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Our field isn't quite “artificial intelligence” – it's “cognitive automation”

294 points by vo2maxer 7y ago

0xdeadbeefbabe 7y ago
it's automation Edit: artificial automation Edit: computer science Edit: pseudo science?
hprotagonist 7y ago
As usual, the monks know what the laity doesn't, and aren't particularly afraid to talk about it. Also as usual, there's still a yawning gap between what domain experts are up to and what non-domain experts think they're up to. That this is true in AI is not surprising; humility comes from knowing that my domain expertise in some fields (and thus a clearer picture of 'what's really going on') is guaranteed to be crippled in other fields. Knowing that by being in some knowledge in-groups requires me to also be in some knowledge out-groups is the beginnings of a sane approach to the world.
- Invictus0 7y ago
  The author is just correcting a misnomer. It is not really accurate to say that machine learning is intelligent at all, so why label it as such? It's confusing for everyone and leads to great misunderstandings.
  - cgriswald 7y ago
    > so why label it as such? Great misunderstandings are often profitable for those who understand.
    - Accujack 7y ago
      Or put more simply... marketing. Every new discovery ever, people wanting to exploit it have done anything necessary to use people's honest interest in new technology and good feeling about human progress to get money or power.
  - PeterisP 7y ago
    Machine learning is a particular narrow result of studying the wider field of artificial intelligence. Just as expert systems, or rdf knowledge representation, or first order logic reasoners, or planning systems - none of them are 'intelligent' but all of them are research results coming from (and being studing in) the discipline of studying how intelligence works and how can something like it be approached artificially. There's lots in the field of AI that is not 'cognitive automation' - many currently popular things and use cases are, but that's not correcting a misnomer, that's a separate term for a separate (and more narrow) thing - even if that narrower thing constitutes the most relevant and most useful part of current AI research. A classic definition of intelligence (Legg&Hutter) is "Intelligence measures an agent’s ability to achieve goals in a wide range of environments". That's a worthwhile goal to study even if (obviously) our artificial sytems are not yet even close to human level according to that criteria; and while it is roughly in the same direction as 'cognitive automation', it's less limited and not entirely the same. For example, 'cognitive automation' pretty much assumes a fixed task to execute/automate, and excludes all the nuances of agentive behavior and motivation, but these are important subtopics in the field of AI. But I am willing to concede that very many people are explicitly working only on the subfield of 'cognitive automation' and that it would be clearer if these people (but not all AI researchers) explicitly said so.
    - corporateslave5 7y ago
      The thing with differentiating machine learning and AI, is that nothing in AI world works except machine learning. It’s just a bunch of old theories and ideas, none of which have panned out
    - joe_the_user 7y ago
      > Machine learning is a particular narrow result of studying the wider field of artificial intelligence. I beg to differ, at least as far as terms go now. Neural networks lived in the "field" of machine learning along with Kernel machines and miscellaneous prediction systems circa the early 2000s. Neural network today are known as AI because ... why? Basically, the histories I've read and remember say that the only difference is now neural networks are successful enough they don't have to hide behind a more "narrow" term - or alternately, the hype train now prefers a more ambitious term. I mean, the Machine Learning reddit is one go-to place for actual researchers to discussion neural nets. Everyone now talks about these as AI because the terms have essentially merged. > A classic definition of intelligence (Legg&Hutter) is "Intelligence measures an agent’s ability to achieve goals in a wide range of environments". Machine learning mostly became AI through neural nets looking really good - but none of that involve them become more oriented to goals, is anything, less so. It was far more - high dimensional curve can actually get you a whole lot and when you do well, you can call it AI.
  - pmelendez 7y ago
    I don't think there are many definitions of machine learning that claim the models to be intelligent. Most of them limit the term to models that can be built from data. Learning is a skill that not necessarily comes with an "intelligent" label attached to it.
    - kkwak 7y ago
      Have we even defined 'intelligent' might mean? As in, we had the Turing test as a bar and we are close to that already. What is intelligence then, last I checked, there wasn't a definitive answer do it. We'll need it so that we can label AI as I properly - or maybe we don't care so much... If it's close enough...
      - hprotagonist 7y ago
        there are several hundred competing definitions of "intelligence". No consensus, as they say, has been reached.
        aSplash0fDerp 7y ago
        Once we start seeing cheaply made, imported yes/no engines (masquerading as AI or knowledge) flooding the market, the definition of intelligence will be lost on marketing anyways (unlimited data, superfood, etc)
        grp000 7y ago
        A predictive model, whether created by ML (regression, SVM, NN, whatever) or something rules-based born out of data analysis is reliant on quality data, which can be expensive to get. There is also a catch 22 where most of the models that are easy to make aren't practically usable because they're not needed in the first place, like a model that tells you if it's a nice day outside; most people would probably take a look at the weather and decide for themselves. On the other hand, a model predicting optimal stocks to buy or self-driving car models are worth a massive amount, but are also really hard to make. Companies will obviously try to sell bad or cheaply made models and may be successful on a small or niche level, but I think most people will recognize the utility and efficacy of a model based on the difficulty of the task it accomplishes relative to their own ability in that task, regardless of buzzwords associated with it. However, a lot of powerful modeling libraries made by really smart people are open source, so maybe what I'm saying is moot apart from sourcing the data.
- aSplash0fDerp 7y ago
  > Also as usual, there's still a yawning gap between what domain experts are up to The best homophone for AI is "beyond be yawned". Comparitive analysis against refined/biased datasets with Kiptronics (knowledge is power electronics/devices) is going to change the world, but spectacular fodder is to be expected.
LoSboccacc 7y ago
not even, it's multivariate regression analysis optimization
- anticensor 7y ago
  It is automated (not automatic) cognition using multivariate regression techniques, after all, there is something to automate.
  - airstrike 7y ago
    I think many take issue with the use of the word "cognition" in that definition.
knolan 7y ago
It’s curve fitting.
- gfodor 7y ago
  It seems unclear if your brain is also curve fitting. Time will tell hopefully.
nickpinkston 7y ago
I see a lot of AI engineers who seem concerned with this particular issue, which I never really understand. Is it because of a perception that most regular people are likely overestimating the speed of which AI is going to overtake human intelligence? Or more about corp management wanting miracles that aren't possible? Why does this matter and always seem to be talked about?
- UncleOxidant 7y ago
  Because there's a history of overhyping ML/AI (whatever you want to call it) leading to AI winters. Winter in this case being kind of like a recession in economic terms - most research funding dries up, etc. We essentially had one of those winters from the late 80s until about a dozen years ago. A lot of laymen now think of AI as being "magic" that can do anything and that's not a good thing when the reality turns out to be different. At this point I don't think we'll see an AI winter as deep as some of the previous ones. But we could certainly see an AI Fall.
  - 0xdeadbeefbabe 7y ago
    The name is overhyped and pretentious by itself, and history bears this out. Who cares if it's an AI fall or winter if it's an AI stupid, because of all the credulous students. Edit: Russel and Norvig's book is good though http://aima.cs.berkeley.edu/ http://aima.cs.berkeley.edu/
  - YeGoblynQueenne 7y ago
    >> Because there's a history of overhyping ML/AI (whatever you want to call it) leading to AI winters. Note that past AI winters have not occurred because of overhyping machine learning. They occurred because of overhyping of symbolic AI that had nothing to do with machine learning. For example, the last AI winter at the end of the '80s happened because of the overhyping of expert systems- which of course are not machine learning systems. Machine learning is not all, not even most, of AI, historically. It's the dominant trend right now, but it was not the dominant trend in the past. The dominant trend until the 1980's was symbolic reasoning.
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cjauvin 7y ago
Following the recent "AI Debate" between Yoshua Bengio and Gary Marcus [0], there was a lot of discussion about the exact definition (or redefinition even, as some argued) of some labels like "deep learning" and "symbol" (what do we mean exactly by these?), I find that it is quite relevant to this discussion. [0] https://www.youtube.com/watch?v=EeqwFjqFvJA https://www.youtube.com/watch?v=EeqwFjqFvJA
deesep 7y ago
When machines transcend beyond their programmed limitations to shape their environment in their own image, then they become truly intelligent.
- ForrestN 7y ago
  Why would a non-human intelligence necessarily have a drive to “shape their environment?” Maybe a non-human intelligence would discover the inevitable end of the habitable universe and opt to just do nothing?
  - whatshisface 7y ago
    Nobody's going to pay for AWS hours for a lazy robot. They'll keep changing it does something. The human drive, which is not essentially rational, will give birth to the machine drive, which won't be rational either.
mcculley 7y ago
I have been wondering why we don't use the term "synthetic intelligence": https://enki.org/2019/08/18/artificial-intelligence-is-a-dumb-term/ https://enki.org/2019/08/18/artificial-intelligence-is-a-dum...
- UncleOxidant 7y ago
  That just replaces the first word with what is essentially a synonym. It's the second word "intelligence" that's the issue.
  - mcculley 7y ago
    "artificial" and "synthetic" aren't exactly synonyms in my mind. If I synthesize glucose, there is nothing artificial about it. It just didn't come from a process developed by evolution. Conversely, artificial leather is nothing like real leather. I'll have to think about that some more.
    - TheRealPomax 7y ago
      But again, it's the "intelligence" part that's the misnomer. Except for John Carmack, no one's trying to invent general intelligence. Every single bit of work is merely automating tasks that when performed by humans requires intelligence... except that too is a misnomer because as humans we literally can't do anything, no matter how mundane, without it "requiring intelligence".
    - kevin_thibedeau 7y ago
      The root artifice has a broader meaning than "fake". Synthesizing something is an application of artifice.
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sebastianconcpt 7y ago
Brilliant observation. And it's even harder than this. The problem is not that we have a problem. The problem is that we have problems. So the solution is not finding a solution to a problem. The solution is finding a metasolution that is valid across time and tribes. Bam! the challenge of being an intelligent being in this universe. No way we can automate that. Only mimic a portion of it and call it intelligence doesn't make it really intelligent.
- carapace 7y ago
  FWIW, check out the Gödel machine: https://en.wikipedia.org/wiki/G%C3%B6del_machine https://en.wikipedia.org/wiki/G%C3%B6del_machine http://people.idsia.ch/~juergen/goedelmachine.html http://people.idsia.ch/~juergen/goedelmachine.html > No way we can automate that. It's a very very interesting question. Personally I believe that "automaton" is almost the opposite of "being". But that's just me, not science or other authority. Certainly, somewhere between virus and human something comes into being (no pun intended.) I don't know of any non-metaphysical argument that we couldn't find some other way to create non-biological general AI. I think we could genetically engineer human DNA to create wetware G"A"I but I put the "artificial" in quotes to indicate that I'm not saying whether that would count as AI or not. I know of a few efforts to create "Daleks" out of human brain organoids, but I don't think anyone has gone beyond the speculative/hype stage with it so far.
  - jacinabox 7y ago
    I was waiting for somebody to goerdlize that
proc0 7y ago
Well said. The definition of intelligence is bastardized for virtually all current AI applications. They are glorified statistical heuristics / stochastic descent as has been mentioned before. The key to approaching actual intelligence as we know it, will be a system that can dynamically model its environment and actors in it, since even insects are able to do this to some extent.
liamcardenas 7y ago
In my opinion, even calling it “cognitive” is too generous. What makes it “cognitive” instead of just “normal” automation? Because it’s dealing with information rather than the physical world? I think a better term is statistical or digital automation.
amrrs 7y ago
François Chollet's discussion with Lex Fridman (first half) is an interesting one on AGI - Video - https://youtu.be/Bo8MY4JpiXE https://youtu.be/Bo8MY4JpiXE
dsr_ 7y ago
All programming is, is the reification of decision making.
GuB-42 7y ago
From the Merriam-Webster dictionary. Definition of cognitive 1 : of, relating to, being, or involving conscious intellectual activity (such as thinking, reasoning, or remembering) 2 : based on or capable of being reduced to empirical factual knowledge Using "cognitive" instead of "intelligence" puts the emphasis on data processing rather that adaptability, which may be a bit more in line with how things are done today. However, it doesn't addresses the core of the debate. The usual "[technology] isn't [AI/cognitive automation] because it can't do [thing humans do], it is just [thing computers do]". Both terms relate to consciousness, and are generally considered fundamentally human qualities. I think there is simply no way out of that debate. Maybe use a term that it sounds completely unrelated to human activity, maybe something like "Big Data Statistical Matching".
eanzenberg 7y ago
Call it whatever you want, I don't care. It's working and improving year over year.
dr_dshiv 7y ago
One thing I find strange is how much we emphasize the artificial nature of the intelligence. AI and automation always occurs in the context of human processes. Nothing is truly autonomous, so why design it as if human involvement is a failure? We can easily design artifacts to enhance human intelligence or team intelligence. Why the focus on the machine part and not the overall system that functionally accomplishes the desired work?
- joe_the_user 7y ago
  > One thing I find strange is how much we emphasize the artificial nature of the intelligence. We really don't know what intelligence (sans qualifications) is. AI has been a term for effort emulate what we roughly think of as "intelligent" behavior. It's far from successful so far and the lack of a "theory of intelligence" is probably part of that. But it's pretty clear what "AI" researchers and systems are doing now is far from intelligence. > AI and automation always occurs in the context of human processes. Nothing is truly autonomous, so why design it as if human involvement is a failure? This argument makes as much sense as "we'll never exceed the speed of light, why act like faster transportation matters". An automated factory still requires some maintenance but it's creation certainly is significant. > We can easily design artifacts to enhance human intelligence or team intelligence. Why the focus on the machine part and not the overall system that functionally accomplishes the desired work? Both approaches matter and since there's really nothing keeping people from doing both of these, people pursue each separately. Moreover, I'd say AI research could do well to cross-pollinate with human-computer interaction theory. But overall, you seem to just not understand why automation matters - automation has brought vast productivity in a variety of fields. It may or may not be possible other further fields but if it is, it will transform the world equivalently.
- carapace 7y ago
  "Intelligence Amplification" (IA) is a thing: https://en.wikipedia.org/wiki/Intelligence_amplification https://en.wikipedia.org/wiki/Intelligence_amplification FWIW, I think that AI offers to offload thinking (whether it delivers or not is another thing) while IA appeals to people who want to improve their own intelligence. Maybe I'm too cynical, but the former seems more popular than the latter.
YeGoblynQueenne 7y ago
>> Our field isn't quite "artificial intelligence" True, but so what? We call it AI and that's that, really. We've been calling it that for 70 years now and it's never been a problem. And let's be absolutely clear that it's not the _name_ that's confusing the public but the way that industry luminaries promise autonomous cars and robotic maids in the next -3 years, or the way that the technology press -the technology press- can't get its shit together to figure out the difference between "machine learning", "deep learning" and "AI" as fields of research and as category labels. Of course the lay public is going to be confused if people who are paid to elucidate complex concepts make a mess of it.
- joe_the_user 7y ago
  > "True, but so what? We call it AI and that's that, really. We've been calling it that for 70 years now and it's never been a problem." That isn't even ... true. AI became "machine learning" in the late 90s/early 2000s and that change happened because the chorus of criticism of "artificial intelligence" had become extremely loud and a less ambitious term served as a refuge.
  - YeGoblynQueenne 7y ago
    AI was renamed into many things in the '80s and '90s, for example "Intelligent Systems" or "Adaptive Systems" etc, and that indeed was done to dissociate research from the bad rep that had accrued for AI. But "machine learning" has been the name of a sub-field of AI since the 1950's and it's never stood for the whole, at least not in conferences, papers or any kind of activity of the field. For example- two of the (still) major conferences in the field are AAAI and IJCAI: the conference of the "Association for the Advancement of Artificial Intelligence" and the "International Joint Conferences of Artificial Intelligence". Neither of those is in any way, shape or form a conference for machine learning only and neither uses machine learning a byname for AI. By contrast, machine learning has its own journal(s actually) and there are specific conferences dedicated to machine learning and deep learning (NeurIPs and ICLR). Additionally, there are many sub-fields of AI that are not machine learning, in name or function: intelligent agents, classical planning, reasoning, knowledge engineering etc etc. The only confusion between "AI" and "machine learning" exists in the minds of tech journalists and the people who get their AI news exclusively from the tech press. P.S. As a side note, the name for what the tech press is doing, referring to the field of AI as "machine learning", is "synecdoche": naming the whole by the name of the part.
shmerl 7y ago
I think a better term you are contrasting it with is artificial mind, not artificial intelligence.
choonway 7y ago
Nope. It's just pattern recognition.
- sgt101 7y ago
  What is? I assume you mean machine learning? Ok... What about one shot learning like lake and tabembauns bpl? What about optimal resource allocation in auctions? Is this recognising patterns in event spaces larger than the number of atoms in the universe?
jariel 7y ago
I don't really agree, and think the misnomer should be applied in the opposite direction: AI should be called 'adaptive algorithms' and it should be just another tool the box of CS people. We're not doing anything that we were not before. There is no new paradigm shift. There is no AI. There's just a slightly new approach to solving problems. That's it. There's somer really nice improvements in computer vision ... and a few other things ... ... but all this talk of 'intelligence' etc. should be brushed aside, it's misleading to everyone. There will be no 'general AI' with our current approaches for a whole variety of reasons. I'm embarrassed at how so many intelligent colleagues drink the kool-aid on this. Take classical ML: it was hyped for a while, now it's not as exciting as 'Deep Learning'. Well, in few years, I think that DL will be there as well: just a tool in the toolbox.
godelski 7y ago
I think it is also important to remember that intelligence isn't clearly defined. It seems a lot of people interpret it in different ways and the definition is closer to pornography (I know it when I see it). I often see two camps, one that defines intelligence to be more human like. Limiting it to really cetaceans and hominids. Maybe including ravens. The other group gives too vague of a definition. Personally, I do not see a problem with having lots of bins. I don't think many disagree that intelligence is a continuum. So why restrict it to very high level bins? Because that's the vernacular usage? I for one vote for the many bin and continuum approach. In this I think you could say that ML has some extremely low level form of intelligence, but I would generally say lower level than that of an ant. In that respect, a multi agent system with the intelligence surpassing that of ants I believe would be extremely impressive.
- Barrin92 7y ago
  >So why restrict it to very high level bins? I don't think it's a quantitative issue (the level of the bin). It's a qualitative issue. When people say that ML lacks intelligence what they're saying is that it lacks robustness, common sense, agent like behaviour, the ability to reason and so on. Intelligence (in humans or animals) does not appear to be just data driven pattern matching. I think we can say this with some confidence given that even the fanciest ML algorithm still hopelessly sucks at performing tasks that are trivial even for barely intelligent animals.
  - m00x 7y ago
    > Intelligence (in humans or animals) does not appear to be just data driven pattern matching. I think we can say this with some confidence given that even the fanciest ML algorithm still hopelessly sucks at performing tasks that are trivial even for barely intelligent animals. Do you mind expanding on this? A lot of the tasks primitive animals can be replicated by Deep RL algorithms given the same environment. Humans adapt to sensor information, memory and a reward/penalty system just like RL. We're just much more advanced and have sophisticated systems to sense and act.
    - 7y ago
jokoon 7y ago
Intelligence doesn't have a lot of scientific ground either. It's pretty hard to define what intelligence is, or at least have a scientific definition that is precise enough. The Turing Test is only a measure, it doesn't help to reach a definition. Practical research will always hit a ceiling if scientists cannot try to define what they're looking for. Even machine learning is not a good definition. There are other attemps, like "sophisticated statistics" or "statistical prediction". Kudos for this tweet.
qwerty456127 7y ago
I'd rather call it cognition imitation if you insist to associate it with cognition or intelligence. In fact it's just brute-force statistics.
- savanaly 7y ago
  Are we so sure human cognition isn't this too?
ratsmack 7y ago
I like this comment: >At the end of the day, "AI" is just glorified statistics (running on increasingly powerful computers).
- raven105x 7y ago
  Depends on what you mean by "AI". SageMaker / GCP / Azure / other ez-bake business "AI", sure. Alas at the forefront, it's much harder to attribute GAN and Transformer performance to statistics.
0xff00ffee 7y ago
I hear this a lot: "Wellll, Machine Learning isn't TRUE artificial intelligence..." Seriously people: are you THAT insecure? I realize it is a cutthroat hiring market and companies are stuffing extra zeroes onto signing bonuses to get anyone with an AI background, so maybe there is lots of jealousy and FOMO. I dunno, but it seems awfully gatekeep-y all of a sudden.
jeromebaek 7y ago
nobody likes "artificial intelligence". laypeople are scared of it. the media blames it for all evils. it brings connotations of frankenstein, arrogant atheism, etc. it's reasonable to want to hide behind a different term.
randomsearch 7y ago
Recently I’ve been reading a lot about AI and ML. It’s quite clear to me what ML is - solving classification and regression problems. There are some fuzzy edges, but that’s true of any discipline. Maybe you invoke Mitchell’s definition and say “well, it’s improving on a task” (as many introductions to ML do) but that’s completely out of step with what people actually treat as ML. There are lots of interesting “learning automation” areas that we’re neglecting - symbolic reasoning being the glaring one for me. AI just seems like a nonsense term to me. Maybe it’d be better if we stopped using it.
cmarschner 7y ago
There has been a lot of debate about the state of AI in recent weeks on Twitter (see #AIDebate). A lot of it was about naming. It occurred to me that very opinionated people had no idea what people in the community are actually doing, and so the whole excercise seemed like a learning experience for them, which seems to be a good thing. The goals of AI (which Google trends classifies as “field of study” - I think that captures it quite well) haven’t changed in decades - to reverse engineer the miracle of human cognition. A certain number of people (like the teams of Yoshua Bengio or Demis Hassabis) have the clear mission to work on just that. The progress in this area is much slower than the perception of the last 5-10 years would suggest. It was just that work from the 90s and 2000s were put to test and quickly outperformed other approaches - symbolic or what we now call “classic machine learning” (e.g. in speech recognition, image classification/detection, machine translation, Information retrieval). All these areas had important and valuable applications in industry and have sucked up a lot of money. But this is only a tiny part of what human cognition entails. Areas around memory, reasoning, consciousness etc. are completely unsolved. Where are we on a scale of 0 to 1000 of solving the problem? Perhaps somewhere between 20 and 50, nobody knows. AI is a north star, and it is a weird development that people have started to call it “AI” again (it felt totally weird about 3-4 years ago when this happened). So, I think the field is still rightly called “AI”. Call the current state of it “system 1”, “differential programming”, “deep learning” or whatever.
blackrock 7y ago
When did Machine Learning become Artificial General Intelligence? When did SVMs become AI? I'm going to take the dissenting viewpoint here. I think AI as it is being sold today (e.g. the Deep Learning).. is bullshit. It's the new snake oil. Everyone is pouring in all this money because of FOMO (Fear of Missing Out). Yes, it's producing some fancy new toys. Beating the best human player at Go. Or doing some facial recognition. Or winning at StarCraft. But I don't even see the point at mastering Chess past a certain level, and I'm certainly not going to bother with mastering a RTS game like StarCraft. The scary thing is if some military planner thinks the StarCraft AI is sufficiently smart enough, to put on military weapons systems, and used to hunt down other humans. Now, if we keep AI to be for these constrained things, then yes, it can produce more toys and products, that can be sold. It's the next evolution in smart products. Corporate America can keep cranking out new and evolved products to sell, and slap an AI sticker on it. And have you noticed? Everyone is slapping an AI sticker on everything. It's like Microsoft and the .Net branding, or Sun and the Java branding all over again. But this time, everyone is calling their little algorithm, AI. But beyond that, it's just another gimmick. Deep Learning is an advanced form of OCR. Facial Recognition is an advanced form of OCR of the face. Do we consider OCR to be AI these days? No, we don't. We just think of it as a wonky pattern recognition engine, that half the time doesn't work, and the other half is frustrating enough, that we just type it out ourselves. In fact, OCR uses a neural network algorithm. It's not an evolution that we need, in order to achieve AI. It's a revolution. And nothing I've seen so far, has convinced me that it can be achieved. In the meanwhile, if you're a newly certified 'AI Expert', then cash in as much as you can. But.. Beware. Winter is coming.
- lm28469 7y ago
  > When did xyz became AI When it started to become a buzzword for PR and more investors money.
radarthreat 7y ago
Cognitive automation is a bit much, progressive pattern matching is more accurate.

294 points by vo2maxer 7y ago

it's automation Edit: artificial automation Edit: computer science Edit: pseudo science?

As usual, the monks know what the laity doesn't, and aren't particularly afraid to talk about it. Also as usual, there's still a yawning gap between what domain experts are up to and what non-domain experts think they're up to. That this is true in AI is not surprising; humility comes from knowing that my domain expertise in some fields (and thus a clearer picture of 'what's really going on') is guaranteed to be crippled in other fields. Knowing that by being in some knowledge in-groups requires me to also be in some knowledge out-groups is the beginnings of a sane approach to the world.

Invictus0 7y ago
The author is just correcting a misnomer. It is not really accurate to say that machine learning is intelligent at all, so why label it as such? It's confusing for everyone and leads to great misunderstandings.
- cgriswald 7y ago
  > so why label it as such? Great misunderstandings are often profitable for those who understand.
  - Accujack 7y ago
    Or put more simply... marketing. Every new discovery ever, people wanting to exploit it have done anything necessary to use people's honest interest in new technology and good feeling about human progress to get money or power.
- PeterisP 7y ago
  Machine learning is a particular narrow result of studying the wider field of artificial intelligence. Just as expert systems, or rdf knowledge representation, or first order logic reasoners, or planning systems - none of them are 'intelligent' but all of them are research results coming from (and being studing in) the discipline of studying how intelligence works and how can something like it be approached artificially. There's lots in the field of AI that is not 'cognitive automation' - many currently popular things and use cases are, but that's not correcting a misnomer, that's a separate term for a separate (and more narrow) thing - even if that narrower thing constitutes the most relevant and most useful part of current AI research. A classic definition of intelligence (Legg&Hutter) is "Intelligence measures an agent’s ability to achieve goals in a wide range of environments". That's a worthwhile goal to study even if (obviously) our artificial sytems are not yet even close to human level according to that criteria; and while it is roughly in the same direction as 'cognitive automation', it's less limited and not entirely the same. For example, 'cognitive automation' pretty much assumes a fixed task to execute/automate, and excludes all the nuances of agentive behavior and motivation, but these are important subtopics in the field of AI. But I am willing to concede that very many people are explicitly working only on the subfield of 'cognitive automation' and that it would be clearer if these people (but not all AI researchers) explicitly said so.
  - corporateslave5 7y ago
    The thing with differentiating machine learning and AI, is that nothing in AI world works except machine learning. It’s just a bunch of old theories and ideas, none of which have panned out
  - joe_the_user 7y ago
    > Machine learning is a particular narrow result of studying the wider field of artificial intelligence. I beg to differ, at least as far as terms go now. Neural networks lived in the "field" of machine learning along with Kernel machines and miscellaneous prediction systems circa the early 2000s. Neural network today are known as AI because ... why? Basically, the histories I've read and remember say that the only difference is now neural networks are successful enough they don't have to hide behind a more "narrow" term - or alternately, the hype train now prefers a more ambitious term. I mean, the Machine Learning reddit is one go-to place for actual researchers to discussion neural nets. Everyone now talks about these as AI because the terms have essentially merged. > A classic definition of intelligence (Legg&Hutter) is "Intelligence measures an agent’s ability to achieve goals in a wide range of environments". Machine learning mostly became AI through neural nets looking really good - but none of that involve them become more oriented to goals, is anything, less so. It was far more - high dimensional curve can actually get you a whole lot and when you do well, you can call it AI.
- pmelendez 7y ago
  I don't think there are many definitions of machine learning that claim the models to be intelligent. Most of them limit the term to models that can be built from data. Learning is a skill that not necessarily comes with an "intelligent" label attached to it.
  - kkwak 7y ago
    Have we even defined 'intelligent' might mean? As in, we had the Turing test as a bar and we are close to that already. What is intelligence then, last I checked, there wasn't a definitive answer do it. We'll need it so that we can label AI as I properly - or maybe we don't care so much... If it's close enough...
    - hprotagonist 7y ago
      there are several hundred competing definitions of "intelligence". No consensus, as they say, has been reached.
      - aSplash0fDerp 7y ago
        Once we start seeing cheaply made, imported yes/no engines (masquerading as AI or knowledge) flooding the market, the definition of intelligence will be lost on marketing anyways (unlimited data, superfood, etc)
        grp000 7y ago
        A predictive model, whether created by ML (regression, SVM, NN, whatever) or something rules-based born out of data analysis is reliant on quality data, which can be expensive to get. There is also a catch 22 where most of the models that are easy to make aren't practically usable because they're not needed in the first place, like a model that tells you if it's a nice day outside; most people would probably take a look at the weather and decide for themselves. On the other hand, a model predicting optimal stocks to buy or self-driving car models are worth a massive amount, but are also really hard to make. Companies will obviously try to sell bad or cheaply made models and may be successful on a small or niche level, but I think most people will recognize the utility and efficacy of a model based on the difficulty of the task it accomplishes relative to their own ability in that task, regardless of buzzwords associated with it. However, a lot of powerful modeling libraries made by really smart people are open source, so maybe what I'm saying is moot apart from sourcing the data.
aSplash0fDerp 7y ago
> Also as usual, there's still a yawning gap between what domain experts are up to The best homophone for AI is "beyond be yawned". Comparitive analysis against refined/biased datasets with Kiptronics (knowledge is power electronics/devices) is going to change the world, but spectacular fodder is to be expected.

LoSboccacc 7y ago

not even, it's multivariate regression analysis optimization

anticensor 7y ago
It is automated (not automatic) cognition using multivariate regression techniques, after all, there is something to automate.
- airstrike 7y ago
  I think many take issue with the use of the word "cognition" in that definition.

knolan 7y ago

It’s curve fitting.

gfodor 7y ago
It seems unclear if your brain is also curve fitting. Time will tell hopefully.

nickpinkston 7y ago

I see a lot of AI engineers who seem concerned with this particular issue, which I never really understand. Is it because of a perception that most regular people are likely overestimating the speed of which AI is going to overtake human intelligence? Or more about corp management wanting miracles that aren't possible? Why does this matter and always seem to be talked about?

UncleOxidant 7y ago
Because there's a history of overhyping ML/AI (whatever you want to call it) leading to AI winters. Winter in this case being kind of like a recession in economic terms - most research funding dries up, etc. We essentially had one of those winters from the late 80s until about a dozen years ago. A lot of laymen now think of AI as being "magic" that can do anything and that's not a good thing when the reality turns out to be different. At this point I don't think we'll see an AI winter as deep as some of the previous ones. But we could certainly see an AI Fall.
- 0xdeadbeefbabe 7y ago
  The name is overhyped and pretentious by itself, and history bears this out. Who cares if it's an AI fall or winter if it's an AI stupid, because of all the credulous students. Edit: Russel and Norvig's book is good though http://aima.cs.berkeley.edu/ http://aima.cs.berkeley.edu/
- YeGoblynQueenne 7y ago
  >> Because there's a history of overhyping ML/AI (whatever you want to call it) leading to AI winters. Note that past AI winters have not occurred because of overhyping machine learning. They occurred because of overhyping of symbolic AI that had nothing to do with machine learning. For example, the last AI winter at the end of the '80s happened because of the overhyping of expert systems- which of course are not machine learning systems. Machine learning is not all, not even most, of AI, historically. It's the dominant trend right now, but it was not the dominant trend in the past. The dominant trend until the 1980's was symbolic reasoning.

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cjauvin 7y ago

Following the recent "AI Debate" between Yoshua Bengio and Gary Marcus [0], there was a lot of discussion about the exact definition (or redefinition even, as some argued) of some labels like "deep learning" and "symbol" (what do we mean exactly by these?), I find that it is quite relevant to this discussion. [0] https://www.youtube.com/watch?v=EeqwFjqFvJA https://www.youtube.com/watch?v=EeqwFjqFvJA

deesep 7y ago

When machines transcend beyond their programmed limitations to shape their environment in their own image, then they become truly intelligent.

ForrestN 7y ago
Why would a non-human intelligence necessarily have a drive to “shape their environment?” Maybe a non-human intelligence would discover the inevitable end of the habitable universe and opt to just do nothing?
- whatshisface 7y ago
  Nobody's going to pay for AWS hours for a lazy robot. They'll keep changing it does something. The human drive, which is not essentially rational, will give birth to the machine drive, which won't be rational either.

mcculley 7y ago

I have been wondering why we don't use the term "synthetic intelligence": https://enki.org/2019/08/18/artificial-intelligence-is-a-dumb-term/ https://enki.org/2019/08/18/artificial-intelligence-is-a-dum...

UncleOxidant 7y ago
That just replaces the first word with what is essentially a synonym. It's the second word "intelligence" that's the issue.
- mcculley 7y ago
  "artificial" and "synthetic" aren't exactly synonyms in my mind. If I synthesize glucose, there is nothing artificial about it. It just didn't come from a process developed by evolution. Conversely, artificial leather is nothing like real leather. I'll have to think about that some more.
  - TheRealPomax 7y ago
    But again, it's the "intelligence" part that's the misnomer. Except for John Carmack, no one's trying to invent general intelligence. Every single bit of work is merely automating tasks that when performed by humans requires intelligence... except that too is a misnomer because as humans we literally can't do anything, no matter how mundane, without it "requiring intelligence".
  - kevin_thibedeau 7y ago
    The root artifice has a broader meaning than "fake". Synthesizing something is an application of artifice.

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sebastianconcpt 7y ago

Brilliant observation. And it's even harder than this. The problem is not that we have a problem. The problem is that we have problems. So the solution is not finding a solution to a problem. The solution is finding a metasolution that is valid across time and tribes. Bam! the challenge of being an intelligent being in this universe. No way we can automate that. Only mimic a portion of it and call it intelligence doesn't make it really intelligent.

carapace 7y ago
FWIW, check out the Gödel machine: https://en.wikipedia.org/wiki/G%C3%B6del_machine https://en.wikipedia.org/wiki/G%C3%B6del_machine http://people.idsia.ch/~juergen/goedelmachine.html http://people.idsia.ch/~juergen/goedelmachine.html > No way we can automate that. It's a very very interesting question. Personally I believe that "automaton" is almost the opposite of "being". But that's just me, not science or other authority. Certainly, somewhere between virus and human something comes into being (no pun intended.) I don't know of any non-metaphysical argument that we couldn't find some other way to create non-biological general AI. I think we could genetically engineer human DNA to create wetware G"A"I but I put the "artificial" in quotes to indicate that I'm not saying whether that would count as AI or not. I know of a few efforts to create "Daleks" out of human brain organoids, but I don't think anyone has gone beyond the speculative/hype stage with it so far.
- jacinabox 7y ago
  I was waiting for somebody to goerdlize that

proc0 7y ago

Well said. The definition of intelligence is bastardized for virtually all current AI applications. They are glorified statistical heuristics / stochastic descent as has been mentioned before. The key to approaching actual intelligence as we know it, will be a system that can dynamically model its environment and actors in it, since even insects are able to do this to some extent.

liamcardenas 7y ago

In my opinion, even calling it “cognitive” is too generous. What makes it “cognitive” instead of just “normal” automation? Because it’s dealing with information rather than the physical world? I think a better term is statistical or digital automation.

amrrs 7y ago

François Chollet's discussion with Lex Fridman (first half) is an interesting one on AGI - Video - https://youtu.be/Bo8MY4JpiXE https://youtu.be/Bo8MY4JpiXE

dsr_ 7y ago

All programming is, is the reification of decision making.

GuB-42 7y ago

From the Merriam-Webster dictionary. Definition of cognitive 1 : of, relating to, being, or involving conscious intellectual activity (such as thinking, reasoning, or remembering) 2 : based on or capable of being reduced to empirical factual knowledge Using "cognitive" instead of "intelligence" puts the emphasis on data processing rather that adaptability, which may be a bit more in line with how things are done today. However, it doesn't addresses the core of the debate. The usual "[technology] isn't [AI/cognitive automation] because it can't do [thing humans do], it is just [thing computers do]". Both terms relate to consciousness, and are generally considered fundamentally human qualities. I think there is simply no way out of that debate. Maybe use a term that it sounds completely unrelated to human activity, maybe something like "Big Data Statistical Matching".

eanzenberg 7y ago

Call it whatever you want, I don't care. It's working and improving year over year.

dr_dshiv 7y ago

One thing I find strange is how much we emphasize the artificial nature of the intelligence. AI and automation always occurs in the context of human processes. Nothing is truly autonomous, so why design it as if human involvement is a failure? We can easily design artifacts to enhance human intelligence or team intelligence. Why the focus on the machine part and not the overall system that functionally accomplishes the desired work?

joe_the_user 7y ago
> One thing I find strange is how much we emphasize the artificial nature of the intelligence. We really don't know what intelligence (sans qualifications) is. AI has been a term for effort emulate what we roughly think of as "intelligent" behavior. It's far from successful so far and the lack of a "theory of intelligence" is probably part of that. But it's pretty clear what "AI" researchers and systems are doing now is far from intelligence. > AI and automation always occurs in the context of human processes. Nothing is truly autonomous, so why design it as if human involvement is a failure? This argument makes as much sense as "we'll never exceed the speed of light, why act like faster transportation matters". An automated factory still requires some maintenance but it's creation certainly is significant. > We can easily design artifacts to enhance human intelligence or team intelligence. Why the focus on the machine part and not the overall system that functionally accomplishes the desired work? Both approaches matter and since there's really nothing keeping people from doing both of these, people pursue each separately. Moreover, I'd say AI research could do well to cross-pollinate with human-computer interaction theory. But overall, you seem to just not understand why automation matters - automation has brought vast productivity in a variety of fields. It may or may not be possible other further fields but if it is, it will transform the world equivalently.
carapace 7y ago
"Intelligence Amplification" (IA) is a thing: https://en.wikipedia.org/wiki/Intelligence_amplification https://en.wikipedia.org/wiki/Intelligence_amplification FWIW, I think that AI offers to offload thinking (whether it delivers or not is another thing) while IA appeals to people who want to improve their own intelligence. Maybe I'm too cynical, but the former seems more popular than the latter.

YeGoblynQueenne 7y ago

>> Our field isn't quite "artificial intelligence" True, but so what? We call it AI and that's that, really. We've been calling it that for 70 years now and it's never been a problem. And let's be absolutely clear that it's not the _name_ that's confusing the public but the way that industry luminaries promise autonomous cars and robotic maids in the next -3 years, or the way that the technology press -the technology press- can't get its shit together to figure out the difference between "machine learning", "deep learning" and "AI" as fields of research and as category labels. Of course the lay public is going to be confused if people who are paid to elucidate complex concepts make a mess of it.

joe_the_user 7y ago
> "True, but so what? We call it AI and that's that, really. We've been calling it that for 70 years now and it's never been a problem." That isn't even ... true. AI became "machine learning" in the late 90s/early 2000s and that change happened because the chorus of criticism of "artificial intelligence" had become extremely loud and a less ambitious term served as a refuge.
- YeGoblynQueenne 7y ago
  AI was renamed into many things in the '80s and '90s, for example "Intelligent Systems" or "Adaptive Systems" etc, and that indeed was done to dissociate research from the bad rep that had accrued for AI. But "machine learning" has been the name of a sub-field of AI since the 1950's and it's never stood for the whole, at least not in conferences, papers or any kind of activity of the field. For example- two of the (still) major conferences in the field are AAAI and IJCAI: the conference of the "Association for the Advancement of Artificial Intelligence" and the "International Joint Conferences of Artificial Intelligence". Neither of those is in any way, shape or form a conference for machine learning only and neither uses machine learning a byname for AI. By contrast, machine learning has its own journal(s actually) and there are specific conferences dedicated to machine learning and deep learning (NeurIPs and ICLR). Additionally, there are many sub-fields of AI that are not machine learning, in name or function: intelligent agents, classical planning, reasoning, knowledge engineering etc etc. The only confusion between "AI" and "machine learning" exists in the minds of tech journalists and the people who get their AI news exclusively from the tech press. P.S. As a side note, the name for what the tech press is doing, referring to the field of AI as "machine learning", is "synecdoche": naming the whole by the name of the part.

shmerl 7y ago

I think a better term you are contrasting it with is artificial mind, not artificial intelligence.

choonway 7y ago

Nope. It's just pattern recognition.

sgt101 7y ago
What is? I assume you mean machine learning? Ok... What about one shot learning like lake and tabembauns bpl? What about optimal resource allocation in auctions? Is this recognising patterns in event spaces larger than the number of atoms in the universe?

jariel 7y ago

I don't really agree, and think the misnomer should be applied in the opposite direction: AI should be called 'adaptive algorithms' and it should be just another tool the box of CS people. We're not doing anything that we were not before. There is no new paradigm shift. There is no AI. There's just a slightly new approach to solving problems. That's it. There's somer really nice improvements in computer vision ... and a few other things ... ... but all this talk of 'intelligence' etc. should be brushed aside, it's misleading to everyone. There will be no 'general AI' with our current approaches for a whole variety of reasons. I'm embarrassed at how so many intelligent colleagues drink the kool-aid on this. Take classical ML: it was hyped for a while, now it's not as exciting as 'Deep Learning'. Well, in few years, I think that DL will be there as well: just a tool in the toolbox.

godelski 7y ago

I think it is also important to remember that intelligence isn't clearly defined. It seems a lot of people interpret it in different ways and the definition is closer to pornography (I know it when I see it). I often see two camps, one that defines intelligence to be more human like. Limiting it to really cetaceans and hominids. Maybe including ravens. The other group gives too vague of a definition. Personally, I do not see a problem with having lots of bins. I don't think many disagree that intelligence is a continuum. So why restrict it to very high level bins? Because that's the vernacular usage? I for one vote for the many bin and continuum approach. In this I think you could say that ML has some extremely low level form of intelligence, but I would generally say lower level than that of an ant. In that respect, a multi agent system with the intelligence surpassing that of ants I believe would be extremely impressive.

Barrin92 7y ago
>So why restrict it to very high level bins? I don't think it's a quantitative issue (the level of the bin). It's a qualitative issue. When people say that ML lacks intelligence what they're saying is that it lacks robustness, common sense, agent like behaviour, the ability to reason and so on. Intelligence (in humans or animals) does not appear to be just data driven pattern matching. I think we can say this with some confidence given that even the fanciest ML algorithm still hopelessly sucks at performing tasks that are trivial even for barely intelligent animals.
- m00x 7y ago
  > Intelligence (in humans or animals) does not appear to be just data driven pattern matching. I think we can say this with some confidence given that even the fanciest ML algorithm still hopelessly sucks at performing tasks that are trivial even for barely intelligent animals. Do you mind expanding on this? A lot of the tasks primitive animals can be replicated by Deep RL algorithms given the same environment. Humans adapt to sensor information, memory and a reward/penalty system just like RL. We're just much more advanced and have sophisticated systems to sense and act.
  - 7y ago

jokoon 7y ago

Intelligence doesn't have a lot of scientific ground either. It's pretty hard to define what intelligence is, or at least have a scientific definition that is precise enough. The Turing Test is only a measure, it doesn't help to reach a definition. Practical research will always hit a ceiling if scientists cannot try to define what they're looking for. Even machine learning is not a good definition. There are other attemps, like "sophisticated statistics" or "statistical prediction". Kudos for this tweet.

qwerty456127 7y ago

I'd rather call it cognition imitation if you insist to associate it with cognition or intelligence. In fact it's just brute-force statistics.

savanaly 7y ago
Are we so sure human cognition isn't this too?

ratsmack 7y ago

I like this comment: >At the end of the day, "AI" is just glorified statistics (running on increasingly powerful computers).

raven105x 7y ago
Depends on what you mean by "AI". SageMaker / GCP / Azure / other ez-bake business "AI", sure. Alas at the forefront, it's much harder to attribute GAN and Transformer performance to statistics.

0xff00ffee 7y ago

I hear this a lot: "Wellll, Machine Learning isn't TRUE artificial intelligence..." Seriously people: are you THAT insecure? I realize it is a cutthroat hiring market and companies are stuffing extra zeroes onto signing bonuses to get anyone with an AI background, so maybe there is lots of jealousy and FOMO. I dunno, but it seems awfully gatekeep-y all of a sudden.

jeromebaek 7y ago

nobody likes "artificial intelligence". laypeople are scared of it. the media blames it for all evils. it brings connotations of frankenstein, arrogant atheism, etc. it's reasonable to want to hide behind a different term.

randomsearch 7y ago

Recently I’ve been reading a lot about AI and ML. It’s quite clear to me what ML is - solving classification and regression problems. There are some fuzzy edges, but that’s true of any discipline. Maybe you invoke Mitchell’s definition and say “well, it’s improving on a task” (as many introductions to ML do) but that’s completely out of step with what people actually treat as ML. There are lots of interesting “learning automation” areas that we’re neglecting - symbolic reasoning being the glaring one for me. AI just seems like a nonsense term to me. Maybe it’d be better if we stopped using it.

cmarschner 7y ago

There has been a lot of debate about the state of AI in recent weeks on Twitter (see #AIDebate). A lot of it was about naming. It occurred to me that very opinionated people had no idea what people in the community are actually doing, and so the whole excercise seemed like a learning experience for them, which seems to be a good thing. The goals of AI (which Google trends classifies as “field of study” - I think that captures it quite well) haven’t changed in decades - to reverse engineer the miracle of human cognition. A certain number of people (like the teams of Yoshua Bengio or Demis Hassabis) have the clear mission to work on just that. The progress in this area is much slower than the perception of the last 5-10 years would suggest. It was just that work from the 90s and 2000s were put to test and quickly outperformed other approaches - symbolic or what we now call “classic machine learning” (e.g. in speech recognition, image classification/detection, machine translation, Information retrieval). All these areas had important and valuable applications in industry and have sucked up a lot of money. But this is only a tiny part of what human cognition entails. Areas around memory, reasoning, consciousness etc. are completely unsolved. Where are we on a scale of 0 to 1000 of solving the problem? Perhaps somewhere between 20 and 50, nobody knows. AI is a north star, and it is a weird development that people have started to call it “AI” again (it felt totally weird about 3-4 years ago when this happened). So, I think the field is still rightly called “AI”. Call the current state of it “system 1”, “differential programming”, “deep learning” or whatever.

blackrock 7y ago

When did Machine Learning become Artificial General Intelligence? When did SVMs become AI? I'm going to take the dissenting viewpoint here. I think AI as it is being sold today (e.g. the Deep Learning).. is bullshit. It's the new snake oil. Everyone is pouring in all this money because of FOMO (Fear of Missing Out). Yes, it's producing some fancy new toys. Beating the best human player at Go. Or doing some facial recognition. Or winning at StarCraft. But I don't even see the point at mastering Chess past a certain level, and I'm certainly not going to bother with mastering a RTS game like StarCraft. The scary thing is if some military planner thinks the StarCraft AI is sufficiently smart enough, to put on military weapons systems, and used to hunt down other humans. Now, if we keep AI to be for these constrained things, then yes, it can produce more toys and products, that can be sold. It's the next evolution in smart products. Corporate America can keep cranking out new and evolved products to sell, and slap an AI sticker on it. And have you noticed? Everyone is slapping an AI sticker on everything. It's like Microsoft and the .Net branding, or Sun and the Java branding all over again. But this time, everyone is calling their little algorithm, AI. But beyond that, it's just another gimmick. Deep Learning is an advanced form of OCR. Facial Recognition is an advanced form of OCR of the face. Do we consider OCR to be AI these days? No, we don't. We just think of it as a wonky pattern recognition engine, that half the time doesn't work, and the other half is frustrating enough, that we just type it out ourselves. In fact, OCR uses a neural network algorithm. It's not an evolution that we need, in order to achieve AI. It's a revolution. And nothing I've seen so far, has convinced me that it can be achieved. In the meanwhile, if you're a newly certified 'AI Expert', then cash in as much as you can. But.. Beware. Winter is coming.

lm28469 7y ago
> When did xyz became AI When it started to become a buzzword for PR and more investors money.

radarthreat 7y ago

Cognitive automation is a bit much, progressive pattern matching is more accurate.