Deep Learning technology has enabled a democratization of Artificial Intelligence: it used to be that you needed a team of people to describe ’s features and it was a long process, with another team of qualified PhDs required to deploy algorithms.
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But nowadays, we have Ph.D. students doing internships where they produce really valuable and viable production-ready results. There are also resources such as TensorFlow , an open-source Machine Learning library that anyone can play around with, as well as hundreds of -focused online courses and summer schools. The democratization of Artificial Intelligence has truly been a revolution, and something we should be proud of.
However, even though we’re not far from solving many of the world’s problems with Artificial Intelligence, if we’re not looking carefully at how Artificial Intelligence is being deployed, we may become complacent and miss out on breakthroughs and opportunities to achieve far greater things.
So how should we be thinking of today? Here are three observations that may help companies considering the use of Artificial Intelligence or those who might be questioning its evolution over the past few years.
We Shouldn’t Be So Scared of Artificial Intelligence
A lot of people fear the consequences of making its own decisions, but the reality is that humans are likely to always have more influence than it may appear at this point in time.
I’m a big believer in the hybrid human- model, and I think even when we do create ‘super intelligence’, there is going to be a human component embedded there. We’re going to see a merge between human and brains. We’re already offloading a lot of our brains into machines on a daily basis: how many of us remember phone numbers anymore?
The question is more: if we’ve got a lot of computational power, what do we direct that computational power towards? These decisions, as well as what we want to solve and create, will likely always have some form of human interaction. We’ll definitely be seeing a combination of human and machine resources when it comes to applying computational power and outcomes.
We’re Not Going to Create a God-Like Algorithm Anytime Soon
What does it mean to create algorithms that are unbiased? The problem is, we can’t eliminate biases completely. So many of our biases and decisions come from many different factors: many are inherent, moral opinions about how things should happen and how society should behave.
As humans, we struggle to get past our own biases, so we may need to accept what a challenge it will be to get rid of machine-learned bias. The best that we can hope to do now is make the best decisions we can as humans, and accept progress over perfection. […]
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