6 and 7 are definitely non-predictions, or a prediction that nothing interesting will happen. 1, 2, 4 and 5 are softly almost true today:
(1) AI Programming—I heard a rumor (don’t have a source on this) that something like 30% of new GitHub commits involve Co-Pilot. I can’t imagine that is really true, seems so implausible, but my prediction can come true if AI code completion becomes very popular.
(2) Household Robots—Every year for the last decade or so some company has demoed some kind of home robot at an electronics convention, but if any of them have actually made it to market, the penetration is very small. Eventually someone will make one that’s useful enough to sell a few hundred or more units. I don’t think a Roomba should qualify as meeting my prediction, which is why I specified a “humanoid” robot.
(3) Self Driving—I stand by what I said, nothing to expand on. I believe that Tesla and Waymo, at least, already have self driving tech good enough, so this is mostly about institutional acceptance.
(4) DRL learning games from pixels—EfficientZero essentially already does this, but restricted to the 57 Atari games. My prediction is that there will be an EfficientZero for all video games.
(5) Turing Test—I think that the Turing test is largely a matter of how long the computer can fool the judge for, in addition to the judge knowing what to look for. Systems from the 70s could probably fool a judge for about 30 seconds. Modern chatbots might be able to fool a competent judge for 10 minutes, and an incompetent judge (naive casual user) for a couple hours at the extreme. I think by 2026 chatbots will be able to fool competent judges for at least 30 minutes, and will be entertaining to naive casual users indefinitely (i.e., people can make friends with their chatbots and it doesn’t get boring quickly if ever.)
For 6 and 7, I’m going to make concrete predictions.
(6) Some research institute or financial publication of repute will claim that AI technology (not computers generally, just AI) will have “added X Trillion Dollars” to the US or world economy, where X is at least 0.5% of US GDP or GWP, respectively. Whether this is actually true might be controversial, but someone will have made the claim. GWP will not be significantly above trendline.
(7) At least two job titles will have less than 50% the number of workers as 2019. The most likely jobs to have been affected are drivers, cashiers, fast food workers, laundry workers, call center workers, factory workers, workers in petroleum-related industries*, QA engineers, and computer programmers. These jobs might shift within the industry such that the number of people working in industry X is similar, but there has to be a job title that shrunk by 50%. For example, the same X million people still work in customer service, but most of them are doing something like prompt engineering on AI chatbots, as opposed to taking phone calls directly.
* This one has nothing to do with AI, but I expect it to happen by 2026 nonetheless.
Let me know if you want to formalize a bet on some website.
For (2) I am less interested in betting than I was previously. Before, I assumed you meant that there would be actual, competent Humaniod robotic maids and butlers for sale in 2026. But now I’m imagining that you meant just any ordinary Humanoid robot on the market, even if doesn’t do what a real human maid or butler does.
Like, I think technically in 1990 companies could have already been selling “Humanoid robotic maids”, but they would’ve been functionally useless. Without some sort of constraint on what actually counts as a robotic maid, I think some random flashy-yet-useless robot that changed hands and made some company $300,000 in revenue might count for the purposes of this bet. And I would prefer not to take a bet with that as a potential outcome.
Some research institute or financial publication of repute will claim that AI technology (not computers generally, just AI) will have “added X Trillion Dollars” to the US or world economy, where X is at least 0.5% of US GDP or GWP, respectively. Whether this is actually true might be controversial, but someone will have made the claim.
This seems like an extremely weak prediction. Institutions, even fairly reputable ones, make fantastic claims like that all the time.
For example, I found one article written in 2019 that says, “By one estimate, AI contributed a whopping $2 trillion to global GDP last year.” It cites the PricewaterhouseCoopers, which according to Wikipedia is “the second-largest professional services network in the world and is considered one of the Big Four accounting firms, along with Deloitte, EY and KPMG.”
Since GWP was about 86.1 trillion USD in 2018, according to the World Bank, this means that PwC thinks that artificial intelligence is already contributing more than 2% of our gross world product, four times more than you expected would be claimed by 2026!
(3) Self Driving—I stand by what I said, nothing to expand on. I believe that Tesla and Waymo, at least, already have self driving tech good enough, so this is mostly about institutional acceptance.
The problem with some of your predictions is that I don’t know how to operationalize them. For example, does L4 self-driving count? What about L3? What source can be used to resolve this question? I’m not currently aware of any source that counts the number of trips done in automobiles in the US, and tabulates them by car type (or self-driving status). So, to bet, we’d either need to get a source, or come up with a different way of operationalizing the question.
(As an aside, I have found that a very high fraction of predictions—even among people who care a lot about betting—tend to be extremely underspecified. I think it’s a non-trivial skill to know how to operationalize bets, and most people just aren’t very good at it without lots of practice. That’s not a criticism of you :). However, I do prefer that you state your predictions very precisely because otherwise we’re just not going to be able to do the bet.)
I think you’re 100% right. Most (>>80%) of the bets I see on Long Bets, or predictions on MetaCalculus, are underspecified to the point where where a human mediator would have to make a judgement call that can be considered unfair to someone. I don’t expect that to change no matter how much work I do, unless I make bets on specific statistics from well known sources, e.g. the stock market, or the CIA World Factbook.
There are possible futures where prediction (3) is obvious. For example, if someone predicted that 50% of trips will be self driving in 2021 (many people did predict that 5 years ago) we can easily prove them wrong without having to debate whether Tesla is L2 or L5 and whether that matters. Teslas are not 50% of the cars on the road, nor are Waymos, so you can easily see that most trips in 2021 are not self driving by any definition. I think there are also future worlds were 95% of cars and trips are L5, most cars can legally autonomously drive anywhere without any humans inside, etc, and in that world there isn’t much to debate about unless you’re really petty. So we could make bets hoping that things will be that obvious, but I don’t think either of us want to do the work to avoid this kind of ambiguity.
I’m happy to consider my bets as paid in Bayes points without any need for future adjudication. So, for all the Bayes points, I’d love to hear what your equivalent predictions are for 2026.
For what it’s worth, here’s my revised (3): Greater than 10% of cars on the road will be legally capable of either L4/L5 OR legally L2/L3 but disengagements will be uncommon, less than once in a typical trip. (Meaning, if you watch a video from the AI DRIVR YouTube channel, there’s less than one disengagement per 20 minutes of driving time.)
I think you’re 100% right. Most (>>80%) of the bets I see on Long Bets, or predictions on MetaCalculus, are underspecified to the point where where a human mediator would have to make a judgement call that can be considered unfair to someone.
To be clear, I have spent a ton of time on Metaculus and I find this impression incorrect. I have spent comparatively little time on Long Bets but I think it’s also wrong there for the most part.
I think you may have accidentally called out parties who are, in my opinion, exemplars of what solid prediction platforms should look like. There are far, far worse parties that you could have called out.
6 and 7 are definitely non-predictions, or a prediction that nothing interesting will happen. 1, 2, 4 and 5 are softly almost true today:
(1) AI Programming—I heard a rumor (don’t have a source on this) that something like 30% of new GitHub commits involve Co-Pilot. I can’t imagine that is really true, seems so implausible, but my prediction can come true if AI code completion becomes very popular.
(2) Household Robots—Every year for the last decade or so some company has demoed some kind of home robot at an electronics convention, but if any of them have actually made it to market, the penetration is very small. Eventually someone will make one that’s useful enough to sell a few hundred or more units. I don’t think a Roomba should qualify as meeting my prediction, which is why I specified a “humanoid” robot.
(3) Self Driving—I stand by what I said, nothing to expand on. I believe that Tesla and Waymo, at least, already have self driving tech good enough, so this is mostly about institutional acceptance.
(4) DRL learning games from pixels—EfficientZero essentially already does this, but restricted to the 57 Atari games. My prediction is that there will be an EfficientZero for all video games.
(5) Turing Test—I think that the Turing test is largely a matter of how long the computer can fool the judge for, in addition to the judge knowing what to look for. Systems from the 70s could probably fool a judge for about 30 seconds. Modern chatbots might be able to fool a competent judge for 10 minutes, and an incompetent judge (naive casual user) for a couple hours at the extreme. I think by 2026 chatbots will be able to fool competent judges for at least 30 minutes, and will be entertaining to naive casual users indefinitely (i.e., people can make friends with their chatbots and it doesn’t get boring quickly if ever.)
For 6 and 7, I’m going to make concrete predictions.
(6) Some research institute or financial publication of repute will claim that AI technology (not computers generally, just AI) will have “added X Trillion Dollars” to the US or world economy, where X is at least 0.5% of US GDP or GWP, respectively. Whether this is actually true might be controversial, but someone will have made the claim. GWP will not be significantly above trendline.
(7) At least two job titles will have less than 50% the number of workers as 2019. The most likely jobs to have been affected are drivers, cashiers, fast food workers, laundry workers, call center workers, factory workers, workers in petroleum-related industries*, QA engineers, and computer programmers. These jobs might shift within the industry such that the number of people working in industry X is similar, but there has to be a job title that shrunk by 50%. For example, the same X million people still work in customer service, but most of them are doing something like prompt engineering on AI chatbots, as opposed to taking phone calls directly.
* This one has nothing to do with AI, but I expect it to happen by 2026 nonetheless.
Let me know if you want to formalize a bet on some website.
For (2) I am less interested in betting than I was previously. Before, I assumed you meant that there would be actual, competent Humaniod robotic maids and butlers for sale in 2026. But now I’m imagining that you meant just any ordinary Humanoid robot on the market, even if doesn’t do what a real human maid or butler does.
Like, I think technically in 1990 companies could have already been selling “Humanoid robotic maids”, but they would’ve been functionally useless. Without some sort of constraint on what actually counts as a robotic maid, I think some random flashy-yet-useless robot that changed hands and made some company $300,000 in revenue might count for the purposes of this bet. And I would prefer not to take a bet with that as a potential outcome.
This seems like an extremely weak prediction. Institutions, even fairly reputable ones, make fantastic claims like that all the time.
For example, I found one article written in 2019 that says, “By one estimate, AI contributed a whopping $2 trillion to global GDP last year.” It cites the PricewaterhouseCoopers, which according to Wikipedia is “the second-largest professional services network in the world and is considered one of the Big Four accounting firms, along with Deloitte, EY and KPMG.”
Since GWP was about 86.1 trillion USD in 2018, according to the World Bank, this means that PwC thinks that artificial intelligence is already contributing more than 2% of our gross world product, four times more than you expected would be claimed by 2026!
I am highly skeptical. Which chatbots are you imagining here?
The problem with some of your predictions is that I don’t know how to operationalize them. For example, does L4 self-driving count? What about L3? What source can be used to resolve this question? I’m not currently aware of any source that counts the number of trips done in automobiles in the US, and tabulates them by car type (or self-driving status). So, to bet, we’d either need to get a source, or come up with a different way of operationalizing the question.
(As an aside, I have found that a very high fraction of predictions—even among people who care a lot about betting—tend to be extremely underspecified. I think it’s a non-trivial skill to know how to operationalize bets, and most people just aren’t very good at it without lots of practice. That’s not a criticism of you :). However, I do prefer that you state your predictions very precisely because otherwise we’re just not going to be able to do the bet.)
I think you’re 100% right. Most (>>80%) of the bets I see on Long Bets, or predictions on MetaCalculus, are underspecified to the point where where a human mediator would have to make a judgement call that can be considered unfair to someone. I don’t expect that to change no matter how much work I do, unless I make bets on specific statistics from well known sources, e.g. the stock market, or the CIA World Factbook.
There are possible futures where prediction (3) is obvious. For example, if someone predicted that 50% of trips will be self driving in 2021 (many people did predict that 5 years ago) we can easily prove them wrong without having to debate whether Tesla is L2 or L5 and whether that matters. Teslas are not 50% of the cars on the road, nor are Waymos, so you can easily see that most trips in 2021 are not self driving by any definition. I think there are also future worlds were 95% of cars and trips are L5, most cars can legally autonomously drive anywhere without any humans inside, etc, and in that world there isn’t much to debate about unless you’re really petty. So we could make bets hoping that things will be that obvious, but I don’t think either of us want to do the work to avoid this kind of ambiguity.
I’m happy to consider my bets as paid in Bayes points without any need for future adjudication. So, for all the Bayes points, I’d love to hear what your equivalent predictions are for 2026.
For what it’s worth, here’s my revised (3): Greater than 10% of cars on the road will be legally capable of either L4/L5 OR legally L2/L3 but disengagements will be uncommon, less than once in a typical trip. (Meaning, if you watch a video from the AI DRIVR YouTube channel, there’s less than one disengagement per 20 minutes of driving time.)
To be clear, I have spent a ton of time on Metaculus and I find this impression incorrect. I have spent comparatively little time on Long Bets but I think it’s also wrong there for the most part.
I think you may have accidentally called out parties who are, in my opinion, exemplars of what solid prediction platforms should look like. There are far, far worse parties that you could have called out.