The problem with articles like this is that they don’t properly represent the spectrum of what an ML project can be for a business.
If all you’re talking about is self-driving cars or voice-operated assistants, then sure, the article’s mostly right. Modern techniques that have revolutionized ML in the past ~15 years have not translated to massive new economic gains in many areas they were anticipated to affect.
But this is the vast vast minority of all ML projects.
Many of the most economically successful ML projects I’ve run in my career are very simple, and ruthlessly focus on business value from the outset. A lot of them involve automating inefficient manual processes, things like spam filtering, phishing detection, fraud detection, automatic keyword tagging, automatic metadata classification in images or text, simple time series forecasting for logistics or consumer demand, simple models for customer churn, and a wide variety of different customized search engines for big & small content collections.
Just for one example, I worked on a project to automatically validate metadata about human models appearing in images, to flag discrepancies between documented ages / ethnicities within legally required model release documentation and the real appearance in images, to find fraud (especially when minors were used in stock photography).
This saved _millions_ of dollars annually in human review & legal costs for when that platform incorrectly approved photography with invalid or fraudulent accompanying release documents for the human models.
In just one project, a team of six engineers paid for itself about 5 times over and the delivered software requires minimal maintenance and only became more valuable as the platform grew larger. In fact that was one of the only times in my career when a non-finance company chose, discretionally, to pay larger bonuses than in employee job agreements as a reward.
That project did happen to use deep neural network for image metadata prediction, but it was fairly mundane and easily trained on 2 average GPU machines from a dataset of only a few hundred thousand images.
Edit: added below
I’ve also observed across several companies that there’s a big variation in outcomes and success of ML based on the level of investment in infrastructure.
It’s not about pumping money in for some crazy GPU cluster or huge framework for massively parallel training, but you do need to separate ML operations away from the ML engineers who research solutions for products and internal stakeholders.
It’s a situation where domain specialty has to be used efficiently or you’ll waste a ton of money and time. If you hire an expensive senior engineer for ML (salary easily north of $200K in large cities), but you task them with managing a database or operating kubernetes or debugging partitions in HDFS, you probably won’t get a good return on your investment.