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
Who decides what is true? It’s difficult to know who to trust and traditionally we looked to the educators, the politicians and the clergy, but they’ve become crowd pleasers rather than crowd leaders.
As a layman in relation to theology, philosophy and science, I’ve been thinking about the nature of truth. There are plenty of facts we all agree on, but the hypotheses that emerge from these facts can vary and that’s when it becomes difficult to agree, or even discuss, what is true. It can be hard to believe in something and hold it lightly enough to genuinely welcome the other point of view or even change your mind.