Obstacles To Machine Learning Adoption

Obstacles To Machine Learning Adoption

Machine Learning is being touted as a solution to some of the world’s most pressing problems. In agriculture it is already helping poorer regions with food production, in healthcare it is being used to contain the spread of contagious diseases and discover new cures. And tech giants are investing tremendous amounts to ensure that its potential is realized. Facebook, Google, Microsoft, and Baidu have spent in excess of $8.5 billion beefing up their AI talent, according to Forbes, while Amazon spends $228 million a year just to find people to run Alexa. To say that the technology is well on its way would appear, at least on the surface, to be an understatement.

However, not everyone is convinced, and investment is yet to really translate into widespread adoption. Indeed, in one recent study from Belatrix Software, ‘Powering the Adoption of Machine Learning’, just 18% of companies asked if they had already started a machine learning initiative in their organizations said they had done so, 40% said that they were investigating it but hadn’t started, and 43% that they had no plans to start one at all.

The reasons for this are varied and complex. Firstly, many simply fear AI, with Hollywood particularly responsible for stoking fears that killer robots are set to wipe us out. This fear isn’t reserved for tin foil hat wearing basement dwellers, though. Even the likes of Stephen Hawking and Tesla founder Elon Musk have urged caution, recently saying publicly that we need proactive regulation of AI. Musk claimed that ‘by the time we are reactive in AI regulation, it’s too late. Normally the way regulations are set up is when a bunch of bad things happens, there’s a public outcry, and after many years a regulatory agency is set up to regulate that industry… It takes forever. That, in the past, has been bad but not something which represented a fundamental risk to the existence of civilization.’

Fear of triggering the apocalypse aside, for many there is the more pressing concern that the technology cannot yet be trusted to the extent that they are willing to turn tasks over to it. In a recent survey of 1,600 senior managers by IT services specialist Infosys Ltd., 54% said that the biggest challenge to adopting AI remains ‘employee fear of change’. This ultimately boils down to whether or not they believe the technology is mature enough yet, and it is clear that common perception is that it is still new, untested, and therefore risky. You could argue that this belief is the result of a lack of education or people claiming it’s not ready for fear it will render their own jobs redundant, but many experts agree. Nikhil Garg, Software Engineering Manager at Quora, for one, told us recently that ‘I think most would agree that the single biggest bottleneck for all machine learning is software engineering. We all collectively in the tech industry are still figuring out the best practices, tools, abstractions, and systems that can enable large organizations to innovate in ML at a huge data scale.’

It is not necessarily that machine learning isn’t ready, though. As with any nascent technology, there is a lack of understanding and skills when it comes to both knowing where and how to apply it.

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