An understanding of AI’s limitations is starting to sink in

Post by p1esk » Mon, Jun 22, 2020, 7:04 PM UTC

inimino wrote: post #400

p1esk wrote: post #399

Earlier quoted context omitted.

To fool you for 20 questions, yes. I'm ~70% confident, so I'll bet you $100 :) To clarify, the common sense problem is a hard one. It is similar to level 5 autonomy driving. That will take a while to solve. But what we are talking about here is kinda like Waymo cars which can drive themselves in ideal weather at slow speeds in Arizona. So in 2.5 years I think the best chatbots will be as far from having common sense…

You're on for $100. I'm > 99% confident that I won't be fooled by any AI before 2023, and would have bet any amount that I could afford to set aside. Ideal weather at slow speeds, with a professional human driver throwing road-condition curveballs at you and challenging your responses? I like my chances.

Post by abj » Wed, Jun 24, 2020, 12:58 PM UTC

levitatorius wrote: post #340

abj wrote: post #97

Earlier quoted context omitted.

There's value, the tech works, but applying it is surprisingly hard. I met someone who dedicates their life to using machine learning to replace/aid/automate pathologists 6+ hour days searching for cancer tumors in lungs. They have been at it for 5 years. There is an insane amount of approvals, red tape, knowing the right people, convincing the hospital to use it - all tasks not related to the tech actually working.…

I am a practicing pathologist and I have seen many attempts and publications to use ML in pathology, which all lack in these aspects: 1. ML is trained on simplified sets (preselected ROIs, limited choice of diagnoses), 2. ML is biased by the experts who labeled learning sets, 3. there is no obvious process of learning from failures after initial training, 4. who is responsible in case of ML error with substantial con…