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A hypothetical situation: AI is tied to a camera of me in my office. Doing basic object identification. I stand up. AI recognizes me, recognizes desk. Recognize

A hypothetical situation: AI is tied to a camera of me in my office. Doing basic object identification. I stand up. AI recognizes me, recognizes desk. Recognizes "human" and recognizes "desk". I sit on desk. Does AI mark it as a desk or as a chair? And let's zoom in on the chair. AI sees "chair". Slowly zoom in on arm of chair. When does AI switch to "arm of chair"? Now, slowly zoom back out. When does AI switch to "chair"? And should it? When does a part become part of a greater whole, and when does a whole become constituent parts? In other words, we have made great strides in teaching AI "physics" or "recognition", but we have made very little progress in teaching it metaphysics (categories, in this case) because half the people working on the problem don't even recognize metaphysics as a category even though without it, they could not perceive the world. Which is also why AI cannot perceive the world the way we do: no metaphysics.

feoren 4y ago
> we have made very little progress in teaching it metaphysics (categories, in this case) That's because ontology, metaphysics, categorization, and all that, is completely worthless bullshit. It's a crutch our limited human brains use, and it causes all sorts of problems. Half of what I do in data modeling is trying to fight against all of the worthless categorizations I come across. There Is No Shelf. Why are categories so bad? Two reasons: 1. They're too easily divorced from their models. Is a tomato a fruit? The questions is faulty: there's no such thing as a "fruit" without a model behind it. When people say "botanically, a tomato is a fruit", they're identifying their model: botany. Okay, are you bio-engineering plants? Or are you cooking dinner? You're cooking dinner. So a tomato is not a fruit. Because when you're cooking dinner, your model is not Botany, it's something culinary, and in any half-decent culinary model, a tomato is a vegetable, not a fruit. So unless we're bio-engineering some plants, shut the hell up about a tomato being a fruit. It's not wisdom/intelligence, it's spouting useless mouth-garbage. And remember that all models are wrong, but some models are useful. Some! Not most. Most models are shit. Categories divorced from a model are worthless, and categories of a shit model are shit. 2. Even good categories of useful models have extremely fuzzy boundaries, and we too often fall into the false dichotomy of thinking something must either "be" or "not be" part of a category. Is an SUV a car? Is a car with a rocket engine on it still a car? Is a car with six wheels still a car? Who cares!? If you're charging tolls for your toll bridge, you instead settle for some countable characteristic like number of axles, and you amend this later if you start seeing lots of vehicles with something that stretches your definition of "axle". In fact the category "car" is worthless most of the time. It's an OK noun, but nouns are only averages; only mental shortcuts to a reasonable approximation of the actual object. If you ever see "class Car : Vehicle", you know you're working in a shit, doomed codebase. And yet you waste time arguing over the definitions of these shit, worthless categories. These worthless things become central to your database and software designs and object hierarchies. Of course you end up with unmaintainable shit. Edit: Three reasons! 3. They're always given too much weight. Male/female: PICK ONE. IT'S VERY IMPORTANT THAT YOU CHOOSE ONE! It is vastly important to our music streaming app that we know whether your skin is black or instead that your ancestors came from the Caucus Mountains or Mongolia. THOSE ARE YOUR ONLY OPTIONS PICK ONE! Employee table: required foreign key to the "Department" table. Departments are virtually meaningless and change all the time! Every time you get a new vice president sitting in some operations chair, the first thing he does is change all the Departments around. You've got people in your Employee table whose department has changed 16 times, but they're the same person, aren't they? Oh, and they're not called "Departments" anymore, they're now "Divisions". Did you change your field name? No, you didn't. Of course you didn't. You have some Contractors in your Employee table, don't you? Some ex-employees that you need to keep around so they show up on that one report? Yeah, you do. Of course you do. Fuck ontology.
theptip 4y ago
I like these examples because they concisely express some of the existing ambiguities in human language. Like, I wouldn’t normally call a desk a chair, but if someone is sitting on the table I’m more likely to - in some linguistic contexts. I think you need LLM plus vision to fully solve this.
- Eisenstein 4y ago
  I still haven't figured out what the difference is between 'clothes' and 'clothing'. I know there is one, and the words each work in specific contexts ('I put on my clothes' works vs 'I put on my clothing' does not), but I have no idea how to define the difference. Please don't look it up but if you have any thoughts on the matter I welcome them.
  - Vecr 4y ago
    What's wrong with "I put on my clothing"? Sounds mostly fine, it's just longer.
    - frosted-flakes 4y ago
      It's not idiomatic. No one actually says that.
      - ghaff 4y ago
        I wouldn't say that as an absolute statement, but in US English (at least the regional dialects I'm most familiar with), "throw on some clothes," "the clothes I'm wearing," etc. certainly sound more natural.
  - yamtaddle 4y ago
    To me, "clothing" fits better when it's abstract, bulk, or industrial, "clothes" when it's personal and specific, with grey areas where either's about as good—"I washed my clothes", "I washed my clothing", though even here I think "clothes" works a little better. Meanwhile, "clothing factory" or "clothing retailer" are perfectly natural, even if "clothes" would also be OK there. "I put on my clothing" reads a bit like when business-jargon sneaks into everyday language, like when someone says they "utilized" something (where the situation doesn't technically call for that word, in its traditional sense). It gets the point across but seems a bit off. ... oh shit, I think I just figured out the general guideline: "clothing" feels more correct when it's a supporting part of a noun phrase, not the primary part of a subject or object. "Clothing factory" works well because "clothing" is just the kind of factory. "I put on my nicest clothes" reads better than "I put on my nicest clothing" because clothes/clothing itself is the object.
narrationbox 4y ago
> Recognizes "human" and recognizes "desk". I sit on desk. Does AI mark it as a desk or as a chair? Not an issue if the image segmentation is advanced enough. You can train the model to understand "human sitting". It may not generalize to other animals sitting but human action recognition is perfectly possible right now.
spacedcowboy 4y ago
Thirty years ago, I was doing an object-recognition PhD. It goes without saying that the field has moved on a lot from back then, but even then hierarchical and comparative classification was a thing. I used to have the Bayesian maths to show the information content of relationships, but in the decades of moving (continent, even) it's been lost. I still have the code because I burnt CD's, but the results of hours spent writing TeX to produce horrendous-looking equations have long since disappeared... The basics of it were to segment and classify using different techniques, and to model relationships between adjacent regions of classification. Once you could calculate the information content of one conformation, you could compare with others. One of the breakthroughs was when I started modeling the relationships between properties of neighboring regions of the image as part of the property-state of any given region. The basic idea was the center/surround nature of the eye's processing. My reasoning was that if it worked there, it would probably be helpful with the neural nets I was using... It boosted the accuracy of the results by (from memory) ~30% over and above what would be expected from the increase in general information load being presented to the inference engines. This led to a finer-grain of classification so we could model the relationships (and derive information-content from connectedness). It would, I think, cope pretty well with your hypothetical scenario. At the time I was using a blackboard[1] for what I called 'fusion' - where I would have multiple inference engines running using a firing-condition model. As new information came in from the lower levels, they'd post that new info to the blackboard, and other (differing) systems (KNN, RBF, MLP, ...) would act (mainly) on the results of processing done at a lower tier and post their own conclusions back to the blackboard. Lather, rinse, repeat. There were some that were skip-level, so raw data could continue to be available at the higher levels too. That was the space component. We also had time-component inferencing going on. The information vectors were put into time-dependent neural networks, as well as more classical averaging code. Again, a blackboard system was working, and again we had lower and higher levels of inference engine. This time we had relaxation labelling, Kalman filters, TDNNs and optic flow (in feature-space). These were also engaged in prediction modeling, so as objects of interest were occluded, there would be an expectation of where they were, and even when not occluded, the prediction of what was supposed to be where would play into a feedback loop for the next time around the loop. All this was running on a 30MHz DECstation 3100 - until we got an upgrade to SGI Indy's <-- The original Macs, given that OSX is unix underneath... I recall moving to Logica (signal processing group) after my PhD, and it took a week or so to link up a camera (an IndyCam, I'd asked for the same machine I was used to) to point out of my window and start categorizing everything it could see. We had peacocks in the grounds (Logica's office was in Cobham, which meant my commute was always against the traffic, which was awesome), which were always a challenge because of how different they could look based on the sun at the time. Trees, bushes, cars, people, different weather conditions - it was pretty good at doing all of them because of its adaptive/constructive nature, and it got to the point where we'd save off whatever it didn't manage to classify (or was at low confidence) to be included back into the model. By constructive, I mean the ability to infer that the region X is mislabelled as 'tree' because the surrounding/adjacent regions are labelled as 'peacock' and there are no other connected 'tree' regions... The system was rolled out as a demo of the visual programming environment we were using at the time, to anyone coming by the office... It never got taken any further, of course... Logica's senior management were never that savvy about potential, IMHO :) My old immediate boss from Logica (and mentor) is now the Director of Innovation at the centre for vision, speech, and signal processing at Surrey university in the UK. He would disagree with you, I think, on the categorization side of your argument. It's been a focus of his work for decades, and I played only a small part in that - quickly realizing that there was more money to be made elsewhere :) 1:https://en.wikipedia.org/wiki/Blackboard_system https://en.wikipedia.org/wiki/Blackboard_system
skibidibipiti 4y ago
[dead]
jjk166 4y ago
There are lots of things people sit on that we would not categorize as chairs. For example if someone sits on the ground, Earth has not become a chair. Even if something's intended purpose is sitting, calling a car seat or a barstool a chair would be very unnatural. If someone were sitting on a desk, I would not say that it has ceased to be a desk nor that it is now a chair. At most I'd say a desk can be used in the same manner as a chair. Certainly I would not in general want an AI tasked with object recognition to label a desk as a chair. If your goal was to train an AI to identify places a human could sit, you'd presumably feed it different training data.
- devoutsalsa 4y ago
  This reminds me of some random Reddit post that says it makes sense to throw things on the floor. The floor is the biggest shelf in the room.
  - JumpCrisscross 4y ago
    > Reddit post that says it makes sense to throw things on the floor Floor as storage, floor as transport and floor as aesthetic space are three incompatible views of the same of object. The latter two being complementary usually outweighs the first, however.
    - number6 4y ago
      Never was there a more compelling argument to tidy up.
      - toss1 4y ago
        Ha! right; don't overload the metaphysics!
    - cwillu 4y ago
      Let me introduce you to the great american artform: the automobile. Storage, transport, and aesthetic, all in one!
dQw4w9WgXcQ 4y ago
> When does AI switch to "chair"? You could ask my gf the same question
yamtaddle 4y ago
"Do chairs exist?" https://www.youtube.com/watch?v=fXW-QjBsruE https://www.youtube.com/watch?v=fXW-QjBsruE Perhaps the desk is "chairing" in those moments. [EDIT] A little more context for those who might not click on a rando youtube link: it's basically an entertaining, whirlwind tour of the philosophy of categorizing and labeling things, explaining various points of view on the topic, then poking holes in them or demonstrating their limitations.
- malfist 4y ago
  I knew this was a vsauce video before I even clicked on the link, haha. Vsause is awesome for mindboggling.
- dredmorbius 4y ago
  That was a remarkably good VSauce video. I had what turned out to be a fairly satisfying thread about it on Diaspora* at the time: <https://diaspora.glasswings.com/posts/65ff95d0fe5e013920f2002590d8e506 https://diaspora.glasswings.com/posts/65ff95d0fe5e013920f200...> TL;DR: I take a pragmatic approach.
kibwen 4y ago
> Which is also why AI cannot perceive the world the way we do: no metaphysics. Let's not give humans too much credit; the internet is rife with endless "is a taco a sandwich?" and "does a bowl of cereal count as soup?" debates. :P
- throwanem 4y ago
  Yeah, we're a lot better at throwing MetaphysicalUncertaintyErrors than ML models are.
pphysch 4y ago
When the AI "marks" a region as a chair, it is saying "chair" is the key with the highest confidence value among some stochastic output vector. It's fuzzy. A sophisticated monitoring system would access the output vectors directly to mitigate volatility of the first rank.
- deleted 4y ago
  [deleted]
edgyquant 4y ago
You’re over thinking it while assuming things have one label. It recognizes it as a desk which is a “thing that other things sit on.”
anigbrowl 4y ago
That's why I think AGI is more likely to emerge from autonomous robots than in the data center. Less the super-capable industrial engineering of companies like Boston Dynamics, more like the toy/helper market for consumers, more like like Sony's Aibo reincarnated as a raccoon or monkey - big enough to be be safely played with or to help out with light tasks, small enough that it has to navigate its environment from first principles and ask for help in many contexts.
amelius 4y ago
The error is in asking for a categorization. Categorizations always fail, ask any biologist.
bnralt 4y ago
> In other words, we have made great strides in teaching AI "physics" or "recognition", but we have made very little progress in teaching it metaphysics (categories, in this case) because half the people working on the problem don't even recognize metaphysics as a category even though without it, they could not perceive the world. A bold claim, but I'm not sure it's one that accurately matches reality. It reminds me of reading about attempts in the 80's to construct AI by having linguists come in and trying to develop rules for the system. From my experience, current methods of developing AI are a lot closer to how most humans think and interact with the world than academic philosophy is. Academic philosophy might be fine, but it's quite possible it's no more useful for navigating the world than the debates over theological minutiae have been.

feoren 4y ago

> we have made very little progress in teaching it metaphysics (categories, in this case) That's because ontology, metaphysics, categorization, and all that, is completely worthless bullshit. It's a crutch our limited human brains use, and it causes all sorts of problems. Half of what I do in data modeling is trying to fight against all of the worthless categorizations I come across. There Is No Shelf. Why are categories so bad? Two reasons: 1. They're too easily divorced from their models. Is a tomato a fruit? The questions is faulty: there's no such thing as a "fruit" without a model behind it. When people say "botanically, a tomato is a fruit", they're identifying their model: botany. Okay, are you bio-engineering plants? Or are you cooking dinner? You're cooking dinner. So a tomato is not a fruit. Because when you're cooking dinner, your model is not Botany, it's something culinary, and in any half-decent culinary model, a tomato is a vegetable, not a fruit. So unless we're bio-engineering some plants, shut the hell up about a tomato being a fruit. It's not wisdom/intelligence, it's spouting useless mouth-garbage. And remember that all models are wrong, but some models are useful. Some! Not most. Most models are shit. Categories divorced from a model are worthless, and categories of a shit model are shit. 2. Even good categories of useful models have extremely fuzzy boundaries, and we too often fall into the false dichotomy of thinking something must either "be" or "not be" part of a category. Is an SUV a car? Is a car with a rocket engine on it still a car? Is a car with six wheels still a car? Who cares!? If you're charging tolls for your toll bridge, you instead settle for some countable characteristic like number of axles, and you amend this later if you start seeing lots of vehicles with something that stretches your definition of "axle". In fact the category "car" is worthless most of the time. It's an OK noun, but nouns are only averages; only mental shortcuts to a reasonable approximation of the actual object. If you ever see "class Car : Vehicle", you know you're working in a shit, doomed codebase. And yet you waste time arguing over the definitions of these shit, worthless categories. These worthless things become central to your database and software designs and object hierarchies. Of course you end up with unmaintainable shit. Edit: Three reasons! 3. They're always given too much weight. Male/female: PICK ONE. IT'S VERY IMPORTANT THAT YOU CHOOSE ONE! It is vastly important to our music streaming app that we know whether your skin is black or instead that your ancestors came from the Caucus Mountains or Mongolia. THOSE ARE YOUR ONLY OPTIONS PICK ONE! Employee table: required foreign key to the "Department" table. Departments are virtually meaningless and change all the time! Every time you get a new vice president sitting in some operations chair, the first thing he does is change all the Departments around. You've got people in your Employee table whose department has changed 16 times, but they're the same person, aren't they? Oh, and they're not called "Departments" anymore, they're now "Divisions". Did you change your field name? No, you didn't. Of course you didn't. You have some Contractors in your Employee table, don't you? Some ex-employees that you need to keep around so they show up on that one report? Yeah, you do. Of course you do. Fuck ontology.

theptip 4y ago

I like these examples because they concisely express some of the existing ambiguities in human language. Like, I wouldn’t normally call a desk a chair, but if someone is sitting on the table I’m more likely to - in some linguistic contexts. I think you need LLM plus vision to fully solve this.

Eisenstein 4y ago
I still haven't figured out what the difference is between 'clothes' and 'clothing'. I know there is one, and the words each work in specific contexts ('I put on my clothes' works vs 'I put on my clothing' does not), but I have no idea how to define the difference. Please don't look it up but if you have any thoughts on the matter I welcome them.
- Vecr 4y ago
  What's wrong with "I put on my clothing"? Sounds mostly fine, it's just longer.
  - frosted-flakes 4y ago
    It's not idiomatic. No one actually says that.
    - ghaff 4y ago
      I wouldn't say that as an absolute statement, but in US English (at least the regional dialects I'm most familiar with), "throw on some clothes," "the clothes I'm wearing," etc. certainly sound more natural.
- yamtaddle 4y ago
  To me, "clothing" fits better when it's abstract, bulk, or industrial, "clothes" when it's personal and specific, with grey areas where either's about as good—"I washed my clothes", "I washed my clothing", though even here I think "clothes" works a little better. Meanwhile, "clothing factory" or "clothing retailer" are perfectly natural, even if "clothes" would also be OK there. "I put on my clothing" reads a bit like when business-jargon sneaks into everyday language, like when someone says they "utilized" something (where the situation doesn't technically call for that word, in its traditional sense). It gets the point across but seems a bit off. ... oh shit, I think I just figured out the general guideline: "clothing" feels more correct when it's a supporting part of a noun phrase, not the primary part of a subject or object. "Clothing factory" works well because "clothing" is just the kind of factory. "I put on my nicest clothes" reads better than "I put on my nicest clothing" because clothes/clothing itself is the object.

narrationbox 4y ago

> Recognizes "human" and recognizes "desk". I sit on desk. Does AI mark it as a desk or as a chair? Not an issue if the image segmentation is advanced enough. You can train the model to understand "human sitting". It may not generalize to other animals sitting but human action recognition is perfectly possible right now.

spacedcowboy 4y ago

Thirty years ago, I was doing an object-recognition PhD. It goes without saying that the field has moved on a lot from back then, but even then hierarchical and comparative classification was a thing. I used to have the Bayesian maths to show the information content of relationships, but in the decades of moving (continent, even) it's been lost. I still have the code because I burnt CD's, but the results of hours spent writing TeX to produce horrendous-looking equations have long since disappeared... The basics of it were to segment and classify using different techniques, and to model relationships between adjacent regions of classification. Once you could calculate the information content of one conformation, you could compare with others. One of the breakthroughs was when I started modeling the relationships between properties of neighboring regions of the image as part of the property-state of any given region. The basic idea was the center/surround nature of the eye's processing. My reasoning was that if it worked there, it would probably be helpful with the neural nets I was using... It boosted the accuracy of the results by (from memory) ~30% over and above what would be expected from the increase in general information load being presented to the inference engines. This led to a finer-grain of classification so we could model the relationships (and derive information-content from connectedness). It would, I think, cope pretty well with your hypothetical scenario. At the time I was using a blackboard[1] for what I called 'fusion' - where I would have multiple inference engines running using a firing-condition model. As new information came in from the lower levels, they'd post that new info to the blackboard, and other (differing) systems (KNN, RBF, MLP, ...) would act (mainly) on the results of processing done at a lower tier and post their own conclusions back to the blackboard. Lather, rinse, repeat. There were some that were skip-level, so raw data could continue to be available at the higher levels too. That was the space component. We also had time-component inferencing going on. The information vectors were put into time-dependent neural networks, as well as more classical averaging code. Again, a blackboard system was working, and again we had lower and higher levels of inference engine. This time we had relaxation labelling, Kalman filters, TDNNs and optic flow (in feature-space). These were also engaged in prediction modeling, so as objects of interest were occluded, there would be an expectation of where they were, and even when not occluded, the prediction of what was supposed to be where would play into a feedback loop for the next time around the loop. All this was running on a 30MHz DECstation 3100 - until we got an upgrade to SGI Indy's <-- The original Macs, given that OSX is unix underneath... I recall moving to Logica (signal processing group) after my PhD, and it took a week or so to link up a camera (an IndyCam, I'd asked for the same machine I was used to) to point out of my window and start categorizing everything it could see. We had peacocks in the grounds (Logica's office was in Cobham, which meant my commute was always against the traffic, which was awesome), which were always a challenge because of how different they could look based on the sun at the time. Trees, bushes, cars, people, different weather conditions - it was pretty good at doing all of them because of its adaptive/constructive nature, and it got to the point where we'd save off whatever it didn't manage to classify (or was at low confidence) to be included back into the model. By constructive, I mean the ability to infer that the region X is mislabelled as 'tree' because the surrounding/adjacent regions are labelled as 'peacock' and there are no other connected 'tree' regions... The system was rolled out as a demo of the visual programming environment we were using at the time, to anyone coming by the office... It never got taken any further, of course... Logica's senior management were never that savvy about potential, IMHO :) My old immediate boss from Logica (and mentor) is now the Director of Innovation at the centre for vision, speech, and signal processing at Surrey university in the UK. He would disagree with you, I think, on the categorization side of your argument. It's been a focus of his work for decades, and I played only a small part in that - quickly realizing that there was more money to be made elsewhere :) 1:https://en.wikipedia.org/wiki/Blackboard_system https://en.wikipedia.org/wiki/Blackboard_system

skibidibipiti 4y ago

[dead]

jjk166 4y ago

There are lots of things people sit on that we would not categorize as chairs. For example if someone sits on the ground, Earth has not become a chair. Even if something's intended purpose is sitting, calling a car seat or a barstool a chair would be very unnatural. If someone were sitting on a desk, I would not say that it has ceased to be a desk nor that it is now a chair. At most I'd say a desk can be used in the same manner as a chair. Certainly I would not in general want an AI tasked with object recognition to label a desk as a chair. If your goal was to train an AI to identify places a human could sit, you'd presumably feed it different training data.

devoutsalsa 4y ago
This reminds me of some random Reddit post that says it makes sense to throw things on the floor. The floor is the biggest shelf in the room.
- JumpCrisscross 4y ago
  > Reddit post that says it makes sense to throw things on the floor Floor as storage, floor as transport and floor as aesthetic space are three incompatible views of the same of object. The latter two being complementary usually outweighs the first, however.
  - number6 4y ago
    Never was there a more compelling argument to tidy up.
    - toss1 4y ago
      Ha! right; don't overload the metaphysics!
  - cwillu 4y ago
    Let me introduce you to the great american artform: the automobile. Storage, transport, and aesthetic, all in one!

dQw4w9WgXcQ 4y ago

> When does AI switch to "chair"? You could ask my gf the same question

yamtaddle 4y ago

"Do chairs exist?" https://www.youtube.com/watch?v=fXW-QjBsruE https://www.youtube.com/watch?v=fXW-QjBsruE Perhaps the desk is "chairing" in those moments. [EDIT] A little more context for those who might not click on a rando youtube link: it's basically an entertaining, whirlwind tour of the philosophy of categorizing and labeling things, explaining various points of view on the topic, then poking holes in them or demonstrating their limitations.

malfist 4y ago
I knew this was a vsauce video before I even clicked on the link, haha. Vsause is awesome for mindboggling.
dredmorbius 4y ago
That was a remarkably good VSauce video. I had what turned out to be a fairly satisfying thread about it on Diaspora* at the time: <https://diaspora.glasswings.com/posts/65ff95d0fe5e013920f2002590d8e506 https://diaspora.glasswings.com/posts/65ff95d0fe5e013920f200...> TL;DR: I take a pragmatic approach.

kibwen 4y ago

> Which is also why AI cannot perceive the world the way we do: no metaphysics. Let's not give humans too much credit; the internet is rife with endless "is a taco a sandwich?" and "does a bowl of cereal count as soup?" debates. :P

throwanem 4y ago
Yeah, we're a lot better at throwing MetaphysicalUncertaintyErrors than ML models are.

pphysch 4y ago

When the AI "marks" a region as a chair, it is saying "chair" is the key with the highest confidence value among some stochastic output vector. It's fuzzy. A sophisticated monitoring system would access the output vectors directly to mitigate volatility of the first rank.

deleted 4y ago
[deleted]

edgyquant 4y ago

You’re over thinking it while assuming things have one label. It recognizes it as a desk which is a “thing that other things sit on.”

anigbrowl 4y ago

That's why I think AGI is more likely to emerge from autonomous robots than in the data center. Less the super-capable industrial engineering of companies like Boston Dynamics, more like the toy/helper market for consumers, more like like Sony's Aibo reincarnated as a raccoon or monkey - big enough to be be safely played with or to help out with light tasks, small enough that it has to navigate its environment from first principles and ask for help in many contexts.

amelius 4y ago

The error is in asking for a categorization. Categorizations always fail, ask any biologist.

bnralt 4y ago

> In other words, we have made great strides in teaching AI "physics" or "recognition", but we have made very little progress in teaching it metaphysics (categories, in this case) because half the people working on the problem don't even recognize metaphysics as a category even though without it, they could not perceive the world. A bold claim, but I'm not sure it's one that accurately matches reality. It reminds me of reading about attempts in the 80's to construct AI by having linguists come in and trying to develop rules for the system. From my experience, current methods of developing AI are a lot closer to how most humans think and interact with the world than academic philosophy is. Academic philosophy might be fine, but it's quite possible it's no more useful for navigating the world than the debates over theological minutiae have been.