| > As long as these issues are not tackled or not researched well enough, then we'll be pretty much be heading into another AI Winter and the hype cycle will go through its trough of disillusionment phase. It's not all or nothing as you present it. ML models can be useful even if they are imperfect - and we should not forget humans aren't perfect either. For example, a model could reduce 50% of the time necessary to enter an invoice into the database. It's imperfect, yet useful. A model need not run alone without any safety. It can have plain old programming rules to validate its outputs, or use human in the loop. > Sure, all you see right now are other students and startups 'applying' deep learning everywhere, but they are hardly advancing the field unlike DeepMind and OpenAI are. On the contrary, I would say that what DeepMind and OpenAI are doing is largely irrelevant for industry. There is a huge number of domains where no ML model has been created, and that is because there are so few people who can make them. The low hanging fruit hasn't been picked yet. It's like electricity at the beginning of the 20th century. The work these students and startups are doing is the good, useful work. You don't need DeepMind grade models to solve most real problems. > creating a AI startup now requires using Google, Amazon or Microsofts data center's for training You can train most useful models on a single machine today. Some, like Logistic Regression, train in seconds or minutes. Others take an hour, or a day. Some heavy ones take a week to train. If you don't do hyper-parameter search or cutting edge research you only need a few runs to get a working model. It's data tagging that usually takes months or years. |