Deep learning job postings have collapsed in the past six months

In my experience a startup of a certain size is founders and sales doing razzle-dazzle, "deeply technical people" jerking off to the latest fad, and no customers.

*PhD or Ph.D.

It's an abbreviation of the Latin "philosophiae doctor" == "doctor of philosophy".

Getting one is as much about persistence as intelligence, and makes you very knowledgeable about one narrow area, so what you say makes sense. Academic researchers usually branch out into other fields and subfields as well, but straight out of a PhD, narrow and deep is what you tend to get.

Most data-related problems, or extraction of knowledge from data, simply doesn't benefit from Deep Learning.

In my experience, what many organizations lack is simple but high-quality "Business Analytics": Reporting & dashboards are developed that look good but jam too much information together. It is often the wrong information:

Something is requested, and the developer develops exactly what was asked. The problem is that it wasn't what was needed because the person making the request couldn't articulate the question in the same terms the developer would understand. The request will say "Give me X & Y" when the real question is "I want to understand the impact of Y on X". The person gets X & Y, looks at it every day in their dashboard, and never sees much that is useful. The initial request should always be the start of a conversation, but that often doesn't happen. A common result are people in departments spending tons of time in Excel sorting, counting, making pivot tables, etc., when all of that could be automated.

This is part of the reason why companies often go looking for some new "silver bullet" to solve their data problems. They don't have the basics down, and don't understand the data problems well enough to seek out a solution.

I've been using voice commands on my android phone, in situations where I can't use my hands. Most often all I want to do is.

1. Start and stop a podcast.

2. Play music

3. Ask for the time

The phone understands me, but then android breaks the flow, so I have to use my hands.

1. It will ask me to unlock the phone first? I have gloves and a mask on. It won't recognize my face, and my gloves don't register touches. Why do I have to unclock the phone to play music in the first place.

2. It gets confused on which app to play the music/podcast on. Wants to open youtube app, or spotify, and so on ...

3. Not consistent. I can say the same thing, and sometimes it will do one things, and another next time.

4. If I'm playing a video, and I want to show it full screen. I have to maximize and touch the screen. Why can't it play full screen be default.

The way I see it, only those companies that had already been using a data oriented approach to business can really reap the benefits of ML. From a company's point of view, ML/AI should be a natural evolution of an existing tool set to better solve problems they have been trying to solve in the past using deterministic methods and then statistical methods, etc. Any other project that is diving right into ML is likely to fail because

1. There's no clear problem statement. They have never formulated one and now trying to bolt ML on to their decision making.

2. They don't have well catalogued data for engineers/scientists to work with because they never tried to do rigorous analysis of data before ML became a thing.

3. Managers have no idea how to deal with data driven insights. What if the results are completely unintuitive to them? Are they going to change their processes abruptly? What if the results are aligned with what they have been always doing? Is it worth paying for something that they have been doing intuitively for decades?

I'm not a data scientist. But the biggest complaint I hear from my colleagues is that they lack data to train models.