8 ms·

Talk about “AI” in the press these days tends to conflate two things

69 points by luu 6y ago

laurieg 6y ago
I agree with the main thrust of the article. However, I don't think this is a particularly new idea. AI has always been this vague, amorphous idea in people's minds. Whenever it becomes concrete progress it generally stops being called AI and gets given a more specific and useful name.
KingOfCoders 6y ago
I wish more people would use ML if it's ML instead of AI.
- freetime2 6y ago
  Isn’t Machine Learning even a bit of an exaggeration, though?
  - forgingahead 6y ago
    Why? The machine learns via essentially trial-and-error to produce a model that best fits the data provided to it. The hope is that the training data is representative of "unseen" data the model later encounters in order to be useful. Seems pretty accurate given the broadness of the topic and variety of use cases.
    - abhinav22 6y ago
      Because it’s regression and statistical analysis. Nothing special about ML except applying it to large(r) datasets, particularly in areas where it works well (various recommendation based models for example).
      - KingOfCoders 6y ago
        How does this differ from you "learning" words of a foreign language?
        loopz 6y ago
        Try to prove electronic computers/TPU works like an organic human.
        drdeca 6y ago
        Why does it have to? If you define “learning” as “being like an organic human”, then sure perhaps it isn’t “learning” under that definition, but that seems like a poor definition, and I don’t think it is how people use the word. What does “learning” mean?
        loopz 6y ago
        Good. Ponder on that question.
        abhinav22 6y ago
        It’s not “learning” anything though. Difference is causation vs correlation: If I learn how to boil water for example, there is close to 100% causation that heating water causes it to boil (notwithstanding we do not know everything about Physics, eg before we would say the earth is flat because we learnt it so, but that was incorrect, ie not everything we learn is true, it’s just a super high level of correlation) However if a machine learning model says the probability of water boiling is 99% after heating water, that is actually correlation. So I would distinguish it from learning In this example there’s not much different. But when you apply to more complex examples, the difference between causation and correlation become clearer
        DubiousPusher 6y ago
        You think people's ability to learn is really distinguished by an ability to discern causation and correlation? That does not comport with modern behavioral science.
    - BoiledCabbage 6y ago
      The question is: Is the machine learning, or is the machine intelligent. The answer is its neither. But we've taken them to be useful analogies and loosely designated them to mean different things. Neither one really applies because in truth, the machine isn't learning. Because we've deemed some types of organs optimizers, others dynamic programing and others learning. It's all arbitrary.
      - DubiousPusher 6y ago
        I don't believe it is entirely arbitrary. Just as there are categorizations of machine, turing machine vs push down automata vs finite state machines, which determine their ability to solve certain problems, there are differences between categories of algorithm and their ability to solve certain problems. There are many "learning" algorithms beyond what is popularly called ML today but they have in common that they use information about their own success or failure to inform future results.
- NalNezumi 6y ago
  And I wish more people would call it what it really is: optimization(regression) & statistics. The sooner the buzzword "AI / ML" dies, the earlier we can shift the wrongly allocated investment to more promising things, (just as blockchain caused all the weird money flow in one direction). Unlike the blockchain, "AI" brigade seems to just have too many people that benefit from keeping it over-hyped so I guess it won't come soon enough.
DubiousPusher 6y ago
I have certainly noticed this but am largely unsurprised by it. I've been much more shocked by the number of people I've worked with in the tech industry who believe that #2 is far more advanced than current evidence suggest and that it will lead inevitably to #1. I'm not opposed to the hypothesis but the number of people I've encountered who really believe current heirarcical neural nets are the end all tech is truly head scratching. Especially when so many technologies such as the Internet have not produced certain expected results and completely surprised with such unexpected ones.
dennist 6y ago
I find a lot of the media (here in Japan anyways) call literally every automation by computers or machines AI. Example, there’s an automated gate that only opens if a scanner finds your body temperature is not too high. AI Gate. The other day was something related to automatic checkout at shops or something where it all boiled down to some computer reading QR codes
- overkalix 6y ago
  The other day I read an article about "uncovering tax fraud with algorithms and AI". The innovation: examining tax records not just from an individual perspective, but from a family perspective. Essentially a split-apply-combine. AI!
  - danielmg 6y ago
    I read an article about a large firm of lawyers removing gendered language from their pro-forma documents. The article claimed they were going to use AI to achieve this. It's not even worth pointing out what is the obvious method for this task.
- NalNezumi 6y ago
  Yea In Japan the AI hype (or fad) arrived with like 3-4 years of delay compared to the west. Thing is it works so well for startups to get funding, it's not just media but investors too. Worked for a startup which sold "AI" interact with customer by detecting human face (and age, gender etc) and having targeted conversation. Turns out it's just a facial recognition and replay of prerecorded voices. I felt really dumb when customers, investors & the CTO somehow were impressed by this basic functionality.
- Ma8ee 6y ago
  That reminds me how in South Africa the traffic lights are called robots.
  - morelisp 6y ago
    A quite good story riffing off that: http://clarkesworldmagazine.com/okorafor-kahiu_10_16/ http://clarkesworldmagazine.com/okorafor-kahiu_10_16/.
yk 6y ago
That's actually a special case of a general phenomenon, which I seen here on a link on HN being called the Conway or Neumann problem (in any case, that blog post did purposefully misattribute for a joke, so I am not too concerned in getting the name right). The thing is, you're reading an newspaper article on anything where you can evaluate the quality with any kind of confidence, and it is invariably garbage, much too much simplified, glances over the interesting bits, and obviously the journalist did not understand the topic. And then you're turning the page, and expect the article on anything else to be much better.
- slavik81 6y ago
  You're referring to the "Gell-Mann Amnesia" effect.
hassanahmad 6y ago
"AI" will be there is almost everything is few years time.
NalNezumi 6y ago
The talk about "AI" is not just misinterpreted in the press, but the ML/AI practitioner themselves too. I can understand academia & researcher hyping it up, they need to chase the funding. But code boot-camp "ML" engineers being all over-hyping is really, really annoying and contribute to the problem far worse. These people are easy to spot: go to an ML/AI meetup or community that also shares latest tech. Since those does not understand the underlying math, they cant follow the paper, the talk or the discussion, so they quickly try to shift the discussion to "AI ethics" or "AI will take over the world" hypothetical useless discussions. These seems to also be the first to run out to promise big things with "AI".
- dvfjsdhgfv 6y ago
  > The talk about "AI" is not just misinterpreted in the press, but the ML/AI practitioner themselves too. Of course, for example when you use the phrase "ML/AI".
- kerng 6y ago
  You might underestimate the speed and velocity of advancement in the field. A few years ago creating artificial data or images that are indistinguishable from real data was impossible. Now machines generate information, like entire videos that are real but fake. This can, and has already (look at facebook and fake profiles), led to big problems and in a few years the world will change quite drastically because of further advances and automation. This can go in good and bad directions... I'm just worried that downplaying it will cause pivoting towards undesirable outcomes.
  - NalNezumi 6y ago
    I don't think I underestimate the speed nor velocity of the advancement, just being realistic about the scope of the advancements. I follow conferences and read ML papers (DL & DL/ML application in Robotics) regularly so I think I have a some grasp on the advancement in the field. GAN sure have been a big step, and have found a great usage. (deepfake etc) but I'd say it is still highly overblown. Like GPT3 it will see wide adaptation enough to make visible improvement (and problems) in certain industries, just as better classifiers changed a lot. I also don't like the blanket term of "automation" because it is like AI: too vague and often used by someone who don't fully grasp HOW hard automation tasks are (and how the complexity scale and differs from the task ). Just as optimism in Self-driving car is now more on low-fire. The over-promise of ML is something akin to people expecting "it to just get better" similar to Moore's law. If you take a look at what direction the algorithms are improving, what incremental advancements are being made the scope of the speed and velocity become much more clear; and just because cars get faster, doesn't mean it will one day fly to space like a rocket. If we one day in short term future see great improvement, it will probably not be the ML improvements, but due external factors (hardware, network speed, etc)
st1x7 6y ago
From the highly recommended classic "AI Meets Natural Stupidity"[1], 1976: As a field, artificial intelligence has always been on the border of respectability, and therefore on the border of crackpottery ... Most AI workers are responsible people who are aware of the pitfalls of a difficult field and produce good work in spite of them. However, to say anything good about anyone is beyond the scope of this paper. [1] - https://www.csee.umbc.edu/courses/graduate/691/fall19/07/papers/AI_Meets_Natural_Stupidity.pdf https://www.csee.umbc.edu/courses/graduate/691/fall19/07/pap...
phkahler 6y ago
The press is parroting the tech industry where AI, ML, and Deep Learning are all conflated and claimed as the magical future of everything, in an effort to boost valuations.
Ma8ee 6y ago
On the other hand, we are today daily using tools that 20 years ago certainly was studied as AI, such as speech recognition and image classification. Maybe they are not using general artificial intelligence to do it, but I think that is beside the point. They are accomplishing something that used to be thought to require some kind of intelligence to accomplish. But as soon we succeed with one of these tasks, the magic disappears among the practitioners, and the goalpost for what is allowed to be called AI is moved. Our understanding what intelligence really is, is very vague to begin with, so what’s wrong with calling progress in machine learning AI even after we actually succeeded with something?
- unishark 6y ago
  I agree there is a moving goalpost for what counts as good enough to call "AI". But with the recent revolution in machine learning the goalpost kind of went backwards. The successes in machine learning problems have tended to come from "adjacent" fields like pattern recognition, information retrieval, plain old statistics, etc. Meanwhile the AI researchers were pursuing more ambitious directions that didn't pan out. Then AI researchers basically "annexed" these lower level methods that worked.
- wolki 6y ago
  There's nothing wrong with it, it can simply lead to confusion. When computers started, people called them 'thinking machines' and, say, multiplication was something that required some intelligent behavior on the part of the person doing it. There's nothing wrong with calling a calculator a thinking machine that implements intelligent behavior, but it helps if everyone is aware that at other times when people talk about 'thinking' and 'intelligent' they mean something quite different. This is not uncommon; when people use the word 'bank' they refer to very different things: river ones, park ones, central ones, rotations of flying objects etc. But everyone should be aware that getting better at building comfortable banks to sit on in the park is not the same as getting better at figuring out the optimal rate of interest or turning planes. It certainly might, but that is a separate argument and terminology can obscure that.
DanielBMarkham 6y ago
The two that are conflated according to this author: Machine intelligence in general, and A specific bundle of technologies. But the goal is specifically to conflate these two, isn't it? If I create a machine that beats a chess grandmaster, does it matter whether or not the machine "knows" how to play chess? If I create an app that takes you to a great restaurant for the mood you're in, does it matter whether or not it understands "mood" or "restaurant"? At some point, the stringing together of specific bundles of technologies becomes indistinguishable to the average person from real intelligence. (This probably happens far, far sooner for the average person than it does tech folks and researchers) At that point, we've created something that for a specific domain _is_ intelligent: it reasons about things in an internal way that is opaque to us and provides what we perceive to be interactive value. Then, that thing just becomes another bundled to be assembled in an even larger chain. True AGI would be able to create and assemble the bundles. But even then, the same end-state occurs: at some point whatever we're calling AGI will be able to do things with bundles of technologies that we perceive to provide interactive value. Will it matter at that point whether or not the machine is "truly" intelligent? (Apologies for the scare quotes, but many of these terms are quite suspect in this conversation and are used in all sorts of ways by authors) To put it bluntly, the goal of AI work is to do cool stuff for reasons we're not exactly sure about. Otherwise it'd just be programming. We are using a lot of programming tools, like NN, to do this. At some point, various groups will fake themselves out and stop working in that area. Whether or not that's intelligence or not, whether or not anything new will ever happen in that field, is beside the point and not in the (commercial) scope of work. Aside from all the formal work and really cool stuff happening, in the end, when it gets used somewhere, this is a "looks good enough to me" situation. We're not looking to create intelligence, we're looking to create an uber-duber supreme version of Eliza for some given problem. The we find other, more complex and interesting problems. Then if we need to we'll join them together (There is a lot of detail that I have ignored, including how GANs play into my argument)
fumblebee 6y ago
There’s a great Wikipedia article which is apt here. https://en.wikipedia.org/wiki/AI_effect?wprov=sfti1 https://en.wikipedia.org/wiki/AI_effect?wprov=sfti1 It’s perhaps best encapsulated by Douglas Hofstadter’s quip: “AI is whatever hasn't been done yet”. The point being, AI is a moving target, and the threshold for reaching it migrates further which each new accomplishment in the field. If you showed the ML of today to someone in the sixties, you’d have a hard time convincing them it wasn’t AI. But I don’t believe this is an issue reserved only for the press. At least anecdotally, practitioners don’t think of it this way either.
- fumblebee 6y ago
  Another apt article: https://link.medium.com/hLkrmNzD9bb https://link.medium.com/hLkrmNzD9bb
Daho0n 6y ago
#2 is like how Tesla sells "Auto-pilot".
karmakaze 6y ago
I'm sorry, this is a pointless discussion. First off AI and MI are used almost synonymously[0] and MI very rarely: - "Artificial Intelligence" 192M hits - "Machine Intelligence" 4.8M hits - "Machine Learning" 153M hits > The word “AI” in these ads has a recognizable meaning, and it is not the same meaning used in the sentence “Elon Musk founded OpenAI because he was worried AI might cause human extinction.” These is not that different, they are the same thing on different time scales. The first usage names the current (moving definition) state, which moves toward the second which is a singular (theoretical) point in development. Finally, is this just a PSA? We already know this. If it doesn't say ML, deep learning, CNN, or other specific term it's a fluff piece, or just trying to get eyeballs on research (which is rational). [0] https://www.dataversity.net/ai-vs-machine-learning-vs-deep-learning/ https://www.dataversity.net/ai-vs-machine-learning-vs-deep-l...
tpoacher 6y ago
My own distinction between ML and AI (which I agree with the article that marketing buzzheads do not care about) is that one is a set of optimization techniques not necessarily involving an agent, and the other involves an agent, who needs to make (intelligent) decisions. The decisions themselves may rely on said ML-based optimization techniques, or they might not. Similarly, optimization techniques may employ an agent-based approach, but equally they may not.
amai 6y ago
Difference between machine learning and AI: If it is written in Python, it's probably machine learning If it is written in PowerPoint, it's probably AI

69 points by luu 6y ago

I agree with the main thrust of the article. However, I don't think this is a particularly new idea. AI has always been this vague, amorphous idea in people's minds. Whenever it becomes concrete progress it generally stops being called AI and gets given a more specific and useful name.

KingOfCoders 6y ago

I wish more people would use ML if it's ML instead of AI.

freetime2 6y ago
Isn’t Machine Learning even a bit of an exaggeration, though?
- forgingahead 6y ago
  Why? The machine learns via essentially trial-and-error to produce a model that best fits the data provided to it. The hope is that the training data is representative of "unseen" data the model later encounters in order to be useful. Seems pretty accurate given the broadness of the topic and variety of use cases.
  - abhinav22 6y ago
    Because it’s regression and statistical analysis. Nothing special about ML except applying it to large(r) datasets, particularly in areas where it works well (various recommendation based models for example).
    - KingOfCoders 6y ago
      How does this differ from you "learning" words of a foreign language?
      - loopz 6y ago
        Try to prove electronic computers/TPU works like an organic human.
        drdeca 6y ago
        Why does it have to? If you define “learning” as “being like an organic human”, then sure perhaps it isn’t “learning” under that definition, but that seems like a poor definition, and I don’t think it is how people use the word. What does “learning” mean?
        loopz 6y ago
        Good. Ponder on that question.
      - abhinav22 6y ago
        It’s not “learning” anything though. Difference is causation vs correlation: If I learn how to boil water for example, there is close to 100% causation that heating water causes it to boil (notwithstanding we do not know everything about Physics, eg before we would say the earth is flat because we learnt it so, but that was incorrect, ie not everything we learn is true, it’s just a super high level of correlation) However if a machine learning model says the probability of water boiling is 99% after heating water, that is actually correlation. So I would distinguish it from learning In this example there’s not much different. But when you apply to more complex examples, the difference between causation and correlation become clearer
        DubiousPusher 6y ago
        You think people's ability to learn is really distinguished by an ability to discern causation and correlation? That does not comport with modern behavioral science.
  - BoiledCabbage 6y ago
    The question is: Is the machine learning, or is the machine intelligent. The answer is its neither. But we've taken them to be useful analogies and loosely designated them to mean different things. Neither one really applies because in truth, the machine isn't learning. Because we've deemed some types of organs optimizers, others dynamic programing and others learning. It's all arbitrary.
    - DubiousPusher 6y ago
      I don't believe it is entirely arbitrary. Just as there are categorizations of machine, turing machine vs push down automata vs finite state machines, which determine their ability to solve certain problems, there are differences between categories of algorithm and their ability to solve certain problems. There are many "learning" algorithms beyond what is popularly called ML today but they have in common that they use information about their own success or failure to inform future results.
NalNezumi 6y ago
And I wish more people would call it what it really is: optimization(regression) & statistics. The sooner the buzzword "AI / ML" dies, the earlier we can shift the wrongly allocated investment to more promising things, (just as blockchain caused all the weird money flow in one direction). Unlike the blockchain, "AI" brigade seems to just have too many people that benefit from keeping it over-hyped so I guess it won't come soon enough.

DubiousPusher 6y ago

I have certainly noticed this but am largely unsurprised by it. I've been much more shocked by the number of people I've worked with in the tech industry who believe that #2 is far more advanced than current evidence suggest and that it will lead inevitably to #1. I'm not opposed to the hypothesis but the number of people I've encountered who really believe current heirarcical neural nets are the end all tech is truly head scratching. Especially when so many technologies such as the Internet have not produced certain expected results and completely surprised with such unexpected ones.

dennist 6y ago

I find a lot of the media (here in Japan anyways) call literally every automation by computers or machines AI. Example, there’s an automated gate that only opens if a scanner finds your body temperature is not too high. AI Gate. The other day was something related to automatic checkout at shops or something where it all boiled down to some computer reading QR codes

overkalix 6y ago
The other day I read an article about "uncovering tax fraud with algorithms and AI". The innovation: examining tax records not just from an individual perspective, but from a family perspective. Essentially a split-apply-combine. AI!
- danielmg 6y ago
  I read an article about a large firm of lawyers removing gendered language from their pro-forma documents. The article claimed they were going to use AI to achieve this. It's not even worth pointing out what is the obvious method for this task.
NalNezumi 6y ago
Yea In Japan the AI hype (or fad) arrived with like 3-4 years of delay compared to the west. Thing is it works so well for startups to get funding, it's not just media but investors too. Worked for a startup which sold "AI" interact with customer by detecting human face (and age, gender etc) and having targeted conversation. Turns out it's just a facial recognition and replay of prerecorded voices. I felt really dumb when customers, investors & the CTO somehow were impressed by this basic functionality.
Ma8ee 6y ago
That reminds me how in South Africa the traffic lights are called robots.
- morelisp 6y ago
  A quite good story riffing off that: http://clarkesworldmagazine.com/okorafor-kahiu_10_16/ http://clarkesworldmagazine.com/okorafor-kahiu_10_16/.

yk 6y ago

That's actually a special case of a general phenomenon, which I seen here on a link on HN being called the Conway or Neumann problem (in any case, that blog post did purposefully misattribute for a joke, so I am not too concerned in getting the name right). The thing is, you're reading an newspaper article on anything where you can evaluate the quality with any kind of confidence, and it is invariably garbage, much too much simplified, glances over the interesting bits, and obviously the journalist did not understand the topic. And then you're turning the page, and expect the article on anything else to be much better.

slavik81 6y ago
You're referring to the "Gell-Mann Amnesia" effect.

hassanahmad 6y ago

"AI" will be there is almost everything is few years time.

NalNezumi 6y ago

The talk about "AI" is not just misinterpreted in the press, but the ML/AI practitioner themselves too. I can understand academia & researcher hyping it up, they need to chase the funding. But code boot-camp "ML" engineers being all over-hyping is really, really annoying and contribute to the problem far worse. These people are easy to spot: go to an ML/AI meetup or community that also shares latest tech. Since those does not understand the underlying math, they cant follow the paper, the talk or the discussion, so they quickly try to shift the discussion to "AI ethics" or "AI will take over the world" hypothetical useless discussions. These seems to also be the first to run out to promise big things with "AI".

dvfjsdhgfv 6y ago
> The talk about "AI" is not just misinterpreted in the press, but the ML/AI practitioner themselves too. Of course, for example when you use the phrase "ML/AI".
kerng 6y ago
You might underestimate the speed and velocity of advancement in the field. A few years ago creating artificial data or images that are indistinguishable from real data was impossible. Now machines generate information, like entire videos that are real but fake. This can, and has already (look at facebook and fake profiles), led to big problems and in a few years the world will change quite drastically because of further advances and automation. This can go in good and bad directions... I'm just worried that downplaying it will cause pivoting towards undesirable outcomes.
- NalNezumi 6y ago
  I don't think I underestimate the speed nor velocity of the advancement, just being realistic about the scope of the advancements. I follow conferences and read ML papers (DL & DL/ML application in Robotics) regularly so I think I have a some grasp on the advancement in the field. GAN sure have been a big step, and have found a great usage. (deepfake etc) but I'd say it is still highly overblown. Like GPT3 it will see wide adaptation enough to make visible improvement (and problems) in certain industries, just as better classifiers changed a lot. I also don't like the blanket term of "automation" because it is like AI: too vague and often used by someone who don't fully grasp HOW hard automation tasks are (and how the complexity scale and differs from the task ). Just as optimism in Self-driving car is now more on low-fire. The over-promise of ML is something akin to people expecting "it to just get better" similar to Moore's law. If you take a look at what direction the algorithms are improving, what incremental advancements are being made the scope of the speed and velocity become much more clear; and just because cars get faster, doesn't mean it will one day fly to space like a rocket. If we one day in short term future see great improvement, it will probably not be the ML improvements, but due external factors (hardware, network speed, etc)

st1x7 6y ago

From the highly recommended classic "AI Meets Natural Stupidity"[1], 1976: As a field, artificial intelligence has always been on the border of respectability, and therefore on the border of crackpottery ... Most AI workers are responsible people who are aware of the pitfalls of a difficult field and produce good work in spite of them. However, to say anything good about anyone is beyond the scope of this paper. [1] - https://www.csee.umbc.edu/courses/graduate/691/fall19/07/papers/AI_Meets_Natural_Stupidity.pdf https://www.csee.umbc.edu/courses/graduate/691/fall19/07/pap...

phkahler 6y ago

The press is parroting the tech industry where AI, ML, and Deep Learning are all conflated and claimed as the magical future of everything, in an effort to boost valuations.

Ma8ee 6y ago

On the other hand, we are today daily using tools that 20 years ago certainly was studied as AI, such as speech recognition and image classification. Maybe they are not using general artificial intelligence to do it, but I think that is beside the point. They are accomplishing something that used to be thought to require some kind of intelligence to accomplish. But as soon we succeed with one of these tasks, the magic disappears among the practitioners, and the goalpost for what is allowed to be called AI is moved. Our understanding what intelligence really is, is very vague to begin with, so what’s wrong with calling progress in machine learning AI even after we actually succeeded with something?

unishark 6y ago
I agree there is a moving goalpost for what counts as good enough to call "AI". But with the recent revolution in machine learning the goalpost kind of went backwards. The successes in machine learning problems have tended to come from "adjacent" fields like pattern recognition, information retrieval, plain old statistics, etc. Meanwhile the AI researchers were pursuing more ambitious directions that didn't pan out. Then AI researchers basically "annexed" these lower level methods that worked.
wolki 6y ago
There's nothing wrong with it, it can simply lead to confusion. When computers started, people called them 'thinking machines' and, say, multiplication was something that required some intelligent behavior on the part of the person doing it. There's nothing wrong with calling a calculator a thinking machine that implements intelligent behavior, but it helps if everyone is aware that at other times when people talk about 'thinking' and 'intelligent' they mean something quite different. This is not uncommon; when people use the word 'bank' they refer to very different things: river ones, park ones, central ones, rotations of flying objects etc. But everyone should be aware that getting better at building comfortable banks to sit on in the park is not the same as getting better at figuring out the optimal rate of interest or turning planes. It certainly might, but that is a separate argument and terminology can obscure that.

DanielBMarkham 6y ago

The two that are conflated according to this author: Machine intelligence in general, and A specific bundle of technologies. But the goal is specifically to conflate these two, isn't it? If I create a machine that beats a chess grandmaster, does it matter whether or not the machine "knows" how to play chess? If I create an app that takes you to a great restaurant for the mood you're in, does it matter whether or not it understands "mood" or "restaurant"? At some point, the stringing together of specific bundles of technologies becomes indistinguishable to the average person from real intelligence. (This probably happens far, far sooner for the average person than it does tech folks and researchers) At that point, we've created something that for a specific domain _is_ intelligent: it reasons about things in an internal way that is opaque to us and provides what we perceive to be interactive value. Then, that thing just becomes another bundled to be assembled in an even larger chain. True AGI would be able to create and assemble the bundles. But even then, the same end-state occurs: at some point whatever we're calling AGI will be able to do things with bundles of technologies that we perceive to provide interactive value. Will it matter at that point whether or not the machine is "truly" intelligent? (Apologies for the scare quotes, but many of these terms are quite suspect in this conversation and are used in all sorts of ways by authors) To put it bluntly, the goal of AI work is to do cool stuff for reasons we're not exactly sure about. Otherwise it'd just be programming. We are using a lot of programming tools, like NN, to do this. At some point, various groups will fake themselves out and stop working in that area. Whether or not that's intelligence or not, whether or not anything new will ever happen in that field, is beside the point and not in the (commercial) scope of work. Aside from all the formal work and really cool stuff happening, in the end, when it gets used somewhere, this is a "looks good enough to me" situation. We're not looking to create intelligence, we're looking to create an uber-duber supreme version of Eliza for some given problem. The we find other, more complex and interesting problems. Then if we need to we'll join them together (There is a lot of detail that I have ignored, including how GANs play into my argument)

fumblebee 6y ago

There’s a great Wikipedia article which is apt here. https://en.wikipedia.org/wiki/AI_effect?wprov=sfti1 https://en.wikipedia.org/wiki/AI_effect?wprov=sfti1 It’s perhaps best encapsulated by Douglas Hofstadter’s quip: “AI is whatever hasn't been done yet”. The point being, AI is a moving target, and the threshold for reaching it migrates further which each new accomplishment in the field. If you showed the ML of today to someone in the sixties, you’d have a hard time convincing them it wasn’t AI. But I don’t believe this is an issue reserved only for the press. At least anecdotally, practitioners don’t think of it this way either.

fumblebee 6y ago
Another apt article: https://link.medium.com/hLkrmNzD9bb https://link.medium.com/hLkrmNzD9bb

Daho0n 6y ago

#2 is like how Tesla sells "Auto-pilot".

karmakaze 6y ago

I'm sorry, this is a pointless discussion. First off AI and MI are used almost synonymously[0] and MI very rarely: - "Artificial Intelligence" 192M hits - "Machine Intelligence" 4.8M hits - "Machine Learning" 153M hits > The word “AI” in these ads has a recognizable meaning, and it is not the same meaning used in the sentence “Elon Musk founded OpenAI because he was worried AI might cause human extinction.” These is not that different, they are the same thing on different time scales. The first usage names the current (moving definition) state, which moves toward the second which is a singular (theoretical) point in development. Finally, is this just a PSA? We already know this. If it doesn't say ML, deep learning, CNN, or other specific term it's a fluff piece, or just trying to get eyeballs on research (which is rational). [0] https://www.dataversity.net/ai-vs-machine-learning-vs-deep-learning/ https://www.dataversity.net/ai-vs-machine-learning-vs-deep-l...

tpoacher 6y ago

My own distinction between ML and AI (which I agree with the article that marketing buzzheads do not care about) is that one is a set of optimization techniques not necessarily involving an agent, and the other involves an agent, who needs to make (intelligent) decisions. The decisions themselves may rely on said ML-based optimization techniques, or they might not. Similarly, optimization techniques may employ an agent-based approach, but equally they may not.

amai 6y ago

Difference between machine learning and AI: If it is written in Python, it's probably machine learning If it is written in PowerPoint, it's probably AI