The O*NET database includes a list of about 20,000 different tasks that American workers currently need to perform as part of their jobs. I’ve found it pretty interesting to scroll through the list, sorted in random order, to get a sense of the different bits of work that add up to the US economy. I think anyone who thinks a lot about AI-driven automation might find it useful to spend five minutes scrolling around: it’s a way of jumping yourself down to a lower level of abstraction. I think the list is also a little bit mesmerizing, in its own right.
One update I’ve made is that I’m now more confident that more than half of present-day occupational tasks could be automated using fairly narrow, non-agential, and boring-looking AI systems. (Most of them don’t scream “this task requires AI systems with long-run objectives and high levels of generality.”) I think it’s also pretty interesting, as kind of a game, to try to imagine as concretely as possible what the training processes might look like for systems that can perform (or eliminate the need for) different tasks on the list.
As a sample, here are ten random tasks. (Some of these could easily be broken up into a lot of different sub-tasks or task variants, which might be automated independently.)
Cancel letter or parcel post stamps by hand.
Inquire into the cause, manner, and circumstances of human deaths and establish the identities of deceased persons.
Teach patients to use home health care equipment.
Write reports or articles for Web sites or newsletters related to environmental engineering issues.
Supervise and participate in kitchen and dining area cleaning activities.
Intervene as an advocate for clients or patients to resolve emergency problems in crisis situations.
Mark or tag material with proper job number, piece marks, and other identifying marks as required.
Calculate amount of debt and funds available to plan methods of payoff and to estimate time for debt liquidation.
Weld metal parts together, using portable gas welding equipment.
Provide assistance to patrons by performing duties such as opening doors and carrying bags.
In general, I think “read short descriptions of randomly sampled cases” might be an underrated way to learn about the world and notice issues with your assumptions/models.
A couple other examples:
I’ve been trying to develop a better understanding of various aspects of interstate conflict. The Correlates of War militarized interstate disputes (MIDs) dataset is, I think, somewhat useful for this. The project files include short descriptions of (supposedly) every case between 1993 and 2014 in which one state “threatened, displayed, or used force against another.” Here, for example, is the set of descriptions for 2011-2014. I’m not sure I’ve had any huge/concrete take-aways, but I think reading the cases: (a) made me aware of some international tensions I was oblivious to; (b) gave me a slightly better understanding of dynamics around ‘micro-aggressions’ (e.g. flying over someone’s airspace); and (c) helped me more strongly internalize the low base rate for crises boiling over into war (since I disproportionately read about historical disputes that turned into something larger).
Last year, I also spent a bit of time trying to improve my understanding of police killings in the US. I found this book unusually useful. It includes short descriptions of every single incident in which an unarmed person was killed by a police officer in 2015. I feel like reading a portion of it helped me to quickly notice and internalize different aspects of the problem (e.g. the fact that something like a third of the deaths are caused by tasers; the large role of untreated mental illness as a risk factor; the fact that nearly all fatal interactions are triggered by 911 calls, rather than stops; the fact that officers are trained to interact importantly differently with people they believe are on PCP; etc.). l assume I could have learned all the same things by just reading papers — but I think the case sampling approach was probably faster and better for retention.
I think it’s possible there might be value in creating “random case descriptions” collections for a broader range of phenomena. Academia really doesn’t emphasize these kinds of collections as tools for either research or teaching.
I’d actually say this is a variety of qualitative research. At least in the main academic areas I follow, though, it seems a lot more common to read and write up small numbers of detailed case studies (often selected for being especially interesting) than to read and write up large numbers of shallow case studies (selected close to randomly).
This seems to be true in international relations, for example. In a class on interstate war, it’s plausible people would be assigned a long analysis of the outbreak WW1, but very unlikely they’d be assigned short descriptions of the outbreaks of twenty random wars. (Quite possible there’s a lot of variation between fields, though.)
I agree with the thrust of the conclusion, though I worry that focusing on task decomposition this way elides the fact that the descriptions of the O*NET tasks already assume your unit of labor is fairly general. Reading many of these, I actually feel pretty unsure about the level of generality or common-sense reasoning required for an AI to straightforwardly replace that part of a human’s job. Presumably there’s some restructure that would still squeeze a lot of economic value out of narrow AIs that could basically do these things, but that restructure isn’t captured looking at the list of present-day O*NET tasks.
The O*NET database includes a list of about 20,000 different tasks that American workers currently need to perform as part of their jobs. I’ve found it pretty interesting to scroll through the list, sorted in random order, to get a sense of the different bits of work that add up to the US economy. I think anyone who thinks a lot about AI-driven automation might find it useful to spend five minutes scrolling around: it’s a way of jumping yourself down to a lower level of abstraction. I think the list is also a little bit mesmerizing, in its own right.
One update I’ve made is that I’m now more confident that more than half of present-day occupational tasks could be automated using fairly narrow, non-agential, and boring-looking AI systems. (Most of them don’t scream “this task requires AI systems with long-run objectives and high levels of generality.”) I think it’s also pretty interesting, as kind of a game, to try to imagine as concretely as possible what the training processes might look like for systems that can perform (or eliminate the need for) different tasks on the list.
As a sample, here are ten random tasks. (Some of these could easily be broken up into a lot of different sub-tasks or task variants, which might be automated independently.)
Cancel letter or parcel post stamps by hand.
Inquire into the cause, manner, and circumstances of human deaths and establish the identities of deceased persons.
Teach patients to use home health care equipment.
Write reports or articles for Web sites or newsletters related to environmental engineering issues.
Supervise and participate in kitchen and dining area cleaning activities.
Intervene as an advocate for clients or patients to resolve emergency problems in crisis situations.
Mark or tag material with proper job number, piece marks, and other identifying marks as required.
Calculate amount of debt and funds available to plan methods of payoff and to estimate time for debt liquidation.
Weld metal parts together, using portable gas welding equipment.
Provide assistance to patrons by performing duties such as opening doors and carrying bags.
In general, I think “read short descriptions of randomly sampled cases” might be an underrated way to learn about the world and notice issues with your assumptions/models.
A couple other examples:
I’ve been trying to develop a better understanding of various aspects of interstate conflict. The Correlates of War militarized interstate disputes (MIDs) dataset is, I think, somewhat useful for this. The project files include short descriptions of (supposedly) every case between 1993 and 2014 in which one state “threatened, displayed, or used force against another.” Here, for example, is the set of descriptions for 2011-2014. I’m not sure I’ve had any huge/concrete take-aways, but I think reading the cases: (a) made me aware of some international tensions I was oblivious to; (b) gave me a slightly better understanding of dynamics around ‘micro-aggressions’ (e.g. flying over someone’s airspace); and (c) helped me more strongly internalize the low base rate for crises boiling over into war (since I disproportionately read about historical disputes that turned into something larger).
Last year, I also spent a bit of time trying to improve my understanding of police killings in the US. I found this book unusually useful. It includes short descriptions of every single incident in which an unarmed person was killed by a police officer in 2015. I feel like reading a portion of it helped me to quickly notice and internalize different aspects of the problem (e.g. the fact that something like a third of the deaths are caused by tasers; the large role of untreated mental illness as a risk factor; the fact that nearly all fatal interactions are triggered by 911 calls, rather than stops; the fact that officers are trained to interact importantly differently with people they believe are on PCP; etc.). l assume I could have learned all the same things by just reading papers — but I think the case sampling approach was probably faster and better for retention.
I think it’s possible there might be value in creating “random case descriptions” collections for a broader range of phenomena. Academia really doesn’t emphasize these kinds of collections as tools for either research or teaching.
EDIT: Another good example of this approach to learning is Rob Besinger’s recent post “thirty-three randomly selected bioethics papers.”
Interesting ideas. Some similarities with qualitative research, but also important differences, I think (if I understand you correctly).
I’d actually say this is a variety of qualitative research. At least in the main academic areas I follow, though, it seems a lot more common to read and write up small numbers of detailed case studies (often selected for being especially interesting) than to read and write up large numbers of shallow case studies (selected close to randomly).
This seems to be true in international relations, for example. In a class on interstate war, it’s plausible people would be assigned a long analysis of the outbreak WW1, but very unlikely they’d be assigned short descriptions of the outbreaks of twenty random wars. (Quite possible there’s a lot of variation between fields, though.)
I agree with the thrust of the conclusion, though I worry that focusing on task decomposition this way elides the fact that the descriptions of the O*NET tasks already assume your unit of labor is fairly general. Reading many of these, I actually feel pretty unsure about the level of generality or common-sense reasoning required for an AI to straightforwardly replace that part of a human’s job. Presumably there’s some restructure that would still squeeze a lot of economic value out of narrow AIs that could basically do these things, but that restructure isn’t captured looking at the list of present-day O*NET tasks.