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AI – the Real World

What is your problem, exactly?

Mind games provide a rich seam for AI. Every year, AI gains more scalps – recently including Go and Poker.
These games share a common feature: they are well defined – you can write down the rules for Go. Developers can solve this narrow problem using specialist techniques.

Real world problems

In the real world, many interesting problems are more open: make ethical investments; write a video codec; drive a car; educate our next generation; negotiate Brexit; make a General AI.
How do we even explain the problem to the AI? The problem starts with defining the problem.
This is normally achieved by using lots of training data – and getting the AI to use inductive logic (if you are doing it properly), or more likely, evolving a neural network, choosing variations which seems to get it about right most of the time as far as you can see.
When it is a program playing a board game, this is fine. When it is a self-driving car, this is risky. Though to be fair, self driving cars are safer than human drivers. The risk is that something unexpected happens – like unusual weather – and millions of cars crash.

Migrating AI into the real world

The pressure for AI is unstoppable, barring concerns about the Fermi Paradox.
Despite popular fiction – and chess programs – not every AI has to be as good as the best person in the world. Assistants such as spelling and grammar checkers, web purchase recommendations and translators give a natural route in. Even steady real world progress adds up to a transformation over a generation.
But anything that makes money directly will lead to a rapid virtuous circle through reinvestment.

Low hanging fruit

Making value is harder than spotting value.
My Granny cynically explained that to sell you need to appeal to three emotions: greed, fear and vanity. Financial markets are simpler – greed and fear is enough. Buy when there is blood on the streets.
When I wrote my AI, for real time image recognition, I planned to use the AI engine for share trading. In the meantime, I made a simple spreadsheet to tide me over. But it turned out that even very simple tactics beat the market so convincingly I didn’t even draw a salary for my first 14 years a Forbidden – until the institutions said it messed up their cost model KPIs.
Disappointingly though, the spreadsheet made money off other traders. Maybe it provided some valuable liquidity in the market as a side effect, but it basically moved wealth from other people to me without actually make any new wealth.
Having said that, my best investments have been long term holdings in tech companies where I’ve got in years before the market has recognised value. In investment, you make your money by choosing well – and waiting. This is where AIs will ultimately win out.
The Paradise Papers of the future will be reporting that the offshore holders are computer programs running in the cloud.
Stephen B Streater
Founder and Director of R&D

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