That Tim Urban piece is great. It's also an interesting time capsule in terms of which AI problems were and were not considered hard in 2015 (when the post was written). From the post:
> Build a computer that can multiply two ten-digit numbers in a split second—incredibly easy. Build one that can look at a dog and answer whether it’s a dog or a cat—spectacularly difficult. Make AI that can beat any human in chess? Done. Make one that can read a paragraph from a six-year-old’s picture book and not just recognize the words but understand the meaning of them? Google is currently spending billions of dollars trying to do it. Hard things—like calculus, financial market strategy, and language translation—are mind-numbingly easy for a computer, while easy things—like vision, motion, movement, and perception—are insanely hard for it.
The children's picture book problem is solved; those billions of dollars were well-spent after all. (See, e.g., DeepMind's recent Flamingo model [1].) We can do whatever we want in vision, more or less [2]. Motion and movement might be the least developed area, but it's still made major progress; we have robotic parkour [3] and physical Rubik's cube solvers [4], and we can tell a robot to follow simple domestic instructions [5]. And Perceiver (again from DeepMind [6]) took a big chunk out of the perception problem.
Getting a computer to carry on a conversation [7], let alone draw art on par with human professionals [8], weren't even mentioned as examples, so laughably out of reach they seemed in the heathen dark ages of... 2015.
And as for recognizing a cat or a dog — that's a problem so trivial today that it isn't even worth using as the very first example in an introductory AI course. [9]
If someone re-wrote this post today, I wonder what sorts of things would go into the "hard for a computer" bucket? And how many of those would be left standing in 2029?
> And as for recognizing a cat or a dog — that's a problem so trivial today
Last time I checked - though it's been a long while I could not check thoroughly owing to other commitments - "«recognizing»" there was "consistently successfully guessing", not "critically defining". It may be that the problem was solved in the latest years, I cannot exclude it - but I have not seen around in the brief "news checking" exercise the signals required for the solution.
> Build a computer that can multiply two ten-digit numbers in a split second—incredibly easy. Build one that can look at a dog and answer whether it’s a dog or a cat—spectacularly difficult. Make AI that can beat any human in chess? Done. Make one that can read a paragraph from a six-year-old’s picture book and not just recognize the words but understand the meaning of them? Google is currently spending billions of dollars trying to do it. Hard things—like calculus, financial market strategy, and language translation—are mind-numbingly easy for a computer, while easy things—like vision, motion, movement, and perception—are insanely hard for it.
The children's picture book problem is solved; those billions of dollars were well-spent after all. (See, e.g., DeepMind's recent Flamingo model [1].) We can do whatever we want in vision, more or less [2]. Motion and movement might be the least developed area, but it's still made major progress; we have robotic parkour [3] and physical Rubik's cube solvers [4], and we can tell a robot to follow simple domestic instructions [5]. And Perceiver (again from DeepMind [6]) took a big chunk out of the perception problem.
Getting a computer to carry on a conversation [7], let alone draw art on par with human professionals [8], weren't even mentioned as examples, so laughably out of reach they seemed in the heathen dark ages of... 2015.
And as for recognizing a cat or a dog — that's a problem so trivial today that it isn't even worth using as the very first example in an introductory AI course. [9]
If someone re-wrote this post today, I wonder what sorts of things would go into the "hard for a computer" bucket? And how many of those would be left standing in 2029?
[1] https://arxiv.org/abs/2204.14198
[2] https://arxiv.org/abs/2004.10934
[3] https://www.youtube.com/watch?v=tF4DML7FIWk
[4] https://openai.com/blog/solving-rubiks-cube/
[5] https://say-can.github.io/
[6] https://www.deepmind.com/open-source/perceiver-io
[7] https://arxiv.org/abs/2201.08239v2
[8] https://openai.com/dall-e-2/
[9] https://www.fast.ai/