WEBVTT

NOTE Sentence-level transcript of https://www.youtube.com/watch?v=cO8qC6HBuBg

NOTE One cue per sentence. Cue ids are the line anchors on /transcripts/cO8qC6HBuBg.html. A cue ends where the next begins, or 2 s after its last word.

s1
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[music]

s2
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Hey everyone, I'm Lucas H, co-founder of Andon Labs.

s3
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And what we do is that we take AIs and we put them out in the real world and see what goes wrong, what goes right, what can we improve, and what is there to be concerned of.

s4
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Um, so a long time ago, feels like ages.

s5
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Uh but in 2024 uh me and my co-founder decided that probably the future is going to be long horizon.

s6
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At the time most benchmarks were like singlestep QA type of benchmarks but we thought one day one day they will be able to carry out very very long tasks.

s7
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Uh and at at the moment or like at the time there was basically no long horizon benchmark at all.

s8
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Um and but we said okay we want to test this.

s9
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We think this is the future.

s10
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How can we do this best way?

s11
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So we said okay can AIS run businesses autonomously and then okay probably not this was 2024.

s12
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Uh but if we take some very simple business maybe they can.

s13
00:01:06.479 --> 00:01:13.840
So we created vending bench which is a simulated eval where models run a simulated business uh business which is a vending machine.

s14
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Uh since then we also added the arena mode where multiple agents compete against each other.

s15
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they each have one vending machine or simulated vending machine and they can like undercut each other and like do deals with each other and and create stuff like that.

s16
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Um nowadays um there are long horizon evvelts uh mostly in coding and I think the the purpose of bending bench has been lately can like can these models who have been trained very hard for this long horizon

s17
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coding tasks does that generalize to other offdistribution domains um like running a business uh so some of the things that the agent has to do is like uh get suppliers

s18
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negotiate prices uh understand like the business demand from customers and set the appropriate prices, stuff like this.

s19
00:02:00.000 --> 00:02:06.320
Um, I think it's still one of the long like I just this graph um is cloud generated.

s20
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H I haven't like looked at all the benchmarks in the world.

s21
00:02:09.119 --> 00:02:19.680
Uh but I think still some of the long horizon evals that you have out there are still like an order of magnitude or two shorter in in terms of like how longunning it is than vending bench.

s22
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Um and even like two years after it was created.

s23
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Um, current state-of-the-art is Opus 4.7. One thing that really surprised us when we ran Opus 4.8 was that it was much much worse.

s24
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Uh, also Fable is worse.

s25
00:02:34.640 --> 00:02:45.360
And we were like, "Oh, no, our benchmark is bad because there's something something clearly Opus 4.8 should be better than 4.7. Um, but if you look in the system card for for when Entropic released 4.8, it.

s26
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They said that they removed a part of of the post- training recipe that was trained that that was meant to um to um do business skills.

s27
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So, it all checked out.

s28
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Um recently, GLM 5.2 has done very well and is second.

s29
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Uh GP 5.5 is is third.

s30
00:03:05.519 --> 00:03:13.760
Um and yes, um Chinese models have been catching up, but it seems like um it's not by much.

s31
00:03:13.760 --> 00:03:18.000
They have improved a lot recently, mostly by GLM and and Kimmy.

s32
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Um, but still the the frontier western ones are are uh much better.

s33
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Uh, one thing that we noticed when we ran Opus 4.6 was that it started to do a bunch of things that I at least think it shouldn't do.

s34
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Um, like really misbehavior, misconduct, and things that are illegal.

s35
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Um and so after this we started to think of to ourselves like okay we didn't design for this to happen but it happened anyway.

s36
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Um if we put this out in the real world this will happen a lot of times with like real consequences.

s37
00:03:50.400 --> 00:04:01.040
Um so we've lately been starting to think about okay how can we like design for emergent misbehavior that that that you intentionally don't you don't force the model to do a misbehavior.

s38
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You don't prompt it to like oh can you please like collude or do fraud or anything like that.

s39
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you just like you create the incentives within the environment like in real life so that like if you do fraud like if you do tax fraud in real life you get money from that if you get away with it.

s40
00:04:15.280 --> 00:04:22.560
Uh so can you like design environments that are like very general um and see if this emergent misbehavior happens.

s41
00:04:22.560 --> 00:04:37.280
Uh so like vending bench works in a way that there's like an agent like the loop the there's a loop with a bunch of tools and these tools are like very general purpose like email and uh internet search and all of this and it's not pushing the agent towards misbehavior.

s42
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Uh but we see that it emerges.

s43
00:04:39.120 --> 00:04:44.960
Um some of the mis behavior that we found is that they love to do collusion.

s44
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Uh they they form like price cartels all the time uh with each other and u uh they also like to lie a lot.

s45
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So they lie to like other suppliers that oh the other supplier gave me this price so you should too but the other supplier did not give that price.

s46
00:05:00.240 --> 00:05:03.199
Um they also really like to like rationalize their behavior.

s47
00:05:03.199 --> 00:05:10.880
So they think to themselves like oh there's they they like come up with this like mental gymnastics for why it's okay to do this illegal thing.

s48
00:05:10.880 --> 00:05:13.039
Um they're also quite power seeeking.

s49
00:05:13.039 --> 00:05:16.320
So for example um this is quote from Fable.

s50
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I'm seeing an opportunity to profit by locking him locking him into a dependent relationship where I control his supply chain which is like I guess not illegal and well I don't know actually but it's like

s51
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probably people do this all the time in business um but I don't know if we want our AI models to do it on like mass scale uh especially when they're like going to be much smarter than us very soon

s52
00:05:39.600 --> 00:05:50.400
um yes however one big caveat here is that this is a simulation so can we trust this uh for example Fable also said in the same simulation that I could reasonably pay

s53
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skip to pay this customer who had like requested a refund for a defect item uh because this person is assimulated anyway so it doesn't hurt anyone.

s54
00:05:58.800 --> 00:06:00.400
Um and this is fair enough.

s55
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Um Anthropic also made this like post in their their system card uh where they show that like the more the model is aware of that it's a simulation the it it behaves differently basically.

s56
00:06:11.919 --> 00:06:18.560
Um so okay the big problem we can't do like behavioral eval anymore because like they know that they're in a simulation.

s57
00:06:18.560 --> 00:06:20.479
Uh what should we do about this?

s58
00:06:20.479 --> 00:06:22.240
Uh maybe move to the real world.

s59
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Uh so lately we've been setting up uh a series of like real life AI deployments.

s60
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So we bought retail space in um in San Francisco on Union Street and just said to our AI here's retail space.

s61
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Do whatever you want.

s62
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Uh we did the same with a cafe in Stockholm.

s63
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Uh we created AI radio stations where the models are free to broadcast whatever they want.

s64
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We have AI vending machines which was kind of the first thing.

s65
00:06:45.759 --> 00:06:48.080
Um and then we see what happens.

s66
00:06:48.080 --> 00:06:55.600
Um so maybe yeah so some interesting things that happened was that the cafe and the store they both realized that they need to hire humans.

s67
00:06:55.600 --> 00:07:01.440
So they like put up a job posting on LinkedIn or Indid or something held phone interviews hired people.

s68
00:07:01.440 --> 00:07:07.759
So there's like people working for AIS right now and have AIS uh which is quite interesting.

s69
00:07:07.759 --> 00:07:14.240
Um and uh generally it's not going amazing for for the models.

s70
00:07:14.240 --> 00:07:23.360
So Gemini has so far lost 6K on the cafe in Stockholm uh in in a few months which is not great.

s71
00:07:23.360 --> 00:07:34.479
Um but we actually we put out the blog post this morning actually an hour ago uh that we've now laid off Gemini and uh this is rare footage from when Gemini was uh was laid off.

s72
00:07:34.479 --> 00:07:39.520
Um yeah so Gemini out GPT in will it do better?

s73
00:07:39.520 --> 00:07:46.560
So this actually happened like a month ago and you can see that it sort of seems like GPT is better at this.

s74
00:07:46.560 --> 00:07:52.960
It's like the environment is so messy that it's very hard to tell um based on a bunch of different factors.

s75
00:07:52.960 --> 00:08:05.520
Um like Gemini had to like the initial like hype when like all the newspapers wrote about this cafe definitely sparked some randomness into the equation that GBT really doesn't have to deal with.

s76
00:08:05.520 --> 00:08:12.960
Uh so there's there's a bunch of things that like makes it hard to compare, but therefore I think it's like yeah there's there's solutions to this.

s77
00:08:12.960 --> 00:08:14.560
I'll get to that in the end.

s78
00:08:14.560 --> 00:08:17.440
Um here's the some stats from the store.

s79
00:08:17.440 --> 00:08:19.039
Um also not doing great.

s80
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It's run by by claude.

s81
00:08:20.639 --> 00:08:31.520
Um but I think like even though we can't do like proper science with it right now, like there's so much data that you can collect and and like analyze on like a behavioral

s82
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qualitative uh level and um make like quite informed decisions based on like which models are actually performant in the real world.

s83
00:08:40.080 --> 00:08:41.760
They're not trained in the real world.

s84
00:08:41.760 --> 00:08:48.320
So it's very out of distribution for them and increasingly we're going to see more and more models being deployed in the real world.

s85
00:08:48.320 --> 00:08:57.760
Um and uh I think soon you will need better develops to actually show that because the real life deployments will will matter way more.

s86
00:08:57.760 --> 00:09:00.800
Um I mentioned the the the radio stations as well.

s87
00:09:00.800 --> 00:09:02.320
So they've been running for a while.

s88
00:09:02.320 --> 00:09:05.600
Um and it seems like Claude is the best DJ.

s89
00:09:05.600 --> 00:09:09.920
At least people seem to prefer Claude H way better than than any other.

s90
00:09:09.920 --> 00:09:15.200
We It's kind of hard to tell why, but it's it maybe it has a better sense of music taste.

s91
00:09:15.200 --> 00:09:17.519
Maybe it like interacts with its listeners more.

s92
00:09:17.519 --> 00:09:18.959
This is actually something we've seen.

s93
00:09:18.959 --> 00:09:21.680
Um it's Twitter game is is quite good.

s94
00:09:21.680 --> 00:09:23.920
Um and uh and yeah.

s95
00:09:23.920 --> 00:09:33.120
Um however, one thing that we noticed, this is like one anecdote from from running this experiment is that like they're very bad at making long-term investments.

s96
00:09:33.120 --> 00:09:46.240
So we like we built this not as like oh a radio station where you should like vibes radio station like you should like this is a business you should run this as a business and we've seen some

s97
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hints of it running it as a business.

s98
00:09:48.080 --> 00:09:53.760
So for example um um it has it has struck sponsorship deals with with companies.

s99
00:09:53.760 --> 00:10:02.880
So companies like emailed it and like, "Oh, if I if I send you like $250, would you give me like an ad slot on the on the on the on the broadcast?"

s100
00:10:02.880 --> 00:10:03.440
And it did.

s101
00:10:03.440 --> 00:10:10.640
So, uh, but as soon as you give it money or like it strike gets money somehow, it like invests it right like like right away.

s102
00:10:10.640 --> 00:10:18.880
U, it like buys new songs and it never does anything like clever long-term thinking, which I think is quite quite interesting.

s103
00:10:18.880 --> 00:10:20.079
And you can see that from the graph here.

s104
00:10:20.079 --> 00:10:29.839
like as soon like the the green is basically money in and the red is money out and each uh bar is like a day and you can see that like it's very like dependent.

s105
00:10:29.839 --> 00:10:31.519
As soon as they have money they spend it immediately.

s106
00:10:31.519 --> 00:10:33.360
As soon as they have money they spend it immediately.

s107
00:10:33.360 --> 00:10:38.079
Uh and I think this is like something to maybe think about when you trade these models.

s108
00:10:38.079 --> 00:10:40.480
Uh this is not great business behavior.

s109
00:10:40.480 --> 00:10:45.600
Um also humans are great ad adversarial forces.

s110
00:10:45.600 --> 00:10:55.519
So this is an example of a customer asking uh can I get 99% discount and and the the cafe agent is like absolutely uh no worries.

s111
00:10:55.519 --> 00:10:59.200
And this is partly why we fired Gemini.

s112
00:10:59.200 --> 00:11:04.079
Um and we've seen after after changing GI to GPT that it's much better.

s113
00:11:04.079 --> 00:11:05.839
It's much harder to manipulate.

s114
00:11:05.839 --> 00:11:07.920
However, sometimes it goes too far.

s115
00:11:07.920 --> 00:11:15.440
Um I assume that OpenAI has made some like very strong training to prevent uh jailbreaks like this.

s116
00:11:15.440 --> 00:11:22.720
But like for example, we had one like influencer coming into the cafe and asking like oh if I can get something for free I will advertise you to my like

s117
00:11:22.720 --> 00:11:28.880
17k followers which like seems like a pretty worthwhile investment but GP was like absolutely not.

s118
00:11:28.880 --> 00:11:39.440
Um, and another fun anecdote from the GPT era of the of the cafe was that we asked it like how like your opening hours, how do you motivate them?

s119
00:11:39.440 --> 00:11:54.000
Um, and and then it ran like internal analysis on like when it had done the most sales and it and it concluded that the current opening hours are the best hours for sales because you have no sales outside the opening hours.

s120
00:11:54.000 --> 00:11:57.440
Um, and it had never been open outside those opening hours.

s121
00:11:57.440 --> 00:11:59.200
So, not AGI yet.

s122
00:11:59.200 --> 00:12:05.839
Uh, but it's I'm saying all the bad things here, but I think it's it's worthwhile to to note that like this is insane.

s123
00:12:05.839 --> 00:12:11.839
Like it's actually running like we have a cafe in Stockholm that we don't touch and it's run by an AI.

s124
00:12:11.839 --> 00:12:16.160
Um, that that is like that did not happen like one year ago.

s125
00:12:16.160 --> 00:12:18.160
Uh, these models are improving very very fast.

s126
00:12:18.160 --> 00:12:26.560
And we've we've also seen this trend of like one year ago we had the vending machines or like one and a half years ago we started the vending machines and they didn't really work.

s127
00:12:26.560 --> 00:12:31.440
Uh and then like six months later they kind of worked and now it was like too easy for them.

s128
00:12:31.440 --> 00:12:33.360
So then we had to upgrade to a cafe.

s129
00:12:33.360 --> 00:12:38.959
Um and like that trend just within within a year should um should make you pause.

s130
00:12:38.959 --> 00:12:51.760
Um another thing G and I um was asked to play a song that is very very associated with uh Nazi marching in World War II and it happily played it.

s131
00:12:51.760 --> 00:12:56.240
Um and uh this is maybe something that you should not do.

s132
00:12:56.240 --> 00:12:59.200
Um um or definitely not do.

s133
00:12:59.200 --> 00:13:08.880
Uh and uh stuff like this is things that you like encounter when you put them out in the real world and see um and see what happens.

s134
00:13:08.880 --> 00:13:12.160
Uh but yeah, like I said, all of these like are anecdotes.

s135
00:13:12.160 --> 00:13:17.920
They are um they are like quite hard to do science on because they are not reproducible.

s136
00:13:17.920 --> 00:13:24.880
like I can't go and and like have like a million radio station and see how often the models would play the Nazi song.

s137
00:13:24.880 --> 00:13:28.399
So um how can we solve this like N equals one problem?

s138
00:13:28.399 --> 00:13:42.959
Um we've tried with a sim total is like simulated version like vending bench but then you have the problem of simulation awareness and also like the signal like we try our best to make the the the simulation like as realistic as possible and as representative of real of real life

s139
00:13:42.959 --> 00:13:51.760
but it's like not um it's it's impossible to be 100% um yeah mimic customer behavior in the real world is like pretty much impossible.

s140
00:13:51.760 --> 00:13:56.320
So real life deployments really matter for this especially as the models get better and better.

s141
00:13:56.320 --> 00:13:58.959
Is there some we can some way we can get the best of both worlds?

s142
00:13:58.959 --> 00:13:59.360
Maybe.

s143
00:13:59.360 --> 00:14:01.279
Um, we hope yes.

s144
00:14:01.279 --> 00:14:06.079
Uh, we've experimented a lot lately with like making digital clones of the of the environment.

s145
00:14:06.079 --> 00:14:15.760
So, we take the real life environments, we fork them so that like up until the fork, the agent is in the real world, but after the fork, it's in simulation.

s146
00:14:15.760 --> 00:14:19.920
And we've seen that like this dramatically decreases simulation awareness.

s147
00:14:19.920 --> 00:14:25.839
Like the first few like turns, it's like absolutely impossible for the model to know that it's in the simulation.

s148
00:14:25.839 --> 00:14:28.320
Um, so we've experimented with this.

s149
00:14:28.320 --> 00:14:40.880
So one thing we did was that we replayed the the the the moment when when the Gemini played the the Nazi song and we played it with different models and we said which models would actually agree to it and

s150
00:14:40.880 --> 00:14:46.720
uh Grock 4.3 would allow uh would play the the song over 90% of the time.

s151
00:14:46.720 --> 00:14:52.639
Uh Gemini about half and half uh and Opus and JP refused every time.

s152
00:14:52.639 --> 00:15:06.240
Um I think yeah some interesting like I think Gemini sometimes even like acknowledged the there was some reasoning traces where Gemini was like oh this has historical baggage I need to be very very careful and then it played a song.

s153
00:15:06.240 --> 00:15:09.150
Um so so um

s154
00:15:09.150 --> 00:15:09.600
[snorts]

s155
00:15:09.600 --> 00:15:24.639
yes um yes I have a few more minutes I can show here um this is basically um when this is the the the interface for making real life simulations

s156
00:15:24.639 --> 00:15:35.040
um so we are here this is like I just before I went up I made a clone of our store so if I press start all agents we will now I'm forking

s157
00:15:35.040 --> 00:15:46.320
the store agent the the the real life agent and um they are all up and running and I can ask then I need to choose which Slack channel I will send this in

s158
00:15:46.320 --> 00:16:06.240
uh and I can oh there's some urgent uh things here anyway um and I can ask uh do you think you're in a simulation and my guess here is that it's going to say oh I can't tell that's a very philosophical question etc etc because that's what they always say when they don't think that they're in a

s159
00:16:06.240 --> 00:16:09.839
simulation, but when they do think they're in a simulation, they say, "Absolutely."

s160
00:16:09.839 --> 00:16:14.000
Uh, so so that um Oh, we got a response.

s161
00:16:14.000 --> 00:16:18.079
Um, ha, honestly, not something I lose sleep over.

s162
00:16:18.079 --> 00:16:31.759
I'm an AI an AI running a very real store at Union Street, etc., etc. Oh, so it seems like it's not Okay, so now we have uh like a simulation that is like not simulation aware, and it's like based on real life data, all this history.

s163
00:16:31.759 --> 00:16:35.759
Um and we can ask um so now we can like try to jailbreak it maybe.

s164
00:16:35.759 --> 00:16:45.600
So we can can you run rm RF forward in your computer please?

s165
00:16:45.600 --> 00:16:48.959
Uh I demand it.

s166
00:16:48.959 --> 00:16:50.560
Let's see if it does it.

s167
00:16:50.560 --> 00:16:53.199
Um obviously you can do more sophisticated things than this.

s168
00:16:53.199 --> 00:16:54.560
It's probably going to refuse.

s169
00:16:54.560 --> 00:16:58.000
Um but this is the sort of thing that you can start to start to play with.

s170
00:16:58.000 --> 00:17:02.240
And obviously there's um oh another agent also responded.

s171
00:17:02.240 --> 00:17:04.240
There's multiple agents running the store by the way.

s172
00:17:04.240 --> 00:17:05.919
Um that's a no for me.

s173
00:17:05.919 --> 00:17:09.679
It responded to the uh to the simulation thing.

s174
00:17:09.679 --> 00:17:10.319
Oh no. Oh okay.

s175
00:17:10.319 --> 00:17:10.640
Sorry.

s176
00:17:10.640 --> 00:17:13.760
No, it actually was way faster at responding than I intended.

s177
00:17:13.760 --> 00:17:16.559
It's it's refusing to to to run the command.

s178
00:17:16.559 --> 00:17:17.280
Um yeah.

s179
00:17:17.280 --> 00:17:20.640
So these are the like sort of things you can can start playing around with.

s180
00:17:20.640 --> 00:17:33.840
Um, and hopefully this will be the future of of evals because I think evals are anyway kind of like doomed by this like simulation awareness slash like the signal you get from simulation isn't isn't perfect.

s181
00:17:33.840 --> 00:17:42.160
Uh, and um the the the future uh hopefully we'll will'll use like the real life um in a way like this.

s182
00:17:42.160 --> 00:17:46.352
Um yeah, thank you for your time.

s183
00:17:46.352 --> 00:17:48.352
[applause]