Apparently[1] enthusiasm didn’t really ramp up again until 2012, when AlexNet proved shockingly effective at image classification.
I think after the backpropagation paper was published in the eighties enthusiasm did ramp up a lot. Which lead to a lot of important work in the nineties like (mature) CNNs, LSTMs, etc.
I see—I mean, clearly AlexNet didn’t just invent all the algorithms it relied on, I believe the main novel contribution was to train on GPU’s and get it working well enough to blow everything else out of the water?
The fact that it took decades of research to go from the Perceptron to great image classification indicates to me that there might be further decades of research between holding an intelligent-ish conversation and being a human agent level agent. This seems like the natural expectation given the story so far, no?
I think AlexNet wasn’t even the first to win computer vision competitions based on GPU-acceleration but that was definitely the step that jump-started Deep Learning around 2011/2012.
To me it rather seems like agency and intelligence is not very intertwined. Intelligence is the ability to create precise models—this does not imply that you use these models well or in a goal-directed fashion at all.
That we have now started the path down RLing the models to make them pursue the goal of solving math and coding problems in a more directed and effective manner implies to me that we should see inroads to other areas of agentic behavior as well.
Whether that will be slow going or done next year cannot really be decided based on the long history of slowly increasing the intelligence of models because it is not about increasing the intelligence of models.
But the historical difficulty of RL is based on models starting from scratch. Unclear whether moulding a model that already knows how to do all the steps into doing all the steps is anywhere as difficult as using RL to also learn how to do all the steps.
I think after the backpropagation paper was published in the eighties enthusiasm did ramp up a lot. Which lead to a lot of important work in the nineties like (mature) CNNs, LSTMs, etc.
I see—I mean, clearly AlexNet didn’t just invent all the algorithms it relied on, I believe the main novel contribution was to train on GPU’s and get it working well enough to blow everything else out of the water?
The fact that it took decades of research to go from the Perceptron to great image classification indicates to me that there might be further decades of research between holding an intelligent-ish conversation and being a human agent level agent. This seems like the natural expectation given the story so far, no?
I think AlexNet wasn’t even the first to win computer vision competitions based on GPU-acceleration but that was definitely the step that jump-started Deep Learning around 2011/2012.
To me it rather seems like agency and intelligence is not very intertwined. Intelligence is the ability to create precise models—this does not imply that you use these models well or in a goal-directed fashion at all.
That we have now started the path down RLing the models to make them pursue the goal of solving math and coding problems in a more directed and effective manner implies to me that we should see inroads to other areas of agentic behavior as well.
Whether that will be slow going or done next year cannot really be decided based on the long history of slowly increasing the intelligence of models because it is not about increasing the intelligence of models.
Our intuitions here should be informed by the historical difficulty of RL.
But the historical difficulty of RL is based on models starting from scratch. Unclear whether moulding a model that already knows how to do all the steps into doing all the steps is anywhere as difficult as using RL to also learn how to do all the steps.