Microsoft’s Kate Crawford: ‘AI is neither artificial nor intelligent’(theguardian.com)
theguardian.com
Microsoft’s Kate Crawford: ‘AI is neither artificial nor intelligent’
https://www.theguardian.com/technology/2021/jun/06/microsofts-kate-crawford-ai-is-neither-artificial-nor-intelligent
91 comments
It sucks when facts get in the way of a fashionable narrative, but the Google AI researcher was fired for demanding the names of an internal review panel who rejected her paper. She had a reputation for accusing her colleagues of bigotry and other toxic behavior.
The r/machinelearning thread provides a more balanced perspective: https://www.reddit.com/r/MachineLearning/comments/k77sxz/d_t...
The r/machinelearning thread provides a more balanced perspective: https://www.reddit.com/r/MachineLearning/comments/k77sxz/d_t...
> the Google AI researcher was fired for demanding the names of an internal review panel who rejected her paper.
That was a last-ditch bid to amicably resolve a situation in which the unique adverse treatment by Google had already reached the point of constructive termination. (And, in fact, that is neither of Google’s simultaneously-presented and incompatible explanations for the reasons for the separation – which were “she resigned and wasn’t fired” and “she was fired for comments on an internal mailing list” – though it does share with Google’s explanations that it fails to explain the situation that already constituted constructive termination and references only events after and responding to that condition.)
That was a last-ditch bid to amicably resolve a situation in which the unique adverse treatment by Google had already reached the point of constructive termination. (And, in fact, that is neither of Google’s simultaneously-presented and incompatible explanations for the reasons for the separation – which were “she resigned and wasn’t fired” and “she was fired for comments on an internal mailing list” – though it does share with Google’s explanations that it fails to explain the situation that already constituted constructive termination and references only events after and responding to that condition.)
When someone accuses colleagues of bigotry and other toxic behaviour, it's typically because those people are perpetrating bigotry and other toxic behaviour.
This needn't be relitigated, but you're at least misleading, if not factually wrong.
The "internal review panel" wasn't at least at the time, known to be a real process. In other words, the sequence of events was that the paper was written and approved via normal channels. After this, the authors were informed that they needed to withdraw it. Confused, they asked for more information (by what process was this decided, and by who, was it peers, legal, an executive who wanted to bury the paper, etc.) and were stonewalled. To my knowledge these questions still haven't really been answered.
I think its very reasonable request to want to know who or what caused your paper to get blocked. In normal peer review, even when it is blind, you can work with the reviewers to update the paper and see their feedback. These opportunities weren't readily provided.
The "internal review panel" wasn't at least at the time, known to be a real process. In other words, the sequence of events was that the paper was written and approved via normal channels. After this, the authors were informed that they needed to withdraw it. Confused, they asked for more information (by what process was this decided, and by who, was it peers, legal, an executive who wanted to bury the paper, etc.) and were stonewalled. To my knowledge these questions still haven't really been answered.
I think its very reasonable request to want to know who or what caused your paper to get blocked. In normal peer review, even when it is blind, you can work with the reviewers to update the paper and see their feedback. These opportunities weren't readily provided.
So it's normal to demand the identities of a blind review panel?
What exactly did I say that was "factually wrong" as you claim?
What exactly did I say that was "factually wrong" as you claim?
> So it's normal to demand the identities of a blind review panel?
It seems reasonable to ask "wait what blind review panel" when one appears out of thin air.
It seems reasonable to ask "wait what blind review panel" when one appears out of thin air.
She did a lot more than that. Here is the email that got her fired:
Hi friends,
I had stopped writing here as you may know, after all the micro and macro aggressions and harassments I received after posting my stories here (and then of course it started being moderated).
Recently however, I was contributing to a document that Katherine and Daphne were writing where they were dismayed by the fact that after all this talk, this org seems to have hired 14% or so women this year. Samy has hired 39% from what I understand but he has zero incentive to do this.
What I want to say is stop writing your documents because it doesn’t make a difference. The DEI OKRs that we don’t know where they come from (and are never met anyways), the random discussions, the “we need more mentorship” rather than “we need to stop the toxic environments that hinder us from progressing” the constant fighting and education at your cost, they don’t matter. Because there is zero accountability. There is no incentive to hire 39% women: your life gets worse when you start advocating for underrepresented people, you start making the other leaders upset when they don’t want to give you good ratings during calibration. There is no way more documents or more conversations will achieve anything. We just had a Black research all hands with such an emotional show of exasperation. Do you know what happened since? Silencing in the most fundamental way possible.
Have you ever heard of someone getting “feedback” on a paper through a privileged and confidential document to HR? Does that sound like a standard procedure to you or does it just happen to people like me who are constantly dehumanized?
Imagine this: You’ve sent a paper for feedback to 30+ researchers, you’re awaiting feedback from PR & Policy who you gave a heads up before you even wrote the work saying “we’re thinking of doing this”, working on a revision plan figuring out how to address different feedback from people, haven’t heard from PR & Policy besides them asking you for updates (in 2 months). A week before you go out on vacation, you see a meeting pop up at 4:30pm PST on your calendar (this popped up at around 2pm). No one would tell you what the meeting was about in advance. Then in that meeting your manager’s manager tells you “it has been decided” that you need to retract this paper by next week, Nov. 27, the week when almost everyone would be out (and a date which has nothing to do with the conference process). You are not worth having any conversations about this, since you are not someone whose humanity (let alone expertise recognized by journalists, governments, scientists, civic organizations such as the electronic frontiers foundation etc) is acknowledged or valued in this company.
Then, you ask for more information. What specific feedback exists? Who is it coming from? Why now? Why not before? Can you go back and forth with anyone? Can you understand what exactly is problematic and what can be changed?
And you are told after a while, that your manager can read you a privileged and confidential document and you’re not supposed to even know who contributed to this document, who wrote this feedback, what process was followed or anything. You write a detailed document discussing whatever pieces of feedback you can find, asking for questions and clarifications, and it is completely ignored. And you’re met with, once again, an order to retract the paper with no engagement whatsoever.
Then you try to engage in a conversation about how this is not acceptable and people start doing the opposite of any sort of self reflection—trying to find scapegoats to blame.
Silencing marginalized voices like this is the opposite of the NAUWU principles which we discussed. And doing this in the context of “responsible AI” adds so much salt to the wounds. I understand that the only things that mean anything at Google are levels, I’ve seen how my expertise has been completely dismissed. But now there’s an additional layer saying any privileged person can decide that they don’t want your paper out with zero conversation. So you’re blocked from adding your voice to the research community—your work which you do on top of the other marginalization you face here.
I’m always amazed at how people can continue to do thing after thing like this and then turn around and ask me for some sort of extra DEI work or input. This happened to me last year. I was in the middle of a potential lawsuit for which Kat Herller and I hired feminist lawyers who threatened to sue Google (which is when they backed off--before that Google lawyers were prepared to throw us under the bus and our leaders were following as instructed) and the next day I get some random “impact award.” Pure gaslighting.
So if you would like to change things, I suggest focusing on leadership accountability and thinking through what types of pressures can also be applied from the outside. For instance, I believe that the Congressional Black Caucus is the entity that started forcing tech companies to report their diversity numbers. Writing more documents and saying things over and over again will tire you out but no one will listen.
Timnit
Hi friends,
I had stopped writing here as you may know, after all the micro and macro aggressions and harassments I received after posting my stories here (and then of course it started being moderated).
Recently however, I was contributing to a document that Katherine and Daphne were writing where they were dismayed by the fact that after all this talk, this org seems to have hired 14% or so women this year. Samy has hired 39% from what I understand but he has zero incentive to do this.
What I want to say is stop writing your documents because it doesn’t make a difference. The DEI OKRs that we don’t know where they come from (and are never met anyways), the random discussions, the “we need more mentorship” rather than “we need to stop the toxic environments that hinder us from progressing” the constant fighting and education at your cost, they don’t matter. Because there is zero accountability. There is no incentive to hire 39% women: your life gets worse when you start advocating for underrepresented people, you start making the other leaders upset when they don’t want to give you good ratings during calibration. There is no way more documents or more conversations will achieve anything. We just had a Black research all hands with such an emotional show of exasperation. Do you know what happened since? Silencing in the most fundamental way possible.
Have you ever heard of someone getting “feedback” on a paper through a privileged and confidential document to HR? Does that sound like a standard procedure to you or does it just happen to people like me who are constantly dehumanized?
Imagine this: You’ve sent a paper for feedback to 30+ researchers, you’re awaiting feedback from PR & Policy who you gave a heads up before you even wrote the work saying “we’re thinking of doing this”, working on a revision plan figuring out how to address different feedback from people, haven’t heard from PR & Policy besides them asking you for updates (in 2 months). A week before you go out on vacation, you see a meeting pop up at 4:30pm PST on your calendar (this popped up at around 2pm). No one would tell you what the meeting was about in advance. Then in that meeting your manager’s manager tells you “it has been decided” that you need to retract this paper by next week, Nov. 27, the week when almost everyone would be out (and a date which has nothing to do with the conference process). You are not worth having any conversations about this, since you are not someone whose humanity (let alone expertise recognized by journalists, governments, scientists, civic organizations such as the electronic frontiers foundation etc) is acknowledged or valued in this company.
Then, you ask for more information. What specific feedback exists? Who is it coming from? Why now? Why not before? Can you go back and forth with anyone? Can you understand what exactly is problematic and what can be changed?
And you are told after a while, that your manager can read you a privileged and confidential document and you’re not supposed to even know who contributed to this document, who wrote this feedback, what process was followed or anything. You write a detailed document discussing whatever pieces of feedback you can find, asking for questions and clarifications, and it is completely ignored. And you’re met with, once again, an order to retract the paper with no engagement whatsoever.
Then you try to engage in a conversation about how this is not acceptable and people start doing the opposite of any sort of self reflection—trying to find scapegoats to blame.
Silencing marginalized voices like this is the opposite of the NAUWU principles which we discussed. And doing this in the context of “responsible AI” adds so much salt to the wounds. I understand that the only things that mean anything at Google are levels, I’ve seen how my expertise has been completely dismissed. But now there’s an additional layer saying any privileged person can decide that they don’t want your paper out with zero conversation. So you’re blocked from adding your voice to the research community—your work which you do on top of the other marginalization you face here.
I’m always amazed at how people can continue to do thing after thing like this and then turn around and ask me for some sort of extra DEI work or input. This happened to me last year. I was in the middle of a potential lawsuit for which Kat Herller and I hired feminist lawyers who threatened to sue Google (which is when they backed off--before that Google lawyers were prepared to throw us under the bus and our leaders were following as instructed) and the next day I get some random “impact award.” Pure gaslighting.
So if you would like to change things, I suggest focusing on leadership accountability and thinking through what types of pressures can also be applied from the outside. For instance, I believe that the Congressional Black Caucus is the entity that started forcing tech companies to report their diversity numbers. Writing more documents and saying things over and over again will tire you out but no one will listen.
Timnit
You have now changed the reason you're claiming she was fired, first it was asking about people's identities now it's that she wanted people to stop working. This leads me to believe that perhaps you reached your conclusion first, and are trying to find evidence to support your preferred result rather than the other way.
But even now your claims are at best only half true, reading the email she says people should stop working on DEI things specifically, as they can't be successful without executive sponsorship and execs only pay lip service. Is that criticism wrong?
But don't let truth get in the way of a good story, right?
But even now your claims are at best only half true, reading the email she says people should stop working on DEI things specifically, as they can't be successful without executive sponsorship and execs only pay lip service. Is that criticism wrong?
But don't let truth get in the way of a good story, right?
Nope, that falls under:
> She had a reputation for accusing her colleagues of bigotry and other toxic behavior.
> She had a reputation for accusing her colleagues of bigotry and other toxic behavior.
She's not accusing an colleagues of bigotry, she's stating leadership doesn't support diversity initiatives enough and this leads to rank and file employees wasting their time. How is that toxic behavior?
And again, what does any of that have to do with a review panel? That's what you claimed she was fired for, the review panel.
And again, what does any of that have to do with a review panel? That's what you claimed she was fired for, the review panel.
Did you read her email? She accused her colleagues of "silencing marginalized voices", among other things. That's an accusation of bigotry.
It really isn't. If you're assuming that the only reason one might do a bad thing is because they are a bad person, then sort of maybe, but even then, bigotry is pretty specific and one can silence marginalized people for all kinds of reasons that aren't bigotry (naivete, for example)
I have no problem with this person who probably had legitimate issues with Google but "You are not worth having any conversations about this, since you are not someone whose humanity (let alone expertise recognised by journalists, governments, scientists, civic organisations such as the electronic frontiers foundation etc) is acknowledged or valued in this company" is a pretty clear and cut accusation of bigotry to me.
How exactly would you fail to acknowledge somebody's humanity thanks to "naivete"? I'm making the presumably safe assumption that she is not a sentient piece of lint.
How exactly would you fail to acknowledge somebody's humanity thanks to "naivete"? I'm making the presumably safe assumption that she is not a sentient piece of lint.
[deleted]
No so veiled, and fully warranted, IMHO.
My first thought was that even giving this brief interview would be a fireable offense for Google's AI Ethics team members.
Good, glad google is facing at least some sort of consequences as minor as they might be. It might even end up snowballing against them.
>Being on the inside, we are often able to see downsides early before systems are widely deployed.
Well they failed to catch that Microsoft Tae would be instantly turned into a internet Nazi bot.
Well they failed to catch that Microsoft Tae would be instantly turned into a internet Nazi bot.
> Time and again, we see these systems producing errors ... and the response has been: “We just need more data.” but... you start to see forms of discrimination... in how they are built and trained to see the world.
Thank goodness this perspective is getting out there.
I have recently been incensed by the opposite view, that the bias is within "the data" only: https://www.youtube.com/watch?v=6jbin15-TcY .
This is wholly false. Machines which analyse the world (ie., actual physical stuff, eg., people) in terms of statistical co-occurances within datasets cannot acquire the relevant understanding of the world.
Consider NLP. It is likely that an NLP system analysing volumes of work on minority political causes will associate minority identifiers (eg., "black") with negative terms ("oppressed", "hostile", "against", "antagonistic"), etc. And thereby introduce an association which is not present within the text.
This is because conceptual association is not statistical association. In such texts the conceptual association is "standing for", "opposing", "suffering from", "in need of". Not "likely to occur with".
There are entire fields sold on a false equivocation between conceptual and statistical association. This equivocation generates novel unethical systems.
AI systems are not mere symptoms of their data. They are unable, by design, to understand the data; and repeat it as-if it were a mere symptom of wordly co-occurance.
Thank goodness this perspective is getting out there.
I have recently been incensed by the opposite view, that the bias is within "the data" only: https://www.youtube.com/watch?v=6jbin15-TcY .
This is wholly false. Machines which analyse the world (ie., actual physical stuff, eg., people) in terms of statistical co-occurances within datasets cannot acquire the relevant understanding of the world.
Consider NLP. It is likely that an NLP system analysing volumes of work on minority political causes will associate minority identifiers (eg., "black") with negative terms ("oppressed", "hostile", "against", "antagonistic"), etc. And thereby introduce an association which is not present within the text.
This is because conceptual association is not statistical association. In such texts the conceptual association is "standing for", "opposing", "suffering from", "in need of". Not "likely to occur with".
There are entire fields sold on a false equivocation between conceptual and statistical association. This equivocation generates novel unethical systems.
AI systems are not mere symptoms of their data. They are unable, by design, to understand the data; and repeat it as-if it were a mere symptom of wordly co-occurance.
I don't know, inference from data is literally how all decisions are fundamentally made. Why wouldn't it be possible to create models that learn this particular pattern?
Here's where the words "data", "pattern", etc. become unhelpful.
Learning, as in what we do, is not learning associations in datasets.
It is learning "associations" between: our body state and the world as we act. It's a sort of: (action, world, body, sensation, prior conceptualisation, ...) association. (Even then, our bodies grow and this is really not a formal process.)
This is, at least, what is necessary to understand what words mean. Words are just tools that we use to coordinate with each other in a shared (physical) world. You really have to be here, with us, to understand them. Words mean what we do with them.
Meaning has a "useful side-effect". It turns out when we are using words their sequencing reveals, on average, some commonalities in their use. Eg., when asking "Can you pass me the salt?" I may go on to ask, "and now the pepper". And thus there is a statistical association between the terms "salt" and "pepper".
But a machine processing only those associations is completely unaware there is anything "salt" to pass, or even that there are objects in the world, or people, or anything. Really, the machine has no connection between its interior and the world, the very connection we have when we use words.
When a machine generates the text "pass me the salt" it doesnt mean it. It cannot. There is no salt it's talking about. It doesnt even know what salt is.
A machine used to make decisions concerning people, unaware of what a person even is, produces new unethical forms of action. Not merely just "being racist because the data is".
Learning, as in what we do, is not learning associations in datasets.
It is learning "associations" between: our body state and the world as we act. It's a sort of: (action, world, body, sensation, prior conceptualisation, ...) association. (Even then, our bodies grow and this is really not a formal process.)
This is, at least, what is necessary to understand what words mean. Words are just tools that we use to coordinate with each other in a shared (physical) world. You really have to be here, with us, to understand them. Words mean what we do with them.
Meaning has a "useful side-effect". It turns out when we are using words their sequencing reveals, on average, some commonalities in their use. Eg., when asking "Can you pass me the salt?" I may go on to ask, "and now the pepper". And thus there is a statistical association between the terms "salt" and "pepper".
But a machine processing only those associations is completely unaware there is anything "salt" to pass, or even that there are objects in the world, or people, or anything. Really, the machine has no connection between its interior and the world, the very connection we have when we use words.
When a machine generates the text "pass me the salt" it doesnt mean it. It cannot. There is no salt it's talking about. It doesnt even know what salt is.
A machine used to make decisions concerning people, unaware of what a person even is, produces new unethical forms of action. Not merely just "being racist because the data is".
We also tend to have decades of experience interacting with other humans and understanding what would be reasonable to want / moral / what would “make sense”.
This is a big part of why I’m bearish on things like fully autonomous self-driving cars until general AI is achieved. Driving is fundamentally a social activity that you participate in with other humans, at least until we built out nation-wide autonomous-only lanes that only allow (through a gate or barrier) autonomous vehicles with self-driving engaged in a way that normal vehicles can’t “sneak in”…I’m not holding my breath. Maybe I’ll see it in my lifetime (30s), maybe not.
This is a big part of why I’m bearish on things like fully autonomous self-driving cars until general AI is achieved. Driving is fundamentally a social activity that you participate in with other humans, at least until we built out nation-wide autonomous-only lanes that only allow (through a gate or barrier) autonomous vehicles with self-driving engaged in a way that normal vehicles can’t “sneak in”…I’m not holding my breath. Maybe I’ll see it in my lifetime (30s), maybe not.
"Bearish" understates my mood.
I think we're drifting into charlatanism, fraud and pseudoscience.
I think we're drifting into charlatanism, fraud and pseudoscience.
Perhaps religious cult territory too. "The Algorithm" [1] is already being used to tell fortunes -- which if it just stays as a few people having fun with horoscopes I don't really care, it seems harmless. But I could also totally picture some cryptocoin charlatan getting revelations about The Spiritual Algorithm or something and starts selling AI for getting into heaven.
---
[1] By "The Algorithm" I just mean the public colloquially reference to trending formulas used on TikTok etc, which I'm sure probably uses machine learning somewhere
---
[1] By "The Algorithm" I just mean the public colloquially reference to trending formulas used on TikTok etc, which I'm sure probably uses machine learning somewhere
I agree. I have the same reactions to these guys as I did to religious zealots when I was a teenager; and later to "transhumanism". Alarm, disbelief, hostility.
There is a new secular religion underneath this.
"Uploading consciousness", "Aren't we all just data systems?", "We've built a neural network with as many (meaningless) neurones as the brain!"
It's the same disgust and denial at being biological animals that all religions foster.
Thankfully here, I think, the religion has predictions. It predicts a self-driving car, a "fourth industrial revolution", and so on. When these fail to materialise, some sanity will return.
(Of course they have already failed, the question is when in the next 5-10yrs people will realise.)
There is a new secular religion underneath this.
"Uploading consciousness", "Aren't we all just data systems?", "We've built a neural network with as many (meaningless) neurones as the brain!"
It's the same disgust and denial at being biological animals that all religions foster.
Thankfully here, I think, the religion has predictions. It predicts a self-driving car, a "fourth industrial revolution", and so on. When these fail to materialise, some sanity will return.
(Of course they have already failed, the question is when in the next 5-10yrs people will realise.)
> It's the same disgust and denial at being biological animals that all religions foster.
Huh, I didn't think of it that way! Good point!
I love history and a few weeks ago I read passage in a history book on a period in the early 19th century dominated by "American radicalism" (a liberal movement) and "transcendentalism" [1], when many hucksters and itinerant pastors profited off of the hype of industrialization. It immediately reminded me of the modern crypto / AI hype. In both cases, it actually is simply more smoke-and-mirrors to remove culpable human elements from a changing system --- by replacing it with "AI" and "crypto" (today), or the "invisible hand" and "radical self-reliance" (the 1820s-1830s US).
[1] https://en.wikipedia.org/wiki/Transcendentalism - and history "rhymed" 100 years later - https://journals.sagepub.com/doi/abs/10.1177/135050842091057... and I would argue that it "rhymes" once again today with Musk following in Ford's footsteps, although with perhaps less intent than Ford
Huh, I didn't think of it that way! Good point!
I love history and a few weeks ago I read passage in a history book on a period in the early 19th century dominated by "American radicalism" (a liberal movement) and "transcendentalism" [1], when many hucksters and itinerant pastors profited off of the hype of industrialization. It immediately reminded me of the modern crypto / AI hype. In both cases, it actually is simply more smoke-and-mirrors to remove culpable human elements from a changing system --- by replacing it with "AI" and "crypto" (today), or the "invisible hand" and "radical self-reliance" (the 1820s-1830s US).
[1] https://en.wikipedia.org/wiki/Transcendentalism - and history "rhymed" 100 years later - https://journals.sagepub.com/doi/abs/10.1177/135050842091057... and I would argue that it "rhymes" once again today with Musk following in Ford's footsteps, although with perhaps less intent than Ford
Hubert Dreyfus's critiques of AI systems (particularly, their all but total inability to bodily engage with a social world) still remain relevant.
> A machine used to make decisions concerning people, unaware of what a person even is, produces new unethical forms of action. Not merely just "being racist because the data is".
Powerful and concise, thank you.
Powerful and concise, thank you.
I think you can create models that learn this particular pattern, but the models being told that oppression is a bad thing will determine that being black is a bad thing and from there that black people are bad.
I think it’s impossible for a human to read a mainstream body of minority political work and not come out with an association between black and oppressed. The entire dominant narrative is that all minority groups are oppressed. That association is definitely present in the text. Maybe it’s the case that we need to explicitly remove all negative associations for things like skin colour (potentially a hard problem in its own right) to generate more egalitarian text. But it’s not merely a matter of AI getting things wrong some negative associations are actually present in the text.
The association between "movie" and "tv" is "played on".
The association between "jellybean" and "apple" is "smells similar".
The association between "black" and "oppression" is "suffering".
The association in each case is NOT likely to co-occur.
AI is not detecting any kind of conceptual association. It is merely recording co-occurance. By interpreting co-occurance (statistical association) as meaningful, you are imparting a conceptual association to text it does not have.
"Statistical association" is just a pun on "conceptual association". Machines do not detect associations; they record frequencies.
The association between "jellybean" and "apple" is "smells similar".
The association between "black" and "oppression" is "suffering".
The association in each case is NOT likely to co-occur.
AI is not detecting any kind of conceptual association. It is merely recording co-occurance. By interpreting co-occurance (statistical association) as meaningful, you are imparting a conceptual association to text it does not have.
"Statistical association" is just a pun on "conceptual association". Machines do not detect associations; they record frequencies.
> AI is not detecting any kind of conceptual association. It is merely recording co-occurance.
Can you explain the famous example of “king - man + woman = queen” through this perspective? Naively it does seem to extend beyond statistical representation as it seems some semantic association is preserved through the mapping of language onto a vector space.
Can you explain the famous example of “king - man + woman = queen” through this perspective? Naively it does seem to extend beyond statistical representation as it seems some semantic association is preserved through the mapping of language onto a vector space.
Well, it's essentially a coincidence mixed in with a bit of superstition.
K - M has no semantics, its more like something you'd find in a teen magazine. "K - M" means as much, "Prince" or "Jesus Christ" or any of a number of words.
The type of association underlying word vectors is just expecting to co-occur.
So there are many cases we can enumerate where our expectation that Q occurs is modulated by M occurring. So if M hasn't occurred in some text, we expect Q rather than K.
(Equally, in biblical literature, we might expect JC to occur; and in disney films, "prince").
And many of these famous examples are just tricks. Very nearly all of the industry sells this technology based on a few cases which happen to turn out "as a lay audience would expect", and neglect to include the many many cases where they do not.
And to be clear, we should not expect "K - M" to be "Q" in anything other than a basically statistical sense, relative to some texts. "King - Man" isn't a semantic operation.
K - M has no semantics, its more like something you'd find in a teen magazine. "K - M" means as much, "Prince" or "Jesus Christ" or any of a number of words.
The type of association underlying word vectors is just expecting to co-occur.
So there are many cases we can enumerate where our expectation that Q occurs is modulated by M occurring. So if M hasn't occurred in some text, we expect Q rather than K.
(Equally, in biblical literature, we might expect JC to occur; and in disney films, "prince").
And many of these famous examples are just tricks. Very nearly all of the industry sells this technology based on a few cases which happen to turn out "as a lay audience would expect", and neglect to include the many many cases where they do not.
And to be clear, we should not expect "K - M" to be "Q" in anything other than a basically statistical sense, relative to some texts. "King - Man" isn't a semantic operation.
> This is because conceptual association is not statistical association. In such texts the conceptual association is "standing for", "opposing", "suffering from", "in need of". Not "likely to occur with".
The better GPT gets, the wronger you will probably be. Why a machine wouldn't be able to abstract conceptual associations from a statistical framework?
The better GPT gets, the wronger you will probably be. Why a machine wouldn't be able to abstract conceptual associations from a statistical framework?
Conceptual associations are not in text. The frequency with which words co-occur says nothing about why they co-occur.
Deep Learning systems merely interpolate across all their training data: ie., they remember every since document they have been shown and merely generate a document which is close to a subset of their previous inputs.
This seems meaningful only because they've stored (a compressed representation of) billions of documents.
There is something, frankly, psychotic in thinking the text these systems generate is meaningful. Run GPT twice on the same input, and the text it generates across runs contradicts itself.
To think these systems are talking to you is to "read a telephone directory as-if it had hidden messages planted by the CIA".
GPT if it says, "I like new york" does not mean that. It hasn't been to new york, and doesnt know what new york is. It has no intention to communicate with you; it has no intentions. It has nothing it wants to say. It isn't saying anything.
It's a trick. An illusion. It's replying fragments of a history of people actually talking to each other. It's a fancy tape recorder. It has never been in the world those people were talking about, and when it repeats their words, it isn't using them to say anything.
Deep Learning systems merely interpolate across all their training data: ie., they remember every since document they have been shown and merely generate a document which is close to a subset of their previous inputs.
This seems meaningful only because they've stored (a compressed representation of) billions of documents.
There is something, frankly, psychotic in thinking the text these systems generate is meaningful. Run GPT twice on the same input, and the text it generates across runs contradicts itself.
To think these systems are talking to you is to "read a telephone directory as-if it had hidden messages planted by the CIA".
GPT if it says, "I like new york" does not mean that. It hasn't been to new york, and doesnt know what new york is. It has no intention to communicate with you; it has no intentions. It has nothing it wants to say. It isn't saying anything.
It's a trick. An illusion. It's replying fragments of a history of people actually talking to each other. It's a fancy tape recorder. It has never been in the world those people were talking about, and when it repeats their words, it isn't using them to say anything.
None of what you say is incompatible with GPT being able to understand these concepts a few generations down the line.
I mean, your central point is that GPT could not possibly understand these concepts because it only perceived them from text, not real life, but... that's kind of true of people too?
I can make observations and guesses about New York, even though I've never been in the US in my life. I can try to understand the hardships faced by minorities, even though I have never suffered from race or gender-based discrimination.
A huge part of everything we know about the world around us comes from information we got from Wikipedia, or TV shows, or Youtube. It's information GPT could be trained on.
You can always make a philosophical argument that even GPT-6 won't "really" what it's saying, but we have yet to see what the upper bound of GPT is given enough computing power. I'd expect most non-philosophical predictions about it can't do to be falsified within a few years.
I mean, your central point is that GPT could not possibly understand these concepts because it only perceived them from text, not real life, but... that's kind of true of people too?
I can make observations and guesses about New York, even though I've never been in the US in my life. I can try to understand the hardships faced by minorities, even though I have never suffered from race or gender-based discrimination.
A huge part of everything we know about the world around us comes from information we got from Wikipedia, or TV shows, or Youtube. It's information GPT could be trained on.
You can always make a philosophical argument that even GPT-6 won't "really" what it's saying, but we have yet to see what the upper bound of GPT is given enough computing power. I'd expect most non-philosophical predictions about it can't do to be falsified within a few years.
GPT has no body. It's not a matter of computing power.
In every case where we estimate the meaning of words we do so in reference to prior concepts learnt by being in the world.
We don't acquire language by watching videos; we watch videos when we have acquired language.
> about it can't do to be falsified within a few years
Such claims have been made since the 1940s, minskey said self-driving cars were a summer project.
AI systems have never acquired what i'm talking about. Ever.
In every case where we estimate the meaning of words we do so in reference to prior concepts learnt by being in the world.
We don't acquire language by watching videos; we watch videos when we have acquired language.
> about it can't do to be falsified within a few years
Such claims have been made since the 1940s, minskey said self-driving cars were a summer project.
AI systems have never acquired what i'm talking about. Ever.
> Such claims have been made since the 1940s, minskey said self-driving cars were a summer project.
> AI systems have never acquired what i'm talking about. Ever.
Well then, I dare you to make falsifiable claims, and we'll see in a few years how things pan out. =D
> AI systems have never acquired what i'm talking about. Ever.
Well then, I dare you to make falsifiable claims, and we'll see in a few years how things pan out. =D
There will be no cars which can drive themselves around cities, as competently as human beings, this century.
https://en.wikipedia.org/wiki/AI_winter
https://en.wikipedia.org/wiki/AI_winter
Relevant presentation by James Mickens:
https://www.usenix.org/conference/usenixsecurity18/presentat...
https://www.usenix.org/conference/usenixsecurity18/presentat...
The speaker starts, "we are computer scientists, we should be more sceptical".
This is a little odd: computer scientists are mathematicians, not scientists: therein lies the issue.
I think the more this is really absorbed, the clearer things become.
* Why are ai models of intelligence logical and not dynamical?
* Why are models rules of association, rather than interpretative frameworks?
* Why is a statistical association deployed without empirical verification?
* Why arent empirical (& verifiable) criteria given against predictive systems?
* Against the mechanisms of these systems?
* Why is ML an experimental discipline without experimental tests?
* Why doesn't ML research employ tests of significance?
* Why is the ability to experiment seen as incidental to intelligence?
* Why is ML sold with a scientific technical vocabulary whose meaning is given metaphorically?
* Why arent ML researchers aware these are metaphors?
* Why aren't scientists of intelligence researcher reviewing and informing the claims of the artificial intelligence community?
* Why do some of the most prominent "scientists" in the world advocate that understanding the world is a matter of mining datasets?
* Why don't these scientists defend the need for actual science in producing accurate models of reality?
* Why aren't computer scientists more sceptical?
This is a little odd: computer scientists are mathematicians, not scientists: therein lies the issue.
I think the more this is really absorbed, the clearer things become.
* Why are ai models of intelligence logical and not dynamical?
* Why are models rules of association, rather than interpretative frameworks?
* Why is a statistical association deployed without empirical verification?
* Why arent empirical (& verifiable) criteria given against predictive systems?
* Against the mechanisms of these systems?
* Why is ML an experimental discipline without experimental tests?
* Why doesn't ML research employ tests of significance?
* Why is the ability to experiment seen as incidental to intelligence?
* Why is ML sold with a scientific technical vocabulary whose meaning is given metaphorically?
* Why arent ML researchers aware these are metaphors?
* Why aren't scientists of intelligence researcher reviewing and informing the claims of the artificial intelligence community?
* Why do some of the most prominent "scientists" in the world advocate that understanding the world is a matter of mining datasets?
* Why don't these scientists defend the need for actual science in producing accurate models of reality?
* Why aren't computer scientists more sceptical?
I am not sure if you are being deliberately obtuse or simply unfamiliar with how ML is designed and implemented. Almost every single point you mentioned does happen in practice. Most are limited by budget and scale, just like real world experiments.
I believe I'm very familiar. Where do you think I've missed something?
> AI is neither artificial nor intelligent. It is made from natural resources and it is people who are performing the tasks to make the systems appear autonomous.
People performing the tasks? As in coding tasks? That’s kinda the definition of artificial. If AI spawned naturally… that’s what wouldn’t be artificial.
Or is she referring to data labeling and forgetting the many non supervised areas of AI? (Not that labeling would make it less artificial)
Or is she suggesting that anything made from natural resources is natural… is anything not made from natural resources? The reason for artificial in artificial intelligence is to juxtapose against biological intelligence not speak to resource usage.
I think arguing on the ‘not intelligent’ front is fine but that’s just kind of a word game. The field seeks intelligence and is fine making stupid ai if it means better ai later. Unless she is getting into notions of a soul or some bs about consciousness in which case we have left the realm of science altogether.
Any way you slice it seems more like an inane point for making a headline than one for substantive discussion.
People performing the tasks? As in coding tasks? That’s kinda the definition of artificial. If AI spawned naturally… that’s what wouldn’t be artificial.
Or is she referring to data labeling and forgetting the many non supervised areas of AI? (Not that labeling would make it less artificial)
Or is she suggesting that anything made from natural resources is natural… is anything not made from natural resources? The reason for artificial in artificial intelligence is to juxtapose against biological intelligence not speak to resource usage.
I think arguing on the ‘not intelligent’ front is fine but that’s just kind of a word game. The field seeks intelligence and is fine making stupid ai if it means better ai later. Unless she is getting into notions of a soul or some bs about consciousness in which case we have left the realm of science altogether.
Any way you slice it seems more like an inane point for making a headline than one for substantive discussion.
It seems to me somewhat similar to an argument put forward by Jaron Lanier: that what is termed artificial intelligence’s (or ‘deep learning’’s etc.) current successes, at least as far as something like the translation services of the big corporations for example, is actually built by leveraging corpuses that are the fruits of a huge amount of individual human effort. Sometimes, say with the ‘Mechanical Turk’ thing that Amazon has, or CAPTCHAs or something, this is a more explicit connection (…bit Wizard of Oz! :) As I understand it, he proposes that an alternative to UBI etc. might be micro-compensations (or transactions) in return for providing this data. This might be a prelude/transition to a stage where our basic needs (on a sort of Mazlow’s hierarchy) were met by A.I., or that there might be increasingly creative or interesting ways to complete some tasks that we could go on refining forever.
> are the fruits of a huge amount of individual efforts
That doesn’t make it any less artificial does it? If anything that makes it more so. That is also again only speaking to supervised learning (and only the fraction of it where datasets are curated not collected) not AI in general
That doesn’t make it any less artificial does it? If anything that makes it more so. That is also again only speaking to supervised learning (and only the fraction of it where datasets are curated not collected) not AI in general
I agree - it’s kind of lazily framed/worded. A.I. is a vast field after all?
That's because that was an inane point used as a headline that didn't really have much to do with what the article is actually about.
It's a criticism of the training data sets used on ai's that were manually tagged by individuals that's led to some extreme biases in ai behaviour resulting in real world consequences.
For a great real world example of this happening right now, there's a fairly large scandal and a whole bunch of angry people over the problems caused by latitude's choice of training material for their ai driven text adventure game.
https://gitgud.io/AuroraPurgatio/aurorapurgatio
It's a criticism of the training data sets used on ai's that were manually tagged by individuals that's led to some extreme biases in ai behaviour resulting in real world consequences.
For a great real world example of this happening right now, there's a fairly large scandal and a whole bunch of angry people over the problems caused by latitude's choice of training material for their ai driven text adventure game.
https://gitgud.io/AuroraPurgatio/aurorapurgatio
Glancing at the table of contents...
> X. Count Grey: Enslavement and Slaughter of Children
>XI. Kyros: Kidnapping, Enslavement, Unethical Experimentation, and Torture
What the hell am I about to read?
> X. Count Grey: Enslavement and Slaughter of Children
>XI. Kyros: Kidnapping, Enslavement, Unethical Experimentation, and Torture
What the hell am I about to read?
The training data for Ai Dungeon.
https://play.aidungeon.io/
https://play.aidungeon.io/
> that didn't really have much to do with what the article is actually about.
That was a quote of the author not just the headline
> It's a criticism of the training data sets used on ai's that were manually tagged by individuals
Again though this is just a fraction of AI. Lumping all of AI into the realm of ‘manually curated supervised learning’ is just incorrect, yet the author seems to think that’s all there is, it’s either that or they are just trying to make a headline.
> For a great real world example of this happening right now
I hardly think a controversy about a random text game on the internet warrants such a serious tone
That was a quote of the author not just the headline
> It's a criticism of the training data sets used on ai's that were manually tagged by individuals
Again though this is just a fraction of AI. Lumping all of AI into the realm of ‘manually curated supervised learning’ is just incorrect, yet the author seems to think that’s all there is, it’s either that or they are just trying to make a headline.
> For a great real world example of this happening right now
I hardly think a controversy about a random text game on the internet warrants such a serious tone
To me she is referring to what she says previously:
> Also, systems might seem automated but when we pull away the curtain we see large amounts of low paid labour, everything from crowd work categorising data to the never-ending toil of shuffling Amazon boxes.
So the artificial and intelligent part is a tiny piece of the task, most of it is done through humans and logistics.
> Also, systems might seem automated but when we pull away the curtain we see large amounts of low paid labour, everything from crowd work categorising data to the never-ending toil of shuffling Amazon boxes.
So the artificial and intelligent part is a tiny piece of the task, most of it is done through humans and logistics.
Again though, ”manual curation of data for supervised learning tasks” is just a fraction of the ai research being done today. Making these claims as to apply to all AI is either ignorant or sensationalist regardless of the truthfulness of the claim itself.
AI is neither artificial nor intelligent. It is made from natural resources and it is people who are performing the tasks to make the systems appear autonomous.
Making something from natural resources does not make it natural. If that were the case, we wouldn't have the word "artificial", since everything we make comes from natural elements and everything would be "natural". The fact that you took natural resources and built something else from it--that it didn't exist in nature already--makes it artificial. AI is definitely artificial.
Making something from natural resources does not make it natural. If that were the case, we wouldn't have the word "artificial", since everything we make comes from natural elements and everything would be "natural". The fact that you took natural resources and built something else from it--that it didn't exist in nature already--makes it artificial. AI is definitely artificial.
I’m not arguing with your point, but let me offer another thought. The crux of this problem as I see it is that people consider themselves as separate from nature. The truth is that we are part of nature and the things we make are still part of nature. The opposite of “natural” is not “artificial” or “man-made” - it’s “supernatural”!
Of course the word “artificial” is useful to classify things for our safety and benefit, but we are not supernatural and so the things we create still exist in nature - like we are the hand of the universe reconfiguring itself.
This artificial separation of the human from nature has been popular in the past and I hope we overcome it.
Of course the word “artificial” is useful to classify things for our safety and benefit, but we are not supernatural and so the things we create still exist in nature - like we are the hand of the universe reconfiguring itself.
This artificial separation of the human from nature has been popular in the past and I hope we overcome it.
I think a better term to use would be perhaps synthetic, which is something man made used to imitate something natural.
So we would call it synthetic intelligence, if we wanted to get pedantic.
So we would call it synthetic intelligence, if we wanted to get pedantic.
Kai-Fu Lee and David Siegel talked about AI in April 2019 and mentioned:
> It's neither artificial nor intelligent
Quote is at 3:40 (topic starts around 3:05) in the YouTube video linked at the end of the article:
https://www.twosigma.com/articles/ai-past-and-future-a-conve...
> It's neither artificial nor intelligent
Quote is at 3:40 (topic starts around 3:05) in the YouTube video linked at the end of the article:
https://www.twosigma.com/articles/ai-past-and-future-a-conve...
Nice catch! How do you even
I had the exact same reaction, but I wonder if I'm too close to it to appreciate what the term might conjure up in the broader (voting) public that isn't in the industry. (FWIW I kind of like the term synthetic intelligence as an alternative.)
I do agree wholeheartedly that, much as in the case of cryptocurrency, there isn't an intuitive link between the product and the resources it requires to create and operate. The fact that GPT-3 would cost millions to reproduce in the commercial market doesn't even compute for me.
I do agree wholeheartedly that, much as in the case of cryptocurrency, there isn't an intuitive link between the product and the resources it requires to create and operate. The fact that GPT-3 would cost millions to reproduce in the commercial market doesn't even compute for me.
Yeah, she seems to be undercutting her argument for the sake of a pithy sounding statement. Her point seems to be not that AI isn't manmade, but rather that it isn't particularly autonomous, given that it is built using data that is collected and labeled through enormous amounts of human labor. Obviously that doesn't have any relation to what is meant by "artificial", though.
I think a better term to describe it is synthetic, which is something man made. Synthetic intelligence kinda has a nice ring to it as well.
But I agree with you, if we had it their way we would have "artificial sweeteners" but "natural sweeteners".
But I agree with you, if we had it their way we would have "artificial sweeteners" but "natural sweeteners".
[deleted]
That's a nice clean breakdown of that wording issue. I get her point though I guess, but I was hoping there might be something deeper.
> ImageNet has now removed many of the obviously problematic people categories – certainly an improvement – however, the problem persists because these training sets still circulate on torrent sites [where files are shared between peers].
This is the scariest part of the article. The idea that some central authority should be censoring and revising datasets to keep up with political orthodoxy, and we should be rooting out unauthorized torrent sharing of unapproved training data.
From a technical point of view, the common reason we pre-traini on imagenet is as the starting point for fine tuning for a specific use case. The diversity and size of the dataset makes good generic feature extractors. If you're using a ML model to identify people as kleptomaniac or drug dealer or other "problematic" labels, you're working on some kind of phrenology and it doesnt take an "AI ethicist" to know you shouldn't do it. But that's not the same as pretraining on imagenet, and certainly doesn't support trying to make datasets align with today's political orthodoxy.
This is the scariest part of the article. The idea that some central authority should be censoring and revising datasets to keep up with political orthodoxy, and we should be rooting out unauthorized torrent sharing of unapproved training data.
From a technical point of view, the common reason we pre-traini on imagenet is as the starting point for fine tuning for a specific use case. The diversity and size of the dataset makes good generic feature extractors. If you're using a ML model to identify people as kleptomaniac or drug dealer or other "problematic" labels, you're working on some kind of phrenology and it doesnt take an "AI ethicist" to know you shouldn't do it. But that's not the same as pretraining on imagenet, and certainly doesn't support trying to make datasets align with today's political orthodoxy.
> The idea that you can see from somebody’s face what they are feeling is deeply flawed. I don’t think that’s possible.
I agree with most of the article but this point I disagree with. Non verbal communication is a huge component in how we interact with each other.
Covid example: When on calls where no cameras are on I get far less feedback when presenting to a 'room' compared to if I was to present in person or have cams on, even if the room stay silent in both situations.
By looking at faces I can see who is distracted, who looks confused and whether what I am saying is being received well or poorly. Did that joke get smiles (polite or genuine ones?) or eye rolls?
Now - do I think the current SOTA algorithms are at this level of nuance? No, definitely not. But to say it isn't at all possible is ridiculous in my opinion
I agree with most of the article but this point I disagree with. Non verbal communication is a huge component in how we interact with each other.
Covid example: When on calls where no cameras are on I get far less feedback when presenting to a 'room' compared to if I was to present in person or have cams on, even if the room stay silent in both situations.
By looking at faces I can see who is distracted, who looks confused and whether what I am saying is being received well or poorly. Did that joke get smiles (polite or genuine ones?) or eye rolls?
Now - do I think the current SOTA algorithms are at this level of nuance? No, definitely not. But to say it isn't at all possible is ridiculous in my opinion
I believe that people (usually) choose to communicate with faces, but just as people can lie with words they can lie with faces.
I agree with most of the content of this article.
Especially this part:
"Ethics are necessary, but not sufficient. More helpful are questions such as, who benefits and who is harmed by this AI system? And does it put power in the hands of the already powerful?"
Distribution of power is the most important political question, more important than distribution of wealth or what we call ethics, that is always biased and hard to measure.
Especially this part:
"Ethics are necessary, but not sufficient. More helpful are questions such as, who benefits and who is harmed by this AI system? And does it put power in the hands of the already powerful?"
Distribution of power is the most important political question, more important than distribution of wealth or what we call ethics, that is always biased and hard to measure.
But anything that benefits Microsoft, one of the most powerful companies in the world, is necessarily putting more power in the hands of the already powerful.
I agree, but wouldn't those questions, or atleast any opinions on the answers to those questions, just be part of ethics?
James Martin called it true in 2001 when he described the coming machine learned systems Alien Intelligences. unintelligible inhuman systems. from After the Internet: Alien Intelligence.
Even in situations where there's an objective reason for AI performing poorly with certain groups (like the worse light contrast on facial details with many photos of dark-skinned people), we go back to default and seek to cast moral judgment on someone for being racist, sexist, or something of the sort. Because you can't blame AI, we blame the people programming it, collecting data for it and training it.
It seems we're trying to prevent a full-on moral panic that would be caused by the realization that "discrimination" is an objective need by the objective nature of the problem in some cases.
For example there are about 14% black people in the US. If you have a FAIR SET OF DATA to train from, 14% of those faces would be black. No, they're not underrepresented, they're literally accurately represented. But this means the AI will be worse with those people. So what do we do? If we artificially up the "quota" and train with 50% black faces, the AI will now underperform with non-black faces.
So then you need to train two networks: black faces and non-black faces, and then you need to "discriminate" between both, and pick the right network depending on the race you're working with.
Math is racist sometimes.
It seems we're trying to prevent a full-on moral panic that would be caused by the realization that "discrimination" is an objective need by the objective nature of the problem in some cases.
For example there are about 14% black people in the US. If you have a FAIR SET OF DATA to train from, 14% of those faces would be black. No, they're not underrepresented, they're literally accurately represented. But this means the AI will be worse with those people. So what do we do? If we artificially up the "quota" and train with 50% black faces, the AI will now underperform with non-black faces.
So then you need to train two networks: black faces and non-black faces, and then you need to "discriminate" between both, and pick the right network depending on the race you're working with.
Math is racist sometimes.
Race aside, you can make this argument for any social strata. Gender, race, nationality, social class, etc... I don't believe most models will perform very different with a balanced training set.
Honestly the hardest part about integrating AI into our daily lives is the fact that we don't really have AI yet. We have made great advances in the fields of machine learning and neural networks, but actually getting a computer to make educated decisions is hard. The current issue is that all of these models are black boxes: an ML model can guess which data will come out, but it can't really fully understand what it knows. It can identify slightly harder to notice patterns, but it can't actually think.
We adopted the phrase "AI" far too early in the field, and I suspect there will be another few decades before we have the technical and scientific capability to make real artifical intelligence a thing.
We adopted the phrase "AI" far too early in the field, and I suspect there will be another few decades before we have the technical and scientific capability to make real artifical intelligence a thing.
> The current issue is that all of these models are black boxes
Not quite. It'd be great if that was the only issue with neural network ML. A much bigger issue is that neural networks have extremely limited applicability. They're great for classification problems when you can have huge training datasets, but for many common problems they're useless.
One obvious class of problems is time series prediction - extremely important in life and for business, and something neural networks are no good for.
Not quite. It'd be great if that was the only issue with neural network ML. A much bigger issue is that neural networks have extremely limited applicability. They're great for classification problems when you can have huge training datasets, but for many common problems they're useless.
One obvious class of problems is time series prediction - extremely important in life and for business, and something neural networks are no good for.
There are many ML time series prediction techniques.
And as a sibling points out, pretraining (not to mention various other low data methods) make big NN models useable even on small datasets.
And as a sibling points out, pretraining (not to mention various other low data methods) make big NN models useable even on small datasets.
> There are many ML time series prediction techniques.
Yes, and they all give worse results than classic math and statistics approaches.
This, in my opinion, is the worst part of ML of all. Instead of clearly stating the limits and boundary conditions of the algorithms you start hearing responses to the tune of "you're doing it wrong" and "wait until X magic pixie dust makes it usable", like you're being sold snake oil instead of algorithms research.
Yes, and they all give worse results than classic math and statistics approaches.
This, in my opinion, is the worst part of ML of all. Instead of clearly stating the limits and boundary conditions of the algorithms you start hearing responses to the tune of "you're doing it wrong" and "wait until X magic pixie dust makes it usable", like you're being sold snake oil instead of algorithms research.
Doesn't transfer learning level the playing field a bit? We already have excellent, off-the-shelf models for NLP, computer vision, ODT, etc... that just need fine-tuning for a particular business or domain problem. I think the 'huge training datasets' requirement is lessening every day.
If you're doing something novel, then sure. I can see that.
If you're doing something novel, then sure. I can see that.
Predicting time series isn't "novel". It's the oldest data science problem in the world. And ML can't solve it yet.
Then go and use the right tool for the right job. I'm not suggesting neural networks are a panacea. I've worked with time-series problems recently and not used NN. NN not being good at one class of problems does not invalidate their usefulness for other classes, like your GP comment is suggesting.
> ...like your GP comment is suggesting
Really? What I literally wrote is "neural networks have extremely limited applicability".
> I'm not suggesting neural networks are a panacea.
That's good. Because ML practitioners are, even if they phrase it differently. (The idea is that since neural networks can fit curves then any problem can be solved by a neural network given enough layers and feature engineering grease.)
Really? What I literally wrote is "neural networks have extremely limited applicability".
> I'm not suggesting neural networks are a panacea.
That's good. Because ML practitioners are, even if they phrase it differently. (The idea is that since neural networks can fit curves then any problem can be solved by a neural network given enough layers and feature engineering grease.)
"Approximate Imitation"?
> But from the beginning there was pushback and more recent work shows there is no reliable correlation between expressions on the face and what we are actually feeling. And yet we have tech companies saying emotions can be extracted simply by looking at video of people’s faces. We’re even seeing it built into car software systems.
I've been involved with a startup where the CEO was certain we're only a step away from replacing humans in recruiting healthcare workers with AI analyzing expressions in self-submitted interview videos, as well as analysis of quizzes. I got pushed aside from that startup for being the 'obnixous technical humanist' (which is a caracterization I wear proudly).
> AI is neither artificial nor intelligent. It is made from natural resources and it is people who are performing the tasks to make the systems appear autonomous.
I've argued as a technological consultant times and times again that the effort they are spending on automating humans out, would be better spent in making those humans happier and augumenting them with an automated system. The fact that the CEO saw his low level employees as just temporary assets ready to be replaced meant that their life in the company was miserable.
> This April, the EU produced the first draft omnibus regulations for AI
I digress. I'm wondering what they're plan of action is since it was a European startup and since April we're theoretically pushed away from using AI to gauge candidates and all of their VC investment money came with the promise of 'automating healthcare recruitment and scaling globally'.
I've been involved with a startup where the CEO was certain we're only a step away from replacing humans in recruiting healthcare workers with AI analyzing expressions in self-submitted interview videos, as well as analysis of quizzes. I got pushed aside from that startup for being the 'obnixous technical humanist' (which is a caracterization I wear proudly).
> AI is neither artificial nor intelligent. It is made from natural resources and it is people who are performing the tasks to make the systems appear autonomous.
I've argued as a technological consultant times and times again that the effort they are spending on automating humans out, would be better spent in making those humans happier and augumenting them with an automated system. The fact that the CEO saw his low level employees as just temporary assets ready to be replaced meant that their life in the company was miserable.
> This April, the EU produced the first draft omnibus regulations for AI
I digress. I'm wondering what they're plan of action is since it was a European startup and since April we're theoretically pushed away from using AI to gauge candidates and all of their VC investment money came with the promise of 'automating healthcare recruitment and scaling globally'.
I am reading her book and so far, I am enjoying it. The book really made me think of externalities for tech that I use and earn a living with. Costs of these externalities are environmental and human suffering. Recommended.
Yes it is, and of course it's not - it's artificial.
If it were intelligent we'd be calling it "synthetic".
If it were intelligent we'd be calling it "synthetic".
It should not matter whether it is artificial or intelligence or neither, as long as it get the jobs done.
Except when people are using the term AI to sell nonsense that doesn't do what it says on the tin.
Discuss amongst yourselves.
Ahh so it is naturally occurring without human artifice non-intelligence. Got it. NOWHANI
Interesting to note she opens with a veiled attack at Google's internal review practices. For those not aware: https://www.google.com/amp/s/www.theverge.com/platform/amp/2...