Cyberspace isn’t much like space at all. It’s crammed full of bytes of information, churning and frothing with intelligence agents who gnash and dissect the data in search of new knowledge, or at least something else to sell us. This is big data and at least one embodiment of artificial intelligence.
Recently, I heard an elegant explanation¹ of machine learning, or the ability of machines to create programs and algorithms that deduce things that they haven’t been programmed to – how machines learn. Consider what would be involved if you had to write program to tell a computer how to distinguish between a cat and a dog. I’d put together a logic chart and add up the cat vs dog points:
| Cat | Dog | |
| Meow noise | 1 | 0 |
| ‘ark-ark’ noise | 0 | 1 |
| retractable claws | 1 | 0 |
| floppy ears | 0 | 1 |
| stripes | 1 | 0 |
| lolling tongue | 0 | 1 |
| it’s ignoring you | 10 | 0 |
| it thinks you are the smartest, most desirable person in the world | 0 | 10 |
and I’m sure you can come up with many other criteria, some less than absolute, such as curly fur (much more common in dogs but not impossible in cats).
In machine learning, you’d give the computer a million videos labelled cat and a million videos labelled dog to watch and let it figure out its own algorithm to tell the difference. Who wouldn’t want the job of watching a million cat and dog videos? Most of us already have. I am curious about the computer’s algorithm: does it use tail wagging frequency, that silly whining noise dogs make, or hissing, as selection criteria?
What if after all that the AI comes to the wrong conclusion. It might decide the true difference between cats and dogs is that cats are the overlords of the planet and dogs are service animals. It’s easy enough for a mere human to decide if the computer has done a good job of differentiating between the two animals. But what happens when they start predicting things we have no prior knowledge of, like how long a pair of socks will last?
And this is a trivial application of artificial intelligence. There is so much data out there, silly names for bigger numbers have emerged. According to this BBC article, 2.5 exabytes (billion gigabytes) of data were generated in one day in 2012 and the US National Security Agency has the capacity to store a yottabyte (one thousand trillion gigabytes) of data. That’s a lot of Facebook likes, tweets, diagnostics at the auto-mechanic, GPS locations, term marks and everything else. If we set AIs to learning from all this data, it seems like a tremendous wealth of knowledge will emerge. This might fall in a few categories:
1. Important and life saving intelligence such as diagnosing serious health events like heart failure and intercepting terrorist plans, so interventions can be made earlier.
2. Efficient systems, such as automated traffic flow to relieve congestion or business processes like finding items (books, events to attend, cheese) people might be interested in based on their preferences.
3. Predictions – varying from novelty (suggestion of what the name of you next pet should be) to kinda useful (prediction of what your partner might like for dinner tonight) to downright world changing (motivational media reports – this is one of my personal dreams).
The biggest question in my mind right now is how do we know if the machines are right?
Sure, we can test each conclusion the machine reaches after it’s made but that will take some time, especially if it’s a long range projection. And who owns the predictions? Is information about me that I don’t know, like what diseases I will develop in my old age, my personal information?
Yikes, I don’t want to go a bad place with such a potentially good thing. Like most new technologies, there is the possibility of misuse and misinformation with machine learning and artificial intelligence. Maybe we can use machine learning to figure out how to avoid the misappropriation of information for improper purposes. That would be cool. A truly self-regulating system.
¹ I believe it was from Steve Brown, Chief Futurist and Evangelist, Intel at the Plenary Session ‘Innovation: Steering Disruption’ of the International Economic Forum of the Americas in Toronto July 8, 2015.
Originally posted on July 15, 2015