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> 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 t

> 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.

> 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.