A Blog by Jonathan Low

 

Jun 19, 2017

Is Artificial Intelligence Being Oversold?

Hype is part and parcel of technology's 'magic.' And anyone who dares to question it risks the accusation of being someone who 'just doesn't get it.'

But it is a leader's job to ask the tough questions everyone else is afraid to venture.

And as we get more experience with automation and its potential, the question, in an economy that tends to reward resource-constraints, is whether AI will eliminate as many jobs - and costs - as projected. Successful executives remember former Intel CEO Andy Grove's admonition that 'only the paranoid survive.' JL

Lionel Laurent reports in Bloomberg:

In a world where CEOs get more credit for cutting costs and buying back shares than opening factories or hiring staff, technology-driven efficiency is a carrot to dangle in front of shareholders. (But) what is the acceptable failure rate of these projects? Outside games like Go or poker, just how suited are machines to the corporate world? Are some algorithms too expensive, as Netflix found out? It’s not yet clear that the cost of automation will be offset by savings in human capital.
Talking about artificial intelligence is in season for Europe’s corporate executives. Just don’t mention its shortcomings.




The C-suite is eager to tout its abilities in riding the 21st-century wave of automation by using sophisticated machine learning or shop-floor robots. Mentions of the phrase “artificial intelligence” on earnings calls are surging, as Bloomberg Intelligence’s Michael McDonough has noted.


In a world where CEOs get more credit for cutting costs and buying back shares than opening factories or hiring staff, technology-driven efficiency is a carrot to dangle in front of shareholders. Stock-market valuations are stretched and spending opportunities are rare—but processing power is abundant and data storage cheap.



That’s why executives are conjuring up the promise of lower costs, more revenue or something in between. Deutsche Telekom and Royal Bank of Scotland are turning to chatbots—a digital replacement for call centers that could shave billions off costs in the next five years. France’s BNP Paribas and publisher Wolters Kluwer are trying to boost revenue, and are using machines to screen financial markets or customer databases and trigger automatic alerts.
Siemens computers are having a go at running gas turbines more efficiently than humans. And don’t forget the blue-collar world: Logistics firms Deutsche Post and DHL are talking up the idea of using robots alongside workers on the warehouse floor.
But there’s remarkably little talk of the limits of automation. What is the acceptable failure rate of these projects? Outside of games like Go or poker, just how suited are machines to the corporate world? Are some algorithms too expensive, as Netflix once found out? There’s a risk that disappointing results lead to an exaggerated corporate pullback, as the Harvard Business Review warned in April.
Machines can fail. Chatbots do so very publicly: Microsoft shut down a bot called Tay after pranksters pushed it to make racist, sexist and pornographic remarks. Earlier this year, Facebook went back to the drawing board after its bots hit a failure rate of 70 percent, according to The Information.
Failure is fine, but the acceptable failure rate of an intelligent vehicle or a computer-controlled turbine is probably different to a bum steer on an electricity bill. That can be the difference between an easy path to cost savings and a complex, long-term investment that doesn’t work as intended.
Then there’s the question of whether machines are always suitable. Machine learning works best in an environment with rules and huge numbers of data points. That might work with cars driving through heavy traffic governed by laws, or with achieving the best price for selling a big block of shares.
It might not work well in deciding where to invest a hedge fund’s money, for example, or recommending products to customers without much previous data to go on. The minute things get fuzzy—either due to a lack of rules, an unclear evaluation of success or a lack of data—artificial intelligence performs poorly, according to Pictet strategist Edgar van Tuyll.


These limitations mean it’s not yet clear that the cost of automation will be offset by savings in human capital. Hiring a data scientist can cost more than $200,000, according to Bloomberg News. Flight-bookings company Amadeus has 40 of them. Siemens says it has more than 200 A.I. specialists running various projects. And even Silicon Valley has its grunt workers: Facebook is hiring 3,000 content moderators, on top of 4,500 existing ones. A.I. cheerleader Amazon has 341,000 employees—three times the number it had in 2012.
There are good reasons to talk about A.I. and boast of its successes. But opening up about failure will help, too


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