AI in Unexpected Places
It’s official – we live in science fiction times. Although it’s not all jetpacks, rayguns, and summer holidays on the moon (yet!), the things we take for granted would make Asimov or Heinlein blush. Our cars can run on solar power instead of gasoline, we carry the concentrated sum of all human knowledge in our pockets, and we’re making steady progress towards space colonisation.
Most importantly, however, our daily lives are becoming more and more influenced by artificial intelligence. Although it wasn’t until Alan Turing’s work that scientists began seriously attempting to emulate processes naturally occurring within human minds. Learning, decision-making, coming up with abstract concepts – all of these things are now more or less available to machines.
In fact, AI has already established itself as one of the fundamental technologies of the modern world – so much so, that we often don’t acknowledge its impact. Here, then, are some of the rather unexpected places where artificial intelligence lurks.
In the modern landscape of multiplayer-focused video gaming, it’s easy to forget how intertwined the worlds of video gaming and artificial intelligence used to be. In fact, early video games were often used as early applications for the then-fledgling field of AI research.
From video games’ earliest beginnings, providing players with a worthwhile challenge has been the primary focus of both game programmers as well as AI researchers. The first algorithms for playing chess were developed as early as the 1950s (with one example being invented by Alan Turing himself!), although it wasn’t until 1997 when IBM’s Deep Blue famously beat Garry Kasparov, scoring AI’s first major win against a human grandmaster.
Chess, of course, isn’t everyone’s cup of tea – a lot of us prefer action to strategy, after all. 1980’s Pac-Man is the first famous application of AI. Pac-Man’s ghosts featured sophisticated algorithms dictating their movement, allowing them to react to the player’s action in real time – something previously unheard of.
The importance of video games to AI research cannot be overstated. Many of the discoveries and innovations made possible thanks to gaming are still in common use today – after all, the same principles guide your Roomba’s pathfinding algorithms and the computer opponents in your Counter-Strike games!
As for video games themselves, AI and machine learning technology has become sufficiently advanced for entire games to be created algorithmically– as the excellent AI Dungeon 2 proves.
Among AI advocates and critics alike, a common talking point is often raised –”there are certain inherent limitations that AI will never be able to overcome, such as creating art”.
Well, that may not be necessarily true. Advances in machine learning allow computer systems to analyse art and generate their own pieces. Although using computers to compose music isn’t anything new, allowing algorithms to create new music is a relatively new phenomenon. Many AI scholars attribute Brian Eno’s work on the Koan system as the starting point of generative music, even though Eno himself has been using similar techniques since at least the mid-1970s.
To make a long story short, AI-powered computer systems are now capable of producing classical and jazz compositions which are almost indistinguishable from music created by humans. Iamus, the computer housed at the University of Malaga, specialises in creating new works of contemporary classical music. Some of Iamus’ works have been collected and performed by the London Symphony Orchestra on the album Iamus. Give it a listen:
Music isn’t the only art form which machines are dabbling in. Recent advances in machine learning, AI-powered image recognition and analysis and automated image compositing made it possible for computers to create their own digital paintings.
The most famous example is probably the DeepDream algorithm, created by Google engineer Alexander Mordvintsev and unleashed upon the world in 2015. Although works produced by DeepDream have been varyingly described as “nightmarish”, “unsettling”, and “blasphemous”, there’s no denying it – the psychedelic visions created by the program are bona fide art.
The glitch art community in particular embraced machine learning algorithms and AI-powered workflows. Applications such as Ganbreeder and Artbreeder have been created by such artists to facilitate the creation of entirely new images and aesthetics. The beauty, as artist Ian Keaveny points out, comes from the subversion and disruption of these algorithms to create something which could not have been thought of by biased human minds.
The world of modern finance is fertile ground for AI research. Already, the field is thoroughly dominated by technology – small surprise, seeing as stock trading was one of the first commercial applications for the internet. Today, millions of transactions are made every second. This is, of course, a perfect setting for AI powered traders.
…Except we haven’t yet seen a successful AI trading solution. Markets are exceptionally volatile, after all – introducing a foolproof algorithm for trading with a high success rate would likely bring down the whole system. AI powered stock trading is simply not compatible with the way modern economics work.
There hasn’t been a better cautionary tale against AI traders than the 2010 “flash crash”, in which one person managed to crash worldwide trading markets for a little over half an hour simply by gaming the way these algorithms work.
This doesn’t mean there isn’t a place for AI and machine learning in the world of finance. Quite contrary – data analysis and pattern recognition performed on large data sets is one area in which AI excels.
AI is everywhere – there’s no denying it. While humanoid-looking robots don’t walk among us (yet!), thinking machines accompany us everywhere. From the virtual assistants in our phones to AI-powered traffic management systems, intelligent computers are here to stay.
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