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I think like anything it's a tool that can be used to great effect. However, I suppose there's a possibility we could become over-reliant on it and blind to th

I think like anything it's a tool that can be used to great effect. However, I suppose there's a possibility we could become over-reliant on it and blind to the sorts of new ideas that you'd generate from manually exploring the data set yourself. For example, if the state-of-the-art AI asteroid discovering program has a blind spot where it never detects a certain type of asteroid, we might stop finding that type of asteroid altogether. Then when the training datasets themselves are mostly derived from asteroids that the AI had previously found, any blind spots and biases could end up self-reinforcing. Then again, is this really all that different to the many human-driven blind spots science has had in the past?

visarga 4y ago
> is this really all that different to the many human-driven blind spots No. Humans are fallible like AI. Most humans can't perfectly apply logical reasoning to common day situations.
- mensetmanusman 4y ago
  They are both fallible because humans supply the data.
  - JoshCole 4y ago
    AI includes supervised learning, but supervised learning is a very narrow part of the field of AI. In some problems, humans don't supply the data. For example, in reinforcement learning, rewards are provided by the environment, but there is no point where humans give labeled examples of what constitutes good play. For an even more extreme example, in evolutionary learning there is no reward provided to the algorithm and there can be no training data visible to the algorithm. In effect, it manages to work as an optimization because sometimes there is a lack of data - something dies before reaching a point where it should produce another generation.
- YeGoblynQueenne 4y ago
  Humans can fail, but humans can also succeed, in applying logical reasoning- not only to common situations, but also to scientific inquiry. That's how we have science, today. If we were only bumbling fools that stumble in the dark we'd still be stuck in dark caves without fire. But we can be -and we often are- much more than that. But not our machine learning systems. Those are stuck in making stupid mistakes and not being able to generalise away from them. For the time being. You can ask GPT-3 a question and it may come up with a smart answer or a dumb answer, and it won't know which is which. But we will. Because, yes, we can make dumb mistakes but we can also understand that they are mistakes, and that they are dumb, and why they are mistakes and dumb, and we can recover from them and stop ourselves from making the same mistakes. We have blind spots, sure, but we can see through them, eventually, with hard work, after a very long time, very haphazardly. But not our machine learning systems. For our machine learning systems the entire world is one big blind spot. That is how humans are different to machine learning systems.
YeGoblynQueenne 4y ago
>> Then again, is this really all that different to the many human-driven blind spots science has had in the past? Yes, because humans can build models that are explanatory as well as predictive, and so can, eventually, get over blind spots. See for example the theory of epicycles and how it was eventually superseded by Kepler's laws and Newton's. The danger with deep learning for science is that we will again be stuck in a rut, relying on (what we think are) good predictive models without explanatory power. In the case of astronomy and the theory of epicycles, the rut lasted more than a thousand years. That is a terrifying thought, considering the mess we currently find ourselves in and the urgent need for true scientific understanding of the world, before we destroy it.

visarga 4y ago

> is this really all that different to the many human-driven blind spots No. Humans are fallible like AI. Most humans can't perfectly apply logical reasoning to common day situations.

mensetmanusman 4y ago
They are both fallible because humans supply the data.
- JoshCole 4y ago
  AI includes supervised learning, but supervised learning is a very narrow part of the field of AI. In some problems, humans don't supply the data. For example, in reinforcement learning, rewards are provided by the environment, but there is no point where humans give labeled examples of what constitutes good play. For an even more extreme example, in evolutionary learning there is no reward provided to the algorithm and there can be no training data visible to the algorithm. In effect, it manages to work as an optimization because sometimes there is a lack of data - something dies before reaching a point where it should produce another generation.
YeGoblynQueenne 4y ago
Humans can fail, but humans can also succeed, in applying logical reasoning- not only to common situations, but also to scientific inquiry. That's how we have science, today. If we were only bumbling fools that stumble in the dark we'd still be stuck in dark caves without fire. But we can be -and we often are- much more than that. But not our machine learning systems. Those are stuck in making stupid mistakes and not being able to generalise away from them. For the time being. You can ask GPT-3 a question and it may come up with a smart answer or a dumb answer, and it won't know which is which. But we will. Because, yes, we can make dumb mistakes but we can also understand that they are mistakes, and that they are dumb, and why they are mistakes and dumb, and we can recover from them and stop ourselves from making the same mistakes. We have blind spots, sure, but we can see through them, eventually, with hard work, after a very long time, very haphazardly. But not our machine learning systems. For our machine learning systems the entire world is one big blind spot. That is how humans are different to machine learning systems.

YeGoblynQueenne 4y ago

>> Then again, is this really all that different to the many human-driven blind spots science has had in the past? Yes, because humans can build models that are explanatory as well as predictive, and so can, eventually, get over blind spots. See for example the theory of epicycles and how it was eventually superseded by Kepler's laws and Newton's. The danger with deep learning for science is that we will again be stuck in a rut, relying on (what we think are) good predictive models without explanatory power. In the case of astronomy and the theory of epicycles, the rut lasted more than a thousand years. That is a terrifying thought, considering the mess we currently find ourselves in and the urgent need for true scientific understanding of the world, before we destroy it.