7 ms·

Researchers propose a new interdisciplinary subfield called “Machine Behaviour”

95 points by benhsu75 8y ago

dqpb 8y ago
> Machines powered by artificial intelligence increasingly mediate our social, cultural, economic and political interactions. What about non-AI powered machines that mediate our social, cultural, economic and political interactions? For example, science journal paywalls.
- SolaceQuantum 8y ago
  In my experience most researchers are more than happy to share with you the paper upon askance via e-mail.
  - DiseasedBadger 8y ago
    If I asked for a paper and they looked at me askance, I'd wonder why I emailed them instead of just asking - since we're in viewing range obviously.
    - taneq 8y ago
      Damn autocarrot.
- deleted 8y ago
  [deleted]
- workingpatrick 8y ago
  Here Ya go: https://sci-hub.tw/10.1038/s41586-019-1138-y https://sci-hub.tw/10.1038/s41586-019-1138-y
hobs 8y ago
Paging Dr. Susan Calvin.
- BWStearns 8y ago
  "Robopsychology!" was my first thought haha.
deleted 8y ago
[deleted]
randcraw 8y ago
It's not clear to me how this "field" of study is any different from existing academic analyses of automation or computing. AI doesn't change what computers do; it affects only how it's done. The problems inherent in automation are as old as windmills, falling water, and certainly electricity. Sure, a few modern fields are affected more than others, but improving voice recognition or visual object identification component by 20% doesn't fundamentally change the system using such code to a degree that it warrants a new academic discipline of study. A new legal field of study on automation makes more sense to me. Algorithmic bias, product or service liability, baseline accountability, the standards of due diligence and safety -- these are rising concerns exacerbated by the recent increase in automation, AI-based or not. But these problems are hardly unique to AI any more than the misdirection of elections was unique to manipulation of digital social media. I'm also doubtful that meaningful solutions to byproducts of automation by corporations and nations can be meaningfully addressed by a bunch of academic computer scientists.
- fromthestart 8y ago
  >AI doesn't change what computers do, if affects only how it's done. Except AI is very much changing how things are done as well as what computers do. Image recognition, natural language processing, self piloted vehicles, these are all novel applications for machines which carry significant real world risks to life and property. Moreover, AI in it's current state is a complex black box with unpredictable outputs for given inputs, which may be chaotic - see, for example, adversarial attacks. If we want a better handle on, say, regularizing outputs in a predictable way, or at least a clearer window into the occluded complexity of neural network decision making, a whole new field may very well be warranted. There's a reason we are calling these program outputs behaviors now; it's a new type of loosely -deterministic computing until we develop a deeper understanding which will not be trivial.
  - drb91 8y ago
    > Image recognition, natural language processing, self piloted vehicles Obviously none of these are AI and I don't see how you're helping anything by insinuating they are.
okintheory 8y ago
I get worried when I see something like this. I'm inclined to believe a substantial number of researchers are currently being deliberately fuzzy about what "AI" can and cannot do. Why not call it algorithms and statistics? I think lay-people have a very skewed understanding of what has already been achieved through AI. They may also not understand the word algorithms but at least it doesn't make them think of Skynet. For example, if you asked a person on the street whether there exists an "artificially intelligent supercomputer" somewhere that could help you plan all aspects of a small but entertaining dinner party, they would probably say yes. They imagine that you could just ask IBM Watson to help out, and he'd tell you what to do. This is completely false. "AI" systems are very fragile, and yes, we could build something that plans dinner parties, but we'd have to start over if we needed to plan a kid's birthday party instead. It's very far from strong AI. Ten years ago when it was mostly IBM telling lies, the end result was a couple of billion dollars wasted by hapless healthcare conglomerates. That's already bad. But now we have people from MIT, Stanford, Harvard, Yale, and more embracing the term AI and relying on unfounded hype to push for funding. It would be much less sexy if we called it "Facebook/Google/etc enable unfair/discriminatory advertising by combining intensive data collection with logistic regression, sometimes in multiple layers, and with some graph algorithms thrown in". But it would be a much better starting point for a well-informed debate. I'm not trying to minimize the importance of algorithms in our world, but a healthy discussion should be based on a sound understanding of the facts on the ground, and AI hype is not helping with that. I strongly prefer the less hyperbolic terminology adopted by someone like Aaron Roth at UPenn, e.g. see the blurb for "The Ethical Algorithm: The Science of Socially Aware Algorithm Design". Bonus AI rant: For the celebration of the new MIT Schwarzman College of Computing, which is a huge expansion of the arguably most important computer science department in the world, there was a discussion panel on AI consisting of MIT President Reif, Henry Kissinger (War criminal?, Theranos board member, wannabe AI expert), Tom Friedman (columnist of limited substance), and Stephen Schwarzman (business man, coined the "increased taxes on carried interest are like Hitler's invasion of Poland" analogy, brought the dough). How the heck is that the inaugural panel!?!
- currymj 8y ago
  most actual researchers are entirely sick of the hype and are desperately trying to correct it, to no avail. i think laypeople just have a strong desire to get overexcited about AI and nothing is going to stop them.
high_derivative 8y ago
My highly cynical take as someone researching in ML is that all these interdisciplinary AI studies, AI policy, etc are mostly grifters trying to get in on the gold-rush. What has AI policy produced so far, really? Algorithm analysis and safety research is done on the algorithmic side, not on the policy side. Every time I have read an AI policy paper, it has been listings of desirable properties with absolutely no input on how to make any real steps towards them. In the meantime, realities of algorithm driven mass surveillance and censorship is moving ahead at a rapid pace. Possible conclusion 1: We just need way more AI policy research. Possible conclusion 2: AI policy is done by the wrong people and deep algorithmic understanding is a pre-requisite. Possible conclusion 3: Current AI policy is just used as a fig-leaf by tech companies who hire a few policy essay-writers without substance. The lack of progress in that area is a feature, not bug. Not mutually exclusive, not exhaustive. Feel free to point to valuable policy research.
- currymj 8y ago
  a common pattern I see in papers written by people on the technical side is: "The correct way of ensuring algorithmic fairness is X" where X is something they already happened to work on. In various papers X could be robust optimization, differential privacy, causal inference, adversarial training, etc. The papers are mostly good, actually, but it's sort of putting the cart before the horse. I think there might be legitimate value in having a bunch of lawyers, sociologists, and philosophers set a target more-or-less in ignorance, and let the people on the technical side try to hit it. Of course even better would be interdisciplinary collaboration but that's hard, there aren't many incentives in its favor (where do you publish? any given venue is worthless to half of the authors). and it requires humility on the part of everyone involved.
  - notbob 8y ago
    > The papers are mostly good, actually Not surprising. Computer scientists have a bit of experience with computation, after all. It would actually be more concerning if a bunch of new stuff was being invented whole cloth. > but it's sort of putting the cart before the horse. Not at all. ML algorithms are just fucking algorithms and computer scientists have been thinking about what it means for an algorithm to be correct since... Turing. And have been proving various theorems about ML algorithms in particular since at least the 60s. Complaints that "AI safety looks a lot like previous CS research" are basically equivalent to observations that "neural nets have been around for a lot longer than alexnet". > I think there might be legitimate value in having a bunch of lawyers, sociologists, and philosophers set a target more-or-less in ignorance, and let the people on the technical side try to hit it. I disagree. This is how you end up with endless navel gazing about trolley problems while actual vehicles kill people by accelerating without control because redundant parts are too expensive and engineers don't have enough voice. Philosophers are rarely interested in honest-to-god engineering ethics, which almost always boils down to "pay well enough to hire good people, and then listen to the good people you're paying good money to have around".
TimTheTinker 8y ago
Is there a rigorous definition of “machine behavior” as a research field? I can see how a good-enough marriage of ML/NN to classic symbolic AI could yield a system capable of higher-order, intention-based “behavior” in complex circumstances. But we’re still a long way from achieving that, as far as I know.
barbecue_sauce 8y ago
Human-Computer Interaction?
- dane-pgp 8y ago
  Human-Cyborg Relations?
deleted 8y ago
[deleted]
aristus 8y ago
"Means and motive matter as much as ends. AIs don’t operate in isolation. Somebody designs them, somebody gathers the data to train them, somebody decides how to use the answers they give. Those human-scale decisions are –or should be– documented and understandable, especially for AIs operating in larger domains for higher stakes. It’s natural to want to ask a programmer how you can trust an AI. More revealing is to ask why they do." https://www.ribbonfarm.com/2018/03/13/justifiable-ai/ https://www.ribbonfarm.com/2018/03/13/justifiable-ai/
killjoywashere 8y ago
If you lead, or aspire to lead, major projects, I submit a reasonable definition of 'major' includes sitting at the table in national capitols with senior policy makers. They will expect you to be as invested in understanding their concerns, and the concerns of their constituents, as they are in trying to understand your project and your objectives. Reading articles like this, and I read and annotated the whole thing, is the homework you are going to have to do. Yes, they should all learn category theory and linear algebra and they haven't. That doesn't mean you suddenly have insight on the evolution of the problem spaces they've spent their entire, obviously successful careers navigating. So maybe take the time pay the penence for your genius, and invest the time to understand something about their efforts as they are fumbling around in the dark. At least they had the decency to put in writing for you to read, or ridicule as the case may be. Also, feel free to look up some of the authors on this paper. They don't suck.
return1 8y ago
There is no way to stop the hype. You can just enjoy watching the waves.

95 points by benhsu75 8y ago

dqpb 8y ago

> Machines powered by artificial intelligence increasingly mediate our social, cultural, economic and political interactions. What about non-AI powered machines that mediate our social, cultural, economic and political interactions? For example, science journal paywalls.

SolaceQuantum 8y ago
In my experience most researchers are more than happy to share with you the paper upon askance via e-mail.
- DiseasedBadger 8y ago
  If I asked for a paper and they looked at me askance, I'd wonder why I emailed them instead of just asking - since we're in viewing range obviously.
  - taneq 8y ago
    Damn autocarrot.
deleted 8y ago
[deleted]
workingpatrick 8y ago
Here Ya go: https://sci-hub.tw/10.1038/s41586-019-1138-y https://sci-hub.tw/10.1038/s41586-019-1138-y

hobs 8y ago

Paging Dr. Susan Calvin.

BWStearns 8y ago
"Robopsychology!" was my first thought haha.

deleted 8y ago

[deleted]

randcraw 8y ago

It's not clear to me how this "field" of study is any different from existing academic analyses of automation or computing. AI doesn't change what computers do; it affects only how it's done. The problems inherent in automation are as old as windmills, falling water, and certainly electricity. Sure, a few modern fields are affected more than others, but improving voice recognition or visual object identification component by 20% doesn't fundamentally change the system using such code to a degree that it warrants a new academic discipline of study. A new legal field of study on automation makes more sense to me. Algorithmic bias, product or service liability, baseline accountability, the standards of due diligence and safety -- these are rising concerns exacerbated by the recent increase in automation, AI-based or not. But these problems are hardly unique to AI any more than the misdirection of elections was unique to manipulation of digital social media. I'm also doubtful that meaningful solutions to byproducts of automation by corporations and nations can be meaningfully addressed by a bunch of academic computer scientists.

fromthestart 8y ago
>AI doesn't change what computers do, if affects only how it's done. Except AI is very much changing how things are done as well as what computers do. Image recognition, natural language processing, self piloted vehicles, these are all novel applications for machines which carry significant real world risks to life and property. Moreover, AI in it's current state is a complex black box with unpredictable outputs for given inputs, which may be chaotic - see, for example, adversarial attacks. If we want a better handle on, say, regularizing outputs in a predictable way, or at least a clearer window into the occluded complexity of neural network decision making, a whole new field may very well be warranted. There's a reason we are calling these program outputs behaviors now; it's a new type of loosely -deterministic computing until we develop a deeper understanding which will not be trivial.
- drb91 8y ago
  > Image recognition, natural language processing, self piloted vehicles Obviously none of these are AI and I don't see how you're helping anything by insinuating they are.

okintheory 8y ago

I get worried when I see something like this. I'm inclined to believe a substantial number of researchers are currently being deliberately fuzzy about what "AI" can and cannot do. Why not call it algorithms and statistics? I think lay-people have a very skewed understanding of what has already been achieved through AI. They may also not understand the word algorithms but at least it doesn't make them think of Skynet. For example, if you asked a person on the street whether there exists an "artificially intelligent supercomputer" somewhere that could help you plan all aspects of a small but entertaining dinner party, they would probably say yes. They imagine that you could just ask IBM Watson to help out, and he'd tell you what to do. This is completely false. "AI" systems are very fragile, and yes, we could build something that plans dinner parties, but we'd have to start over if we needed to plan a kid's birthday party instead. It's very far from strong AI. Ten years ago when it was mostly IBM telling lies, the end result was a couple of billion dollars wasted by hapless healthcare conglomerates. That's already bad. But now we have people from MIT, Stanford, Harvard, Yale, and more embracing the term AI and relying on unfounded hype to push for funding. It would be much less sexy if we called it "Facebook/Google/etc enable unfair/discriminatory advertising by combining intensive data collection with logistic regression, sometimes in multiple layers, and with some graph algorithms thrown in". But it would be a much better starting point for a well-informed debate. I'm not trying to minimize the importance of algorithms in our world, but a healthy discussion should be based on a sound understanding of the facts on the ground, and AI hype is not helping with that. I strongly prefer the less hyperbolic terminology adopted by someone like Aaron Roth at UPenn, e.g. see the blurb for "The Ethical Algorithm: The Science of Socially Aware Algorithm Design". Bonus AI rant: For the celebration of the new MIT Schwarzman College of Computing, which is a huge expansion of the arguably most important computer science department in the world, there was a discussion panel on AI consisting of MIT President Reif, Henry Kissinger (War criminal?, Theranos board member, wannabe AI expert), Tom Friedman (columnist of limited substance), and Stephen Schwarzman (business man, coined the "increased taxes on carried interest are like Hitler's invasion of Poland" analogy, brought the dough). How the heck is that the inaugural panel!?!

currymj 8y ago
most actual researchers are entirely sick of the hype and are desperately trying to correct it, to no avail. i think laypeople just have a strong desire to get overexcited about AI and nothing is going to stop them.

high_derivative 8y ago

My highly cynical take as someone researching in ML is that all these interdisciplinary AI studies, AI policy, etc are mostly grifters trying to get in on the gold-rush. What has AI policy produced so far, really? Algorithm analysis and safety research is done on the algorithmic side, not on the policy side. Every time I have read an AI policy paper, it has been listings of desirable properties with absolutely no input on how to make any real steps towards them. In the meantime, realities of algorithm driven mass surveillance and censorship is moving ahead at a rapid pace. Possible conclusion 1: We just need way more AI policy research. Possible conclusion 2: AI policy is done by the wrong people and deep algorithmic understanding is a pre-requisite. Possible conclusion 3: Current AI policy is just used as a fig-leaf by tech companies who hire a few policy essay-writers without substance. The lack of progress in that area is a feature, not bug. Not mutually exclusive, not exhaustive. Feel free to point to valuable policy research.

currymj 8y ago
a common pattern I see in papers written by people on the technical side is: "The correct way of ensuring algorithmic fairness is X" where X is something they already happened to work on. In various papers X could be robust optimization, differential privacy, causal inference, adversarial training, etc. The papers are mostly good, actually, but it's sort of putting the cart before the horse. I think there might be legitimate value in having a bunch of lawyers, sociologists, and philosophers set a target more-or-less in ignorance, and let the people on the technical side try to hit it. Of course even better would be interdisciplinary collaboration but that's hard, there aren't many incentives in its favor (where do you publish? any given venue is worthless to half of the authors). and it requires humility on the part of everyone involved.
- notbob 8y ago
  > The papers are mostly good, actually Not surprising. Computer scientists have a bit of experience with computation, after all. It would actually be more concerning if a bunch of new stuff was being invented whole cloth. > but it's sort of putting the cart before the horse. Not at all. ML algorithms are just fucking algorithms and computer scientists have been thinking about what it means for an algorithm to be correct since... Turing. And have been proving various theorems about ML algorithms in particular since at least the 60s. Complaints that "AI safety looks a lot like previous CS research" are basically equivalent to observations that "neural nets have been around for a lot longer than alexnet". > I think there might be legitimate value in having a bunch of lawyers, sociologists, and philosophers set a target more-or-less in ignorance, and let the people on the technical side try to hit it. I disagree. This is how you end up with endless navel gazing about trolley problems while actual vehicles kill people by accelerating without control because redundant parts are too expensive and engineers don't have enough voice. Philosophers are rarely interested in honest-to-god engineering ethics, which almost always boils down to "pay well enough to hire good people, and then listen to the good people you're paying good money to have around".

TimTheTinker 8y ago

Is there a rigorous definition of “machine behavior” as a research field? I can see how a good-enough marriage of ML/NN to classic symbolic AI could yield a system capable of higher-order, intention-based “behavior” in complex circumstances. But we’re still a long way from achieving that, as far as I know.

barbecue_sauce 8y ago

Human-Computer Interaction?

dane-pgp 8y ago
Human-Cyborg Relations?

deleted 8y ago

[deleted]

aristus 8y ago

"Means and motive matter as much as ends. AIs don’t operate in isolation. Somebody designs them, somebody gathers the data to train them, somebody decides how to use the answers they give. Those human-scale decisions are –or should be– documented and understandable, especially for AIs operating in larger domains for higher stakes. It’s natural to want to ask a programmer how you can trust an AI. More revealing is to ask why they do." https://www.ribbonfarm.com/2018/03/13/justifiable-ai/ https://www.ribbonfarm.com/2018/03/13/justifiable-ai/

killjoywashere 8y ago

If you lead, or aspire to lead, major projects, I submit a reasonable definition of 'major' includes sitting at the table in national capitols with senior policy makers. They will expect you to be as invested in understanding their concerns, and the concerns of their constituents, as they are in trying to understand your project and your objectives. Reading articles like this, and I read and annotated the whole thing, is the homework you are going to have to do. Yes, they should all learn category theory and linear algebra and they haven't. That doesn't mean you suddenly have insight on the evolution of the problem spaces they've spent their entire, obviously successful careers navigating. So maybe take the time pay the penence for your genius, and invest the time to understand something about their efforts as they are fumbling around in the dark. At least they had the decency to put in writing for you to read, or ridicule as the case may be. Also, feel free to look up some of the authors on this paper. They don't suck.

return1 8y ago

There is no way to stop the hype. You can just enjoy watching the waves.