Why we should embrace AI, not fear it
The media has not got a clue about artificial intelligence (AI). Or technology. ‘Robots are coming for your job’ is a popular cry, but the next day, it’s fears about AI beginning World War III.
Not only do robots and AI have very little to do with each other, but AI is at a very early stage. What’s more, it can be split into several separate technologies.
The masses are being misled into fearing automation and a nebulous super-intelligence, but it’s those with a working knowledge of how AI works – and how it can be exploited – that will be best prepared for the future of work.
There is no definite answer, but it’s got nothing to do with robot overlords. AI is a field of computer science that examines if we can teach a computer to ‘think’.
AI as a phrase has been around since 1956 when it was coined by American computer scientist John McCarthy, six years after English mathematician Alan Turing had published a paper called ‘Computing machinery and intelligence’ in 1950.
AI is generally split into various subsets that try to emulate specific things that humans do. Speech recognition mimics hearing, natural language processing mimics writing and speaking, image recognition and face scanning mimic sight, and machine learning mimics thinking.
That’s a lot of different, often unrelated technologies; AI is an umbrella term, and certainly not a general purpose technology.
Research into AI is currently riding the wave of increased computing power and big data. Together they make AI both possible and imperative; as a society we now produce way too much data to ever process ourselves or get any insight from. Collected data is growing 40% a year, and it’s mostly going to waste.
The existence of all this data also means that AI software has enough information not only to work with, but to learn from. Is this AI’s big moment? Venture capitalists and technology giants such as Amazon, Google, Facebook, Microsoft and Apple think so, and are investing heavily in research.
It’s these companies that have unimaginably huge data-sets collected in the last few decades, and a vested interest in automating tasks on that data. Together they’re becoming the arbiters of AI know-how, so it’s AI techniques developed by Google et al. that are being used by scientists to trawl through data to get new insights.
There’s about to be an AI-powered knowledge explosion.
Machine learning is the act of computer scientists training a computer to do something. It’s about automating repetitive tasks, essentially training a computer to recognize patterns, and categorize data.
The classic example is image recognition or ‘AI vision’; give a computer a large number of images containing labeled objects, and the computer can learn to identify them automatically. The computer creates what AI researchers call a neural network; a virtual brain connection similar to a basic process in the human brain.
However, creating a neural network like this takes a lot of human labor, and also a lot of processing power. Google AI and the University of Texas recently used AI on a labeled data-set of signals from the Kepler space telescope to discover two exoplanets when astronomers had failed to find anything.
It’s also being used to identify cracks in reactors, and even help engineers at the UK’s Joint European Torus facility capture and deploy nuclear fusion energy.
This is supervised machine learning, and while it’s getting better at not forgetting, its usefulness at predicting patterns in data is hamstrung by the data it is fed.
