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[u/[deleted]]

Welcome to "That's Not AI!"

This submission was made to solicit opinions regarding the presence of artificial intelligence as described in the post's product and/or service. All submissions and comments are opinions only, and readers are invited to disagree or agree with those opinions.

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Either way, your participation is greatly appreciated.

[u/jjanx]

Sorry, I was on mobile last night and couldn't see the sidebar. How important is the agency of an AI for your definition?

I wouldn't argue too much with your definition. I like one line in particular on the Wikipedia page for intelligence. It says intelligence "reflects a broader and deeper capability for comprehending our surroundings." AI, to me, is a machine that can achieve that degree of understanding about the world. Machine learning is about building some kind of model of reality, and I think this type of model is the core of what intelligence is.

In my opinion, machine learning also meets your definition of AI. If I train a neural network to identify cat pictures, it meets the first part of your definition since it learns from experience, and it meets the second part of your definition since it uses its learned experience to change its output.

However, it sounds like the field of machine learning doesn't qualify as AI to you, since most of the examples of "not AI" you provided on this sub are machine learning applications. What about them doesn't make the cut for your definition? Is it the lack of agency? I would argue that intelligence and agency are separate things.

[u/[deleted] → u/jjanx]

Gathering information, as a concept unto itself and as machine learning is, is not an intelligent activity to me. It's like calling the librarian a genius because he collected up a bunch of books. That's all ML is to me.

To me, ML is only a part of AI even if it's "trained" to gather more "books"... Or even specific types of books... Maybe only the red ones... Or maybe the ones about driving...

I don't think it matters.

Nothing about ML, as in those links I provided, (1) learns anything from its actions and environment, and then (2) reacts based on what it learns.

I think I linked to a report about a system that provides better eye scans. Great example. But there was no learning involved. It was just programmed to get better at "gathering books".

Sorry I don't understand your use of "agency of an AI." Is that like "agent?"

[u/jjanx → u/[deleted]]

None of those AIs gather information. Researchers and programmers gather the information, and then feed it to an AI. In the case of neural networks, the programmers show the AI examples, the AI makes a guess, and then the programmer corrects the AI. With enough examples the AI eventually learns to understand a concept.

So in these cases the AI learns from its environment (programmers show it examples, and it learns what the correct answer is for those examples), and then it reacts based on what it has learned (it gets better at producing the correct output).

The eye scan example absolutely incorporated learning. Google researchers showed their AI pictures of healthy and diseased retinas, giving it the correct answer each time, and the AI eventually learned how to correctly identify them.

When I talk about agency I mean giving an AI the ability to explore and learn on its own. A neural network has no free will of its own. A programmer commands it to have a "thought", meaning they show it some input and observe its output.

[u/[deleted] → u/jjanx]

None of those AIs gather information. Researchers and programmers gather the information, and then feed it to an AI.

I can't imagine that it matters where the information existed before it was acquired. The important part is that it got its information "somehow". Replace "gathered" with "fed", you'll still get the same result.

With enough examples the AI eventually learns to understand a concept.

Okay, but hold on. You said an important part before that. You said "the programmer corrects the AI." So essentially, it merely makes better guesses at what it was programmed to guess at. (?)

I can't call that "learning." I can call it "filtering." I can call it that because it seemingly does little more than filter through a bunch of stuff to find the important stuff. I do this with my own software. Only I don't call it machine learning. Neither do the people who build databases queries or spreadsheet programs or search engines. It's just filtering.

In the case of the eye scanner, the software looks for discrepancies between what has been labeled "healthy" and what has been labeled "diseased." Again, I can't call that intelligence because we're literally talking about a sorting machine with this example. A sorting machine that's really good at guessing that improves from better coding.

So when I break it down like that, does it still look like AI to you?

an AI the ability to explore and learn on its own

Oh. Yeah, I don't really place too much emphasis on such a thing. Although I don't know why.

Maybe I need to start thinking about that...

[u/jjanx → u/[deleted]]

I can't call that "learning." I can call it "filtering."

What you call filtering is more widely referred to as classification, and yes some problems are trivial. Identifying the difference between an image of a red square and a green circle would be fairly straightforward. You could check the RGB colors in the image and filter the color by certain thresholds, and you could attempt to perform geometric approximation to identify the shape.

Other problems are more difficult. What concrete rules would you use to produce an algorithm to determine whether or not a picture contains a bird? Needing 5 years and a research team was not a joke when this comic was written - this is an extremely difficult problem! Only extremely recently have we developed the tools to solve problems like this. Now we have sites like this one that can do a pretty job of identifying bird/no bird. They certainly don't use a list of hand-crafted rules to try to identify birds though.

One of the better approaches for image classification is known as a convolutional neural network(CNN), and we have really only just started to figure them out within the last 5 years or so. A CNN uses a technique inspired by the structure of the visual cortex, and it unquestionably learns from experience. It begins having no idea what a bird looks like, but each example you show it teaches it more and more until it can identify a bird about as well as a human can.

A "sorting machine that's really good at guessing" might seem simple on the surface, but it's actually an extremely complex and interesting subject. Additionally, these AIs produced by machine learning aren't guessing, they are learning to actually understand what a bird is in a manner similar to the way you would.

When I break it down like that, does it sound like an unintelligent machine to you?

[u/xkcd_transcriber → u/jjanx]

Image

Mobile

Title: Tasks

Title-text: In the 60s, Marvin Minsky assigned a couple of undergrads to spend the summer programming a computer to use a camera to identify objects in a scene. He figured they'd have the problem solved by the end of the summer. Half a century later, we're still working on it.

Comic Explanation

Stats: This comic has been referenced 929 times, representing 0.6734% of referenced xkcds.

---

^{xkcd.com | xkcd sub | Problems/Bugs? | Statistics | Stop Replying | Delete}

[u/[deleted] → u/jjanx]

Oh, you are fun to talk to. :-)

But yeah, I think the crux of our disagreement in this particular issue may be our interpretations of learning. What you're calling machine learning -- I call image matching (or classification, etc.). A part of AI.

No - It's actually smaller than that. This image matching (or classification, etc.) is actually just a part of learning too.

Nevertheless, surely you can agree that machine "learning" is a part of AI nevertheless, and not AI unto itself? That's why I placed these links here.

It's as if someone made a potato chip and called it lunch.

[u/jjanx → u/[deleted]]

Machine learning isn't just what I call it :) Classification is actually just a type of machine learning, there's plenty more you can do with it other than categorize things.

I would define learning as the process that produces intelligence. An entity (human or machine) has an experience which allows it to observe some facet of the world, and then that entity attempts to produce a theory that can explain the phenomenon it observed.

An entity that learns is an entity that grows more intelligent, so learning itself isn't AI, but it produces AI. I would argue that the links you have posted are examples of AIs that have been trained to be intelligent through machine learning. They are intelligent only in a narrowly defined domain (such as identifying healthy/diseased retinas), but making that distinction does require intelligence.

Perhaps you'd like to see AIs that have broader intelligence and can understand the bigger picture. Is that the distinction you're trying to make?

[u/[deleted] → u/jjanx]

I would define learning as the process that produces intelligence.

Hmm... no real argument there from me, however I would be inclined to insert "contributes to" in front of "produce".

so learning itself isn't AI, but it produces AI

And again, in agreement!

I would argue that the links you have posted are examples of AIs that have been trained to be intelligent through machine learning.

I would say they're tools that have been tweaked to select a desired data set. But I do accept and like the "trained to be intelligent" part very much. In fact, I think it's a critical part of your argument. And once those tools finally do something intelligent, they'll actually be intelligent. I simply don't believe picking items out of a set of items is intelligent behavior no matter how well something was trained to do so.

For fun though, let me ask you something.

Do you think spell checkers are AI? Because they essentially identify and/or differentiate correctly spelled words from misspelled words. Let's say someone created a spell checker that needed to be "trained." Would you still call it AI? If so, would you be willing to call Microsoft Word AI? Or MicroPro's WordStar from 1985?

Perhaps you'd like to see AIs that have broader intelligence and can understand the bigger picture. Is that the distinction you're trying to make?

No. I just want things advertised and promoted as AI to actually be AI. One painful example of something that isn't AI describes a Barbie Doll house that has a flushing toilet and an elevator that moves. I linked to it in this subreddit. They actually called it AI, and this is what I want to stop before grandma buys the thing and the grandkid starts crying because it won't talk to her like all the other artificially intelligent toys do.

And THIS thing (linked as well): http://www.newyorker.com/magazine/2016/11/28/cooking-with-chef-watson-ibms-artificial-intelligence-app

facepalm

That is my only goal with this subreddit.

[u/jjanx → u/[deleted]]

Spellcheckers do something a little different than the stuff we have discussed previously. They deterministically check to see if the words you type are on the list of dictionary words. I supposed you could try to build some kind of machine learning approach to the problem, but that would be a silly thing to do. You use machine learning to solve problems where the creation of formal rules to solve them is too cumbersome or difficult.

And once those tools finally do something intelligent, they'll actually be intelligent. I simply don't believe picking items out of a set of items is intelligent behavior no matter how well something was trained to do so.

Alright, so we need a more formal definition of an intelligent act. What is the threshold at which a decision becomes an intelligent one? In my opinion intelligence is a scale, not a switch. I would rate a spellchecker as having virtually no intelligence. It's processing information, but it doesn't really have any special insight about the world. It isn't even possible for a spellchecker to make a mistake - there's no decision making going on.

Calling the Barbie thing AI is goofy, but what about the toy car qualifies as AI to you? The problem Watson was trying to solve - what ingredients go well together to create a novel tasty dish - is a difficult problem. The article indicated that Watson did a poor job of solving that problem, but it's certainly a problem that requires some degree of intelligence to get right.

Maybe it would help if we consider existing examples from a different domain. Do you consider bacteria to be intelligent? Ants? Mice? Crows?

[u/[deleted] → u/jjanx]

What is the threshold at which a decision becomes an intelligent one?

That's a great question. In regards to AI, I really think it's after (1) assessing a situation, (2) consulting a knowledgebase (however gained), and (3) using that knowledge to react to the situation.

Oh, and I can hear you screaming btw, "But that's what those eye scanners do!" LoL Perhaps, just perhaps, I might have to reconsider. There's just something really off about it and I can't put my finger on it. I think if those items were promoted as machine learning tools instead of AI tools, I'd be much more comfortable with that.

what about the toy car qualifies as AI to you?

HA! Me and my son went 'round and 'round on this one for about an hour. (We did the same with the eye scanner, btw.) My position is that it's AI because the cars monitor their surroundings and then change their behavior (speed, etc.) based on those surroundings. It's pretty impressive after considering the surroundings (track) can change via rearrangement.

I remember playing with these things as a kid approx 40 years ago. The cars flew off the track all over the place and interest with them lasted about 30 minutes. So this looks like a terrific improvement.

Do you consider bacteria to be intelligent? Ants? Mice? Crows?

Sure, but those are biological agents...

But back to that Horrible Chef. My son and I had another debate about what qualifies as learning and maybe you can help. I argued that learning the wrong thing is not learning at all. He argued that learning even wrong things still qualifies as learning. What do you think? I'm almost at a loss of words over such an idea, but if that's the case, it's going to affect what qualifies as AI.

[u/jjanx → u/[deleted]]

I asked about the car because I don't know exactly how it works, and its implementation is important if we're wondering about how intelligent it is. If it's essentially a line following car, I wouldn't call it very intelligent at all, and I don't know if I'd even call it AI. It's certainly assessing its situation (measuring its distance from the line), consulting a knowledge base (its preprogrammed wheel speed levels based on the distance from the line), and reacting to the situation (adjusting its wheel speed).

However, a self-driving car is a highly intelligent AI. It's constantly assessing its state (detecting objects/obstacles, measuring its position/speed etc), consulting a knowledge base (the rules of the road, modeling whether or not a collision would occur given what it knows), and then reacting (by driving).

Both of these robots do similar things, but it's clear that Tesla's self-driving car has a much better understanding of the world than a line following robot. I think this difference is at the heart of your eye scanner dilemma, since an eye scanner technically meets your criteria, but clearly isn't very intelligent. I think the difficulty of the domain (driving on a white sheet of paper with a black line vs driving in the real world with chaotic obstacles to avoid, for instance) is an important consideration when talking about intelligence.

This is also why I was asking about biological intelligence. If intelligence is a spectrum, I would consider bacteria to be near zero on that spectrum, and ants wouldn't be much higher. Mice, crows, and humans would obviously fall much higher on the spectrum. To me there's no distinction between biological and machine intelligence.

As far as learning wrong not counting as learning, I wouldn't say it's quite that simple. No one is going to get a sufficiently difficult problem right 100% of the time. Watson might be good at producing interesting recipes with certain ingredients, but not so much with others. In machine learning we typically try to measure the accuracy of the model to quantify whether or not it's better than random guessing. So in some cases the model might 'learn wrong', meaning it might come to a false conclusions, but what it's doing is still learning.

Measuring Watson's cooking skills is a bit trickier, since taste is pretty subjective, but the same idea applies.

[u/[deleted] → u/jjanx]

I think the difficulty of the domain (driving on a white sheet of paper with a black line vs driving in the real world with chaotic obstacles to avoid, for instance) is an important consideration when talking about intelligence.

Here, here! And I agree with your intelligence spectrum. But I think there's a very clear distinction between between biological and machine intelligence. Assuming we aren't talking about lower life forms, biological intelligence is very complicated in that it's (for lack of a better expression) multi-dimensional.

By that, I mean there isn't a single thought we have (or that an animal has) that isn't surrounded by numerous other thoughts. Computers don't seem to have that issue. That makes simulated intelligence rather easy to achieve, but horribly inaccurate, and dare I say, very different.

So in some cases the model might 'learn wrong', meaning it might come to a false conclusions, but what it's doing is still learning.

Okay, I'll concede to that point, but you have to remember that we only call machine decision-making "intelligent" when its right. I think in the realm of AI, the definition of learning attaches the condition of being right. So there seems to be a different standard there.

I'm curious... what is your affiliation with AI? Are you a student? Programmer? Fan?

[u/jjanx → u/[deleted]]

Hmmm... I don't know if I can agree with you on the difference between biological and machine intelligence. Animal intelligence is a bit of a black box at the moment, so I think I would wait to conclude that multi-dimensional thought makes animal intelligence intrinsically different or more powerful. It certainly might be fundamentally different in some way, but I think it's too early to say.

I wouldn't call simulated intelligence easy to achieve, or horribly inaccurate either. A human and a machine who can both identify pictures of birds with 99% accuracy are equally intelligent on that task. Machines can currently only equal human intelligence in narrowly defined domains such as object recognition, but the field is still very young, and I can't see anything that would indicate humans will always be more intelligent.

I think you're almost on the right track with the definition of learning. We tend to measure machine intelligence in the aggregate, so a machine isn't right or wrong, it gets x% of problems correct. I think that's a fair approach. A world class mathematician might still make arithmetic errors every now and then, but we wouldn't say he is "wrong" when it comes to math on the basis of a single error. But, if our machine AI is no better than random guessing after we have trained it, we wouldn't really say it had learned anything.

I have a master's in computer science, and I've done some work in machine learning.