1940 Data Processing.
1940 Data Processing. (source: US Census Bureau on Flickr)

In this episode, I chat with Marc Warner, CEO of ASI, a data science and business analytics consultancy and training organization in London. We talk about artificial intelligence, speculating about the future and looking at current real-world business applications of AI. We also talk about a survey Warner recently conducted with data science companies in London, where he uncovered a data scientist skills cap.

Here are some highlights from our chat:

The last problem we ever solve

If we create a general intelligence in a safe and beneficial manner, the gains to humanity could be absolutely enormous; it really could be the last problem we ever have to solve. After that, we spin up our AGI in the cloud and basically everything else is taken care of. Having said that, if we mess up this transition somehow and things go badly, then it could end up being literally the last problem we ever solve and terrible things happen. Hopefully, the actions that we can take now have the ability to influence the probability of either outcome.

AI in the short term

We see a really interesting cross section of what people are actually doing with machine learning and artificial intelligence right now. It's really exciting. Conventionally, software's been used for decades in the form of expert systems where humans would code in explicit sets of rules, and nowadays you just don't need to provide those rules; the machine learning algorithms can pick out the understanding themselves from the data, and it's actually much, much more effective.

For things like custom analytics or recommendation or speech recognition or image tagging, all of these are being used across more and more domains. One of the things that this enables is a continuing personalization of services, so things like smart personal assistants or personalized health care are all going to be in the relatively short-term future of using these tools in the commercial environment.

AI's fundamental shift: Supplemental thinking

If we look at a human as sort of an abstract information processing unit, there are a few things we do: we receive information, we store it, we process it, and then we transmit it. With the advent of the printing press, suddenly we had the ability to store and transmit information with high fidelity and at scale, but in all of human history, pretty much all the processing of information that's ever been done has been done inside a human's head. It's only now, for the first time ever, that we can supplement this ability to think, to process information with machines, and that seems to me to be quite a fundamental shift.

Winning at AI

Probably the winners of this shift are those that are thinking today about which parts of their business are most vulnerable. Alongside this, obviously investing in things like data collection and fairly sensible ideas around using open source tools and cloud infrastructure. It means that they're giving themselves the smallest amount of fixed costs to move on any new trends that come out. I guess this is sort of fundamentally tied to my previous answer, in that if you don't think you can predict the exact specifics of what a change is going to be, you want to make yourself as agile as possible to deal with it in the moment.

Marc Warner will be speaking more about artificial intelligence and the future of data science at Strata + Hadoop World London 2016.