Machine learning is the cornerstone of many technologies we use every day; without it we would be interacting with computers as we did 20 years ago – using them to compute things. With the advent of more powerful processors, we can harness this computing power and enable the machines to start to learn for themselves.
To be clear – they do still need input, they still follow patterns and still need to be programmed – they are not sentient machines. They are machines which can find more information that just 2+2=4. What machine learning is very useful for is extracting meaning from large datasets and spotting patterns. We use it here at Speechmatics to enable us to train our ASR to learn a different language on significantly less data than would have been possible even 15 years ago.
We are now in a world which is starting to find more and more uses for machine learning (eventually the machines will find uses for it themselves, the ‘singularity’, but we aren’t there yet!). Your shopping suggestions, banking security and tagging friends on Facebook are all initial uses for it, but the sky is literally the limit. There is no reason why eventually the Jetson’s flying cars wouldn’t be powered by machine learning algorithms, or why I Robot style cars couldn’t be controlled by a central hub. Machine learning could also be used to help out humans; to assist air traffic controllers by directing planes to a holding pattern, or help teachers to identify struggling pupils based on test results.
Machine learning coupled with neural networks (large networks of processors which begin to simulate a brain) can unlock even more power from machine learning. Whilst at Speechmatics we like to think we are changing the world – the reality is research into deep neural networks and machine learning are starting to unravel the way some of the most vicious illnesses operate. The mechanisms of HIV and Aids, as well as simulating flu transmission can both lead to a better understanding of how they operate.
As The Royal Society stated in a recent article on the possibilities of machine learning, they are calling for more research into machine learning ‘to ensure the UK make the most of opportunities’. The progress we have made so far is astounding and with an exciting prospect ahead, we at Speechmatics are continually innovating and researching artificial intelligence.
What is most exciting is to think how things could end up looking in the future. Today your mobile phone had more computing power than that which NASA used to put man on the moon. The phone which you use to combine candies into rows and crush them, has nearly 1 million times the processing power of the machine which landed on the moon. So just as it was hard for the scientists of the 60s to consider what we could do with more computing power (Angry Birds probably wasn’t in their thoughts), it is just as impossible for us to determine what we can do with machine learning backed up by current computing power.
So, welcome to the future. A future where computers no longer just compute. A future where processors think.
Luke Berry, Speechmatics