AI gets such a bad rap. People only think of unsolved or sort-of-solved problems as AI, and don't give AI any credit for the problems it has solved, I guess because by definition those problems seem easy.
Think how much Microsoft Office and competitors have amplified business productivity over the last 20 years (yeah yeah, make your jokes too). Word and Powerpoint and Excel are full of AI whether it's spellcheck or auto-fill, drawing algorithms like "fill with color", etc. So many things that were AI research papers of the 70s, 80s, or 90s. And those innovations continue today.
Logistics companies rely on huge amounts of optimization and problem-solving. Finding routes for drivers and deliveries, planning schedules, optimizing store layouts, etc. -- that's AI.
Employees use AI tools to improve their lives and productivity whether it's a rideshare or maps app to get to work, speech-to-text, asking Siri for answers, translating web pages, etc. All of this comes out of research in AI or related fields.
How many office jobs don't require someone to use a search engine to find and access information related to a query? Information retrieval is one million percent AI.
Robotics and automated manufacturing has been huge for a long time -- robotics is closely connected AI and related problems like control theory.
The best applications of AI have almost always been to support and enhance human decisionmaking, not replace it.
That effect is explained by the fact that the problems in AI research are thought to be solvable only by intelligent agents. This can often turn out to be false. Many problems that seemingly require intelligence don't actually require it.
Nick Bostrom wrote the following few lines in his book "Superintelligence: Paths, Dangers, Strategies" (which I absolutely recommend reading):
> There is an important sense, however, in which chess-playing AI turned out to be a lesser triumph than many imagined it would be. It was once supposed, perhaps not unreasonably, that in order for a computer to play chess at grandmaster level, it would have to be endowed with a high degree of general intelligence.
But you know what? It could have been solved with general intelligence. But by defining it as some kind of major breakthrough a bunch of money got spent on solving just that one problem and then declaring victory by the players. I predict that this will happen for anything that gets pointed at as 'the' example of what a general AI would solve.
A better benchmark would be that a single piece of software that is not specialized is able to solve two of these problems.
This is trivial to do right now: Build a software program that recognizes the problem it is being set(limited list of problems it can handle at first, ie. extract text from an image, tell me if an image is a cat or a dog, play a chess game, play a go game) and once it has identified the problem get it to call into a specialized program for that problem(chess playing, OCR whatever).
As you want to support more use cases, you just need to plug in a new subprogram and modify the top level problem recognizer.
A chat bot sitting on top of a bunch of specialized systems, basically.
This is clearly still not general intelligence and probably not super useful, but hey.
The true benchmark for intelligence is a piece of software that is presented with some pattern and told to turn it into some other pattern and then figures out how to do it. This probably requires general and deep language comprehension(ie. get me a list of emails for environmental managers at US public water utilities. system needs to have some idea about google, some idea about the EPA and their web resources as a starting point for getting that dataset, some ability to google, read and understand web page data etc).
Obviously you can cheat by just having a giant database of information, but at some point you have to be able to answer questions that are not in the database.
Back in the early 90s, my grad AI class referred to AI as the "incredible shrinking field". The class kicked off with a black-and-white newsreel style interview from the 1950's with an MIT professor[1] who says, to eternal breathless infamy, something along the lines of "we'll have machines that can think within five years!"
Part of the challenge is/was the line of thinking "surely if we can solve Hard Problem X, we'll have intelligence!" This turned out to be entirely wrongheaded, since a vast litany of Hard Problems X turned out to have plain old algorithmic solutions.
[1] I keep hoping this shows up online somewhere. It was shot using a machine room as the set, where mid-century modern furniture had been brought in for guest and host!
Maybe in a few years we'll have bridged the gap between computer and machine capabilities. Not by deciding computers think, but by realizing that what humans do is computing.
That indeed looks like it, thanks. It also looks like old memory mixed up bits and pieces of it; the interview wasn't in fact set in the machine room shots.
Every time someone write software, someone comes along after-the-fact to try to claim it as "artificial intelligence", no matter how little (or absolutely nothing) the software in question has to do with whatever's being sold that year as "AI".
AI used to be sold as human-like or even superhuman intelligence. Now, it just means "running software".
Normally, we would not only want a proof that something works but also verifiable explanation of HOW something works.
Medicine is a problem because we don't understand how many of the chemicals work but the problem is so important (and also it is big business) that we just let it pass and we are happy that we can get some results even if we don't completely understand how we get them.
>"Do you know what they call alternative medicine that has been proven to work?...Medicine."
I don't like this quote. It is mostly true, yet it is not completely true. But because it is mostly true so many treat it as if it is completely true and thus miss the edge cases where it is not true.
Take for example certain substances that are deemed by the US federal government to have no medical value. They cannot be medicines. Yet they are alternative medicines and even prescribed by doctors. Their are even states who go against the federal government by saying these substances do have medical value. And if you go back a few decades, you'll find a time where mainstream science largely ignored any medical benefits of these substances, likely due to pressure from the federal government, and yet at that time those substances were alternative medicines that worked.
The medical establishment doesn't think that eg marijuana is "alternative medicine" or without medical value: the DEA does. The fact that the medical establishment now accepts marijuana as a therapeutic tool is evidence of the quote: it was "alternative medicine" until there was sufficient evidence that it worked. Other drugs which are Schedule 1 (ie without medical value _according to the DEA_) are in various stages of this process (eg MDMA for PTSD).
That's not same thing as saying that there isn't anything which is currently considered "alternative medicine" that works. Rather, once an alternative medicine has sufficient evidence going for it (and is better understood), it becomes less mystical and is accepted as mainstream medicine.
>The medical establishment doesn't think that eg marijuana is "alternative medicine" or without medical value: the DEA does.
Does the DEA and the FDA not play a role in what is considered a medicine or an alternative medicine?
>The fact that the medical establishment now accepts marijuana as a therapeutic tool is evidence of the quote: it was "alternative medicine" until there was sufficient evidence that it worked.
This is effectively making the claim that while there were problems in the past, the fixing of past problems can be taken as evidence that current problems do not exist. I'm arguing the opposite. That problems in the past, without fixing the systematic causes of those problems, is a reliable indicator that we are still at risk of those same problems.
Think of it like code. If your code had bugs but you fixed them, should you assume that A: my code had bugs but now they are gone and my code is bug free or B: the process by which I write code allows for bugs, and while I have fixed those bugs I haven't fundamentally changed how I write code so I am still at risk for having bugs. I find the latter the more reasonable approach.
>That's not same thing as saying that there isn't anything which is currently considered "alternative medicine" that works.
When people use that quote, I find that this is exactly what they are saying in the majority of cases. That if something is currently considered alternative medicine, it doesn't work because otherwise it would be considered medicine.
A claim that as our knowledge progresses alternative medicine that works becomes medicine is a much more reasonable claim, because it recognizes there is a timeline and since we aren't at the end of that timeline we allow for some alternative medicine actually work with the recognition in the future it will be refined into medicine.
And if someone wanted to make the claim that the majority of alternative medicine doesn't work, I would also fully agree. My issue is people taking a guideline that is generally true and applying it as if it is always true.
I mean if I take some herb and (successfully) use it to treat abdominal pain without knowing what that herb is or how it works, would that herb be classified as medicine or not?
I have read description of many medicines and found out that we still don't know how those medicines work. But they are still called medicines.
If your herb was shown to improve abdominal pain in a certain population through use of well-executed, replicated randomized control trials, I think it would count as medicine, yes.
Knowing how something works is better than not knowing. But you can still have a pretty good idea that it does have the desired effect (i.e. verify cause and effect) via randomized controlled trials.
Of course, alternative medicine proponents rarely do randomized controlled trials.
If the alternative medicine is widely known, you won't be able to get a patent on it. Without a patent you won't be able to make a profit. Without a profit there's no incentive to spend the enormous money required for a randomized controlled trial.
But if the alternative medicine is widely known to work, it means the effect is powerful enough that a cheap RCT done at a local medical university should be more than enough to demonstrate it.
If it can't, then frankly, you don't really know it works.
The point is that without a monetary payout at the end, you're going to have trouble incentivizing someone to do the study no matter how cheap it is. And don't forget the opposite, if there's a current medical treatment for the condition you're testing they will try to block you at every step.
You can pester scientists, they have their own separate set of perverted incentives - academic status - which, in this case, works in your favor.
As for the opposite case - if there was a thing you wanted to test that had a strong effect, then the company that owns the current treatment would happily fund you the tests in exchange for dibs if the tests pans out.
What're some examples of things that were called "alternative medicine" but that were later proven to work and regarded as "medicine"?
Edit: Since people are bringing up ancient examples and kind of missing the point of the question: I'm not looking for examples from the Roman Empire here. Let's stick with the past < 50 years. Maybe something your parents might actually remember being dismissed as "alternative medicine" (whatever that meant at the time) but which now is clearly just accepted "medicine". Basically, try to find something that's in the spirit of the question. The goal is obviously to find things that modern medicine actually previously dismissed and later accepted, not to find a loophole in the question.
The Australian doctor who proved ulcers and stomach cancer could be caused by bacterial infection was dismissed until he inoculated himself and cured it with antibiotics.
I'm personally very grateful to that doctor. I used to suffer from quite painful stomach ulcers in my teens. I was scheduled for an endoscopy but the doctor I was referred to had obviously heard of this research. One week of antibiotics later and I have never suffered from it again.
These aren't "finding loopholes in the question". Most "alternative medicine" things that work aren't new information. Humans have had literal millenia to figure out how to deal with ailments. The easiest methods that people can think of to try to solve a problem have been done by someone, and were passed around when they worked.
There's things like "gargle with salt water to cure a sore throat". That's commonly done remedy that was done forever and we now understand the science behind it. This page[0] labels it under an "alternative medicine" tag, as does this page[1]:
The entire premise of this discussion was that modern medicine currently has a flaw/double-standard/whatever about what it considers alternative medicine. Bringing up examples where medicine was flawed a century ago is very much finding a loophole and missing the point of the question.
I don't think the quote meant that "modern medicine currently has a flaw/double-standard/whatever about what it considers alternative medicine" just as there is no flaw (necessarily) in what is considered AI. I think it says that many of tomorrow's treatments are considered fringe today since they have not yet been fully examined.
As long as pain and death exist our medical knowledge is incomplete. That doesn't mean it isn't the best we have or that the process isn't the best we are capable of, it just means the job isn't done (or, in some cases, mistakes or oversights occurred).
The saying is also intentionally provocative, and in my interpretation is meant to promote curiosity and open-mindedness rather than an interest in any specific alternative therapy.
An example of an alternative therapy gone mainstream (among many others in the thread that I think fit) is folic acid supplementation for those who many become pregnant. It was not universally practiced even after some physicians had linked it to spina bifida prevention (this is my layman's understanding of history, not medical advice or judgment): https://pediatrics.aappublications.org/content/106/4/825
I would say many alternative therapies revolve around some supplement or another. Only rarely does the medical community agree that it is of high importance (if they do, again, I'm not a doctor I might be very wrong) and the previously alternative therapy become mainstream.
I'm not so sure. I think the task is to find a treatment and a point in time where the treatment was considered alternative medicine, was then proven to work, and thereafter was considered medicine.
The conclusion then is that there are things we consider medicine today that used to be alternative medicine.
> I'm not so sure. I think the task is to find a treatment and a point in time where the treatment was considered alternative medicine, was then proven to work, and thereafter was considered medicine.
That would imply examples from 400 BC would have the same relevance as examples from 50 years ago. I assumed it was obvious that that's not the case, but it seems I was wrong.
One that is relatively modern is everything cannabis, though depending on your perspective the "alternative medicine" or "medicine" stages may be questionable.
Artemisinin is an antimalarial drug derived from a plant used in traditional Chinese medicine to treat fevers, including malaria. First discovered and isolated in 1972.
St. John's Wort started out as alternative medicine but is now officially approved and prescribed for treating depression in many European countries -- I believe this got started in the 1990s.
I think this kind of thing is quite rare in medicine, though. Whereas it's not hard to find stuff that was done by AI researchers that somehow no longer counts as AI.
Awesome, thanks for the examples. It got me going in a direction I didn't expect the discussion to go though: were these examples of traditional medicine or alternative medicine? They seem to be the former whereas this discussion was about the latter - alternative and traditional medicine are not the same thing [1]:
> Complementary medicine refers to therapies that complement traditional western (or allopathic) medicine and is used together with conventional medicine, and alternative medicine is used in place of conventional medicine. Alternative medicine refers to therapeutic approaches taken in place of traditional medicine and used to treat or ameliorate disease.
The difference (as I've understood it anyway) is "alternative medicine" implies you contradict modern medical practices, which was the basis for the comment I replied to. And which (I guess not surprisingly) medical experts recommend against. So I think the kind of example you'd want is a medical treatment that modern medicine previously recommended against, but that ended up vouching later. Because this was all a response to a comment that suggested people had a double-standard and were moving the goalposts or something like that (hence the AI comparison). Merely showing the science went from "we don't know if X is a good idea" to "we know X is a good idea" isn't finding an inconsistency.
I can't think of any of those from recent history. Medicine has gotten pretty good.
Exceptions may be in mental health. Somebody else mentioned psychedelic drugs, which might be FDA-approved for some conditions soon, but all official medical sources currently treat them as extremely dangerous.
However, I do think cherry-picking the effective treatments from naturopathy and TCM and turning them into mainstream medicine is similar in spirit to cherry-picking the successful approaches from 80s and 90s AI research and turning them into "just algorithms". But there are a lot more successful old-school AI techniques than there are effective alternative medicine treatments.
Sanitation guided by germ theory might never have been explicitly called "alternative medicine", pre-dating common use of the term, but when Miasma Theory was dominant germ theory was just that.
Does rely on accepting "cleaning things" as medicine. It's certainly a great way to prevent disease. Does it count as "alternative preventative medicine"?
By "alternative medicine" I'm not talking 19th century views here, especially given the comparison to AI. Can you pick something from like the past half-century or so? Which people called alternative medicine but which now they call medicine?
The modern medical field has authorities who largely determine what is medicine and what is "alternative medicine." I can't imagine anything which today is considered "medicine" that isn't approved by the FDA; can you?
AI doesn't have such an authority. New AI methods can be deployed without asking for permission from a governing body.
19th century views on medicine is actually a better comparison than something from the last fifty years, because advancements in modern medicine is largely shaped by governing authorities.
The FDA is a US body, so if you take a global perspective I'm sure you'll find lots of examples of things that are 'alternative medicine' according to the FDA but just 'medicine' according to the equivalent bodies in other countries. Someone brought up St. John's Wort as an example above, apparently now legitimate in some European countries.
I guess it depends how you define alternative medicine. The bark of a willow tree, a traditional remedy, from which aspirin is derived is given as an example in the beat poem.
The point was you don't have to define it at all. You just need some things that were previously dismissed as "alternative medicine" in the past (say) 50 years, but which are now widely seen as ordinary medicine. I'm looking for examples a lot of people might actually remember from their own lives here... if you have to go back a century to find something then that doesn't count (sorry).
Why doesn't it count? Your restriction seems very arbitrary and makes it much harder to find an answer. There are too many counter examples muddying the space, such as vitamin C curing colds.
The entire premise of this discussion was that modern medicine currently has a flaw/double-standard/whatever about what it considers alternative medicine. Bringing up examples where medicine was flawed a century ago isn't exactly proving anything. I would've hoped this would be obvious but apparently it was not.
Human nature is what defines the difference between alternative and regular. It also defines what is a double standard. You didn't need to explicitly mention it for it to be relevant to the discussion.
Okay, I won't stop you. Go ahead and bring up examples from Hippocrates's time then. Human nature hasn't changed much on these timescales so you will be making a very strong point about modern medicine with such examples.
I haven't brought up examples that old, and neither has anyone else in this thread. Typical straw man. I just pointed out that you were being unnecessarily restrictive and that this would hinder the effort to get the examples you seek.
In many ways modern medicine changed a lot in the last half century. Similarly to how modern culture changed a lot. In particular the way in which we believe we have discovered everything there is to know is constantly evolving.
Not all hyperbole are straw men, the original question was about how the current distinction between medicine and non-medicine works. Historical examples are relevant, but not what intended in the question.
I may be off on this but I believe low carb diets as a treatment for prediabetics/diabetics was considered pseudoscience for decades by most doctors [1], largely associated with fad diets like Atkins and keto. Over the last decade or two that has slowly been changing. Holistic approaches in general have been gaining steam and taking back that word from the naturopaths.
artemisinin. Has been used for millenia to effectively treat a lot of things (naturally so because of its being super-peroxide), anecdotally/personally - the wormwood was part of the folk medicine toolbox of my Ukrainian grandmother, yet
ISTR that seaweed was a folk remedy for goiter. It contains relatively high amounts of iodine, which prevents thyroid conditions.
I also vaguely recall that the doctrine of signatures wasn't a complete and abject failure, but some cursory Googling indicates that maybe it was after all.
Effective treatments as been subsumed into "mainstream" medicine, perhaps with a bit of purification/standardization along the way, while the ineffective and unproven stuff remains "alternative medicine".
If it somehow turns out that homeopathy is an effective therapy for something (besides dehydration), it'll end up with a procedure code, the hospital pharmacy will stock 10x tincture of whatever, and eventually it will be just another mainstream medical treatment.
I have an interesting one from within the past month, although I don't think the phrase "alternative medicine" was used:
Trump was mocked in the media for asking if getting UV light inside the body was a possible treatment for covid19, as if he was suggesting an alternative medicine treatment. And light therapy does have that long a history.
Thing is, this is not only a known treatment used in the past (only falling out of favor in the 50s), there are studies from the past few decades about how well UV light works on viruses, and it really is being investigated today for use on SARS-CoV-2.
Eh, I think we still have the Turing test, and AFAIK it hasn't had a case where it's passed without raising eyebrows for potentially trying to cover up for deficiencies. I think the threshold needs to be high (maybe "beyond a reasonable doubt"?) and reproducible with high probability: I'd say at least X% (I don't know what X should be, but it should be much higher than 50%) of the adults that engage in or read the conversation would need to be convinced it's between two typical humans in their societies. Certainly nothing that leaves room for e.g. excuses like the computer being a 13-year-old from another country, or that leaves inadequate time for the participants to gauge the plausibility...
This is because intelligence isn't like most of the other words we use in computer science. It's a word with a history of deep philosophical debate and no clear answer. We haven't come to an agreement on what is intelligence in biological systems so it may be premature to call anything artificially intelligent.
Perhaps that is the point. If we can understand it, it's not intelligence. We're trying to make computers do things we can't understand, and if we do, we've reached our goal.
Until then we have Machine Learning or gradient descent or whatever else.
I was wondering what happened to the 'Computer-aided' notion? It was very representative of the utility and provenance. Noone claimed intelligence but literally just the help side of the computers. AI as well could stand for 'Assisted Inference'.
> Logistics companies rely on huge amounts of optimization and problem-solving.
Having worked at the largest tech based Logistics company in India, I can say, we did rely on optimization and problem-solving, but none of them involved AI, they were mathematical models and not black box.
> Having worked at the largest tech based Logistics company in India, I can say, we did rely on optimization and problem-solving, but none of them involved AI, they were mathematical models and not black box.
I was going to say the same thing, but I worked at BMW and VW and spearheaded several initiatives with Corporate partners that relied more on optimizing via mathematical models/data sets within the warrantied parts/Takata airbag recall at BMW and the Tdi Diesel-gate buy program at VW. It entailed lots of data analysis and some trial by error on my part that eventually got us a favourable result, not AI.
AI can be useful, one day, but I'm returning back to Supply Chain analytics and Logistics and not much seems to have changed in those years. I submitted my proposal for a Supply Chain Analytics course as my final project that I drew up in 2017 at BMW and got 99.7% for my thorough, and more importunately to me, relevant analysis and execution of a scheme to optimize leadtime and overall turn over using the means and methods available back then.
A part of me wishes I could just run an algo/AI protocol with predictive modeling to do that all for me, as it almost got me fired several times trying to deploy it and I had to go over managements head and straight to the owners and corporate to get them to try it.
Luckily, I was able to negotiate a return to VW as result of my scheme, who were hemorrhaging Billions, were getting execs thrown in prison at the time so were way more receptive to ideas of cost cutting. Crazy days...
* AI initially inserted 'trail,' instead of a commonly used phrase 'trial by error' perhaps proving how autocorrect/spellcheck AI usecases are still not where they need to be to prove the point of the aforementioned post.
There's my bet on a less hyped but very prevalent future. Really good real mathematical models of things, that is, deriving systems for your product which can predict and control from scientific laws, not just black box automation.
Of course this isn't new, but a broad focus on specializing on developing talent to create these models and methodology and tech to help would be.
Lots of operations research, planning, optimization, and control theory came out of funding streams that were very much in the auspices of Artificial Intelligence. In most universities, "Artificial Intelligence" is still the name of the course where Computer Science students first encounter everything related to OR, optimization, planning, etc.
It's only since 2013 or so that AI = ML = DL.
> if not for the absence of calculation-by-machine, Isaac Newton and Archimedes would have been doing AI.
From the Stanford Encyclopedia of Philosophy entry on Leibniz's Philosophy of the Mind [1]:
"He believed that such a language would perfectly mirror the processes of intelligible human reasoning. It is this plan that has led some to believe that Leibniz came close to anticipating artificial intelligence. At any rate, Leibniz's writings about this project (which, it should be noted, he never got the chance to actualize) reveal significant insights into his understanding of the nature of human reasoning. This understanding, it turns out, is not that different from contemporary conceptions of the mind, as many of his discussions bear considerable relevance to discussions in the cognitive sciences."
By that definition, it would be hard to separate out anything a computer does which isn't AI, which is I think not very useful.
AI "gets a bad rap" because it is past its hype peak. The number of people realizing that slapping an AI sticker on your product is largely BS and doesn't impress any more is growing, and AI isn't magic to everybody that can solve every problem. It's the same overhype cycle which happens with just about everything.
Why would we have such a term as AI, if not to distinguish it from algorithmic?
I thought the whole point was to get out of the business of having to write down procedures for everything, to have the computer approach novel problems like a programmer does.
Is this only a contemporary perspective? Seems like that's where the goalposts were at least 15 years ago.
>Finding routes for drivers and deliveries, planning schedules, optimizing store layouts, etc. -- that's AI.
If a path-finding algorithm that I can write on paper is AI, AI has completely lost all meaning. Let's not call graph traversal and sorting "AI" please.
(1) Optimization is much, much bigger than graph traversal and sorting.
(2) Modern route-finding algorithms are to your on-paper-Dijkstra what a rocket ship is to your bicycle.
(3) I think you're under the same misconception I'm talking about: graph traversal is absolutely a fundamental of AI. Ask anyone what the main AI textbook is, they'll tell you it's Russell and Norvig: http://aima.cs.berkeley.edu/
The first topic they cover is graph traversal and search.
No, you're just projecting the modern bar for AI into the past. AI roughly means "things human brains can do that computers can't": when computing was primarily straightforward and analytical ("calculating"), then relatively more sophisticated algorithms that could "solve problems" like mazes absolutely were on the AI frontier. The fact that they've since retreated so far from "things computers can't do" is just a function of the fact that it was an early success in the field.
Your comment is just a crystallization of what the parent comment is talking about: claiming something isn't AI because "pft that's such an easy, solved problem" _after_ it's solved defines away the possibility that AI can solve problems.
If anything the opposite is true. When Minsky et al set out to define AI what they really meant was 'thinking machines'.
If anything the opposite has happened. In a painful attempt to push forward notions of success in AI almost purely mechanical tasks have been claimed to be AI, while there is virtually no progress on building machines that can think.
I mean sure you can claim all day that the navigation system in your car calculates you a billion routes per second and if that's intelligence my smart-toaster is probably more intelligent than everyone here together, but it completely misses the point, and the reason why people have expanded the term so much is because there has been so little progress on genuine intelligence.
> If anything the opposite is true. When Minsky et al set out to define AI what they really meant was 'thinking machines'.
Yes, there's two word senses, the theoretical and the colloquial. You're referring to the former, and I'm referring to the latter; the latter is a lot more relevant to this thread's topic, which is public perception of AI and its value. Wikipedia actually has a pretty good concise description of these two senses:
> Leading AI textbooks define the field as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.[1] Colloquially, the term "artificial intelligence" is often used to describe machines (or computers) that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving"[2]
(The [2] citation is of Russell & Norvig's '09 edition, substantially predating the recent mass-interest in AI)
> if that's intelligence my smart-toaster is probably more intelligent than everyone here together
The leap from "this is reasonably described as a step on the road to intelligence" to "being really good at this step means you're _really_ intelligent" is obviously nonsense, though I suspect you know that. The fact that a cat has the ability to orient itself and navigate home and am amoeba couldn't is a sign of relative intelligence; but if your cat has a better sense of direction than you, it obviously doesn't make it smarter than you.
>AI roughly means "things human brains can do that computers can't"
Ok, then nothing being done today (neural nets, gan, etc) on computers is AI. What a dumb definition.
>claiming something isn't AI because "pft that's such an easy, solved problem"
Nope. It's not AI because instructions were directly written by a programmer of how to path-find. There was no input training where the program learned how to pathfind. It just had one method from day 1 that hasn't deviated since.
i mean, these kinds of simple CS201 algorithms are still at the core of a lot of things that definitely count as AI by any reasonable definition.
In particular, the best superhuman poker-playing systems use counterfactual regret minimization, which is literally just traversing a tree and updating small arrays of numbers at each node, augmented with some clever heuristics to make it scale to realistic poker scenarios. there aren't even any neural networks involved.
But this very much was the main focus of AI only a few decades ago. If AI has changed that much such that AI 20 years ago is no longer AI now, perhaps modern AI researchers should find a different name? I very much mean this. Previous-generation AI was very much about graph algorithms, whether via LISP, Prolog, RDF, reasoning expert systems, parsing, etc. These are all graph concepts
What you're picking up on is that the old school vision of how AI would be achieved (Minsky, Chomsky etc) had some early success with simple games (which they then touted as the forerunners of AI) and then stopped. Data driven ML approaches are completely different.
Physics once held that there was a substance called aether rather than vacuum, and chemistry that fire came from phlogiston, but we didn't need to rename the discipline when reasoning evolved.
The existence of the aether isn't borne out by experiments; rather, we have justification in believing in its non-existence.
The algorithms of AI of decades past still work.
That's a big difference.
E.g. SHRDLU can be built today and you can have a conversation with it about its world of blocks.
Algorithms are artifacts of mathematics. To calculate distance between two points, we still use sqrt(dx^2 + dy^2); it doesn't go out of fashion due to advancements in topology.
The techniques aren't that new; all that is new is having the gigabytes upon gigabytes of RAM to run them, not too mention CPU power, and scads of data.
I knew what a neural network was, and understood it as part of AI, when Wham! was in the Top 40 charts.
The hate isn't for the "new" techniques, I think; just for the posers who claim to be AI experts because they know how to use some Python library or whatnot.
You don't have AI creds if you have no background in the symbolic stuff.
We'll have to disagree. You can be perfectly well credentialled without having years of studying techniques that aren't very successful. It is really rewriting history to pretend that old style NN and backpropagation is the same as modern systems -- implementation techniques matter.
> Physics once held that there was a substance called aether rather than vacuum, and chemistry that fire came from phlogiston, but we didn't need to rename the discipline when reasoning evolved.
The key difference being that physical theories based on aether never actually worked, whereas computer programs based on graph algorithms solved many problems they set out to solve.
A huge amount of stuff can and should be automated by mundane programming. Since that hasn't been done yet, AI isn't about to automate all of the stuff that shouldn't be automated by mundane programming.
You're proving the point of the original post. Today's mundane programing used to be considered AI. See: search, fuzzy logic, character recognition. Or even more mundane: object-oriented programming, interpreted languages, and tons of generic algorithms used in daily life, all of which came out of AI labs.
Well arguably an automated feature is one that is at least somewhat more intelligent than the manual version (e.g. spell-check vs. a dictionary).
Whereas on the other end of the scale people are aimlessly using subtle AI-related techniques like neural networks and calling the result "intelligent" even if it is anything but.
> Logistics companies rely on huge amounts of optimization and problem-solving. Finding routes for drivers and deliveries, planning schedules, optimizing store layouts, etc. -- that's AI.
Is it? These have largely been done with MILP and related tools for decades, and those approaches have never been called AI. What AI techniques are you thinking of here?
Anyway, AI gets a bad rap because there's so much hype and snake oil out there that the signal is being lost in the noise. I'll be much better disposed towards it once the next AI winter hits. Like for instance, I have great respect for anybody selling an expert system at this point in time.
Is this the point in the curve we redefine anything successful as using 'AI' because it uses a computer program?
Literally the only things you mention using AI/ML techniques are "speech-to-text, asking Siri for answers, translating web pages" and search engines, which are external to business process. Internal search is usually a disaster because processes are document based rather than web/publication based.
There are ML applications used for business, like speech recog, pricing, marketing and so on, but not so much for business processes. Where are the AI tools for parsing legal contracts? Agents for finding and negotiating prices with suppliers? Filling out regulatory reports?
The classical, as in old fashioned, 70s, 80s definition of AI is solving any problem using heuristics. This is contrasted with algorithmic problem solving; which is impossible to do if your problem is intractable (you can't have an efficient 3SAT solver if you don't use heuristics). The canonical example is CSP (constraint satisfaction problem) solvers: people legit thought, encoding knowledge in a Prolog-like CSP solver will allow us to create AI. AI researchers before the "AI Winter" thought AI is all about encoding the information in a way computers can use heuristic to solve problems like humans.
When the shapes are hand drawn, and the boundaries have gaps in them, heuristics are needed. Same if filling an object in a photo - where are the edges, exactly? That kind of object recognition is familiar ML stuff. The pictures in this paper show quite a few examples of filling problems: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.655...
I don't know about MS Office, specifically, though.
Imagine taking someone who didn't know English words or sounds, sitting them down with a document and dictionary, and asking them to spell-check it. If they could figure out that "butiful" should be "beautiful" in less than a week I'd call that really freaking intelligent. AI researchers of 40 years ago would be astounded by what Word can do.
Route planning is essentially "the" fundamental AI problem. You need to get from A to B and there are 2^100 ways to do it. How do you pick the best one?
Just because you were taught routing in your AI class doesn't make it AI. It is a topic in graph theory and combinatorics. Dynamic programming is a useful technique for solving traditional AI problem formulations, but it isn't AI.
In contrast, your example of the human is like modern AI, specifically unsupervised learning. I'm sure I could get a human doing that kind of pattern matching quite good at it quite quickly.
If I didn't have a learning system I could still write an algorithm for it quite quickly, by computing the Levenshtein distance. But that algorithm would notoriously not understand the context of the sentence, probabilities of words, etc. And indeed Word/Android/iOS spell correct still gets it really wrong much of the time. But the AI used is quite different from old timey backtracking.
> Logistics companies rely on huge amounts of optimization and problem-solving. Finding routes for drivers and deliveries, planning schedules, optimizing store layouts, etc. -- that's AI.
Your definition of AI seems to be particularly broad. There are several general and specific algorithms designed to handle the examples you gave and they have been working well for the last few decades, some even earlier and have been implemented on computers later. E.g. linear programming was designed to solve several classes of programs and was initially done on paper. Lumping all this as "AI" doesn't seem fair to me.
Well, we can't dismiss the overhype either. Watson and all that. IBM alone is likely responsible for much of the disappointment, with its constant ads around Watson solving the Universe's toughest problems, when what they really seemed to be after is somebody to figure out how to take advantage of this thing they built.
Because laymen, movies and nowadays even tech consultants don’t know (or don’t want to know, in the case of the latter, for financial reasons) what AI is and picture advanced ML, fancy neural networks, acrobatic talking robots, Skynet, GAI, etc. instead.
Which is way more cool and gives you more dreams (or money).
Everything you outlined is a use case that is powered by machine learning algorithms. You can discuss all of them without using the phrase "AI." They all likely started with rules engines that solved a real business problem and iterated over time to use more sophisticated back ends.
The thing that I don't appreciate in this field is that companies seem more focused on discussing their "AI" than the actual problems they hope to solve. When I see this, my mind jumps to the assumption that these companies are either: 1) looking for easy valuation multiples. 2) looking for a way to impress VCs/investors. 3) they don't actually understand the problem they are solving. This is admittedly a big stereotype on my part, but this stereotype was built on pattern recognition over time.
Think how much Microsoft Office and competitors have amplified business productivity over the last 20 years (yeah yeah, make your jokes too). Word and Powerpoint and Excel are full of AI whether it's spellcheck or auto-fill, drawing algorithms like "fill with color", etc. So many things that were AI research papers of the 70s, 80s, or 90s. And those innovations continue today.
Logistics companies rely on huge amounts of optimization and problem-solving. Finding routes for drivers and deliveries, planning schedules, optimizing store layouts, etc. -- that's AI.
Employees use AI tools to improve their lives and productivity whether it's a rideshare or maps app to get to work, speech-to-text, asking Siri for answers, translating web pages, etc. All of this comes out of research in AI or related fields.
How many office jobs don't require someone to use a search engine to find and access information related to a query? Information retrieval is one million percent AI.
Robotics and automated manufacturing has been huge for a long time -- robotics is closely connected AI and related problems like control theory.
The best applications of AI have almost always been to support and enhance human decisionmaking, not replace it.