3 ms·

This is the other side of the classic XKCD "Tasks" (https://xkcd.com/1425/ https://xkcd.com/1425/). A non-technical person in 2014 (when the above was original

This is the other side of the classic XKCD "Tasks" (https://xkcd.com/1425/ https://xkcd.com/1425/). A non-technical person in 2014 (when the above was originally published) would likely have the same conception of the difficulty of recognizing a bird from an image as they would in 2022, even though the task itself has gone from near-insurmountable to off-the-shelf-library in eight years. Even as Imagen and Dall-E 2 amaze us today, these feats will likely be commonplace in a few years. The non-technical may have only a vague sense that their new TikTok filter is doing something that was impossible only a few years prior.

futureshock 4y ago
Exactly and I was thinking of that XKCD. Very much case in point, I have the Merlin Bird ID app which can determine species from ridiculously blurry photos and can also identify hundreds of birds from their calls alone in noisy environments. In 2014 I would have sworn this would be impossible.
DonHopkins 4y ago
The tooltip you get when you hover your cursor over the comic: "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." I'm working with his son Henry Minsky and other great people at Leela AI on that same old problem, applying hybrid symbolic-connectionist constructivist AI by combining neat neural networks with scruffy symbolic logic to understand video, and it's mind boggling what is possible now: https://leela.ai/ https://leela.ai/ >Our AI system, Leela, is motivated by intrinsic curiosity. Leela creates theories about cause and effect in her world, and then conducts experiments to test these theories. Leela can connect all her knowledge and use this network to make plans, reason about goals, and communicate using grounded natural language. >Leela has at her core a hybrid symbolic-connectionist network. This means that she uses a dynamic combination of artificial neural networks and symbol networks to learn. Hybrid networks open the door to AI agents that can build their own abstractions on the fly, while still taking full advantage of the power of deep learning. https://en.wikipedia.org/wiki/Neats_and_scruffies https://en.wikipedia.org/wiki/Neats_and_scruffies >Neats and scruffies: Neat and scruffy are two contrasting approaches to artificial intelligence (AI) research. The distinction was made in the 70s and was a subject of discussion until the middle 80s. In the 1990s and 21st century AI research adopted "neat" approaches almost exclusively and these have proven to be the most successful. >"Neats" use algorithms based on formal paradigms such as logic, mathematical optimization or neural networks. Neat researchers and analysts have expressed the hope that a single formal paradigm can be extended and improved to achieve general intelligence and superintelligence. >"Scruffies" use any number of different algorithms and methods to achieve intelligent behavior. Scruffy programs may require large amounts of hand coding or knowledge engineering. Scruffies have argued that the general intelligence can only be implemented by solving a large number of essentially unrelated problems, and that there is no magic bullet that will allow programs to develop general intelligence autonomously. >The neat approach is similar to physics, in that it uses simple mathematical models as its foundation. The scruffy approach is more like biology, where much of the work involves studying and categorizing diverse phenomena. We're looking for talented engineers and designers to help, including neats and scruffies working together! https://leela.ai/jobs/ https://leela.ai/jobs/

futureshock 4y ago

Exactly and I was thinking of that XKCD. Very much case in point, I have the Merlin Bird ID app which can determine species from ridiculously blurry photos and can also identify hundreds of birds from their calls alone in noisy environments. In 2014 I would have sworn this would be impossible.

DonHopkins 4y ago

The tooltip you get when you hover your cursor over the comic: "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." I'm working with his son Henry Minsky and other great people at Leela AI on that same old problem, applying hybrid symbolic-connectionist constructivist AI by combining neat neural networks with scruffy symbolic logic to understand video, and it's mind boggling what is possible now: https://leela.ai/ https://leela.ai/ >Our AI system, Leela, is motivated by intrinsic curiosity. Leela creates theories about cause and effect in her world, and then conducts experiments to test these theories. Leela can connect all her knowledge and use this network to make plans, reason about goals, and communicate using grounded natural language. >Leela has at her core a hybrid symbolic-connectionist network. This means that she uses a dynamic combination of artificial neural networks and symbol networks to learn. Hybrid networks open the door to AI agents that can build their own abstractions on the fly, while still taking full advantage of the power of deep learning. https://en.wikipedia.org/wiki/Neats_and_scruffies https://en.wikipedia.org/wiki/Neats_and_scruffies >Neats and scruffies: Neat and scruffy are two contrasting approaches to artificial intelligence (AI) research. The distinction was made in the 70s and was a subject of discussion until the middle 80s. In the 1990s and 21st century AI research adopted "neat" approaches almost exclusively and these have proven to be the most successful. >"Neats" use algorithms based on formal paradigms such as logic, mathematical optimization or neural networks. Neat researchers and analysts have expressed the hope that a single formal paradigm can be extended and improved to achieve general intelligence and superintelligence. >"Scruffies" use any number of different algorithms and methods to achieve intelligent behavior. Scruffy programs may require large amounts of hand coding or knowledge engineering. Scruffies have argued that the general intelligence can only be implemented by solving a large number of essentially unrelated problems, and that there is no magic bullet that will allow programs to develop general intelligence autonomously. >The neat approach is similar to physics, in that it uses simple mathematical models as its foundation. The scruffy approach is more like biology, where much of the work involves studying and categorizing diverse phenomena. We're looking for talented engineers and designers to help, including neats and scruffies working together! https://leela.ai/jobs/ https://leela.ai/jobs/