Intonet

Intelligence quotient

Who remembers the 1968 movie A Space Odyssey which introduced us to the computer called HAL?

I certainly do and despite it being a gripping tale, it appeared farfetched. The name was an acronym, Heuristically Programmed ALgorithmic Computer, which in those days may as well have been gibberish for something that did not exist.

But what is the reality today?

HAL has been left far behind. Today, we have the web, social media, artifi cial intelligence (AI), and trawling bots. We also have companies of fabled riches that acquired wealth by data mining and creating what we call big data.

But what is big data and what makes it so valuable?

The answer lies with statisticians who love to count things and calculate the odds of certain things happening. It is also how the various reward programs work at your favourite shopping centre.

Arvo Elias,

Cybercons

We all understand what odds are: fl ip a coin and it will either land as heads or tails, the odds are half heads and half tails or 50 percent. A very untidy boy only has green and red socks jumbled in his drawer: how many socks does he have to take out when blindfolded to be sure that he has a pair to wear; obviously he has to take out three socks even though he won’t know whether he will be wearing red or green socks, so the odds are 33.3 percent.

Your supermarket does much the same things because everything you buy is coded and then analysed. They may discover that everyone who buys iced vovos also buys Mrs, Murgatroid’s jam. Bingo! The more times they see that combination the higher the confi dence factor becomes and if you turn this into a guessing game you will soon see that your bets are becoming very accurate with an increasingly smaller error count as your total number of samples increases. Ka-ching! Big data!

And there you have your fi rst algorithms! The data is our behaviour interpreted in a manner which gives someone a huge advantage over us. They can guess, nay, even predict how we behave or what we are most likely to do. This is the start of artifi cial intelligence.

Today, we build machines that can process mountains of information, learn our various guessing games and generate the rules that point to a result the next time a similar single piece of information is seen by them. By analysing millions of chess games, a computer with superior processing speed does beat the world’s chess champion.

Some of this type of intelligence application can be put to incredibly good use by us. A great example is the work that is now done with robots performing surgery. Not just the more common procedures but also high precision brain surgery, to me a terrifying concept.

Now let me introduce you to, and quote from, a research article published by Google:

“Modern mobile devices have access to a wealth of data suitable for learning models, which in turn can greatly improve the user experience on the device. For example, language models can improve speech recognition and text entry, and image models can automatically select good photos. However, this rich data is oft en privacy sensitive, large in quantity, or both, which may preclude logging to the data centre and training there using conventional approaches.

“We advocate an alternative that leaves the training data distributed on the mobile devices and learns a shared model by aggregating locally computed updates. We term this decentralized approach Federated Learning.”