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> some magic AI that will do everything perfectly Total straw man. He actually uses an intern as an example in the very next sentence after what you quoted, as

> some magic AI that will do everything perfectly Total straw man. He actually uses an intern as an example in the very next sentence after what you quoted, as you would expect them to be able to read and get up to speed on a new area regardless of what it was. Meanwhile SOTA in NLP is a system that can be built to answer a single kind of question but can't explain why it did so or do anything useful if given an explanation of why its answer was wrong.

Mathnerd314 7y ago
There are deep models like BERT that do pre-training and then need minimal training to do multiple tasks such as question answering, entailment, sentiment analysis, etc. I don't know about "explaining" an answer but there are debuggers that find errors in data sets: https://arxiv.org/pdf/1603.07292.pdf https://arxiv.org/pdf/1603.07292.pdf. But as I said, I don't see why an artist would suddenly get up to speed as a construction worker. He seems to overestimate the capacity of interns as well.
- inimino 7y ago
  Deeply familiar with BERT. It lacks the very ability he is describing, to adapt itself, get up to speed, and collect relevant information in a new field, because that's simply not how it works. It can't possibly explain itself because it lacks any mechanism of introspection that could possibly give it that ability. It's an expensive way of gaining a very accurate language model that can be tweaked and get good results on a lot of tasks, but it doesn't understand what it's doing. It can't argue for its position or explain why it thinks whatever it thinks. It's not operating on that level of reasoning, at all. An artist understands the goals of construction work, and can pick up the skills necessary along the way, because we can understand a goal and have a wide variety of cognitive tools to let us know how we are doing. If you've worked closely with BERT you already know that interns have nothing to worry about, not just from the current crop of tools that includes BERT, but from the entire line of deep learning research, short of a sudden and dramatic shift in direction.

Mathnerd314 7y ago

There are deep models like BERT that do pre-training and then need minimal training to do multiple tasks such as question answering, entailment, sentiment analysis, etc. I don't know about "explaining" an answer but there are debuggers that find errors in data sets: https://arxiv.org/pdf/1603.07292.pdf https://arxiv.org/pdf/1603.07292.pdf. But as I said, I don't see why an artist would suddenly get up to speed as a construction worker. He seems to overestimate the capacity of interns as well.

inimino 7y ago
Deeply familiar with BERT. It lacks the very ability he is describing, to adapt itself, get up to speed, and collect relevant information in a new field, because that's simply not how it works. It can't possibly explain itself because it lacks any mechanism of introspection that could possibly give it that ability. It's an expensive way of gaining a very accurate language model that can be tweaked and get good results on a lot of tasks, but it doesn't understand what it's doing. It can't argue for its position or explain why it thinks whatever it thinks. It's not operating on that level of reasoning, at all. An artist understands the goals of construction work, and can pick up the skills necessary along the way, because we can understand a goal and have a wide variety of cognitive tools to let us know how we are doing. If you've worked closely with BERT you already know that interns have nothing to worry about, not just from the current crop of tools that includes BERT, but from the entire line of deep learning research, short of a sudden and dramatic shift in direction.