It feels as if AI is everywhere at the moment. In almost any publication that I read there seems to be at least one article that refers to it.

The tone feels cautious. I don’t think anyone is ready to come down too far on the utopian side; rather, commentators think about the risks and generally promote that side. And that isn’t a bad thing.

In the recent issues of high-profile publications like [New Scientist](https://www.newscientist.com/issue/3133/), [Science](http://science.sciencemag.org/), and [IEEE Spectrum](http://spectrum.ieee.org/magazine/2017/June), the ethics of AI gets a fair amount of space.

What all this means to me is that we just don’t know what is going on. There is a lot happening, but how does it all link together? And what are the implications?

I think it is fair to say that entrepreneurs and big business will at least _try_ to make AI work in almost all industries. Not long ago I thought this would happen at a blinding pace and we would be wondering about how to occupy all the un- and under-employed people. I don’t think this anymore. Or, more accurately, I think this is still quite some time away.

I see the first broad application of AI as an augmenting influence. I see wide applications in science to enhance discovery, and I see it used to automate boring (and error-prone) manual processes that — honestly — no human wants to do.

So, to me AI means a new wave of mobile and enterprise apps that aim to assist with decision-making.

### The Reckoning

Be that as it may, a reckoning is coming. AI will be relied on for something it isn’t meant for, and a bad outcome will ensue. This is the unintended consequence we would like to avoid, but will sooner-rather-than-later have to consider.

Researchers are working on ways to [visualise and better explain](http://theprincipledalgorithm.com/index.php/2017/07/10/arxiv-explainable-artificial-intelligence/) how deep [learning](https://hackernoon.com/tagged/learning) models produce their outputs. But of course, even if they succeed there needs to be governance in place to ensure that problems are picked up.

On the back of the data privacy movement, and the introduction of the new EU data protection laws next year ([GDPR](http://www.eugdpr.org/)), there is a growing discussion about what this new regime for data will mean (just Google any major law firm in Europe, America, and Asia to see examples).

### It all comes back to Data

Data is certainly the [currency of the 21st Century](https://dupress.deloitte.com/dup-us-en/deloitte-review/issue-13/data-as-the-new-currency.html).

It is the fuel for the input, processing, and output of business processing across the globe that is the fundamental issue. How is business and [government](https://hackernoon.com/tagged/government) going to assure citizens that their personal details are not going to wind up in the next hack?

Sure, we can implement some convoluted data governance measures. We can also enact laws to punish negligence. But the data is still vulnerable.

I see encryption and user-friendly applications of the blockchain as a viable solution to data security. It’s not fool-proof, but it is better than trying to rely on humans too much to keep data secure!

And don’t forget the ethics of machine making decisions for us. As New Scientist asks: “should we…”?

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