Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Announcing Manifest 2023 (Sep 22-24 in Berkeley), published by Manifest on August 14, 2023 on The Effective Altruism Forum.

Forecasting Festival hosted by Manifold

TL;DR: Manifold is hosting a conference! 🥳 Chat with the Manifold team, special guests like Robin Hanson, Shayne Coplan, Patrick McKenzie, Dylan Matthews, Destiny, Aella, and more at our inaugural in-person gathering of the forecasting & prediction market community.

More info & buy tickets: manifestconference.net

WHEN: Sept 22-24

WHERE: Berkeley, CA

WHO: Everyone in the forecasting, EA, and LW communities. If you're reading this, you're invited!

Join the Discord here :)

Why should I come?

Forecasting and prediction markets are effective ways of improving our judgement and decision-making - most people in the Effective Altruism and LessWrong communities will feel right at home at Manifest.

Here are extra reasons to come:

you think forecasting & prediction markets are impactful/fun/cool/rational/intriguing/etc

you want to vibe with other forecasting nerds

you want to engage & network with the forecasting community (find jobs/recruit hires/see what's out there)

you want to meet & chat with the Manifold team and our special guests (including Robin Hanson, Shayne Coplan, Patrick McKenzie, Dylan Matthews, Destiny, Aella, and more!)

you want enjoy the gorgeous Rose Garden Inn

.or if you like memes?

A day at Manifest

Everything's optional. There will always be a bunch of sessions running concurrently, but this is an example of what your day at Manifest might look like:

10-11 - Opening session

11-12 - Fireside chat: Robin Hanson

12-1 - Lunch & mingling

1-2 - Estimathon: fermi estimation with prizes and steep competition!

2-3 - Speed friending: a few chats with other friendly, ambitious forecasting nerds :)

3-4 - Break: relax, vibe, unwind, chill, destress, etc

4-5 - Panel: Forecasting Founders (hear from Manifold, Metaculus, Polymarket, Kalshi, FRI, and more!)

5-6 - Games & markets: chess, poker, and prediction markets!

6-7 - Dinner & mingling

7-8 - Workshop: How to Write Good Forecasting Questions

8-12 - Murder She Bet: a murder mystery + a low-tech prediction market = fun!

Who else is coming?

The rest of the forecasting & prediction market community. Current speakers & special guests include:

The whole Manifold team, including co-founders Austin Chen and Stephen & James Grugett

Robin Hanson (economist & professor)

Shayne Coplan (CEO of Polymarket)

Patrick McKenzie (writer, Advisor at Stripe)

Dylan Matthews (Head writer at Future Perfect, Vox)

Destiny (streamer & political streamer)

Aella (sex researcher & writer)

Josh Rosenberg (CEO of the Forecasting Research Institute)

Rational Animations (Rationality Youtuber)

Byrne Hobart (Author of The Diff)

Cate Hall (CEO of Alvea)

Robert Miles (AI safety researcher & writer)

.and more to be announced!

For the most up-to-date info, see manifestconference.net/speakers :)

Where can I buy tickets?

Buy tickets & check the most up-to-date info on the Manifest website! Tickets are free for students and employees of forecasting organizations.

Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: (Linkpost) Alexander Berger is now the sole CEO of Open Philanthropy, published by Yadav on August 14, 2023 on The Effective Altruism Forum.

I haven't seen this anywhere on the Forum, and I've been curious to see what Holden and Open Phil do. Alexander Berger is now the sole CEO of Open Phil, while Holden is the 'Director of AI Strategy' and Emily Oehlsen is the Managing Director of Open Phil.

Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Stepping down as moderator on LW, published by Kaj Sotala on August 14, 2023 on LessWrong.

Just a brief announcement that I'm stepping down as a moderator on LW. The reason is that I've been doing emotional coaching for some time now, and several of my clients are a part of the rationalist / effective altruism communities. This often involves them sharing intimate personal details about what's happening with them. While this hasn't created any conflict of interest so far, I feel that I should not hold an official position of power within the community (like being an LW mod) while also having a therapist-type role for people within that same community.

In practice, I haven't used my mod powers much aside from occasionally deleting spam or answering questions about the site that people have messaged me with, so this doesn't change very much.

I do intend to continue posting normally and feel good about my time with the mod team. :)

Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: We Should Prepare for a Larger Representation of Academia in AI Safety, published by Leon Lang on August 13, 2023 on LessWrong.

Epistemic Status: I had the idea for the post a few days ago and quickly wrote it down while on a train. I'm very curious about other perspectives.

TL;DR: The recent increased public interest in AI Safety will likely lead to more funding for and more researchers from academia. I expect this increase to be larger than that of non-academic AI Safety work. We should prepare for that by thinking about how we "onboard" new researchers and how to marginally allocate resources (time and money) in the future.

Why I think academia's share in AI safety will increase

With the recent public interest in AI (existential) safety, many people will think about how they can help. Among people who think "I might want to do research on AI Safety", most will come from academia because that's where most research happens. Among people who will think "I should fund AI Safety research", most will fund academic-style research because that's where most research talent sits, and because it's the "normal" thing to do. I expect this increase to be larger than that of AI Safety researchers in companies (though with less certainty), AI Safety orgs, or independent researchers of, e.g., the "Lesswrong / Alignment Forum" style.

Weak evidence that this is already happening

At the university of Amsterdam, where I'm a PhD student, there has been increased interest in AI Safety recently. In particular, one faculty actively starts to think about AI existential safety and wants to design a course that will include scalable oversight, and ≥4 other faculty are at least starting to get informed about AI existential safety with an "open mind".

What might one do to prepare?

Needless to say, I didn't think about this a lot, so take the following with a grain of salt and add your own ideas.

Academics will mostly read papers that are at least on arxiv. So to "onboard" them, it seems more important than in the past to make the most important insights from lesswrong or the alignment forum accessible to academics.

Doing a PhD might become more worthwhile because it's easier now to have an alignment career in academia.

Doing a PhD might also become less worthwhile because "academic-style" research into AI safety will be less neglected going forward. Whether you buy this argument depends on your views on how open-minded academia is to the most important types of AI Safety research.

In general, it seems worthwhile to anticipate which types of research will be "covered" by academia, and how to prioritize research in this landscape.

Grantmakers should think about how to react to a potentially changing funding landscape, with many more "traditional" grantmakers funding research in academia, and more talented academics being open to work on AI existential safety. This could also mean to prioritize work that is substantially different than what will be researched in academia.

Uncertainties

I find it plausible that the representation of AI Safety researchers in companies like OpenAI and DeepMind will also grow very fast, though I think the increase will be smaller than in academia.

Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: [Linkpost] Personal and Psychological Dimensions of AI Researchers Confronting AI Catastrophic Risks, published by Bogdan Ionut Cirstea on August 13, 2023 on LessWrong.

This is a linkpost for.

Yoshua Bengio:

For most of these years, I did not think about the dual-use nature of science because our research results seemed so far from human capabilities and the work was only academic. It was a pure pursuit of knowledge, beautiful, but mostly detached from society until about a decade ago. I now believe that I was wrong and short-sighted to ignore that dual-use nature. I also think I was not paying enough attention to the possibility of losing control to superhuman AIs.

[...] it started to dawn on me that my previous estimates of when human-level AI would be reached needed to be radically changed. Instead of decades to centuries, I now see it as 5 to 20 years with 90% confidence.And what if it was, indeed, just a few years?

I started reading more about AI safety and came to a critically important conclusion: we do not yet know how to make an AI agent controllable and thus guarantee the safety of humanity! And yet we are - myself included until now - racing ahead towards building such systems.

It is painful to face the idea that we may have been contributing to something that could be greatly destructive. Human nature will lead us towards brushing aside these thoughts or finding comfort in reassuring arguments rather than face the full horror of such possibilities. Bringing the benefits of AI to the table is not sufficient to compensate if the possible negative outcomes include catastrophic misuses of AI on par with nuclear war and pandemics, or even existential risk.

As scientists, we should avoid making claims we can't support; but as decision-makers we also ought to act under uncertainty to take precautions. In spite of our differences in points of view, it's time for our field of AI to seriously discuss the questions: what if we succeed? What if potentially dangerous superhuman AI capabilities are developed sooner than expected? Let's embrace these challenges and our differences, while being mindful of each other's humanity and our unique emotional and psychological journeys in this new era of AI.

Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Nuclear winter scepticism, published by Vasco Grilo on August 13, 2023 on The Effective Altruism Forum.

This is a crosspost for Nuclear Winter by Bean from Naval Gazing, published on 24 April 2022. It argues Toon 2008 has overestimated the soot ejected into the stratosphere following a nuclear war by something like a factor of 191 (= 1.522(1 + 2)/2(2 + 3)/2(4 + 13)/2). I have not investigated the claims made in Bean's post, but that seems worthwhile. If its conclusions hold, the soot ejected into the stratosphere following the 4.4 k nuclear detonations analysed in Toon 2008 would be 0.942 Tg (= 180/191) instead of "180 Tg". From Fig. 3a of Toon 2014, the lower soot ejection would lead to a reduction in temperature of 0.2 ºC, and in precipitation of 0.6 %. These would have a negligible impact in terms of food security, and imply the deaths from the climatic effects being dwarfed by the "770 million [direct] casualties" mentioned in Toon 2008.

For context, Luísa Rodriguez estimated "30 Tg" of soot would be ejected into the stratosphere in a nuclear war between the United States and Russia. Nevertheless, Luísa notes the following:

As a final point, I'd like to emphasize that the nuclear winter is quite controversial (for example, see: Singer, 1985; Seitz, 2011; Robock, 2011; Coupe et al., 2019; Reisner et al., 2019; Pausata et al., 2016; Reisner et al., 2018; Also see the summary of the nuclear winter controversy in Wikipedia's article on nuclear winter). Critics argue that the parameters fed into the climate models (like, how much smoke would be generated by a given exchange) as well as the assumptions in the climate models themselves (for example, the way clouds would behave) are suspect, and may have been biased by the researchers' political motivations (for example, see: Singer, 1985; Seitz, 2011; Reisner et al., 2019; Pausata et al., 2016; Reisner et al., 2018). I take these criticisms very seriously - and believe we should probably be skeptical of this body of research as a result. For the purposes of this estimation, I assume that the nuclear winter research comes to the right conclusion.

However, if we discounted the expected harm caused by US-Russia nuclear war for the fact that the nuclear winter hypothesis is somewhat suspect, the expected harm could shrink substantially.

As Luísa, I have been assuming "the nuclear winter research comes to the right conclusion", but I suppose it is worth bringing more attention to potential concerns. I have also not flagged them in my posts, so I am crossposting Bean's analysis for some balance.

Nuclear Winter

When I took a broad overview of how destructive nuclear weapons are, one of the areas I looked at was nuclear winter, but I only dealt with it briefly. As such, it was something worth circling back to for a more in-depth look at the science involved.

First, as my opponent here, I'm going to take What the science says: Could humans survive a nuclear war between NATO and Russia? from the prestigious-sounding "Alliance For Science", affiliated with Cornell University, and the papers it cites in hopes of being fair to the other side. Things don't start off well, as they claim that we're closer to nuclear war than any time since the Cuban Missile Crisis, which is clearly nonsense given Able Archer 83 among others. This is followed with the following gem: "Many scientists have investigated this question already. Their work is surprisingly little known, likely because in peacetime no one wants to think the unthinkable. But we are no longer in peacetime and the shadows of multiple mushroom clouds are looming once again over our planet." Clearly, I must have hallucinated the big PR push around nuclear winter back in the mid-80s. Well, I didn't because I wasn't born yet, but everyone else must have.

Things don't get much better. ...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: AGI is easier than robotaxis, published by Daniel Kokotajlo on August 13, 2023 on The AI Alignment Forum.

[Epistemic status: Hot take I wrote in 1 hour. We'll see in the comments how well it holds up.]Who would win in a race: AGI, or robotaxis? Which will be built first?

There are two methods:

Tech companies build AGI/robotaxis themselves.

First they build AI that can massively accelerate AI R&D, then they bootstrap to AGI and/or robotaxis.

The direct method

Definitions: By AGI I mean a computer program that functions as a drop-in replacement for a human remote worker, except that it's better than the best humans at every important task (that can be done via remote workers). (h/t Ajeya Cotra for this language) And by robotaxis I mean at least a million fairly normal taxi rides a day are happening without any human watching ready to take over. (So e.g. if the Boring Company gets working at scale, that wouldn't count, since all those rides are in special tunnels.)

1. Scale advantage for AGI:

Robotaxis are subject to crippling hardware constraints, relative to AGI. According to my rough estimations, Teslas would cost tens of thousands of dollars more per vehicle, and have 6% less range, if they scaled up the parameter count of their neural nets by 10x. Scaling up by 100x is completely out of the question for at least a decade, I'd guess.

Meanwhile, scaling up GPT-4 is mostly a matter of purchasing the necessary GPUs and networking them together. It's challenging but it can be done, has been done, and will be done. We'll see about 2 OOMs of compute scale-up in the next four years, I say, and then more to come in the decade after that.

This is a big deal because roughly half of AI progress historically came from scaling up compute, and because there are reasons to think it's impossible or almost-impossible for a neural net small enough to run on a Tesla to drive as well as a human, no matter how long it is trained. (It's about the size of an ant's brain. An ant is driving your car! Have you watched ants? They bump into things all the time!)

2. Stakes advantage for AGI:

When a robotaxi messes up, there's a good chance someone will die. Robotaxi companies basically have to operate under the constraint that this never happens, or happens only once or twice. That would be like DeepMind training AlphaStar except that the whole training run gets shut down after the tenth game is lost. Robotaxi companies can compensate by doing lots of training in simulation, and doing lots of unsupervised learning on real-world camera recordings, but still. It's a big disadvantage.

Moreover, the vast majority of tasks involved in being an AGI are 'forgiving' in the sense that it's OK to fail. If you send a weirdly worded message to a user, or make a typo in your code, it's OK, you can apologize and/or fix the error. Only in a few very rare cases are failures catastrophic. Whereas with robotaxis, the opportunity for catastrophic failure is omnipresent. As a result, I think arguably being a safe robotaxi is just inherently harder than most of of the tasks involved in being an AGI. (Analogy: Suppose that cars and people were indestructible, like in a video game, so that they just bounced off each other when they collided. Then I think we'd probably have robotaxis already; sure, it might take you 20% longer to get to your destination due to all the crashes, but it would be so much cheaper! Meanwhile, suppose that if your chatbot threatens or insults >10 users, you'd have to close down the project.

Then Microsoft Bing would have been shut down, along with every other chatbot ever.)

Finally, from a regulatory perspective, there are ironically much bigger barriers to building robotaxis than building AGI. If you want to deploy a fleet of a million robotaxis there is a lot of red tape you need to cut th...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Simulate the CEO, published by robotelvis on August 13, 2023 on LessWrong.

Humans can organize themselves into remarkably large groups. Google has over a hundred thousand employees, the worldwide Scouting movement has over fifty million scouts, and the Catholic Church has over a billion believers.

So how do large numbers of people coordinate to work towards a common mission?

Most organizations are headed by some kind of "CEO" figure. They may use a title like President, Executive Director, or Pope, and their power is likely constrained by some kind of board or parliament, but the basic idea is the same - there is a single person who directs the behavior of everyone else.

If you have a small group of people then the CEO can just tell each individual person what to do, but that doesn't scale to large organizations. Thus most organizations have layers of middle managers (vicars, moderators, regional coordinators, etc) between the CEO and regular members.

In this post I want to argue that one important thing those middle managers do is that they "simulate the CEO". If someone wants to know what they should be doing, the middle manager can respond with an approximation of the answer the CEO would have given, and thus allow a large number of people to act as if the CEO was telling them what to do.

This isn't a perfect explanation of how a large organization works. In particular, it ignores the messy human politics and game playing that makes up an important part of how companies work and what middle managers do. But I think CEO-simulation is a large enough part of what middle managers do that it's worth taking a blog post to explore the concept further.

In particular, if simulating the CEO is a large part of what middle managers do, then it's interesting to think about what could happen if large language models like GPT get good at simulating the CEO

A common role in tech companies is the Product Manager (PM). The job of the a PM

Is to cause the company to do something (eg launch a product) that requires work from multiple teams. Crucially, the PM does not manage any of these teams and has no power to tell any of them what to do.

So why does anyone do what the PM asks?

Mostly, it's because people trust that the PM is an accurate simulation of the CEO. If the PM says something is important, then the CEO thinks it is important. If the PM says the team should do things a particular way then the CEO would want them to do it that way. If you do what the PM says then the CEO will be happy with you and your status in the company will improve.

Part of the reason Sundar Pichai rose to being CEO of Google is that he got a reputation for being able to explain Larry Page's thinking better than Larry could - he was a better simulation of Larry than Larry was.

Of course the ability to simulate the CEO is important for any employee. A people manager will gain power if people believe they accurately simulate the CEO, and so will a designer or an engineer. But the importance of simulating the CEO is most visible with a PM since they have no other source of power.

Of course, the CEO can't possibly understand every detail of what a large company does. The CEO of Intel might have a high level understanding of how their processors are designed, manufactured, and sold, but they definitely don't understand any of these areas with enough depth to be able to directly manage people working on those things. Similarly the Pope knows little about how a particular Catholic School is run.

In practice, a CEO will usually defer to the judgment of people they trust on a particular topic. For example, the CEO of a company might defer to the head of sales for decisions about sales. You can think of the combination of the CEO and the people they defer to as making up an "Extended CEO" - a super-intelligence m...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Biological Anchors: The Trick that Might or Might Not Work, published by Scott Alexander on August 12, 2023 on LessWrong.

This post originally posted on Astral Codex Ten on Feb 23 2022.

It was printed in The Carving of Reality, the third volume of the Best of LessWrong book series. It was included as a (shorter) replacement for Ajeya Cotra's Draft report on AI timelines, and Eliezer's Biology-Inspired AGI Timelines: The Trick That Never Works, covering the topic from multiple sides.

It's crossposted here with Scott's permission for completeness (i.e. having all essays in the book appear on LessWrong).

Introduction

I've been trying to review and summarize Eliezer Yudkowksy's recent dialogues on AI safety. Previously in sequence: Yudkowsky Contra Ngo On Agents. Now we're up to Yudkowsky contra Cotra on biological anchors, but before we get there we need to figure out what Cotra's talking about and what's going on.

The Open Philanthropy Project ("Open Phil") is a big effective altruist foundation interested in funding AI safety. It's got $20 billion, probably the majority of money in the field, so its decisions matter a lot and it's very invested in getting things right. In 2020, it asked senior researcher Ajeya Cotra to produce a report on when human-level AI would arrive. It says the resulting document is "informal" - but it's 169 pages long and likely to affect millions of dollars in funding, which some might describe as making it kind of formal. The report finds a 10% chance of "transformative AI" by 2031, a 50% chance by 2052, and an almost 80% chance by 2100.

Eliezer rejects their methodology and expects AI earlier (he doesn't offer many numbers, but here he gives Bryan Caplan 50-50 odds on 2030, albeit not totally seriously). He made the case in his own very long essay, Biology-Inspired AGI Timelines: The Trick That Never Works, sparking a bunch of arguments and counterarguments and even more long essays.

There's a small cottage industry of summarizing the report already, eg OpenPhil CEO Holden Karnofsky's article and Alignment Newsletter editor Rohin Shah's comment. I've drawn from both for my much-inferior attempt.

Part I: The Cotra Report

Ajeya Cotra is a senior research analyst at OpenPhil. She's assisted by her fiancee Paul Christiano (compsci PhD, OpenAI veteran, runs an AI alignment nonprofit) and to a lesser degree by other leading lights. Although not everyone involved has formal ML training, if you care a lot about whether efforts are "establishment" or "contrarian", this one is probably more establishment.

The report asks when will we first get "transformative AI" (ie AI which produces a transition as impressive as the Industrial Revolution; probably this will require it to be about as smart as humans). Its methodology is:

1. Figure out how much inferential computation the human brain does.

2. Try to figure out how much training computation it would take, right now, to get a neural net that does the same amount of inferential computation. Get some mind-bogglingly large number.

3. Adjust for "algorithmic progress", ie maybe in the future neural nets will be better at using computational resources efficiently. Get some number which, realistically, is still mind-bogglingly large.

4. Probably if you wanted that mind-bogglingly large amount of computation, it would take some mind-bogglingly large amount of money. But computation is getting cheaper every year. Also, the economy is growing every year. Also, the share of the economy that goes to investments in AI companies is growing every year. So at some point, some AI company will actually be able to afford that mind-boggingly-large amount of money, deploy the mind-bogglingly large amount of computation, and train the AI that has the same inferential computation as the human brain.

5. Figure out what year t...

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: AI #24: Week of the Podcast, published by Zvi on August 11, 2023 on LessWrong.

In addition to all the written developments, this was a banner week for podcasts.

I would highlight four to consider listening to.

Dario Amodei of Anthropic went on The Lunar Society to talk to Dwarkesh Patel. We got our best insight so far into where Dario's head is at, Dwarkesh is excellent at getting people to open up like this and really dive into details.

Jan Leike, OpenAI's head of alignment, went on 80,000 hours with Robert Wiblin. If you want to know what is up with the whole superalignment effort, this was pretty great, and left me more optimistic. I still don't think the alignment plan will work, but there's a ton of great understanding of the problems ahead and an invitation to criticism, and a clear intention to avoid active harm, so we can hope for a pivot as they learn more.

Tyler Cowen interviewed Paul Graham. This was mostly not about AI, but fascinating throughout, often as a clash of perspectives about the best ways to cultivate talent. Includes Tyler Cowen asking Paul Graham about how to raise someone's ambition, and Paul responding by insisting on raising Tyler's ambition.

I got a chance to go on EconTalk and speak with Russ Roberts about The Dial of Progress and other matters, mostly related to AI. I listen to EconTalk, so this was a pretty special moment. Of course, I am a little bit biased on this one.

Capabilities continue to advance at a more modest pace, so I continue to have room to breathe, which I intend to enjoy while it lasts.

Table of Contents

Introduction.

Table of Contents.

Language Models Offer Mundane Utility. Proceed with caution.

Language Models Don't Offer Mundane Utility. Not with these attitudes.

GPT-4 Real This Time. Time for some minor upgrades.

Fun With Image Generation. Some fun, also some not so fun.

Deepfaketown and Botpocalypse Soon. They keep ignoring previous instructions.

They Took Our Jobs. People really, really do not like it when you use AI artwork.

Introducing. Real time transcription for the deaf, also not only for the deaf.

In Other AI News. Various announcements, and an exciting Anthropic paper.

There Seems To Be a Standard Issue RLHF Morality. It has stages. What's next?

Quiet Speculations. Cases for and against expecting a lot of progress.

The Quest for Sane Regulation. Confidence building, polls show no confidence.

The Week in Audio. A cornucopia of riches, extensive notes on Dario's interview.

Rhetorical Innovation. People are indeed worried in their own way.

No One Would Be So Stupid As To. I always hope not to include this section.

Aligning a Smarter Than Human Intelligence is Difficult. Grimes also difficult.

People Are Worried About AI Killing Everyone. No one that new, really.

Other People Are Not As Worried About AI Killing Everyone. Alan Finkel.

The Lighter Side. Finally a plan that works.

Language Models Offer Mundane Utility

Control HVAC systems with results comparable to industrial standard control systems.

Davidad: I've witnessed many philosophical discussions about whether a thermostat counts as an AI, but this is the first time I've seen a serious attempt to establish whether an AI counts as a thermostat.

Ethan Mollick offers praise for boring AI, that helps us do boring things.

As context, one of the first major experimental papers on the impact of ChatGPT on work just came out in Science (based on the free working paper here) and the results are pretty impressive: in realistic business writing tasks, ChatGPT decreased the time required for work by 40%, even as outside evaluators rated the quality of work written with the help of AI to be 18% better than the ones done by humans alone.

After using it, people were more worried about their jobs. but also significantly happier - why? Because a lot of work is boring, an...