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I completely agree. One cannot pass fair laws on topics one doesn't understand. It's even worse if understanding is based on input from a consultant or lobbyist

I completely agree. One cannot pass fair laws on topics one doesn't understand. It's even worse if understanding is based on input from a consultant or lobbyist! There aren't many reasons an engineer would want to jump into the political fray besides altruism. It's like choosing to join the largest, most corrupt, bureaucratic, change-averse, and valueless company that ever existed. For lawyers, that probably sounds like a dream come true. My perspective as an engineer: Yes, there are huge important problems I would like to help solve. No, I don't think I'm willing or capable of solving that class of problems in a system like that.

sharadov 7y ago
Incentivize engineers/technologists to join the government, that's what China has been pushing for the past 3 decades. Partner with the FAANG companies, have them sponsor their employees to go on a paid sabbatical wherein they can contribute to technology solutions within government. I know some form of this was happening under Obama.
- opportune 7y ago
  I would totally be willing to do this, however I fear the actual implementation would be more of the same: - Terrible consultants, middlemen, and lifers making progress too hard or impossible. - Constantly changing or unclear requirements - Making penny-wise, pound-foolish decisions for political reasons or budgeting idiosyncracies - Resistance from lawmakers since it would give more government influence to people from states that lean towards some of other party (put more bluntly, Republicans don't want to give a bunch of liberal or left-leaning people from California, Washington, and New York more influence) - The government only seeing the value in surveillance/military software that people are likely to refuse to work on. Not sponsoring software that actually directly helps people domestically What I think we need instead is something like the US Digital Service, along with some other government programs, rolled into a Department of Technology that will hopefully have a completely different culture, payscale, etc. compared to the rest of the government. And maybe even something like jury duty, but for software people, engineers, scientists, etc. so it's more representative of the general public and not the old-school government culture.
  - jointpdf 7y ago
    > Terrible consultants, middlemen, and lifers making progress too hard or impossible. Can you elaborate on this? I hear lots of criticism around inept and ill-intentioned consultants and “govvies” alike, but I think a lot of it is a bit unfairly harsh. There are certainly some adverse incentives (chiefly that it’s easy enough to coast along with minimal effort in these jobs if you’re inclined), but I’d say the slight majority of individuals in this space are qualified, capable, and motivated to improve the world via good work—technical or otherwise. Slowly but surely, projects get done and positive change is made. > Not sponsoring software that actually directly helps people domestically This really isn’t true. Every US agency has technology initiatives, some more effective than others. See the work that the Department of Veterans Affairs does, as one example. The US government also spends ~$750B on grants annually, much of which goes to fundamental research as well as tech projects. If you really want to dig in, there’s an API to access data on every item of spending (e.g. contract awards, grants): https://www.usaspending.gov/#/ https://www.usaspending.gov/#/
    - opportune 7y ago
      >Terrible consultants, middlemen, and lifers making progress too hard or impossible. I can mostly only echo hearsay, however I have been tangentially involved in government-focused projects. The big issue I see with the government getting involved in tech is that they don't know what they really want, they don't know how to filter out bullshit/upsells, and they'll frequently overpay for things they don't need and underpay for things that would be very helpful. I'm not one of those people who wants to defund the government or privatize everything, however I do think the government incentive structure needs to be redesigned and become less cushy while simultaneously becoming more lucrative, accompanied by a huge culture change. The government isn't inherently inefficient, it's only like that because we let it be, one has only to look at other countries (see: Estonia, China) to see how much more effective we could be here. Regarding actual spending, I want to measure that by its effectiveness, not by the total amount spent. The government could blow all its money on tech initiatives and EMR upgrades and have nothing to show for it, and I wouldn't call that helping people domestically. To address your examples, a lot of those grants are for defense contractors (which only indirectly helps people, at best, and is itself a pretty corrupt old-boys industry). Others are for things like direct academic research though, which I do think is decently well run in general, but isn't really a direct government relationship to tech.
ismail 7y ago
The question becomes, how much understanding is enough? Do we need AI engineers passing laws or training courses for politicians? Can the AI be treated like a black box [0] and the regulation still be effective? I think in some cases the answer would be yes. A simple thought experiment demonstrates this. Example: Consider regulation for AI in relation to job losses. Does one need to know the details of AI tech to develop regulation effectively? I would argue not. The economic impacts can be analysed. At some tipping point, job losses Lead to a cycle of: reduced demand -> reduced Revenue in companies -> reductions in jobs -> reduced demand and so on and on. To regulate this, one would have to model the economic impacts as best as one could. Understand what jobs are most likely to be lost, in which industries. Understand the key things to monitor (I.e replacement job rate. Assuming jobs are created in other industries). And pass regulation to mitigate some of the risks or make sure the number of Job losses never hit the tipping point. None of these items leads me to believe the person passing the regulation requires knowledge of AI at an engineers level. There is the Conant-Ashby Theorem or good regulator theory [1]. This states “Every good regulator of the system must be a model of the system” [2] In this instance the regulation is to deal with ECONOMIC impacts. Therefore an economic model is needed, while impacted by the tech the tech does not need to be modelled. Thus my conclusion. This poses an interesting question, could all eventualities be catered for in the regulation? There is other theories that could help us answer this question. Happy to hear different or contradictory views. [0] https://en.m.wikipedia.org/wiki/Black_box#Other_theories https://en.m.wikipedia.org/wiki/Black_box#Other_theories [1] https://en.m.wikipedia.org/wiki/Good_regulator https://en.m.wikipedia.org/wiki/Good_regulator [2] http://pespmc1.vub.ac.be/books/Conant_Ashby.pdf http://pespmc1.vub.ac.be/books/Conant_Ashby.pdf
- sailfast 7y ago
  Enough to reason about what the thing does in a basic manner and potential pros and cons? That seems like a straightforward heuristic but maybe I'm an optimist haha. You don't need to know the ins and outs of the mathematics to understand what encryption does, but you should be able to reason about how data is encrypted and what is necessary to decrypt, and what law provides you to request decryption. You sure as hell should also be able to grasp that, like the atomic bomb, once out in the world it's there to stay and you can't really prevent it from being used. As for economic impact - I find that measure does not really get ahead of the problem. In the case of machine learning if you're regulating economic impact you're decades late. The first-movers already have the data sets, the training has occurred and now you're living in their world. I think it's important to understand a bit more upstream so that you can get ahead of these kinds of issues. Furthermore, a baseline understanding of these algorithms and their potential biases / impacts of misclassification is important to understand to ensure we don't compromise society by their mis-use, or shift scales in a terrible way unintentionally. The earlier the better, and economic impacts are probably a lagging indicator, or even worse considered a huge boon for those areas that have companies doing this work but using labor from elsewhere to train their models.
ismail 7y ago
I have added the link to the good regulator theorem, “Every good regulator of the system must be a model of the system” paper on HN. https://news.ycombinator.com/item?id=20761459 https://news.ycombinator.com/item?id=20761459

sharadov 7y ago

Incentivize engineers/technologists to join the government, that's what China has been pushing for the past 3 decades. Partner with the FAANG companies, have them sponsor their employees to go on a paid sabbatical wherein they can contribute to technology solutions within government. I know some form of this was happening under Obama.

opportune 7y ago
I would totally be willing to do this, however I fear the actual implementation would be more of the same: - Terrible consultants, middlemen, and lifers making progress too hard or impossible. - Constantly changing or unclear requirements - Making penny-wise, pound-foolish decisions for political reasons or budgeting idiosyncracies - Resistance from lawmakers since it would give more government influence to people from states that lean towards some of other party (put more bluntly, Republicans don't want to give a bunch of liberal or left-leaning people from California, Washington, and New York more influence) - The government only seeing the value in surveillance/military software that people are likely to refuse to work on. Not sponsoring software that actually directly helps people domestically What I think we need instead is something like the US Digital Service, along with some other government programs, rolled into a Department of Technology that will hopefully have a completely different culture, payscale, etc. compared to the rest of the government. And maybe even something like jury duty, but for software people, engineers, scientists, etc. so it's more representative of the general public and not the old-school government culture.
- jointpdf 7y ago
  > Terrible consultants, middlemen, and lifers making progress too hard or impossible. Can you elaborate on this? I hear lots of criticism around inept and ill-intentioned consultants and “govvies” alike, but I think a lot of it is a bit unfairly harsh. There are certainly some adverse incentives (chiefly that it’s easy enough to coast along with minimal effort in these jobs if you’re inclined), but I’d say the slight majority of individuals in this space are qualified, capable, and motivated to improve the world via good work—technical or otherwise. Slowly but surely, projects get done and positive change is made. > Not sponsoring software that actually directly helps people domestically This really isn’t true. Every US agency has technology initiatives, some more effective than others. See the work that the Department of Veterans Affairs does, as one example. The US government also spends ~$750B on grants annually, much of which goes to fundamental research as well as tech projects. If you really want to dig in, there’s an API to access data on every item of spending (e.g. contract awards, grants): https://www.usaspending.gov/#/ https://www.usaspending.gov/#/
  - opportune 7y ago
    >Terrible consultants, middlemen, and lifers making progress too hard or impossible. I can mostly only echo hearsay, however I have been tangentially involved in government-focused projects. The big issue I see with the government getting involved in tech is that they don't know what they really want, they don't know how to filter out bullshit/upsells, and they'll frequently overpay for things they don't need and underpay for things that would be very helpful. I'm not one of those people who wants to defund the government or privatize everything, however I do think the government incentive structure needs to be redesigned and become less cushy while simultaneously becoming more lucrative, accompanied by a huge culture change. The government isn't inherently inefficient, it's only like that because we let it be, one has only to look at other countries (see: Estonia, China) to see how much more effective we could be here. Regarding actual spending, I want to measure that by its effectiveness, not by the total amount spent. The government could blow all its money on tech initiatives and EMR upgrades and have nothing to show for it, and I wouldn't call that helping people domestically. To address your examples, a lot of those grants are for defense contractors (which only indirectly helps people, at best, and is itself a pretty corrupt old-boys industry). Others are for things like direct academic research though, which I do think is decently well run in general, but isn't really a direct government relationship to tech.

ismail 7y ago

The question becomes, how much understanding is enough? Do we need AI engineers passing laws or training courses for politicians? Can the AI be treated like a black box [0] and the regulation still be effective? I think in some cases the answer would be yes. A simple thought experiment demonstrates this. Example: Consider regulation for AI in relation to job losses. Does one need to know the details of AI tech to develop regulation effectively? I would argue not. The economic impacts can be analysed. At some tipping point, job losses Lead to a cycle of: reduced demand -> reduced Revenue in companies -> reductions in jobs -> reduced demand and so on and on. To regulate this, one would have to model the economic impacts as best as one could. Understand what jobs are most likely to be lost, in which industries. Understand the key things to monitor (I.e replacement job rate. Assuming jobs are created in other industries). And pass regulation to mitigate some of the risks or make sure the number of Job losses never hit the tipping point. None of these items leads me to believe the person passing the regulation requires knowledge of AI at an engineers level. There is the Conant-Ashby Theorem or good regulator theory [1]. This states “Every good regulator of the system must be a model of the system” [2] In this instance the regulation is to deal with ECONOMIC impacts. Therefore an economic model is needed, while impacted by the tech the tech does not need to be modelled. Thus my conclusion. This poses an interesting question, could all eventualities be catered for in the regulation? There is other theories that could help us answer this question. Happy to hear different or contradictory views. [0] https://en.m.wikipedia.org/wiki/Black_box#Other_theories https://en.m.wikipedia.org/wiki/Black_box#Other_theories [1] https://en.m.wikipedia.org/wiki/Good_regulator https://en.m.wikipedia.org/wiki/Good_regulator [2] http://pespmc1.vub.ac.be/books/Conant_Ashby.pdf http://pespmc1.vub.ac.be/books/Conant_Ashby.pdf

sailfast 7y ago
Enough to reason about what the thing does in a basic manner and potential pros and cons? That seems like a straightforward heuristic but maybe I'm an optimist haha. You don't need to know the ins and outs of the mathematics to understand what encryption does, but you should be able to reason about how data is encrypted and what is necessary to decrypt, and what law provides you to request decryption. You sure as hell should also be able to grasp that, like the atomic bomb, once out in the world it's there to stay and you can't really prevent it from being used. As for economic impact - I find that measure does not really get ahead of the problem. In the case of machine learning if you're regulating economic impact you're decades late. The first-movers already have the data sets, the training has occurred and now you're living in their world. I think it's important to understand a bit more upstream so that you can get ahead of these kinds of issues. Furthermore, a baseline understanding of these algorithms and their potential biases / impacts of misclassification is important to understand to ensure we don't compromise society by their mis-use, or shift scales in a terrible way unintentionally. The earlier the better, and economic impacts are probably a lagging indicator, or even worse considered a huge boon for those areas that have companies doing this work but using labor from elsewhere to train their models.

ismail 7y ago

I have added the link to the good regulator theorem, “Every good regulator of the system must be a model of the system” paper on HN. https://news.ycombinator.com/item?id=20761459 https://news.ycombinator.com/item?id=20761459