Hysteria of Hype

Somewhere around the web, there’s a cycle of hype which generally pins down where we are in terms of a hype cycle. I have not the time to go looking for it now but put simply, it has bunch of stages. I have decided it is too complicated for the tech sector.

Basically, the point at which you start seeing comments around X is the next big thing is the point at which something else is the next big thing. Sounds contradictory? Well yeah, it is.

Most people talking about the next big thing being X tend not to know a whole lot about X. Their primary objective is to make money off X. They do not really care what X achieves, so long as it makes them money.

Five years ago up to oh I don’t know, middle of 2014, early 2015 sometime, Big Data Is The Next Big Thing. Being blunt about it, there has been very little obvious Life Changing going on courtesy of Big Data and that is because by the time people started screaming about big data in the media and talking about how it was the future, it had ceased to be the future in the grand scheme of things. Artificial intelligence and machine learning, now they are the next big thing.

I have to declare an interest in machine learning and artificial intelligence – I wrote my masters dissertation on the subject of unsupervised machine learning and deep learning. However, I am still going to say that machine learning and artificial intelligence are a) a long way short of what we need them to be to be the next big thing b) were the next big thing at the time everyone was saying that big data is the next big thing.

It is particularly galling because of Alpha Go and the hysteria that engendered. Grown men talking about how this was the N.

Right now, artificial intelligence is still highly task limited. Sure it is fantastic that a machine can beat a human being at Go. In another respect, it isn’t even remotely special. AlphaGo was designed to do one thing, it was fed with data to do one thing. Go, and chess to some extent, are the same thing as brute forcing a password. Meanwhile, the processes designed to win games of Go and chess are not generally also able to learn to be fantastic bridge players, for example. Every single bit of progress has to be eked out, at high costs. Take machine translation. Sure, Google Translate is there, and maybe it opens a few doors, but it is still worse than a human translator. Take computer vision. It takes massive deep learning networks to even approximate human performance for identifying cats.

I’m not writing this to trash machine learning, artificial intelligence and the technologies underpinning both. I’m saying that when we have a discussion around AI and ML being the next big thing, or Big Data being the next thing, we are having the equivalent of looking at a 5 year old playing Twinkle Twinkle Little Star and declaring he or she will be the next Yehudi Menuhin. It doesn’t work like that.

Hype is dangerous in the tech sector. It overpromises and then, screams blue murder when delivery does not happen. Artificial intelligence does not need this. It’s been there before with the AI winter and the serious cuts in research. Artificial intelligence doesn’t need to be picked on by the vultures looking for the next big thing because those vultures aren’t interested in artificial intelligence. They are only interested in the rentability of it. They will move on when artificial intelligence fails to deliver. They will find something else to hype out of all order. And in the meantime, things which need time to make progress – and artificial intelligence has made massive jumps in the last 5 or 6 years – will be hammered down for a while.

For the tl;dr version, once you start talking about something being the next big thing, it no longer is.

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