1
00:00:00,000 --> 00:00:05,200
All right, everyone, get ready because today we're taking a deep dive into a paper that's

2
00:00:05,200 --> 00:00:08,400
going to make you rethink how you think about AI.

3
00:00:08,560 --> 00:00:13,760
Yeah, it's called LLMs as method actors. Sounds kind of like Hollywood, right?

4
00:00:13,800 --> 00:00:17,520
Yeah, it does sound like a movie title. But trust me, this is hardcore AI research.

5
00:00:17,520 --> 00:00:23,920
Exactly. And it's all about pushing the limits of how we can use AI to tackle those really

6
00:00:23,920 --> 00:00:24,920
tough problems.

7
00:00:24,920 --> 00:00:28,640
The thing that grabbed me about this paper is that they use this surprisingly challenging

8
00:00:28,640 --> 00:00:33,440
game to test their ideas. You know, that connections word puzzle from the New York Times.

9
00:00:33,440 --> 00:00:35,400
Oh, yeah, that one's a real brain bender.

10
00:00:35,440 --> 00:00:40,160
Well, they use that to see if thinking of AI as actors instead of just thinkers could

11
00:00:40,160 --> 00:00:41,480
actually help them perform better.

12
00:00:41,520 --> 00:00:44,840
And the results, well, let's just say even the researchers were kind of blown away by

13
00:00:44,840 --> 00:00:47,040
how well this whole method acting thing worked.

14
00:00:47,120 --> 00:00:51,840
Some of the AI systems they tested actually ended up beating human experts at the game.

15
00:00:51,880 --> 00:00:53,320
Pretty wild stuff, right?

16
00:00:53,440 --> 00:00:58,360
It is. So let's break this down a bit. Starting with the core concept. This method

17
00:00:58,360 --> 00:01:03,840
actors model treats those powerful language AI systems, we call them LLMs for large

18
00:01:03,840 --> 00:01:06,400
language models like they're actors upon a stage.

19
00:01:06,680 --> 00:01:11,560
Exactly. The prompts they get, those are like their scripts and their responses are

20
00:01:11,560 --> 00:01:13,400
basically their performances.

21
00:01:13,600 --> 00:01:14,240
Makes sense.

22
00:01:14,240 --> 00:01:19,760
The idea is that just like actors, LLMs are amazing at imitating human language, but

23
00:01:19,760 --> 00:01:22,880
they don't necessarily get the deeper meaning behind what they're saying.

24
00:01:23,080 --> 00:01:26,880
I think we've all been there, right? Like when you have to write a super confident

25
00:01:26,880 --> 00:01:30,480
email, even when you're feeling anything but confident.

26
00:01:30,520 --> 00:01:31,480
Aha. Yeah.

27
00:01:31,960 --> 00:01:33,960
That's kind of what LLMs are doing all the time.

28
00:01:34,320 --> 00:01:35,200
They're putting on a show.

29
00:01:35,440 --> 00:01:36,600
I like that analogy.

30
00:01:36,720 --> 00:01:38,680
And that's where this connections game comes in.

31
00:01:39,040 --> 00:01:41,160
This puzzle isn't just about knowing a bunch of words.

32
00:01:41,200 --> 00:01:45,240
It's about making connections, spotting subtle links, being creative.

33
00:01:45,720 --> 00:01:47,920
All that stuff that AI usually has trouble with.

34
00:01:47,960 --> 00:01:51,720
Yeah. For those who haven't played it, you get this grid of words four by four.

35
00:01:52,280 --> 00:01:55,240
And you got to find the four groups of four that share some kind of unique

36
00:01:55,240 --> 00:01:55,760
connection.

37
00:01:55,760 --> 00:01:57,080
It's a lot harder than it sounds.

38
00:01:57,120 --> 00:01:57,640
Oh, it is.

39
00:01:57,720 --> 00:01:59,080
It's deceptively difficult.

40
00:01:59,160 --> 00:02:02,600
Even the New York Times themselves has said that AI shouldn't be able to solve

41
00:02:02,600 --> 00:02:03,800
these puzzles reliably.

42
00:02:03,920 --> 00:02:04,240
Okay.

43
00:02:04,240 --> 00:02:06,400
So that's where this research gets really interesting.

44
00:02:06,440 --> 00:02:06,760
Right.

45
00:02:06,760 --> 00:02:07,680
Because they did it.

46
00:02:07,760 --> 00:02:08,720
They found a way.

47
00:02:08,920 --> 00:02:10,040
So how'd they pull it off?

48
00:02:10,160 --> 00:02:12,280
They didn't just throw any old AI at this problem, right?

49
00:02:12,320 --> 00:02:12,560
Nope.

50
00:02:13,000 --> 00:02:16,280
They tried a bunch of different approaches to see what worked best.

51
00:02:16,440 --> 00:02:16,880
Like what?

52
00:02:17,000 --> 00:02:19,560
Well, they started with a simple vanilla prompting approach.

53
00:02:19,680 --> 00:02:22,760
Just giving the AI the puzzle and saying, go solve it.

54
00:02:22,800 --> 00:02:23,160
Okay.

55
00:02:23,200 --> 00:02:23,680
Makes sense.

56
00:02:23,680 --> 00:02:25,680
But then they got fancier.

57
00:02:25,720 --> 00:02:29,840
They tried things like chain of thought prompting, where the AI kind of thinks

58
00:02:29,840 --> 00:02:31,920
out loud as it's working through the problem.

59
00:02:31,960 --> 00:02:32,760
Oh, that's interesting.

60
00:02:32,800 --> 00:02:34,720
So like giving the AI some training wheels.

61
00:02:34,760 --> 00:02:35,560
Exactly.

62
00:02:35,720 --> 00:02:40,000
And then they even gave the AI a bunch of examples from past connections puzzles

63
00:02:40,280 --> 00:02:42,320
and specific instructions to follow.

64
00:02:42,560 --> 00:02:45,520
Think of it like giving them a really detailed script to work from.

65
00:02:45,640 --> 00:02:45,880
Okay.

66
00:02:45,880 --> 00:02:50,440
So we've got basic prompting, thinking out loud and the super detailed

67
00:02:50,440 --> 00:02:51,280
script approach.

68
00:02:51,760 --> 00:02:52,600
What comes next?

69
00:02:52,600 --> 00:02:54,280
Well, then they brought in the big guns.

70
00:02:54,920 --> 00:02:57,160
Two different method acting approaches.

71
00:02:57,320 --> 00:02:59,200
Ooh, sounds intense.

72
00:02:59,280 --> 00:03:02,400
And let me tell you, these ended up crushing all the other methods.

73
00:03:02,440 --> 00:03:02,720
Okay.

74
00:03:02,720 --> 00:03:04,960
So let's dive into this method acting magic.

75
00:03:05,400 --> 00:03:06,960
What did that actually look like?

76
00:03:07,120 --> 00:03:10,400
How do you turn an AI into a method actor for a word puzzle?

77
00:03:10,600 --> 00:03:13,280
The one that really stole the show is called Actor 2.

78
00:03:13,360 --> 00:03:15,720
It took this whole acting thing to a whole new level.

79
00:03:16,480 --> 00:03:17,080
Picture this.

80
00:03:17,080 --> 00:03:21,480
The AI is given this crazy scenario, like it's some kind of puzzle solving

81
00:03:21,480 --> 00:03:23,240
expert who's got to defuse a bomb.

82
00:03:23,280 --> 00:03:23,760
Whoa.

83
00:03:24,080 --> 00:03:25,000
Talk about pressure.

84
00:03:25,040 --> 00:03:25,440
Right.

85
00:03:25,760 --> 00:03:30,280
The fate of the world rests on its ability to crack this connections puzzle.

86
00:03:30,320 --> 00:03:30,600
Okay.

87
00:03:30,600 --> 00:03:31,240
I'm hooked.

88
00:03:31,640 --> 00:03:34,240
So the AI is in character, feeling the heat.

89
00:03:35,160 --> 00:03:37,560
But how does it actually tackle the puzzle itself?

90
00:03:37,920 --> 00:03:39,200
It's a two step process.

91
00:03:39,320 --> 00:03:41,280
First, there's this brainstorming stage.

92
00:03:41,720 --> 00:03:46,840
The AI gets these templates based on common types of connections from past puzzles.

93
00:03:46,840 --> 00:03:51,520
You know, like synonyms, pop culture references, words that can all be followed by the same

94
00:03:51,520 --> 00:03:54,280
word, all those sneaky links that make you go, aha.

95
00:03:54,520 --> 00:03:55,960
So it's not just shooting in the dark.

96
00:03:56,120 --> 00:03:59,120
It's actually using patterns from previous puzzles to get started.

97
00:03:59,240 --> 00:03:59,960
Exactly.

98
00:04:00,200 --> 00:04:01,760
And here's where things get even cooler.

99
00:04:01,800 --> 00:04:05,680
The AI actually cycles through these templates, almost like an actor trying out

100
00:04:05,680 --> 00:04:08,680
different interpretations of a scene until it finds the one that clicks.

101
00:04:08,720 --> 00:04:11,200
I'm starting to see how this method acting thing works.

102
00:04:11,560 --> 00:04:13,520
But what happens after all that brainstorming?

103
00:04:13,520 --> 00:04:16,720
How does the AI figure out which connections are the real deal?

104
00:04:16,960 --> 00:04:19,280
That's where stage two comes in discernment.

105
00:04:20,400 --> 00:04:24,320
Now, the AI has to pick the strongest guesses from its brainstorming session.

106
00:04:25,080 --> 00:04:26,000
But here's the thing.

107
00:04:26,040 --> 00:04:31,440
AI can sometimes hallucinate, meaning it can make stuff up that sounds kind of plausible,

108
00:04:31,440 --> 00:04:32,760
but it's totally wrong.

109
00:04:32,760 --> 00:04:33,120
Oh, yeah.

110
00:04:33,440 --> 00:04:34,560
AI going off the rails.

111
00:04:34,600 --> 00:04:35,440
That's a classic.

112
00:04:35,480 --> 00:04:39,360
So to prevent the AI from going down a rabbit hole, the researchers had to come up with

113
00:04:39,360 --> 00:04:41,240
some clever ways to keep it grounded.

114
00:04:41,240 --> 00:04:45,120
So basically, they needed to make sure the AI was really understanding the connections

115
00:04:45,120 --> 00:04:47,200
and not just getting lucky with random guesses.

116
00:04:47,240 --> 00:04:47,800
Exactly.

117
00:04:47,840 --> 00:04:52,520
They came up with some really ingenious tests to see if the AI was the real deal.

118
00:04:52,560 --> 00:04:53,040
Okay.

119
00:04:53,040 --> 00:04:57,520
So we've got this dramatic scenario, brainstorming discernments, the whole production.

120
00:04:58,160 --> 00:05:00,880
But the big question is, did it actually work?

121
00:05:01,520 --> 00:05:04,040
Did this method acting AI live up to the hype?

122
00:05:04,320 --> 00:05:06,080
The results were pretty mind blowing.

123
00:05:06,120 --> 00:05:10,360
Remember how that basic AI only solved like 27% of the puzzles?

124
00:05:10,400 --> 00:05:10,920
Yeah.

125
00:05:10,920 --> 00:05:14,040
Well, the method acting AI solved a whopping 86%.

126
00:05:14,080 --> 00:05:14,480
Wait a second.

127
00:05:14,520 --> 00:05:15,600
86%.

128
00:05:15,640 --> 00:05:16,520
That's incredible.

129
00:05:16,560 --> 00:05:21,120
So basically pretending to be a bomb diffusing puzzle master actually made the AI a better

130
00:05:21,120 --> 00:05:21,560
thinker.

131
00:05:21,800 --> 00:05:22,800
It really seems that way.

132
00:05:22,800 --> 00:05:26,360
It's like looking at a problem from a completely different angle can sometimes lead to those

133
00:05:26,400 --> 00:05:27,480
aha moments.

134
00:05:27,520 --> 00:05:28,400
That's amazing.

135
00:05:28,640 --> 00:05:33,640
And they actually compared the AI's performance to real humans playing the game.

136
00:05:33,640 --> 00:05:34,080
It did.

137
00:05:34,080 --> 00:05:36,280
It was like a human versus machine showdown.

138
00:05:36,320 --> 00:05:37,440
Who came out on top?

139
00:05:37,440 --> 00:05:41,720
Well, the best method acting AI actually beat out novice human players.

140
00:05:41,760 --> 00:05:42,760
No way.

141
00:05:42,760 --> 00:05:43,200
Yeah.

142
00:05:43,440 --> 00:05:45,200
And here's the really fascinating part.

143
00:05:45,760 --> 00:05:49,920
The AI even failed on puzzles in a similar way to how humans do.

144
00:05:49,960 --> 00:05:50,400
Really?

145
00:05:50,440 --> 00:05:55,760
The easier puzzles were no problem, but those really tricky nuanced connections stumped the

146
00:05:55,760 --> 00:05:57,920
AI just like they often stump us.

147
00:05:57,960 --> 00:06:02,400
So it's almost like the AI is developing this human like understanding of the game,

148
00:06:02,760 --> 00:06:04,640
both in its successes and its failures.

149
00:06:04,680 --> 00:06:05,400
Exactly.

150
00:06:05,400 --> 00:06:07,520
That's what makes this research so intriguing.

151
00:06:07,560 --> 00:06:10,600
It suggests that AI isn't just mimicking human thinking.

152
00:06:10,840 --> 00:06:15,040
It might actually be embodying different ways of thinking, learning to approach problems

153
00:06:15,040 --> 00:06:16,960
in a more nuanced and intuitive way.

154
00:06:17,320 --> 00:06:17,840
Mind blown.

155
00:06:18,560 --> 00:06:19,000
Okay.

156
00:06:19,000 --> 00:06:25,120
So we've got this method acting AI crushing connections puzzles showing us that maybe

157
00:06:25,120 --> 00:06:27,760
just maybe AI can learn to think a bit more like us.

158
00:06:28,440 --> 00:06:30,360
But then a new challenger enters the arena.

159
00:06:30,600 --> 00:06:33,040
Tell us about Open AI's 01 preview.

160
00:06:33,280 --> 00:06:34,000
Oh yeah.

161
00:06:34,000 --> 00:06:36,000
This is where things get really interesting.

162
00:06:36,560 --> 00:06:40,960
Just when the researchers thought things couldn't get any wilder, Open AI drops this

163
00:06:40,960 --> 00:06:44,720
super powered AI model specifically designed for reasoning tasks.

164
00:06:44,960 --> 00:06:47,520
It's like the heavyweight champion stepping into the ring.

165
00:06:47,560 --> 00:06:47,840
Okay.

166
00:06:47,840 --> 00:06:48,360
Fight Night.

167
00:06:48,720 --> 00:06:52,840
How did this new AI 01 preview stack up against our method acting champ?

168
00:06:52,880 --> 00:06:57,360
Remember that basic AI that only solved 27% of the puzzles in one shot?

169
00:06:57,600 --> 00:07:01,760
Well, 01 preview with just a single prompt solved a mind blowing 79%.

170
00:07:01,800 --> 00:07:02,400
Whoa.

171
00:07:02,400 --> 00:07:03,960
That's almost three times better.

172
00:07:03,960 --> 00:07:04,560
It is.

173
00:07:04,560 --> 00:07:07,880
And get this with a little bit of feedback, meaning it could make a few guesses and

174
00:07:07,880 --> 00:07:08,840
learn from its mistakes.

175
00:07:09,080 --> 00:07:11,520
01 preview actually solved every single puzzle.

176
00:07:11,520 --> 00:07:11,880
Okay.

177
00:07:11,880 --> 00:07:13,680
That's just next level puzzle solving skills.

178
00:07:13,720 --> 00:07:13,920
Yeah.

179
00:07:13,920 --> 00:07:15,560
But here's where it gets really meta.

180
00:07:15,760 --> 00:07:19,720
Even 01 preview, this reasoning superstar actually got a boost from the method acting

181
00:07:19,720 --> 00:07:20,160
approach.

182
00:07:20,400 --> 00:07:21,120
You got it.

183
00:07:21,400 --> 00:07:21,520
Yeah.

184
00:07:21,520 --> 00:07:25,560
Even though it was already a powerhouse, it solved the puzzles more perfectly, like

185
00:07:25,560 --> 00:07:29,040
without any wrong guesses when they use that whole actor framing.

186
00:07:29,040 --> 00:07:34,000
So even the most advanced AI out there can benefit from thinking about how they

187
00:07:34,000 --> 00:07:36,960
perform a task, not just the pure logic behind it.

188
00:07:37,000 --> 00:07:38,200
That's the key takeaway.

189
00:07:38,240 --> 00:07:40,760
It's like giving the AI a strategy, a roadmap.

190
00:07:40,800 --> 00:07:44,520
It's saying, okay, pretend you're an expert puzzle solver about to diffuse a bomb.

191
00:07:44,520 --> 00:07:45,360
What would you do?

192
00:07:45,600 --> 00:07:47,200
And the results speak for themselves.

193
00:07:47,240 --> 00:07:51,840
It's incredible to think that this acting framework could unlock so much potential,

194
00:07:51,840 --> 00:07:54,680
even in AI systems that are already super advanced.

195
00:07:54,880 --> 00:07:58,640
So this method acting thing, it's really shaking things up in the AI world.

196
00:07:58,640 --> 00:08:01,600
It's like pushing the boundaries of what we thought these systems could do.

197
00:08:01,880 --> 00:08:04,640
Makes you wonder, where else could we use this idea?

198
00:08:04,680 --> 00:08:06,080
That's what's so exciting about it.

199
00:08:06,080 --> 00:08:08,040
It's not just about AI getting better at games.

200
00:08:08,040 --> 00:08:12,040
It's about them getting better at things that require creativity, intuition, you

201
00:08:12,040 --> 00:08:13,880
know, that spark of human understanding.

202
00:08:14,080 --> 00:08:17,920
Like writing amazing marketing copy or navigating those super complex legal

203
00:08:17,920 --> 00:08:18,560
documents.

204
00:08:18,600 --> 00:08:19,360
Exactly.

205
00:08:19,600 --> 00:08:21,240
Or even composing music.

206
00:08:21,440 --> 00:08:23,840
Things that need more than just raw information.

207
00:08:24,040 --> 00:08:25,720
They need that human touch.

208
00:08:25,720 --> 00:08:29,080
This research is really changing how we think about AI and what it can do.

209
00:08:29,400 --> 00:08:34,400
If we can tap into this method acting approach, who knows what we could achieve.

210
00:08:34,440 --> 00:08:38,680
It's like giving AI a whole new set of tools, a new way to tackle problems,

211
00:08:38,680 --> 00:08:40,240
and the results could be incredible.

212
00:08:40,280 --> 00:08:43,000
So listeners, here's something to chew on.

213
00:08:44,000 --> 00:08:46,680
Think about something you do that's tough to explain logically.

214
00:08:46,720 --> 00:08:50,120
Yeah, something that's more about intuition and feel like designing a

215
00:08:50,120 --> 00:08:53,360
website or writing a story or coming up with a business strategy.

216
00:08:53,360 --> 00:08:57,240
Could we teach AI to do those things using this method acting approach?

217
00:08:57,240 --> 00:09:01,000
Could we create AI actors that can actually embody those skills?

218
00:09:01,280 --> 00:09:02,440
That's a wild thought.

219
00:09:02,720 --> 00:09:04,880
Makes you wonder what the future holds for AI.

220
00:09:05,160 --> 00:09:08,480
What happens when we combine the raw power with this new way of thinking?

221
00:09:08,720 --> 00:09:10,760
This LLMs as method actors paper.

222
00:09:11,000 --> 00:09:12,880
It's like a window into that future.

223
00:09:13,160 --> 00:09:15,080
A future where AI isn't just a tool.

224
00:09:15,120 --> 00:09:18,920
It's a collaborator, a partner in problem solving and creativity.

225
00:09:19,160 --> 00:09:21,680
That's what makes this deep dive so cool.

226
00:09:21,680 --> 00:09:23,480
It's not just about the technical stuff.

227
00:09:23,520 --> 00:09:24,680
It's about the big picture.

228
00:09:24,720 --> 00:09:28,360
It's about pushing the limits and exploring what's possible with AI.

229
00:09:28,400 --> 00:09:29,480
And who knows?

230
00:09:29,520 --> 00:09:33,840
Maybe someday we'll see AI method actors winning awards right alongside

231
00:09:33,840 --> 00:09:38,280
human actors, pushing the boundaries of creativity in ways we can't even imagine yet.

232
00:09:38,720 --> 00:09:39,520
That's the thought.

233
00:09:40,400 --> 00:09:43,400
Until then, keep those brains buzzing and we'll catch you on the next deep

234
00:09:43,400 --> 00:09:52,000
dive into the world of AI.