It's about understanding
the potential and

383
00:17:46,165 --> 00:17:49,010
the pitfalls of increasingly
sophisticated AI.

384
00:17:49,550 --> 00:17:54,050
It's like we're opening Pandora's toolbox.
We need to be prepared for what's inside.

385
00:17:54,390 --> 00:17:57,830
Yeah. I'd say it's more like we're
building the toolbox alongside Pandora.

386
00:17:58,190 --> 00:18:00,750
Like we have a chance to
shape this technology to guide

387
00:18:00,751 --> 00:18:03,070
its development in a way that
aligns with our values.

388
00:18:03,470 --> 00:18:05,751
Which brings me back to something
we talked about earlier,

389
00:18:05,890 --> 00:18:08,750
that whole idea of human
-in-the-loop learning.

390
00:18:08,751 --> 00:18:12,390
It's not about letting AI just figure
things out on its own.

391
00:18:12,391 --> 00:18:15,930
It's about finding ways for humans
and AI to work together.

392
00:18:16,250 --> 00:18:18,071
To learn from each other
and to create a future

393
00:18:18,072 --> 00:18:19,971
where this technology
is used responsibly.

394
00:18:20,430 --> 00:18:23,386
I completely agree. And that's something
the researchers emphasized in the paper too.

395
00:18:23,410 --> 00:18:27,050
They talk about the need for
more robust self-evaluation mechanisms.

396
00:18:27,570 --> 00:18:31,390
Where AI can essentially double check its
own work and identify potential errors.

397
00:18:32,050 --> 00:18:35,310
They also talk about incorporating human
feedback into the learning process

398
00:18:35,311 --> 00:18:40,190
so that AI can benefit from our experience,
our intuition, and even our mistakes.

399
00:18:40,910 --> 00:18:43,630
So it's like having a good
editor for your AI's code.

400
00:18:43,890 --> 00:18:45,750
Someone who can catch those
typos and logical

401
00:18:45,751 --> 00:18:47,990
fallacies before they become
major problems.

402
00:18:48,190 --> 00:18:49,290
Exactly. Exactly.

403
00:18:50,010 --> 00:18:53,670
So as we wrap up this deep dive
into the world of AI planning.

404
00:18:54,810 --> 00:18:57,330
I'm curious to hear your take
on the big picture.

405
00:18:57,870 --> 00:18:59,590
What's your takeaway from
all this research?

406
00:18:59,970 --> 00:19:02,430
Are you optimistic about the
future of AI planning?

407
00:19:02,431 --> 00:19:04,470
You know,
I'm actually feeling quite hopeful.

408
00:19:04,750 --> 00:19:04,990
Yeah.

409
00:19:05,350 --> 00:19:07,390
Yes, there are challenges.
There are limitations.

410
00:19:07,570 --> 00:19:10,890
But this research shows that
we are making progress.

411
00:19:11,210 --> 00:19:11,310
Right.

412
00:19:11,650 --> 00:19:13,770
We're starting to understand
how AI learns,

413
00:19:13,771 --> 00:19:16,930
how it plans,
and where it still needs our guidance.

414
00:19:17,530 --> 00:19:20,450
It's like we're starting to
see the matrix to

415
00:19:20,451 --> 00:19:22,630
borrow a phrase from another
AI-themed classic.

416
00:19:22,890 --> 00:19:24,775
Right. And the more
we understand, the better

417
00:19:24,776 --> 00:19:26,850
equipped we are to shape
this technology for good.

418
00:19:27,170 --> 00:19:27,550
Exactly.

419
00:19:27,970 --> 00:19:28,010
Yeah.

420
00:19:28,011 --> 00:19:32,010
I think the key is to approach AI with
a healthy ghost of excitement and caution.

421
00:19:32,870 --> 00:19:35,610
To celebrate the breakthroughs,
to learn from the setbacks,

422
00:19:36,070 --> 00:19:38,430
and to never lose sight of the
human element in all of this.

423
00:19:38,790 --> 00:19:41,710
Beautifully put, it's a
reminder that AI isn't

424
00:19:41,711 --> 00:19:44,110
just about lines of code
or complex algorithms.

425
00:19:44,630 --> 00:19:48,830
It's about us. Our potential,
our responsibility, and our shared future.

426
00:19:49,270 --> 00:19:51,290
And that's a future worth planning for.

427
00:19:51,830 --> 00:19:56,950
So we're talking about AI that can like
plan almost as well as humans in some cases.

428
00:19:56,951 --> 00:19:57,430
Right.

429
00:19:57,670 --> 00:20:00,650
But it's still making those
like classic AI blunders.

430
00:20:01,090 --> 00:20:01,230
Yeah.

431
00:20:01,530 --> 00:20:05,650
Which I have to admit are kind of funny
when you're not relying on the AI to,

432
00:20:05,770 --> 00:20:07,870
you know,
actually function in the real world.

433
00:20:07,871 --> 00:20:10,670
Yeah, it's definitely a case of
laugh-now-think later.

434
00:20:10,970 --> 00:20:11,070
Right.

435
00:20:11,610 --> 00:20:13,790
Because the implications of
like increasingly

436
00:20:13,791 --> 00:20:16,882
sophisticated AI are
huge, especially when it

437
00:20:16,883 --> 00:20:19,070
comes to things like
safety and reliability.

438
00:20:19,590 --> 00:20:22,270
Right. A misplaced block in a
simulation is one thing,

439
00:20:22,430 --> 00:20:24,110
but a misplaced weld on a bridge.

440
00:20:24,730 --> 00:20:24,910
Exactly.

441
00:20:25,210 --> 00:20:26,050
That's a whole different story.

442
00:20:26,051 --> 00:20:28,250
Exactly. And that's why this
research is so important.

443
00:20:28,730 --> 00:20:30,990
It's not just about making
AI that can plan.

444
00:20:31,390 --> 00:20:33,503
It's about making
sure it can plan well in a

445
00:20:33,504 --> 00:20:36,030
way that aligns with,
you know, our values.

446
00:20:36,250 --> 00:20:39,710
Okay. So how do we make sure
our AI friends are planning responsibly?

447
00:20:40,010 --> 00:20:41,866
What did the researchers have
to say about that?

448
00:20:41,890 --> 00:20:43,690
Well, they had a few
really interesting ideas.

449
00:20:43,890 --> 00:20:48,290
One of them is this idea of
like robust self-evaluation mechanisms.

450
00:20:48,590 --> 00:20:48,770
Okay.

451
00:20:49,190 --> 00:20:52,070
Basically, it's like giving the AI
a little internal critic.

452
00:20:52,410 --> 00:20:52,670
Okay.

453
00:20:52,671 --> 00:20:57,430
A way for it to kind of
double check its own work and

454
00:20:57,431 --> 00:21:01,530
catch any potential errors or biases
before they become big problems.

455
00:21:01,690 --> 00:21:03,250
So like AI quality control.

456
00:21:03,650 --> 00:21:07,130
Exactly. And another important factor they
highlighted is the need

457
00:21:07,131 --> 00:21:09,630
for more human feedback in
the learning process.

458
00:21:09,870 --> 00:21:10,170
Right.

459
00:21:10,410 --> 00:21:13,810
After all, we humans have been planning
and problem solving for millennia.

460
00:21:14,430 --> 00:21:17,850
We've got a lot of experience and
mistakes that AI can learn from.

461
00:21:17,851 --> 00:21:20,170
Right. Makes sense. It's like
having a mentor or

462
00:21:20,171 --> 00:21:24,210
teacher to guide the AI as it's learning,
helping it to understand not

463
00:21:24,211 --> 00:21:27,430
just the what,
but also the why behind certain decisions.

464
00:21:27,830 --> 00:21:30,210
Precisely. And that kind of
guidance can be invaluable,

465
00:21:30,250 --> 00:21:33,010
especially when it comes to things
like ethics in common sense,

466
00:21:33,550 --> 00:21:36,170
which are notoriously difficult to
program into AI.

467
00:21:36,530 --> 00:21:38,530
Right. So it sounds like
the key to developing

468
00:21:38,531 --> 00:21:41,950
truly effective and responsible
AI planners is

469
00:21:41,951 --> 00:21:46,070
to find that sweet spot between letting
the AI learn and explore on its own.

470
00:21:46,071 --> 00:21:48,770
But also providing it
with the right kind of

471
00:21:48,771 --> 00:21:51,250
human guidance and
feedback along the way.

472
00:21:51,430 --> 00:21:54,790
Absolutely. It's about finding that
balance between pushing

473
00:21:54,791 --> 00:21:57,364
the boundaries of what AI
can do, but also making sure

474
00:21:57,365 --> 00:21:59,490
that it's always aligned
with our values and goals.

475
00:21:59,930 --> 00:22:02,030
And that brings us back to
the bigger picture, doesn't it?

476
00:22:02,150 --> 00:22:02,430
It does.

477
00:22:02,790 --> 00:22:05,790
What does all this research mean for
the future of AI planning?

478
00:22:06,130 --> 00:22:10,410
Are we on the coast of a world where
robots are seamlessly integrating

479
00:22:10,411 --> 00:22:13,590
into our lives,
mixing our drinks, building our houses,

480
00:22:13,910 --> 00:22:14,510
driving our cars?

481
00:22:14,511 --> 00:22:17,410
Or are we still a long way
off from that kind of future?

482
00:22:17,930 --> 00:22:19,730
Well, I think it's safe to
say that we're not quite

483
00:22:19,731 --> 00:22:22,870
at the Jetsons level of
AI integration just yet.

484
00:22:23,110 --> 00:22:23,270
Okay.

485
00:22:23,530 --> 00:22:26,550
But this research does offer a glimpse
into what might be possible.

486
00:22:27,110 --> 00:22:29,510
Like we're seeing AI that can
not only follow instructions,

487
00:22:29,511 --> 00:22:34,970
but also learn, adapt, and even come up
with creative solutions to complex problems.

488
00:22:35,170 --> 00:22:37,170
And as that technology
continues to evolve,

489
00:22:37,790 --> 00:22:41,990
it's up to us to make sure it evolves in
a way that benefits humanity as a whole.

490
00:22:42,250 --> 00:22:43,630
That's a pretty big responsibility.

491
00:22:43,631 --> 00:22:46,430
It is,
but it's also an incredible opportunity.

492
00:22:47,190 --> 00:22:49,970
We have the chance to shape
the future of AI,

493
00:22:50,470 --> 00:22:53,770
to guide its development in a
way that aligns with

494
00:22:53,771 --> 00:22:56,410
our values and helps us
create a better world.

495
00:22:56,890 --> 00:22:59,470
A world where AI isn't
something to be feared,

496
00:22:59,810 --> 00:23:01,170
but something to be embraced.

497
00:23:01,890 --> 00:23:04,150
A tool that can help us solve
some of the world's most

498
00:23:04,151 --> 00:23:08,750
pressing problems and unlock a whole
new level of human potential.

499
00:23:09,090 --> 00:23:11,490
Exactly. And I think that's a
future worth striving for.

500
00:23:11,491 --> 00:23:12,750
I'm with you on that one.

501
00:23:13,250 --> 00:23:17,230
But before we get too caught up in
visions of a utopian AI-powered future,

502
00:23:17,590 --> 00:23:19,390
let's bring things back down to
Earth for a moment and

503
00:23:19,391 --> 00:23:22,390
talk about some of the practical
implications of this research.

504
00:23:22,910 --> 00:23:24,990
What are some of the
like real world applications

505
00:23:24,991 --> 00:23:27,870
of this kind of AI
planning technology?

506
00:23:28,270 --> 00:23:30,070
Oh, the possibilities are
pretty much endless.

507
00:23:30,790 --> 00:23:33,230
One area where I think we'll see
a huge impact is in robotics.

508
00:23:33,830 --> 00:23:34,050
Okay.

509
00:23:34,051 --> 00:23:38,090
Imagine robots that can not
only perform tasks autonomously,

510
00:23:38,570 --> 00:23:42,410
but also adapt to changing environments
and learn from their experiences.

511
00:23:42,850 --> 00:23:45,450
So we're talking about robots that can
actually think on their feet,

512
00:23:45,451 --> 00:23:49,250
so to speak, and make decisions in
real time based on the situation at hand.

513
00:23:49,530 --> 00:23:53,170
Exactly. And that has huge implications
for everything from manufacturing

514
00:23:53,171 --> 00:23:56,730
and logistics to healthcare and
even space exploration.

515
00:23:56,731 --> 00:24:00,410
Right. Imagine robots that can
like work alongside

516
00:24:00,411 --> 00:24:03,149
human surgeons in the
operating room, providing

517
00:24:03,150 --> 00:24:05,610
them with like real time
feedback and assistance.

518
00:24:05,611 --> 00:24:06,170
Right.

519
00:24:06,450 --> 00:24:09,466
Or robots that can explore
hazardous environments

520
00:24:09,467 --> 00:24:12,310
like deep sea trenches
or the surface of Mars.

521
00:24:12,311 --> 00:24:15,670
It's mind blowing when you think about it.
And that's just the tip of the iceberg.

522
00:24:16,090 --> 00:24:16,150
Right.

523
00:24:16,770 --> 00:24:22,030
We could also see AI planning being used
to like optimize traffic flow in cities,

524
00:24:22,031 --> 00:24:24,710
develop personalized treatment plans
for patients,

525
00:24:24,711 --> 00:24:28,670
or even create more efficient
and sustainable energy grids.

526
00:24:29,130 --> 00:24:32,310
It sounds like we're on the
verge of a technological revolution.

527
00:24:32,890 --> 00:24:37,090
One that has the potential to transform
our world in ways we can only imagine.

528
00:24:37,430 --> 00:24:40,090
And the best part is that
this revolution is already underway.

529
00:24:40,610 --> 00:24:43,930
We're seeing incredible advances in
AI every single day.

530
00:24:44,130 --> 00:24:46,950
And the research we've been discussing
today is a perfect example of that.

531
00:24:47,330 --> 00:24:49,210
It's an exciting time to be alive,
wouldn't you say?

532
00:24:49,211 --> 00:24:52,229
Absolutely. But as with
any revolution, there are

533
00:24:52,230 --> 00:24:54,910
sure to be challenges and
obstacles along the way.

534
00:24:55,690 --> 00:24:57,950
What are some of the biggest
hurdles we'll need to overcome

535
00:24:57,951 --> 00:25:01,210
in order to fully realize the
potential of AI planning?

536
00:25:01,910 --> 00:25:03,790
Well, one of the biggest challenges
is going to be

537
00:25:03,791 --> 00:25:07,370
ensuring that these AI systems
are safe and reliable.

538
00:25:07,870 --> 00:25:08,010
Right.

539
00:25:08,530 --> 00:25:09,830
After all,
we're talking about machines that

540
00:25:09,831 --> 00:25:12,370
are going to be making decisions that
could have a real impact on our lives.

541
00:25:12,371 --> 00:25:15,170
Right. We don't want our
self-driving cars taking

542
00:25:15,171 --> 00:25:17,530
us on a detour through
a construction zone,

543
00:25:17,550 --> 00:25:21,290
or our robot surgeons accidentally
removing the wrong organ.

544
00:25:21,630 --> 00:25:22,630
Exactly.

545
00:25:22,990 --> 00:25:26,170
So a lot of work needs to be done
to ensure that these systems are robust,

546
00:25:26,570 --> 00:25:30,670
reliable, and capable of
handling unexpected situations.

547
00:25:31,210 --> 00:25:34,670
And that's not just a technical challenge,
it's also a social and ethical one.

548
00:25:34,910 --> 00:25:34,990
Right.

549
00:25:34,991 --> 00:25:38,290
We need to make sure that these AI
systems are aligned with our values

550
00:25:38,291 --> 00:25:40,870
and that they're used in a way
that benefits all of humanity,

551
00:25:41,370 --> 00:25:42,690
not just a select few.

552
00:25:43,110 --> 00:25:46,870
Absolutely. We need to be having
conversations about the

553
00:25:46,871 --> 00:25:52,190
ethical implications of AI right now before
this technology advances even further.

554
00:25:52,930 --> 00:25:57,710
What are the potential risks and benefits?
How do we ensure that AI is used for good?

555
00:25:58,110 --> 00:26:00,990
These are questions that we need to
be asking ourselves as a society.

556
00:26:01,490 --> 00:26:03,030
And there are no easy answers, are there?

557
00:26:03,031 --> 00:26:04,070
No, there aren't. Yeah.

558
00:26:04,290 --> 00:26:07,536
But that's what makes this such a
fascinating and important topic to explore.

559
00:26:07,537 --> 00:26:10,810
Because the future of
AI is not something that's

560
00:26:10,811 --> 00:26:14,430
going to be decided by
algorithms or lines of code.

561
00:26:14,790 --> 00:26:14,950
Right.

562
00:26:15,290 --> 00:26:18,150
It's going to be shaped by the
choices that we make today.

563
00:26:18,530 --> 00:26:22,170
So let's make sure we choose wisely.
We'll delve deeper into the

564
00:26:22,171 --> 00:26:25,890
ethical considerations of
AI planning and explore what the future

565
00:26:25,891 --> 00:26:27,990
holds for this rapidly
evolving technology.

566
00:26:27,991 --> 00:26:32,590
It's like we're standing at a crossroads
with multiple paths laid out before us.

567
00:26:33,110 --> 00:26:36,290
Each path representing a different
potential future for AI.

568
00:26:36,610 --> 00:26:36,670
Right.

569
00:26:37,270 --> 00:26:40,350
And the choices that we make today will
determine which path we end up taking.

570
00:26:40,550 --> 00:26:42,206
So it's not just about
the technology itself,

571
00:26:42,230 --> 00:26:44,750
but about how we choose to
use it and how we

572
00:26:44,751 --> 00:26:48,130
prepare for its impact on
society as a whole.

573
00:26:48,470 --> 00:26:51,730
Exactly. Technology is a tool.
And like any tool,

574
00:26:52,090 --> 00:26:53,730
it can be used for good or for ill.

575
00:26:53,950 --> 00:26:54,090
Right.

576
00:26:54,091 --> 00:26:58,130
It's up to us to make sure that
AI is used to enhance our lives,

577
00:26:58,590 --> 00:26:59,650
not to diminish them.

578
00:27:00,110 --> 00:27:02,510
So where do we even
begin with that? How do we

579
00:27:02,511 --> 00:27:05,630
ensure that AI is developed
and deployed responsibly?

580
00:27:06,250 --> 00:27:07,990
Well, I think education is key. Okay.

581
00:27:08,330 --> 00:27:11,650
The more people understand about AI,
how it works, its potential implications,

582
00:27:12,210 --> 00:27:14,290
the better equipped will be
to make informed

583
00:27:14,291 --> 00:27:17,110
decisions about its development
and its use.

584
00:27:17,330 --> 00:27:19,630
Right. We don't want AI
to be this mysterious

585
00:27:19,631 --> 00:27:22,410
black box that only a
select few understand.

586
00:27:22,411 --> 00:27:22,890
Right.

587
00:27:23,050 --> 00:27:26,110
We need to kind of demystify it,
make it accessible to everyone.

588
00:27:26,230 --> 00:27:28,350
Absolutely. And that's something
we've been trying

589
00:27:28,351 --> 00:27:31,370
to do with this deep dive,
to break down these complex concepts,

590
00:27:31,371 --> 00:27:33,890
make them relevant,
understandable for everyone.

591
00:27:34,050 --> 00:27:37,850
Right. Because at the end of the day,
AI is going to impact all of us.

592
00:27:38,090 --> 00:27:38,330
It is.

593
00:27:38,570 --> 00:27:40,990
So we all have a stake
in shaping its future.

594
00:27:41,410 --> 00:27:43,910
I couldn't agree more.
And beyond education,

595
00:27:44,730 --> 00:27:46,950
I think it's crucial that we
have these open and

596
00:27:46,951 --> 00:27:50,670
honest conversations about the ethical
implications of AI.

597
00:27:51,170 --> 00:27:51,410
Right.

598
00:27:51,411 --> 00:27:53,490
And these conversations
need to involve everyone,

599
00:27:53,510 --> 00:27:55,470
not just tech experts
and policy makers.

600
00:27:55,590 --> 00:27:57,490
Right. Because AI doesn't exist
in a vacuum.

601
00:27:57,710 --> 00:27:58,050
Exactly.

602
00:27:58,310 --> 00:28:02,690
It's going to be interacting with
our existing social structures,

603
00:28:02,930 --> 00:28:04,070
our values, our biases.

604
00:28:04,490 --> 00:28:04,730
It is.

605
00:28:04,930 --> 00:28:08,550
So we need to be really thoughtful about
how we integrate it into our world.

606
00:28:08,870 --> 00:28:11,430
Exactly. We need to be
considering questions like,

607
00:28:11,770 --> 00:28:15,410
how do we ensure that AI
is used fairly and equitably?

608
00:28:15,411 --> 00:28:19,350
How do we prevent AI
from like perpetuating

609
00:28:19,351 --> 00:28:22,250
or even amplifying
existing social biases?

610
00:28:23,090 --> 00:28:26,266
How do we protect
privacy in a world where AI

611
00:28:26,267 --> 00:28:28,930
is constantly collecting
and analyzing data?

612
00:28:29,290 --> 00:28:32,431
Those are some really big questions. And
I have a feeling there are no easy answers.

613
00:28:32,490 --> 00:28:33,170
You're right. There aren't.

614
00:28:33,270 --> 00:28:33,390
Yeah.

615
00:28:33,750 --> 00:28:35,570
But that's okay. The important thing
is that we're asking

616
00:28:35,571 --> 00:28:37,852
these questions and that we're
having these conversations.

617
00:28:38,390 --> 00:28:40,870
Because the more we grapple
with these ethical dilemmas,

618
00:28:40,871 --> 00:28:43,230
the better prepared will be
to navigate the

619
00:28:43,231 --> 00:28:45,270
challenges and the opportunities
that AI presents.

620
00:28:45,271 --> 00:28:48,910
So as we stand on the cusp of this,
like AI revolution,

621
00:28:49,510 --> 00:28:53,490
what are you most excited about? And like,
what are you most wary of?

622
00:28:53,770 --> 00:28:54,770
Honestly.

623
00:28:55,150 --> 00:28:58,510
I'm excited about the potential for
AI to help us

624
00:28:58,511 --> 00:29:00,470
solve some of the world's
most pressing problems.

625
00:29:00,950 --> 00:29:04,750
Things like climate change, poverty,
disease. Imagine if we could

626
00:29:04,751 --> 00:29:08,630
harness the power of
AI to develop like clean energy solutions,

627
00:29:08,631 --> 00:29:11,389
create more equitable
economic systems, or

628
00:29:11,390 --> 00:29:14,170
even cure diseases like
cancer and Alzheimer's.

629
00:29:14,171 --> 00:29:16,890
Now that's a few try can get behind.
But what about the flip side?

630
00:29:17,270 --> 00:29:20,710
What are some of the potential
downsides of AI that keep you up at night?

631
00:29:21,250 --> 00:29:24,550
Well, one concern that I have is
the potential for job displacement.

632
00:29:25,330 --> 00:29:29,570
As AI becomes more sophisticated,
it's inevitable that it's going

633
00:29:29,571 --> 00:29:32,430
to automate tasks
that are currently performed by humans.

634
00:29:32,630 --> 00:29:36,970
And that could lead to significant
job losses in certain sectors.

635
00:29:37,130 --> 00:29:41,350
That's a valid concern, especially in
industries that are already heavily automated.

636
00:29:41,870 --> 00:29:44,710
Right. So I think it's crucial that
we start thinking like now

637
00:29:44,711 --> 00:29:48,510
about how we're going to adapt
to this changing job market.

638
00:29:48,870 --> 00:29:51,570
How do we retrain workers whose
jobs are at risk?

639
00:29:51,970 --> 00:29:54,390
How do we create new opportunities
in these emerging fields?

640
00:29:54,970 --> 00:29:57,410
These are questions that we need
to be addressing proactively.

641
00:29:57,530 --> 00:30:01,971
Because the last thing we want is for AI
to exacerbate existing social inequalities.

642
00:30:02,370 --> 00:30:04,752
Exactly. We need to make
sure that the benefits of

643
00:30:04,753 --> 00:30:08,050
AI are shared by everyone,
not just a select few.

644
00:30:08,051 --> 00:30:12,910
So as we wrap up this deep dive
into the world of AI planning,

645
00:30:13,330 --> 00:30:16,070
what's the one thing you want our listeners
to take away from this conversation?

646
00:30:16,890 --> 00:30:19,950
I want them to remember that the
future of AI is not predetermined.

647
00:30:20,930 --> 00:30:24,570
It's not something that's going to be
decided by algorithms or lines of code.

648
00:30:24,970 --> 00:30:27,430
It's going to be shaped by the
choices that we make today.

649
00:30:27,810 --> 00:30:30,030
So let's make sure we choose wisely.

650
00:30:30,930 --> 00:30:32,570
To our listeners,
thank you for joining us on this

651
00:30:32,571 --> 00:30:34,850
incredible journey into the world
of AI planning.

652
00:30:34,851 --> 00:30:37,250
We hope this deep dive has
left you feeling informed,

653
00:30:37,750 --> 00:30:39,130
intrigued,
and maybe even a little

654
00:30:39,131 --> 00:30:42,910
bit inspired to learn more about this
fascinating and rapidly evolving field.

655
00:30:43,510 --> 00:30:47,190
Until next time, keep exploring,
keep questioning, and keep diving deep.