Google Translate has developed an understanding of the meaning behind words so that it can translate directly from one language to another using the concepts behind phrases rather than a word by word translation.
This means it can be taught to translate from French to German and from German to Chinese and because it understands language at a conceptual level it can translate French into Chinese without going via German; it matches concepts not words.
Should we be worried by this latest revelation of a Neural Machine that has created its own internal language that nobody understands?
I’m not sure.
Imagine an algorithm to determine where to concentrate health-care research. If its inputs are biased towards one section of society, accidentally rather than by design, wouldn’t it develop a skewed view of the world?
Wouldn’t it favour some people over others?
Yes, but we already have a healthcare system that does that, don’t we? And, this could be less biased because it would be much more effective at using large volumes of data to determine the best outcome overall.
The difference is that in a world of “bias in, bias out” and opaque algorithms nobody, not even the creators, would know why it made the choices it did.
Maybe this is a price worth paying.
As this TechCrunch article says, “Neural networks may be complex, mysterious and little creepy, but it’s hard to argue with their effectiveness.”
photo credit: Adi Korndörfer … brilliant ideas via photopin (license)
Are machines that learn for themselves the stuff of nightmares or a vision of a wonderful utopian future?
The answer, of course, is neither.
We all know that technology is neutral, even though we forget a lot of the time. But there is that niggling doubt. What if they broke through the barrier and became sentient and intelligent?
It’s possible, but probably a long way off.
Artificial Intelligence and robots are hot topics for Science Fiction at the moment and I’m one of those who believe we should use fiction to help us imagine the future so we can be better prepared for it. Good or bad.
The more of us that have a basic understanding of how the tech works the richer the debate about how it’s used will be, so I was pleased to find some fun stuff from Google that starts to demystify machine learning.
Here’s an AI experiment that tests a neural network to see if it can guess what you’re sketching.
I’m rubbish at drawing but it guessed 2 out of my 5 doodles and as the designers say, “The more you play with it, the more it will learn.”
Take a look – https://aiexperiments.withgoogle.com/quick-draw
Christopher’s neck was bruised where they’d held him down while forcibly removing his arms and legs. He’d fought them hard, but it had been pointless; here he was, dumped by the side of the road in an old damp car seat, helpless and homeless.
Tears were rolling down his face and he could do nothing about them.
How could it have come to this? Less than a year ago he’d taken an affordable loan from a company that owned massive driverless trucks. He’d replaced his arms and legs with prosthetics to become a highly paid and highly sought after new-breed trucker with enough strength to load and unload the huge cargos.
Now look at him. Useless. Slumped on a dirty seat in the gutter with the small begging bowl the bailiffs had graciously left in front of him.
A group of people approached and his hopes rose. As they got close he called out. ‘Please. Help me.’