An understanding of AI’s limitations is starting to sink in
The trouble is that people have been sold this idea that ML&#x2F;AI can do amazing things, without properly being told that really the things it can do are quite narrowly-scoped.  They&#x27;ve been sold the Star Trek computer idea.<p>For example, years ago I was working on a prototype&#x2F;proof-of-concept thing for instrumenting industrial machinery with stick-on small computers.  Simple stuff - attach accelerators, temperature, humidity etc sensors to existing machines and collect the data and send it back.<p>The management thought we&#x27;d be able to apply machine learning on the data to get &quot;business insights&quot; from the all-powerful machine. They didn&#x27;t know what these insights might be, just that ML&#x2F;AI would generate them and therefore make the business a fuck-ton of money because AI generated novel new &quot;business insights&quot; that no one had thought of before and so transform the business. They thought it was just a magic box that would generate unbounded magic answers for their needs by passing in just some temperature and humidity readings or whatever, and then it would tell them they need to make more brown bread and less bagels in the North East region etc.<p>In reality, as I understand it, currently ML&#x2F;AI requires us to know what the possible answers can be before we even begin training the network.  So the classic example is it needs to know that the possible MNIST digits are 0-9, or that you are looking for one of 100 image classes etc.<p>You cant train a network with the MNIST digits, and then have that network tell you what shares to buy or sell.<p>Sure you can lop off the final layer and repurpose some of the middle layers, but you still need to train it to classify the inputs into categories you define up front. It won&#x27;t give you a novel answer that you have not trained it for.<p>... at least that is how I understand it.  Things may have changed over the past 5 years or so.<p>That said, I do agree there have been some cool things lately like machine vision etc. I don&#x27;t think it will be that huge an industry though - it feels like a lot of it is largely just commoditised now (which is good) and it will be just like any other library you pick - like picking a UI framework for a web app. Just pick up a pre-trained network from modelzoo and get on with your real business requirements for 99% of people using ML, while the other 1% (at FAANGs et al) and academia churn out new models.