While its easy to say that machine learning is the Next Big Thing, there are still many unanswered questions around the technology.

Everyone in tech is struggling to find out what machine learning will mean for companies in the broader economy or what machine learning means for all the rest of us, and what important problems it might actually be able to solve.

The Silicon Valley genius Benedict Evans has taken up the task of finding answers to the above questions. Evans, formerly a tech analyst, is a partner at venture capital fund Andreessen Horowitz or a16z as everyone calls it.

In his June 22 blogpost, Evans said that machine learning helps find patterns or structures in data that are implicit and probabilistic rather than explicit, that previously only people and not computers could find.

"They address a class of questions that were previously ‘hard for computers and easy for people’, or, perhaps more usefully, ‘hard for people to describe to computers’."Evans, who sports Harry Potter’s glasses and closely resembles Dustin Hoffman from The Graduate, takes the example of washing machines to elaborate on his point.

"Washing machines are robots, but they're not ‘intelligent’. They don't know what water or clothes are. Moreover, they're not general purpose even in the narrow domain of washing - you can't put dishes in a washing machine, nor clothes in a dishwasher".

According to Evans, washing machines are just another kind of automation, no different conceptually to a conveyor belt or a pick-and-place machine.

"Equally, machine learning lets us solve classes of problem that computers could not usefully address before, but each of those problems will require a different implementation, and different data, a different route to market, and often a different company. Each of them is a piece of automation. Each of them is a washing machine."

So what all can machine learning do?

Machine learning may deliver better results for questions we're already asking about data we already have.

Machine learning lets us ask new questions of the data we already have. For example, a lawyer doing discovery might search for 'angry’ emails, or 'anxious’ or anomalous threads or clusters of documents, as well as doing keyword searches

Third, machine learning opens up new data types to analysis - computers could not really read audio, images or video before and now, increasingly, that will be possible.

At the end of the blog, Evans noted that today most of the companies are relying heavily on machine learning for their technological needs but they often ask one very similar question: What are the other things that this will enable, and what are the unknown unknowns that it will find?

"We’ve probably got ten to fifteen years before that starts getting boring," wrote Evans.

First Published:Jun 25, 2018 1:37 PM IST