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AI in the enterprise: Get ready for a whole new era of smart software fueled by mountains upon mountains of data
Our Register Debates continue in which we spar over hot topics and YOU decide which side is right – by reader vote
Register Debate Welcome back to the inaugural Register Debate in which we pitch our writers against each other on contentious topics in IT and enterprise tech, and you – the reader – decide the winning side. The format is simple: a motion was proposed, for and against arguments were published on Monday, another round of arguments today, and a concluding piece on Friday summarizing the brouhaha and the best reader comments.
During the week you can cast your vote using the embedded poll, choosing whether you're in favor or against the motion. The final score will be announced on Friday, revealing whether the for or against argument was most popular. It's up to our writers to convince you to vote for their side.
For our first debate, the motion is: Artificial intelligence in the enterprise is just yesterday's dumb algorithms rebranded as AI
And now, arguing AGAINST the motion is DAVE CARTWRIGHT...
For a very long time, I was of the opinion that “artificial intelligence application” was synonymous with “a really cool script.” I don’t feel bad about having had this misconception, though, because in practical terms that’s what it often was: a script or application that was nothing special but which the marketing bods were labeling as AI.
Of course, algorithms have been with us for centuries, and will continue to underpin technology for centuries to come. There’s even a thing called the General Problem Solving Algorithm, whose application spans the past, present and future.
The thing is, though, an algorithm can’t be intelligent in its own right. An algorithm is a collection of logic: it executes a set of instructions (sometimes sequentially, sometimes in parallel) and changes the flow of instruction execution based on the state of the system. IF something THEN do something ELSE do another something. You need more than an algorithm for there to be any perception of intelligence. Today's artificial intelligence is much more than the algorithms we're perhaps all familiar with.
When I wrote that last paragraph, I nearly used the word “decision” about the execution flow. But then I thought: no, that would be the wrong word. A decision is, to quote the Concise Oxford Dictionary I have beside me, “a conclusion or resolution reached … after consideration.” Your program doesn’t deliberate what to do when it hits an IF statement – it just looks at the facts and does what you’ve programmed it to do.
You can’t be wise with logic alone, you need more
And going back to the “I” word itself, my copy of the dictionary tells me that intelligence is, among other things, “the intellect; the understanding; quickness of understanding; wisdom.” The word “wisdom” leaps out: you can’t be wise with logic alone, you need more – and Chambers’ online dictionary helpfully spells it out clearly, defining intelligence as: “the ability to use memory, knowledge, experience, understanding, reasoning, imagination and judgement in order to solve problems and adapt to new situations.”
This last definition really spells out the difference between algorithms and AI: data. Memory, knowledge and experience all depend on data. Understanding isn’t possible without facts and context, which again means data. Even imagination is based in part on memories and experiences. I could go on, but you get the point.
“Aha!” you cry, “AI is just yesterday’s algorithms plus data.” Nice try, but nope. Data as a concept is nothing new, but the quantities of data we’re now able to work with are unprecedented. Yesterday’s algorithms simply weren’t able to work with the data now available to us, and so they weren’t written in a way that would let them scale and allow us to re-use them now.
And the pressure is being piled on to make those volumes of available data even bigger: for example, there’s an excellent paper by Professor Dame Wendy Hall, “Growing the artificial intelligence industry in the UK,” which makes 18 recommendations – the first three of which relate to improving the sharing, availability and mining of data.
The final element of yesterday’s algorithms that makes them unsuitable for AI is how they work: the average legacy algorithm is built using logic as I described in the third paragraph, but that no longer works. I mentioned that algorithms don’t make decisions – and that’s right, until you give them enough data to do so. But it’s not just a case of throwing data at the algorithm – you need a new algorithm that throws away the rigid logic and exploits the data.
AI needs to “use memory, knowledge, experience, understanding, reasoning, imagination and judgement in order to solve problems and adapt to new situations.” And yesterday’s algorithms just can’t do that. ®
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