Opinion: Machine learning is the new oil

20-Nov-2017

By Alex Pollak

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There is so much discussion about artificial intelligence (AI), or machine learning, that the general population would be excused for going a little glazed in the eyes at its mention.

People can touch and feel a mobile phone, so they understand its importance. Mobility has become part of the infrastructure to support high bandwidth services like video, music and search engines, all of which helped create the diversity of the internet.

But what of the data that is being accessed with the mobile device? How should we think about that?

The answer is that every time someone is tagged in a photo, uses Siri to make a phone call, or uses a digital map to navigate, AI is part of the process.

Connected devices everywhere are the prelude to the rollout of machine learning services that will change the way people live their lives.

Machine learning is now so widespread that it is a key part of virtually every significant datacentre and cloud business in the world, such as Amazon, Google, Microsoft, Alibaba and Facebook.

For example, many Americans already know Amazon’s Alexa, the voice-activated speaker, which makes soap and nappies arrive with the vodka order on a Tuesday. But they don’t know that Alexa moonlights as Lex, and he provides the processing power to run the annoying chatbot that keeps you busy ahead of handing you on to someone who can actually answer your questions.

For businesses using Amazon Web Services, Lex is available to process 4000 speech requests and 1000 text requests for the low price of US$16.75.

How about facial recognition software? Amazon offers this for free for the first 5000 images a month for one year. After that, it’s $1 per 1000 images processed, or $1000 per one million images.

Machine learning is now so widespread that there is a pricing matrix for it, and getting a taste of the services costs no more than a six-pack.

Who might use machine learning? Well, any retailer who wishes to greet the customers as they walk in the store by their first name.

How about using machine learning in fraud protection? An executive of a company involved in this is quoted on Amazon’s site as saying “In order to counter evolving forms of fraud, we needed to build and train a larger number of more targeted and more precise machine-learning models. Once you start catching a form of fraud, the fraudsters themselves will change their strategy—so it’s a constantly evolving problem.”

Meaning that if you make an acquisition in Brisbane, and a few seconds later log a different transaction, but for the same amount, which pops up in Ireland, it can be hard to tell whether it is fraudulent. Sure, you and your Uber driver will know, but multiply it by the millions of transactions per second, and you can see where this is heading.

Malcolm Turnbull talks about cross-referencing the feed from the nation’s security cameras with the licence photos of its drivers – a machine learning problem requiring a massive dataset of images integrated in real time with video capture, for deployment at times when crowd safety is an issue.

It’s a fact that the companies with the largest datasets have the ability to generate the best information on those customers, as long as the tools are there to support it, which is the game the big disruptors are playing. Data has already been called the new oil by The Economist.

But making AI available on demand is also good for small business, because it leverages their otherwise limited resources to provide transactions.

Naturally there are some downsides. Snapchat, one of Google’s largest datacentre customers, is paying US$400 million a year for the use of that processing power – an amount that the company just lost in the quarter.

At its worst, machine learning datacentres have the ability to effectively levy a tax on every user of their services, which is shaping up to be a lot of users. Price-gouging is a very real possibility, as is cartel behaviour.

But we think data companies still have a long way to run, which is why investment in these companies is a key part of the holdings of Loftus Peak.

Alex Pollak is chief investment officer at Loftus Peak

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