The dangerous rush to build AI expertise

From Lyft’s biggest AI challenge is getting engineers up to speed | VentureBeat

Machine learning and deep learning AI have gone from the niche realm of PhDs to tools that will be used throughout all types of companies. That equates to a big skills gap, says Gil Arditi, product lead for Lyft’s Machine Learning Platform.

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Today, of course, any engineer with a modicum of experience can spin up databases on user-friendly cloud services. That’s the path that AI processes have to travel, he says. Luckily, machine learning is making AI more accessible to newbies without a PhD in statistics, mathematics, or computer science.

“Part of the promise of machine learning in general but deep learning in particular … is that there actually is not a lot of statistical modeling,” said Arditi. “Instead of giving to the machines exact formulas that will address the problem, you just give it the tools and treat it like a black box.”

From LinkedIn plans to teach all its engineers the basics of using AI | VentureBeat

Today, of course, any engineer with a modicum of experience can spin up databases on user-friendly cloud services. That’s the path that AI processes have to travel, he says. Luckily, machine learning is making AI more accessible to newbies without a PhD in statistics, mathematics, or computer science.

“Part of the promise of machine learning in general but deep learning in particular … is that there actually is not a lot of statistical modeling,” said Arditi. “Instead of giving to the machines exact formulas that will address the problem, you just give it the tools and treat it like a black box.”

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The academy isn’t designed to give engineers an academic grounding in machine learning as a discipline. It’s designed instead to prepare them for using AI in much the same way that they’d use a system like QuickSort, an algorithm for sorting data that’s fed into it. Users don’t have to understand how the underlying system works, they just need to know the right way to implement it.

That’s the goal for LinkedIn, Agarwal said. Thus far, six engineers have made it through the AI academy and are deploying machine learning models in production as a result of what they learned. The educational program still has a ways to go (Agarwal said he’d grade it about a “C+ at the moment) but it has the potential to drastically affect LinkedIn’s business.

From Tech Giants Are Paying Huge Salaries for Scarce A.I. Talent – The New York Times

Typical A.I. specialists, including both Ph.D.s fresh out of school and people with less education and just a few years of experience, can be paid from $300,000 to $500,000 a year or more in salary and company stock, according to nine people who work for major tech companies or have entertained job offers from them. All of them requested anonymity because they did not want to damage their professional prospects.

Well-known names in the A.I. field have received compensation in salary and shares in a company’s stock that total single- or double-digit millions over a four- or five-year period. And at some point they renew or negotiate a new contract, much like a professional athlete

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Most of all, there is a shortage of talent, and the big companies are trying to land as much of it as they can. Solving tough A.I. problems is not like building the flavor-of-the-month smartphone app. In the entire world, fewer than 10,000 people have the skills necessary to tackle serious artificial intelligence research, according to Element AI, an independent lab in Montreal.

Two thoughts:

  • This is unprecedented in the last two decades. Not even the raise of virtualization or cloud computing triggered such a massive call to action.
  • Do you really think that all these education programs and all these rushed experts will spend any significant time on the ethical aspects of AI and long-term implications of algorithmic bias?