“White collar robots” are closer than you think

Robotic process automation is software you train to do work around the office. It’s “today’s technology”. But “tomorrow’s technology” is already here.

Yesterday I wrote about “white collar” robots, or, to give them their full title, robotic process automation (RPA) robots.

RPA robots don’t look like robots. They’re basically software you train to do the donkey work around the office.

RPA robots can be trained to do any type of repetitive task such as managing customer databases, organising logins for new employees or maintaining email lists. They’re flexible (as in, they can do any type of repetitive task) and easy to set up (as in, no IT guys required).

They’re designed to mimic a human worker, so they sit “on top” of all a company’s existing IT systems. The RPA robot just gets a login for whatever systems it’s going to be working on, a couple of days training from the rest of the team, and away it goes. Once it’s up and running, each RPA robot can do the work of between two and five employees. They do the boring repetitive grunt work that nobody enjoys.

Here’s an example – do you remember when there used to be a 24 hour wait before you could use your new mobile phone? What was happening was this: you gave all your details to the person in the phone shop – direct debit, home address etc. The person in the shop would pass your details along to back-office staff. The back office staff would input them on the system. The whole rigmarole took a day or so.

A few years ago, one of the big four UK mobile operators decided to try RPA robots. Within a couple of months a team of four people, helped by RPA robots, were able to process between 400,000 and 500,000 transactions per month. Customers were set up on their phones instantly. For the phone company, the three-year return on investment in RPA robots was 650%.

All very impressive. But Professors Mary Lacity and Leslie Willcocks – who are experts in process automation – call RPA “today’s technology”. It’s quick and easy to set up, doesn’t cost a great deal, and it can handle a lot of common tasks.

They’re even more excited about “tomorrow’s technology”. It’s called cognitive intelligence (CI).

So many acronyms

To explain CI, I’ll start with RPA.

RPA is good for very specific tasks. It uses rules – “if this, then that” or IFTTT – to do the work. IFTTT rules can be configured to be used in lots of different settings. But there’s a limit to what they can do. They need to be instructed very specifically.

Cognitive intelligence robots don’t use IFTTT rules. They’re more of an example of the “deep learning” algorithms I talked about in last week’s Risk and Reward. Deep learning algorithms are much more flexible than IFTTT-style rules. They’re more like a human.

Here’s how I described the difference between the two types of robot back then:

The problem with a lot of old-school approaches is that they relied heavily on programming. They tried to come up with a very smart set of instructions which would show the computer how to interpret the world, and learn from it.

Machine learning has come very far in the last few years because it’s broken away from that approach. The big breakthrough is called “deep learning”. “Deep learning” algorithms don’t learn by following rigid rules. Instead they interact with the world and figure it out by trial and error.

Did you hear about the AlphaGo robot which beat a world champion human player at the game of “go” earlier on this year? That’s a good example of deep learning.

Everyone was very excited about that because go is a different sort of game to chess (which computers cracked twenty years ago). Go has many many more possible moves than chess, which means it can’t be cracked by brute computing power.

To win at go, you need intuition. And it was thought that computers can’t have intuition. AlphaGo proved that was possible. The computer studied millions of games of online games of go. By studying those games and simulating them, it was able to figure out from them which types of moves were more likely to lead to a win in a given situation. Since every game of go is unique it couldn’t just copy the previous win-ning moves. But it could intuit the difference between a good move and a bad one.

Deep learning algorithms like AlphaGo are the reason why everyone’s so excited about machine learning these days. When you set them loose on big data sets, they’re capable of amazing things. If you give them data about how drivers use the roads, they can learn to drive. Give them data about how people speak and they can learn language. A company in the penny share letter portfolio uses them to train bad habits out of teenage drivers. Another uses them to tell employers who to hire and who to fire. Deep learning algorithms are everywhere, once you start looking.

CI is what you get when you build a “white collar robot” using deep learning technologies. Here are some of the characteristics of CI robots:

– They can deal with messy, unstructured inputs/data/instructions. RPA robots need very specific, stand-ardised inputs.

– They can communicate through natural language. They can understand language and speak it back.

– They use inference and intuition. RPA robots use IFTTT rules.

– Their outputs are more probabilistic than deterministic. So they can describe things in terms of “shades of grey”.

When you read about deep learning, it’s usually in the context of Facebook or Google or some other Silicon Valley giant. Those companies are using deep learning to improve their search, teach cars to drive and cre-ate robotic assistants.

But here’s the thing. CI and RPA robots aren’t just for Silicon Valley. There are a couple of highly promising startups building these robots right here in the UK. For example two years ago, Google dropped $500m to acquire a tiny London-based deep learning startup called DeepMind. DeepMind didn’t even have a product at the time – Google wanted the company because it employed the best talent in the field.

As I mentioned yesterday, I’m looking into this area for my Penny Share Letter subscribers. I’ll share more with you when I can. And if you’d like to see my full research, you can subscribe to The Penny Share Letter here.

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