Ironically, I worked on a product that had a classic use case for machine learning during this time period and still had great difficulty getting results.
It was difficult to attract top ML talent no matter how much we offered. Everyone wanted to work for one of the big, recognizable names in the industry for the resume name recognition and a chance to pivot their way into a top role at a leading company later.
Meanwhile, we were flooded with applicants who exaggerated their ML knowledge and experience to an extreme,
hoping to land high paying ML jobs through hiring managers who couldn’t understand what they were looking for. It was easy to spot most of these candidates after going through some ML courses online and creating a very basic interview problem, but I could see many of these candidates successfully getting ML jobs at companies that didn’t know any better. Maybe they were going to fake it until they made it, or maybe they were counting on ML job performance being notoriously difficult to quantify on big data sets.
Dealing with 3rd party vendors and consulting shops wasn’t much better. A lot of the bigger shops were too busy with never ending lucrative contracts to take on new work. A lot of the smaller shops were too new to be able to show us much of a track record. Their proposals often boiled down to just implementing some famous open source solution on our product and letting us handle the training. Thanks, but we can do that ourselves.
I get the impression that it is (or was) more lucrative to start your own ML company and hope for an acquisition than to do the work for other companies. We tried to engage with several small ML vendors in our space and more than half of them came back with suggestions that we simply acquire them for large sums of money. Meanwhile, one of the vendors we engaged with was acquired by someone else and, of course, their support dried up completely.
Ultimately we found a solution from a vendor that had prepared a nice solution for our exact problem.the contracts were drawn up in a way that wouldn’t be too disastrous if (when?) they were acquired.
I have to wonder if an industry-wide slowdown to the ML frenzy is exactly what we need to give people and companies time to focus on solving real problems instead of just chasing easy money.
I find your post kind of interesting. I develop software in a non-AI field and have been following and experimenting with AI on the side for a long time. Academics seem intent on publishing papers, not finding solutions to creating value. Corporate AI seems focused on sizzle not substance.
It is so frustrating to see the potential in the AI world and realize almost no one is really interested in building it.
I agree that it's a shame that many research results do not get to be "industrialized" and actually used, but also I feel like many research results are created in such a sterile way that they wouldn't be applicable to real world scenarios.
I think what we got really good at is "perceptive" ML, like speech and image recognition, and those things do see industry applications, like self-driving cars or voice assistants.
I'd be interested to know where you see unrealized potential.
It was difficult to attract top ML talent no matter how much we offered. Everyone wanted to work for one of the big, recognizable names in the industry for the resume name recognition and a chance to pivot their way into a top role at a leading company later.
Meanwhile, we were flooded with applicants who exaggerated their ML knowledge and experience to an extreme, hoping to land high paying ML jobs through hiring managers who couldn’t understand what they were looking for. It was easy to spot most of these candidates after going through some ML courses online and creating a very basic interview problem, but I could see many of these candidates successfully getting ML jobs at companies that didn’t know any better. Maybe they were going to fake it until they made it, or maybe they were counting on ML job performance being notoriously difficult to quantify on big data sets.
Dealing with 3rd party vendors and consulting shops wasn’t much better. A lot of the bigger shops were too busy with never ending lucrative contracts to take on new work. A lot of the smaller shops were too new to be able to show us much of a track record. Their proposals often boiled down to just implementing some famous open source solution on our product and letting us handle the training. Thanks, but we can do that ourselves.
I get the impression that it is (or was) more lucrative to start your own ML company and hope for an acquisition than to do the work for other companies. We tried to engage with several small ML vendors in our space and more than half of them came back with suggestions that we simply acquire them for large sums of money. Meanwhile, one of the vendors we engaged with was acquired by someone else and, of course, their support dried up completely.
Ultimately we found a solution from a vendor that had prepared a nice solution for our exact problem.the contracts were drawn up in a way that wouldn’t be too disastrous if (when?) they were acquired.
I have to wonder if an industry-wide slowdown to the ML frenzy is exactly what we need to give people and companies time to focus on solving real problems instead of just chasing easy money.