We’ve come a long way since the speaking clock, but transactions over the phone or online can still sometimes be frustrating affairs. Endless options repeated just to get your bank balance, and not having a 24 hour service can be a pain. But if you want to ask something, anything about the new BMW electric car, the i3, you’ll get a swift and expert response… from a computer. The BMW I genius is a remarkable programme, known as “The Brain”.
Dimitry Aksenov, 21 years old, founded technology company London Brand Management in 2011. The company provides an AI service for big companies who want to outsource customer/staff interactions to computers.
BMW UK marketing director Chris Brownridge said:
“BMW I Genius is capable of understanding each question and responding accurately every time as if you were talking to an expert from the company. The system operates around the clock, allowing the consumer to ask any question relating to the “i” cars but without the hassle of having to pick up the phone or go into a dealership.”
LBM’s system is cloud-based, and so it can be accessed from anywhere . It can deal with thousands of enquiries simultaneously, and its database has a virtually unlimited memory capacity. It’s the equivalent of having hundreds or even thousands of call centre staff, with the added advamtage that it remembers and learns, and there is no downtime. Much better then than our human brain?
Aksenov provides the technology to brands under licence with a one-off implementation fee to “teach” the system. Unlike hiring humans, the AI only has to learn once and that’s it for good. He said:
“Within five years we will have a system that truly knows more than a human could ever know and is more efficient at delivering information. It will replace many of the boring jobs that are currently done by humans. Unfortunately, this may take some jobs from the economy by replacing human beings with a machine. But it is the future.”
Do you remember the computer HAL 9000 in Stanley Kubrick’s 1960’s science fiction film “2001 A Space Odyssey”? Forget the fact that it went a bit OTT and mission crazy towards the end, one of the interesting things about HAL (which stood for Heuristically programmed ALgorithmic computer) was that it could understand and converse in English. No need for inputting via a keyboard, or translation into machine code. Think “lexical semantics”.
Ever since the release of the film linguists and computer scientists have tried to get computers to understand human language by programming the semantics of language as software. We already have programmes that can understand and distinguish numbers and certain words on our mobiles, when we pay bills over the phone, and even in computer games, but a computer that can understand and be fluent in a human language has eluded us.
That may be changing- a University of Texas at Austin linguistics Professor, Katrin Erk, is using supercomputers to develop a new method for helping computers learn natural language.
Instead of hard-coding human logic or deciphering dictionaries to try to teach computers language, Erk decided to try a different tactic: feed computers a vast body of texts (as an input of human knowledge) and use the implicit connections between the words to create a map of relationships.
“An intuition for me was that you could visualize the different meanings of a word as points in space. You could think of them as sometimes far apart, like a battery charge and criminal charges, and sometimes close together, like criminal charges and accusations (“the newspaper published charges…”). The meaning of a word in a particular context is a point in this space. Then we don’t have to say how many senses a word has. Instead we say: ‘This use of the word is close to this usage in another sentence, but far away from the third use.'”
I have to say that as a human, I had some trouble getting my head round that quote! Perhaps we should be looking at how babies learn language and try to replicate that learning in a computer. But back to Erk’s work, to create a model that can accurately recreate the intuitive ability to distinguish word meaning will require a lot of text and a lot of analytical crunching power.
“The lower end for this kind of a research is a text collection of 100 million words. If you can give me a few billion words, I’d be much happier. But how can we process all of that information? That’s where supercomputers come in.”
So we need a mega computer to help us devise a computer that will not only understand us, but communicate intelligently with us. If this could be achieved, how close wiuld such a computer be to a sentient entity? What if it’s first words to us once we switch on this fully loaded language-conversant computer are “I’m hungry”! Well, as long it doesn’t start singing “Daisy Daisy” and switching off life support systems… But perhaps HAL 9001 will be better behaved.