22 ms·

Asking the Right Questions About AI

107 points by tim_sw 9y ago

ocdtrekkie 9y ago
Yonatan and I no longer see eye to eye on a lot of things, but this is a really good introductory post to how AI works and certain ethical questions around it. It's a long, but extremely enjoyable read.
danso 9y ago
This is easily one of the best explanations of AI and explorations of its implications that I've read. Probably because I largely agree with the author, but have never been able to express such assertions so clearly: > These are rarely new problems; rather, the formal process of explaining our desires to a computer — the ultimate case of someone with no cultural context or ability to infer what we don’t say — forces us to be explicit in ways we generally aren’t used to. It was also interesting to read his inside account about Google Photos's "Gorilla Incident". I had always assumed it was due to training on limited data but that just didn't make sense as Google would not have been lacking for data in 2015.
- TeMPOraL 9y ago
  The article is good, I agree. The line you quoted is not specific to AI/ML, it's the fundamental rule of programming (and why I absolutely love it as a tool for understanding) - the machine is the ultimate bullshit antagonist. You can't handwave away your lack of understanding of a problem, like you can do with people. And the process of trying to code a problem (e.g. for a simulation) forces you to learn just about everything you don't yet comprehend about it.
  - danso 9y ago
    > The line you quoted is not specific to AI/ML, it's the fundamental rule of programming Very good point. Though the OP's observation comes with additional poignancy/force in the context of artificial intelligence. A lot of people are fine with the idea of machines being told explicitly what to when it comes to doing "mechanical" things. I would guess most of these laypeople do not realize that the same sausage-making process is involved when it comes to having machines do human-like things.
  - natestemen 9y ago
    > You can't handwave away your lack of understanding of a problem well we do use packages written by others all the time which we don't necessarily need to have a complete understanding of.
justinpombrio 9y ago
> Humans are terrible at driving cars: that’s why 35,000 people were killed by them in the US alone in 2015. Ahg misleading numbers. People are actually surprisingly good at driving. Wikipedia says that the US driving fatality rate (which is worse than most other countries) is "7.1 road fatalities per 1 billion vehicle-km". You can tell it's low because it's measured in "per billion vehicle-km". EDIT: When I said "misleading numbers", I should have said "misleading units". The number 35k people killed in 2015 measures part of the toll that driving takes on the human race, and it is terrible. However, the units of deaths/year is not a measurement of how good or bad people are at driving. Deaths/mile-driven is such a measurement.
- danso 9y ago
  On what basis do you say that the OP's numbers are "misleading"? What's the objective benchmark on what number of fatalities/accidents is acceptable per billion miles? Also, fatal accidents are not the only kind of error possible when it comes to driving.
  - justinpombrio 9y ago
    > On what basis do you say that the OP's numbers are "misleading"? It's misleading because of the units its presented in: deaths/year. Those units measure (part of) the cost of driving on the human race. They do not represent how good or bad people are at driving. > What's the objective benchmark on what number of fatalities/accidents is acceptable per billion miles? I don't mean to say that the current number of deaths is OK! I want cars to get safer. However, when you say "people are bad at driving, and kill 35k people/year", it sounds like it's a low bar for self-driving cars to pass. However, when you say "people kill 7 people per billion km driven", it's clear that the bar is high. Tesla Autopilot has killed 2 people so far (I believe?); has it driven 280 million km?
    - cryptoz 9y ago
      > Tesla Autopilot has killed 2 people so far (I believe?); has it driven 280 million km? Ha, what? Tesla Autopilot cannot kill anyone. The drivers of those cars are responsible, not Tesla. You are making things up. Tesla has been very clear that you are responsible for the vehicle when Autopilot is engaged. It is not a self-driving car. Also this is nonsense: > It's misleading because of the units its presented in: deaths/year. Those units measure (part of) the cost of driving on the human race. They do not represent how good or bad people are at driving. Deaths/year is a perfectly valid way to measure and discover that humans are bad at driving. If humans were not bad at driving, that number would be a lot lower, close to 0. Also, > However, when you say "people are bad at driving, and kill 35k people/year", it sounds like it's a low bar for self-driving cars to pass. No, it does not sound like a low bar to pass. Driving is hard.
jacinabox 9y ago
> Intuitively, this seems obvious and valuable — yet when this is mentioned around ML professionals, their faces turn colors and they try to explain that what’s requested is physically impossible. Machine learning models have an attention mechanism right? So just classify whatever has the model's attention.
- ahartman00 9y ago
  Not sure why you were downvoted, I'm assuming this was a good faith question. I'm going to try to answer your question, but I'm still learning, so take this with a grain of salt. Some models have attention, but it is not a requirement. In models that do have attention, it may or may not be simple. In image captioning for example, you can do just that. See [1] for some pictures of this happening. But in this example, they are stepping through a caption, and seeing where the attention is focused for each word. Works fine for a short caption. Videos are just many images and sound, but already this is going to be more difficult. For natural language processing, you will be stepping through very many words. Some models use characters instead of words. Not sure how you could even make sense of that. I have seen people look at the attention in machine translation. Not sure how it would work for sentiment analysis. So you aren't exactly wrong, but you are greatly over simplifying this for the models where it is possible(based on my understanding). Overall, machine learning is very much a black box, despite efforts to the contrary. It also doesn't solve some of the other problems. For example, knowing the model is focusing on zip code doesn't help you remove bias that comes from the data. 1. https://arxiv.org/pdf/1502.03044.pdf https://arxiv.org/pdf/1502.03044.pdf
aalleavitch 9y ago
I wonder if humanity is going to become a lot more introspective once it becomes a parent to another intelligence. Things look a lot different once you start seeing your own biases reflected in the child you're trying to raise to be better than yourself...
- BoiledCabbage 9y ago
  Unfortunately not. Humanity has plenty of opportunity to examine it's biases now and for the most part, most choose to ignore them or deny them. Just like people think They're smarter than they are, and are right more often than they actually are, and more in control of their decision making than are, they also think they're less biased than they are. There is plenty of opportunity to confront that now around the world and for the most part people dont. Humanity will decide to address biases when we decide / are convinced it's the right think to do, not because availability of more data.
  - dwaltrip 9y ago
    Maybe we are already trying, but it's pretty damn hard?
bluetwo 9y ago
One of these days, sooner or later, this is what is going to happen: Some people being toted about by an autonomous car are going to be killed. The NTSB is going to blame the car for making bad decisions. The car maker is going to blame the condition of the road, signs, or painted lines. They are going to pin the blame on the state government. Insurance companies will point fingers at each other, depending on who they represent. Then, everyone is going to sue everyone. The state is going to have to explain WHY it let these cars on roads that were not maintained at a level of being 100% compatible with the level of technology in existence. The state will say they were told the technology was safe, and pull out fancy presentations that were put together with no intention of ever ending up in court. Technology companies will shrug and say it was marketing speak and, well, you knew the risks. Then everyone will start covering their butts, and AI in autonomous cars will stop become a thing for awhile.
- erikpukinskis 9y ago
  So? Exactly what you describe happens all the time in every field.
- willbw 9y ago
  An accident will happen yes. There are about 3,287 road deaths per day globally[1] already however so hopefully people will view this in context. [1] http://asirt.org/initiatives/informing-road-users/road-safety-facts/road-crash-statistics http://asirt.org/initiatives/informing-road-users/road-safet...
- jimrandomh 9y ago
  I think this is giving the NTSB too little credit. Remember, the people who work at the NTSB deal with statistics all day, and for the most part genuinely care about reducing traffic deaths. So I think that, for their part, it'll look more like the way they've handled investigations into crashes with current partial automation (ie Tesla's autopilot): with a lot of emphasis on the overall effect of the system on accident rates, rather than on one specific crash.
- danso 9y ago
  I'm not a particular optimist about AI, but I think your cynical take doesn't account enough for the potential changes in mindset and policy that could occur by the time an autonomous car can take full blame for a big disaster. First of all, that incident will likely have been preceded by years of semi-autonmous driving becoming more and more mainstream, which means there will be more incidents similar to the Tesla autopilot fatality a couple years ago. And as we've seen, Tesla was unpunished in the NTSB's report, and self-driving investment continued unabated, so there's reason to believe that future fatalities won't cause major outrage because the public will have found ways to rationalize it. Five to 10 years of mainstream semi-autonomous driving may be enough to do 2 things: - Strongly reduce the demand for people to learn how to drive themselves, or want to drive at all (especially among the senior-age population). Keep in mind that trustworthy semi/mostly-autonomous driving will likely make Uber/Lyft even more day-to-day afforadable. - Strongly increase people's comfort with AI and robotics. Sure, AI already has huge impact and influence on society. But not in the day-to-day tangibility that comes with confidently trusting your daily drive to AI. Society's appreciation (and/or complacency) toward AI will likely be at a much higher level by the time fully-auto cars become mainstream. So when that AI-instigated-driving-disaster finally happens, you have a status quo that's generally favorable toward AI, including the passage of laws that make road conditions more favorable toward AI, both in reducing operational hazards and in mitigating legal culpability. Finally, you have a society far more dependent on self-driving cars -- you really think the millions of people who are 90 to 100% dependent on AI-personal-transportation are going to give up their livelihoods (nevermind their investments in the actual hardware) because of one big wreck? I'm too cynical to think that society will become more intellectual and rational about things. But I believe that when fully-auto cars are approved, there will be too much acceptance/complacancy for outrage to cause the industry to roll back. I agree that there will be lot of butt-covering between all the stakeholders when this fatal accident happens -- but I argue that there will be plenty of butt-covering well before this kind of situation.
TeMPOraL 9y ago
Point 3.2 is golden, and I'm very happy people seem to be no longer afraid to publish the ugly truth here (it's the second article I've seen touching this over the past month), instead of being in complete denial. AI doesn't care about our nice "ought to be" fake worlds, and algorithms themselves don't have social biases. So when an ML algorithm spits out something that goes against the prevailing ideologies, it's most likely the fact in the data. If the data is good (i.e. it represents the real world properly), then the ideologies should be questioned.
- solipsism 9y ago
  So when an ML algorithm spits out something that goes against the prevailing ideologies, it's most likely the fact in the data. You seem to have missed the subtlety in the author's treatment of this subject. His point is exactly that the data is not necessarily "good". It's biased because we are biased [1], and you have to be aware of that when interpreting results. It's the data that needs to be questioned. [1] if three white teenagers are arrested for a crime, not only are news media much less likely to show their mug shots, but they’re less likely to refer to them as “white teenagers.”
  - TeMPOraL 9y ago
    I didn't miss it; I address it in the very next sentence - "If the data is good (i.e. it represents the real world properly), then the ideologies should be questioned." There is a if-then clause there. The point about mugshots shows perfectly the problem of what happens when the data is biased. But then the corollary is, sometimes the data is good. The relevant quote from article: > If you try to manually “ignore race” by not letting race be an input to your model, it comes in through the back door: for example, someone’s zip code and income predict their race with great precision. This is what happens with loan or crime data sets, and it made plenty of noise in the news recently - but only the kind of noise in which people say it's obviously the algorithm that's broken, because it doesn't fit the "polite fiction" they'd like to believe.
    - danso 9y ago
YeGoblynQueenne 9y ago
>> As I write this, I’m going to use the terms “artificial intelligence” (AI) and “machine learning” (ML) more or less interchangeably. There’s a stupid reason these terms mean almost the same thing: it’s that “artificial intelligence” has historically been defined as “whatever computers can’t do yet.” For years, people argued that it would take true artificial intelligence to play chess, or simulate conversations, or recognize images; every time one of those things actually happened, the goalposts got moved. The phrase “artificial intelligence” was just too frightening: it cut too close, perhaps, to the way we define ourselves, and what makes us different as humans. So at some point, professionals started using the term “machine learning” to avoid the entire conversation, and it stuck. But it never really stuck, and if I only talked about “machine learning” I’d sound strangely mechanical — because even professionals talk about AI all the time. That's a typically unhistorical explanation of the relationship between machine learning and AI, which is really quite simple: machine learning is a field in the broader reseach subject of AI. "Professionals" did not "at some point" start using machine learning instead of AI. The field got a name when it solidified into a field, just like Natural Language Processing did, or Machine Vision. In fact, to use "machine learning" as a byword for "AI" is not different than using "NLP" or "Machine Vision" as a byword for AI. It is that wrong and makes it that evident that the person using the terms doesn't really understand where they come from.
- joe_the_user 9y ago
  The field got a name when it solidified into a field, just like Natural Language Processing did, or Machine Vision. I'd say a claim of machine learning being "solidified into a field" is somewhere between false and meaningless. Just look at neural networks. These may be the most successful machine learning or AI technique ever but they're also the technique most dependent on ad-hoc tweaks and tuning of anything so-far. Which seems rather the oppose of solidified.
  - YeGoblynQueenne 9y ago
    Machine learning has a journal and a conference, like neural networks do. They're AI fields. I don't know what your definition is.
YTGRK 9y ago
https://youtu.be/I2l8xkjTUh4 https://youtu.be/I2l8xkjTUh4

107 points by tim_sw 9y ago

Yonatan and I no longer see eye to eye on a lot of things, but this is a really good introductory post to how AI works and certain ethical questions around it. It's a long, but extremely enjoyable read.

danso 9y ago

This is easily one of the best explanations of AI and explorations of its implications that I've read. Probably because I largely agree with the author, but have never been able to express such assertions so clearly: > These are rarely new problems; rather, the formal process of explaining our desires to a computer — the ultimate case of someone with no cultural context or ability to infer what we don’t say — forces us to be explicit in ways we generally aren’t used to. It was also interesting to read his inside account about Google Photos's "Gorilla Incident". I had always assumed it was due to training on limited data but that just didn't make sense as Google would not have been lacking for data in 2015.

TeMPOraL 9y ago
The article is good, I agree. The line you quoted is not specific to AI/ML, it's the fundamental rule of programming (and why I absolutely love it as a tool for understanding) - the machine is the ultimate bullshit antagonist. You can't handwave away your lack of understanding of a problem, like you can do with people. And the process of trying to code a problem (e.g. for a simulation) forces you to learn just about everything you don't yet comprehend about it.
- danso 9y ago
  > The line you quoted is not specific to AI/ML, it's the fundamental rule of programming Very good point. Though the OP's observation comes with additional poignancy/force in the context of artificial intelligence. A lot of people are fine with the idea of machines being told explicitly what to when it comes to doing "mechanical" things. I would guess most of these laypeople do not realize that the same sausage-making process is involved when it comes to having machines do human-like things.
- natestemen 9y ago
  > You can't handwave away your lack of understanding of a problem well we do use packages written by others all the time which we don't necessarily need to have a complete understanding of.

justinpombrio 9y ago

> Humans are terrible at driving cars: that’s why 35,000 people were killed by them in the US alone in 2015. Ahg misleading numbers. People are actually surprisingly good at driving. Wikipedia says that the US driving fatality rate (which is worse than most other countries) is "7.1 road fatalities per 1 billion vehicle-km". You can tell it's low because it's measured in "per billion vehicle-km". EDIT: When I said "misleading numbers", I should have said "misleading units". The number 35k people killed in 2015 measures part of the toll that driving takes on the human race, and it is terrible. However, the units of deaths/year is not a measurement of how good or bad people are at driving. Deaths/mile-driven is such a measurement.

danso 9y ago
On what basis do you say that the OP's numbers are "misleading"? What's the objective benchmark on what number of fatalities/accidents is acceptable per billion miles? Also, fatal accidents are not the only kind of error possible when it comes to driving.
- justinpombrio 9y ago
  > On what basis do you say that the OP's numbers are "misleading"? It's misleading because of the units its presented in: deaths/year. Those units measure (part of) the cost of driving on the human race. They do not represent how good or bad people are at driving. > What's the objective benchmark on what number of fatalities/accidents is acceptable per billion miles? I don't mean to say that the current number of deaths is OK! I want cars to get safer. However, when you say "people are bad at driving, and kill 35k people/year", it sounds like it's a low bar for self-driving cars to pass. However, when you say "people kill 7 people per billion km driven", it's clear that the bar is high. Tesla Autopilot has killed 2 people so far (I believe?); has it driven 280 million km?
  - cryptoz 9y ago
    > Tesla Autopilot has killed 2 people so far (I believe?); has it driven 280 million km? Ha, what? Tesla Autopilot cannot kill anyone. The drivers of those cars are responsible, not Tesla. You are making things up. Tesla has been very clear that you are responsible for the vehicle when Autopilot is engaged. It is not a self-driving car. Also this is nonsense: > It's misleading because of the units its presented in: deaths/year. Those units measure (part of) the cost of driving on the human race. They do not represent how good or bad people are at driving. Deaths/year is a perfectly valid way to measure and discover that humans are bad at driving. If humans were not bad at driving, that number would be a lot lower, close to 0. Also, > However, when you say "people are bad at driving, and kill 35k people/year", it sounds like it's a low bar for self-driving cars to pass. No, it does not sound like a low bar to pass. Driving is hard.

jacinabox 9y ago

> Intuitively, this seems obvious and valuable — yet when this is mentioned around ML professionals, their faces turn colors and they try to explain that what’s requested is physically impossible. Machine learning models have an attention mechanism right? So just classify whatever has the model's attention.

ahartman00 9y ago
Not sure why you were downvoted, I'm assuming this was a good faith question. I'm going to try to answer your question, but I'm still learning, so take this with a grain of salt. Some models have attention, but it is not a requirement. In models that do have attention, it may or may not be simple. In image captioning for example, you can do just that. See [1] for some pictures of this happening. But in this example, they are stepping through a caption, and seeing where the attention is focused for each word. Works fine for a short caption. Videos are just many images and sound, but already this is going to be more difficult. For natural language processing, you will be stepping through very many words. Some models use characters instead of words. Not sure how you could even make sense of that. I have seen people look at the attention in machine translation. Not sure how it would work for sentiment analysis. So you aren't exactly wrong, but you are greatly over simplifying this for the models where it is possible(based on my understanding). Overall, machine learning is very much a black box, despite efforts to the contrary. It also doesn't solve some of the other problems. For example, knowing the model is focusing on zip code doesn't help you remove bias that comes from the data. 1. https://arxiv.org/pdf/1502.03044.pdf https://arxiv.org/pdf/1502.03044.pdf

aalleavitch 9y ago

I wonder if humanity is going to become a lot more introspective once it becomes a parent to another intelligence. Things look a lot different once you start seeing your own biases reflected in the child you're trying to raise to be better than yourself...

BoiledCabbage 9y ago
Unfortunately not. Humanity has plenty of opportunity to examine it's biases now and for the most part, most choose to ignore them or deny them. Just like people think They're smarter than they are, and are right more often than they actually are, and more in control of their decision making than are, they also think they're less biased than they are. There is plenty of opportunity to confront that now around the world and for the most part people dont. Humanity will decide to address biases when we decide / are convinced it's the right think to do, not because availability of more data.
- dwaltrip 9y ago
  Maybe we are already trying, but it's pretty damn hard?

bluetwo 9y ago

One of these days, sooner or later, this is what is going to happen: Some people being toted about by an autonomous car are going to be killed. The NTSB is going to blame the car for making bad decisions. The car maker is going to blame the condition of the road, signs, or painted lines. They are going to pin the blame on the state government. Insurance companies will point fingers at each other, depending on who they represent. Then, everyone is going to sue everyone. The state is going to have to explain WHY it let these cars on roads that were not maintained at a level of being 100% compatible with the level of technology in existence. The state will say they were told the technology was safe, and pull out fancy presentations that were put together with no intention of ever ending up in court. Technology companies will shrug and say it was marketing speak and, well, you knew the risks. Then everyone will start covering their butts, and AI in autonomous cars will stop become a thing for awhile.

erikpukinskis 9y ago
So? Exactly what you describe happens all the time in every field.
willbw 9y ago
An accident will happen yes. There are about 3,287 road deaths per day globally[1] already however so hopefully people will view this in context. [1] http://asirt.org/initiatives/informing-road-users/road-safety-facts/road-crash-statistics http://asirt.org/initiatives/informing-road-users/road-safet...
jimrandomh 9y ago
I think this is giving the NTSB too little credit. Remember, the people who work at the NTSB deal with statistics all day, and for the most part genuinely care about reducing traffic deaths. So I think that, for their part, it'll look more like the way they've handled investigations into crashes with current partial automation (ie Tesla's autopilot): with a lot of emphasis on the overall effect of the system on accident rates, rather than on one specific crash.
danso 9y ago
I'm not a particular optimist about AI, but I think your cynical take doesn't account enough for the potential changes in mindset and policy that could occur by the time an autonomous car can take full blame for a big disaster. First of all, that incident will likely have been preceded by years of semi-autonmous driving becoming more and more mainstream, which means there will be more incidents similar to the Tesla autopilot fatality a couple years ago. And as we've seen, Tesla was unpunished in the NTSB's report, and self-driving investment continued unabated, so there's reason to believe that future fatalities won't cause major outrage because the public will have found ways to rationalize it. Five to 10 years of mainstream semi-autonomous driving may be enough to do 2 things: - Strongly reduce the demand for people to learn how to drive themselves, or want to drive at all (especially among the senior-age population). Keep in mind that trustworthy semi/mostly-autonomous driving will likely make Uber/Lyft even more day-to-day afforadable. - Strongly increase people's comfort with AI and robotics. Sure, AI already has huge impact and influence on society. But not in the day-to-day tangibility that comes with confidently trusting your daily drive to AI. Society's appreciation (and/or complacency) toward AI will likely be at a much higher level by the time fully-auto cars become mainstream. So when that AI-instigated-driving-disaster finally happens, you have a status quo that's generally favorable toward AI, including the passage of laws that make road conditions more favorable toward AI, both in reducing operational hazards and in mitigating legal culpability. Finally, you have a society far more dependent on self-driving cars -- you really think the millions of people who are 90 to 100% dependent on AI-personal-transportation are going to give up their livelihoods (nevermind their investments in the actual hardware) because of one big wreck? I'm too cynical to think that society will become more intellectual and rational about things. But I believe that when fully-auto cars are approved, there will be too much acceptance/complacancy for outrage to cause the industry to roll back. I agree that there will be lot of butt-covering between all the stakeholders when this fatal accident happens -- but I argue that there will be plenty of butt-covering well before this kind of situation.

TeMPOraL 9y ago

Point 3.2 is golden, and I'm very happy people seem to be no longer afraid to publish the ugly truth here (it's the second article I've seen touching this over the past month), instead of being in complete denial. AI doesn't care about our nice "ought to be" fake worlds, and algorithms themselves don't have social biases. So when an ML algorithm spits out something that goes against the prevailing ideologies, it's most likely the fact in the data. If the data is good (i.e. it represents the real world properly), then the ideologies should be questioned.

solipsism 9y ago
So when an ML algorithm spits out something that goes against the prevailing ideologies, it's most likely the fact in the data. You seem to have missed the subtlety in the author's treatment of this subject. His point is exactly that the data is not necessarily "good". It's biased because we are biased [1], and you have to be aware of that when interpreting results. It's the data that needs to be questioned. [1] if three white teenagers are arrested for a crime, not only are news media much less likely to show their mug shots, but they’re less likely to refer to them as “white teenagers.”
- TeMPOraL 9y ago
  I didn't miss it; I address it in the very next sentence - "If the data is good (i.e. it represents the real world properly), then the ideologies should be questioned." There is a if-then clause there. The point about mugshots shows perfectly the problem of what happens when the data is biased. But then the corollary is, sometimes the data is good. The relevant quote from article: > If you try to manually “ignore race” by not letting race be an input to your model, it comes in through the back door: for example, someone’s zip code and income predict their race with great precision. This is what happens with loan or crime data sets, and it made plenty of noise in the news recently - but only the kind of noise in which people say it's obviously the algorithm that's broken, because it doesn't fit the "polite fiction" they'd like to believe.
  - danso 9y ago

YeGoblynQueenne 9y ago

>> As I write this, I’m going to use the terms “artificial intelligence” (AI) and “machine learning” (ML) more or less interchangeably. There’s a stupid reason these terms mean almost the same thing: it’s that “artificial intelligence” has historically been defined as “whatever computers can’t do yet.” For years, people argued that it would take true artificial intelligence to play chess, or simulate conversations, or recognize images; every time one of those things actually happened, the goalposts got moved. The phrase “artificial intelligence” was just too frightening: it cut too close, perhaps, to the way we define ourselves, and what makes us different as humans. So at some point, professionals started using the term “machine learning” to avoid the entire conversation, and it stuck. But it never really stuck, and if I only talked about “machine learning” I’d sound strangely mechanical — because even professionals talk about AI all the time. That's a typically unhistorical explanation of the relationship between machine learning and AI, which is really quite simple: machine learning is a field in the broader reseach subject of AI. "Professionals" did not "at some point" start using machine learning instead of AI. The field got a name when it solidified into a field, just like Natural Language Processing did, or Machine Vision. In fact, to use "machine learning" as a byword for "AI" is not different than using "NLP" or "Machine Vision" as a byword for AI. It is that wrong and makes it that evident that the person using the terms doesn't really understand where they come from.

joe_the_user 9y ago
The field got a name when it solidified into a field, just like Natural Language Processing did, or Machine Vision. I'd say a claim of machine learning being "solidified into a field" is somewhere between false and meaningless. Just look at neural networks. These may be the most successful machine learning or AI technique ever but they're also the technique most dependent on ad-hoc tweaks and tuning of anything so-far. Which seems rather the oppose of solidified.
- YeGoblynQueenne 9y ago
  Machine learning has a journal and a conference, like neural networks do. They're AI fields. I don't know what your definition is.

YTGRK 9y ago

https://youtu.be/I2l8xkjTUh4 https://youtu.be/I2l8xkjTUh4