trentmkelly/LessWrong-43k huggingface dataset:
Modifying Jones' "AI Dilemma" Model
by harsimony — 2024-03-04T21:55:39.334Z
Link: https://splittinginfinity.substack.com/p/modifying-jones-ai-dilemma-model

Code for some of the equations and plots can be found here.

Charles I. Jones’ paper The A.I. Dilemma: Growth versus Existential Risk tackles the question of whether or not to pursue AI development in the face of existential risk. I found the approach both interesting and accessible and I recommend it to people interested in understanding these sorts of problems.

I want to illustrate the model and suggest a few modifications. For the purposes of this post, we’re going to assume society can make a collective decision about AI (a big if!). We’re also going to assume that those decisions are made from the perspective of a total utilitarian social planner.

To start, we’re going to jump directly to the more advanced model discussed in section 3. In this section, Jones assumes that individuals have CRRA utility functions with risk-aversion parameter greater than 1. This has the consequence that individual utility has an upper-bound regardless of how much they consume.

I think this is a good assumption, especially for a decision involving so much risk. For one, people’s measured gamma is around 1.5. In finance, using methods like the Kelly criterion (log utility, gamma= 1) results in very risky allocations, possibly due to its sensitivity to incorrectly estimated probabilities. All of this pushes us in the direction of having risk-averse utility functions.

Now, the total utility for a society is the sum of the individual utilities, discounted over time:

Here u(c_t) is the utility function with consumption c at time t:

Consumption grows at rate g:

and N_t is the population at time t:

Where n is the population growth rate. Jones breaks down *n *further as *n = b - m *where b is the birth rate and m is the mortality rate.

Plugging everything into the integral we get a total utility for a given growth rate and mortality rate:

Okay, now let’s think about how we use this utility in our decisions. Today, we plod along with a certain growth rate (~2%) and mortality rate (~1%), call these g_0 and m_0. AI will presumably change both of these things to g_ai and m_ai. Perhaps, as Jones considers, AI will raise the growth rate to 10% and lower mortality to 0.5% (resulting in a 200 year lifespan). AI also presumably comes with a one-time existential risk delta. For this gamble to be worthwhile, the utility of a future with AI combined with the risk of dying has to be greater than the utilit...
Benefits of adding poison to your DMT
by George3d6 — 2024-03-04T20:35:08.352Z
Link: https://morelucid.substack.com/p/benefits-of-adding-poison-to-your

DMT might be one of the best psychedelics out there, its effects are short-lived, it can be quite intense, and it allows for careful modulation of the dosage through trial and error.

With most psychedelics, working towards a dosage that won’t induce a difficult experience (read: hellish and psychotic) is tiresome. Their effects last for a long time, and the vast majority come with some amount of side effects if taken often or in large quantities.

Its more infamous cousin, 5-MeO-DMT, has a similar profile regarding safety and ease of titrating the dosage. As far as I’ve been told, its phenomenal character is such that it would be more unpleasant than DMT, but I can’t speak to that.

Thus, the two ideal candidates for introducing people to psychedelics.

Ayahuasca

Of course, this is a purely theoretical point, since reality operates on the principle of maximum irony, most people encounter these two compounds in a setting where they are mixed with various dangerous chemicals that:

a) Pose a risk of death and cause unpleasant (sometimes permanently damaging) bodily side effects (tachycardia, arrhythmia, hypertension, diarrhea, serotonin syndrome)

b) Reduces the intensity of the trip while making it last for hours instead of a few minutes

What is Ayahuasca? It used to be the collective name given to psychoactive beverages consumed by people in South America. But analogous beverages exist in other cultures, and its use has been spread throughout esoterically inclined circles beyond South America, your neighborhood’s shaman in Berkeley can probably cook it just as well as his traditional counterpart in the Amazon.

That being said, what’s common among almost all of the brews fitting the label is that they contain either DMT or 5-MeO-DMT and MAOI.

MAOs and DMT

Monoamine oxidases oxidize monoamines. Which is to say that it quickly destroys compounds like DMT and 5-MeO-DMT. Inhibiting them lets monoamines stroll through the body, hitching a ride into the vascular system, all the way to the brain.

The odd thing about monoamines is that, in spite of their potential abundance in our diet, they can be absorbed in an intact state and can cross the blood-brain barrier; The only defense we have against sharing in large quantities of neurotransmitters from the plants and animals we eat are MAOs.

Having MAOs is nice, they make it so that we can eat cheese or grapefruit without hav...
Notes on Awe
by David Gross — 2024-03-04T20:23:16.671Z

This post examines the virtue related to awe. As with my other posts in this sequence, I’m less interested in breaking new ground and more in gathering and synthesizing whatever wisdom I could find on the subject. I wrote this not as an expert on the topic, but as someone who wants to learn more about it. I hope it will be helpful to people who want to know more about this virtue and how to nurture it.

What is this virtue?

“The fairest thing we can experience is the mysterious. It is the fundamental emotion which stands at the cradle of true art and true science. He who knows it not and can no longer wonder, no longer feel amazement, is as good as dead, a snuffed-out candle.” ―Albert Einstein[1]

Awe is most typically described as an emotion, but it (or something similar like “elevation” or “wonder”) frequently also gets named among the virtues. A virtue is a characteristic habit; an emotion usually is not, unless it expands to become part of one’s personality. So this is a little confusing.

Part of the problem is that the virtue associated with awe does not seem to have a name in English, so we use the name of the emotion as a placeholder. The virtue, I suggest, has two main non-emotional components:

Openness to (or maybe “welcoming of” or “seeking out”) experiences of awe.
Skill in processing those experiences so as to get the most value from them (not being merely “awe-struck”).

What is awe?

The modern literature on awe seems to have settled on a definition that goes something like this:[2]

Awe is when a person encounters something so tremendous that they struggle to accommodate it into their understanding, and they temporarily lose themselves in this struggle, with an attentive and perhaps anxious fascination.

We can break this down into components: 1) an overwhelming stimulus, 2) a struggle to fit it into one’s worldview, 3) a leaning-in to this struggle. The virtue of awe includes the willingness or eagerness to engage in such a struggle and the skill to do it well (one does not recoil from the tremendous, or surrender hopelessly if at first one fails to come to grips with it).

The immediately following sections describe the typical characteristics of awe-provoking stimuli and of the emotional experience of awe. Further along, I will consider the components of the virtue: how to welcome the struggle that awe invites us to, and how to engage with it productively.

Fi...
Boston's Line 1
by jefftk — 2024-03-04T19:30:06.723Z

Over on r/mbta people

were
speculating: if Boston's rapid transit lines were numbered, like

some

other

cities,
which line would get the honor of being first? The

Orange
Line, as the main line of the historical

Boston
Elevated Railway? The

Red Line

,
with the highest (pre-covid) ridership? The

Green Line,
as the oldest line (and the oldest subway in North America)? The

Blue Line,
as, uh, the only line to run cars that switch between catenary and third
rail?

It turns out there's an actual answer! From 1964
to 1966,
before switching to colors
in 1967, the MTA (and then the MBTA) used numbers. Here's the 1966
system map:

The lines were:

Harvard to Ashmont (Red Line, Ashmont Branch)

Everett to Forest Hills (Orange Line)

Bowdoin to Wonderland (Blue Line)

Lechmere to Riverside (Green Line, D)

(I originally noticed the claim that they used to be numbered on
Wikipedia, but then when I went looking for maps I couldn't find any
examples and marked it as citation
needed. Then I found that the parallel claim on the Blue Line
cited the (incredible!) Changes
to Transit Service in the MBTA district, which has this on p8. I
realized that I'd been looking at maps that were too early, and when I
checked the years immediately preceding the introduction of the
colors I found it. I've now updated
the page with a citation to the 1966 map.)

Comment via: facebook, mastodon
Anthropic release Claude 3, claims >GPT-4 Performance
by LawrenceC — 2024-03-04T18:23:54.065Z
Link: https://www.anthropic.com/news/claude-3-family

Today, we're announcing the Claude 3 model family, which sets new industry benchmarks across a wide range of cognitive tasks. The family includes three state-of-the-art models in ascending order of capability: Claude 3 Haiku, Claude 3 Sonnet, and Claude 3 Opus. Each successive model offers increasingly powerful performance, allowing users to select the optimal balance of intelligence, speed, and cost for their specific application.

Better performance than GPT-4 on many benchmarks

The largest Claude 3 model seems to outperform GPT-4 on benchmarks (though note slight differences in evaluation methods):

Opus, our most intelligent model, outperforms its peers on most of the common evaluation benchmarks for AI systems, including undergraduate level expert knowledge (MMLU), graduate level expert reasoning (GPQA), basic mathematics (GSM8K), and more. It exhibits near-human levels of comprehension and fluency on complex tasks, leading the frontier of general intelligence.

Important Caveat: With the exception of GPQA, this is comparing against gpt-4-0314 (the original public version of GPT-4), and not either of the GPT-4-Turbo models (gpt-4-1106-preview, gpt-4-0125-preview). The GPT-4 entry for GPQA is gpt-4-0613, which performs significantly better than -0314 on benchmarks. Where the data exists, gpt-4-1106-preview consistently outperforms Claude 3 Opus. That being said, I do believe that Claude 3 Opus probably outperforms all the current GPT-4 models on GPQA. Maybe someone should check by running GPQA evals on one of the GPT-4-Turbo models?

Also, while I haven't yet had the chance to interact much with this model, but as of writing, Manifold assigns ~70% probability to Claude 3 outperforming GPT-4 on the LMSYS Chatbot Arena Leaderboard.

https://manifold.markets/JonasVollmer/will-claude-3-outrank-gpt4-on-the-l?r=Sm9uYXNWb2xsbWVy

Synthetic data?

According to Anthropic, Claude 3 was trained on synthetic data (though it was not trained on any customer-generated data from previous models):

Also interesting that the model can identify the synthetic nature of some of its evaluation tasks. For example, it provides the following response to a synthetic recall text:

Is Anthropic pushing the frontier of AI development?

Several people have pointed out that this post seems to take a different stance on race dynamics than was expressed previously:

As we push the boundaries of AI capab

...
Anomalous Concept Detection for Detecting Hidden Cognition
by Paul Colognese — 2024-03-04T16:52:52.568Z

Thanks to Johannes Treutlein, Erik Jenner, Joseph Bloom, and Arun Jose for their discussions and feedback.

Summary

Monitoring an AI’s internals for features/concepts unrelated to the task the AI appears to be performing may help detect when the AI is performing

hidden cognition

. For example, it would be very suspicious if an AI tasked with booking a holiday for a user was found to be using internal features/concepts related to “bioweapons” or “viruses”.

The ability to detect hidden cognition could help prevent negative outcomes that result from some forms of

deceptive alignment

/

scheming

.

In this post, we will:

Introduce the idea of concepts as units of knowledge used in cognition to solve tasks.
Introduce and discuss the idea of Anomalous Concept Detection for revealing hidden cognition.
Explore how existing interpretability techniques, such as probes or sparse autoencoders, could be used to detect hidden cognition and discuss the possibility of testing these approaches in the near future.

Introduction

An AI may be able to hide cognition that leads to negative outcomes from certain oversight processes (such as

deceptive alignment

/

scheming

). Without being able to detect this hidden cognition, an overseer may miss the opportunity to intervene and prevent the associated negative outcomes. The ability to detect and interrupt hidden cognition may be critical to preventing irreversibly bad outcomes, such as when the outcomes themselves are hard to detect, or the outcomes make future monitoring/interventions more difficult (as is the case when the outcome is some form of a decision to be

deceptive

, removing an off-switch, etc.)

For example, an AI managing a city's energy grid may begin to prioritize its own operational efficiency over the city’s actual energy needs. Knowing that its overseers will not approve of this goal, it pursues its goal via hidden cognition, undetected until issues arise.

One hope is that traces of the hidden cognition are present in the AI’s internals and could be detected and interpreted. This is part of the potential of interpretability as an alignment research agenda (see

Hubinger

,

Olah

,

Nanda

).

Although we may be far from being able to reverse engineer frontier models as a means of detecting hidden cognition, the recent development of

Sparse

Autoencoders

, suggest that we may soon have automate...
INTERVIEW: StakeOut.AI w/ Dr. Peter Park
by jacobhaimes — 2024-03-04T16:35:06.542Z
Link: https://into-ai-safety.github.io/episode/interview/episode-14/

Hey everyone! This week's episode of the Into AI Safety podcast is an interview with Dr. Peter Park. Along with Harry Luk and one other cofounder, he started StakeOut.AI, a non-profit with the goal of making AI go well, for humans.

Unfortunately, due to funding pressures, the organization recently had to dissolve, but the founders continue to contribute positively towards society in their respective roles.

Nonetheless, the interview gives great coverage of some of the first struggles and accomplishments that have happened since "AI" hit the main stream.

Note that the interview will be broken up into 3 episodes, and this one is only the first in the series. It is 15th episode of the podcast, which began right here on LessWrong.

As I have mentioned previously, any feedback, advice, comments, etc. is greatly appreciated.

Spotify
Apple Podcasts
Amazon Music
Housing Roundup #7
by Zvi — 2024-03-04T15:00:08.192Z

Legalize housing. It is both a good slogan and also a good idea.

The struggle is real, ongoing and ever-present. Do not sleep on it. The Housing Theory of Everything applies broadly, even to the issue of AI. If we built enough housing that life vastly improved and people could envision a positive future, they would be far more inclined to think well about AI.

In Brief

What will AI do to housing? If we consider what the author here calls a ‘reasonably optimistic’ scenario and what I’d call a ‘maximally disappointingly useless’ scenario, all AI does is replace some amount of some forms of labor. Given current AI capabilities, it won’t replace construction, so some other sectors get cheaper, making housing relatively more expensive. Housing costs rise, the crisis gets more acute.

Chris Arnade says we live in a high-regulation low-trust society in America, and this is why our cities have squalor and cannot have nice things. I do not buy it. I think America remains a high-trust society in the central sense. We trust individuals, and we are right to do so. We do not trust our government to be competent, and are right not to do so, but the problem there is not the lack of trust.

Reading the details of Arnade’s complaints pointed to the Housing Theory of Everything and general government regulatory issues. Why are so many of the things not nice, or not there at all? Homelessness, which is caused by lack of housing. The other half, that we spend tons of money for public works that are terrible, is because such government functions are broken. So none of this is terribly complicated.

Matt Yglesias makes the case against subsidizing home ownership. Among other things, it creates NIMBYs that oppose building housing, it results in inefficient allocation of the housing stock, it encourages people to invest in a highly concentrated way we otherwise notice is highly unwise and so on. He does not give proper attention to the positives, particularly the ability to invest in and customize a place of one’s own, and does not address the ‘community buy-in’ argument except to notice that one main impact of that, going NIMBY, is an active negative. Also he does not mention that the subsidies involved increase inequality, and the whole thing makes everyone who needs to rent much worse off. I agree that our subsidies for homeownership are highly inefficient and dumb. A neutral ...
The Solution to Sleeping Beauty
by Ape in the coat — 2024-03-04T06:46:35.337Z

This is the eighth post in my series on Anthropics. The previous one is Lessons from Failed Attempts to Model Sleeping Beauty Problem. The next one is Beauty and the Bets.

Introduction

Suppose we take the insights from the previous post, and directly try to construct a model for the Sleeping Beauty problem based on them.

We expect a halfer model, so

P(Heads&Monday)=P(Heads)=1/2

On the other hand, in order not repeat Lewis' Model's mistakes:

P(Heads|Monday)=1/2

But both of these statements can only be true if

P(Monday)=1

And, therefore, apparently, P(Tuesday) has to be zero, which sounds obviously wrong. Surely the Beauty can be awaken on Tuesday!

At this point, I think, you wouldn't be surprised, if I tell you that there are philosophers who are eager to bite this bullet and claim that the Beauty should, indeed, reason as if she can't possible be awoken on Tuesday. I applaud their dedication to brute-forcing the problem, and leave them at that, preferring to follow less bizarre approaches.

As I mentioned in the previous post, we need to find the core flawed assumption in all three models and fix it before we will be able to construct the correct model. That's what we are going to do now.

On the other hand, this drawback may create an impression that my analysis from the previous post is wrong, and that's the reason why my insights are leading to a dead end. That actually one of the three models is sound for the Sleeping Beauty problem, despite all the issues mentioned previously.

Thankfully, there is a way to kill both birds with one stone.

Statistical Analysis

Let's simulate Sleeping Beauty experiment multiple times and write down the day and the coin side on every awakening. Here is an implementation in Python:

def sleepingBeauty():
days = ['Monday']
if random.random() >= 0.5: # result of the coin toss
coin = 'Tails'
days += ['Tuesday']
else:
coin = 'Heads'
return days, coin

ListSB = []
for i in range(n):
days, coin = sleepingBeauty()
for day in days:
ListSB.append(coin+'&'+day)

And then do the same with all the models.

def modelElga():
rand = random.random()
if rand <= 0.33
return 'Heads&Monday'
elif rand <= 0.66:
return 'Tails&Monday'
else:
return 'Tails&Tuesday'

ListE = []
for i in range(1.5*n):
outcome = modelElga()
ListE.append(outcome)

def modelLewis():
if rando

...
Are we so good to simulate?
by KatjaGrace — 2024-03-04T05:20:03.535Z

If you believe that,—

a) a civilization like ours is likely to survive into technological incredibleness, and

b) a technologically incredible civilization is very likely to create ‘ancestor simulations’,

—then the Simulation Argument says you should expect that you are currently in such an ancestor simulation, rather than in the genuine historical civilization that later gives rise to an abundance of future people.

Not officially included in the argument I think, but commonly believed: both a) and b) seem pretty likely, ergo we should conclude we are in a simulation.

I don’t know about this. Here’s my counterargument:

‘Simulations’ here are people who are intentionally misled about their whereabouts in the universe. For the sake of argument, let’s use the term ‘simulation’ for all such people, including e.g. biological people who have been grown in Truman-show-esque situations.

In the long run, the cost of running a simulation of a confused mind is probably similar to that of running a non-confused mind.

Probably much, much less than 50% of the resources allocated to computing minds in the long run will be allocated to confused minds, because non-confused minds are generally more useful than confused minds. There are some uses for confused minds, but quite a lot of uses for non-confused minds. (This is debatable.) Of resources directed toward minds in the future, I’d guess less than a thousandth is directed toward confused minds.

Thus on average, for a given apparent location in the universe, the majority of minds thinking they are in that location are correct. (I guess at at least a thousand to one.)

For people in our situation to be majority simulations, this would have to be a vastly more simulated location than average, like >1000x

I agree there’s some merit to simulating ancestors, but 1000x more simulated than average is a lot - is it clear that we are that radically desirable a people to simulate? Perhaps, but also we haven’t thought much about the other people to simulate, or what will go in in the rest of the universe. Possibly we are radically over-salient to us. It’s true that we are a very few people in the history of what might be a very large set of people, at perhaps a causally relevant point. But is it clear that is a very, very strong reason to simulate some people in detail? It feels like it might be salient because it is what m

...