3 ms·
I think the "confident but inaccurate" problem is one of the biggest hurdles for most of the powerful usecases of these models. We don't seem too far from being
by rurp 4y ago
I think the "confident but inaccurate" problem is one of the biggest hurdles for most of the powerful usecases of these models. We don't seem too far from being able to automate filler text or stock images, which is certainly useful but only so valuable, but a long way off from more technical usecases like law or medicine. Being right 90% of the time but completely wrong 10%, without any way to distinguish the two, isn't going to get very far in many fields of work.
- schwoll 4y agoI had it give me book recommendations on a particular topic. It straight up made up a fake book with a fake author and attributed it to a publisher. It did this with confidence. I even emailed the publisher to see if they had ever carried that book. Never heard of it. The problem seems to be whether or not the content was generated via language modeling or via direct references. When it crosses into language modeling it is just making stuff up on the fly. This sometimes works for programming, but for specific things that connect to the real world it can be phenomenally wrong.