Today few conversations in tech and digital transformation are complete without a reference to Artificial Intelligence (AI).
However, AI is a topic that’s been around since 1950.
That’s when Alan Turing (cracker of the Enigma code in WW2), published his seminal research paper ‘Computing Machinery and Intelligence’. This posed the question ‘Can machines think?’, and introduced the Turing Test, which tested a machine’s ability to be indistinguishable from a human, in terms of its reactions.
Despite initial interest, there was no major scientific breakthrough that would have kept momentum going. As a result, government funding dried up, with interest only reviving periodically. The concept of Artificial Intelligence remained on the periphery of most technological discussions.
Man v machine
That all changed in 1997.
Chess grandmaster Garry Kasparov lost to IBM’s artificially intelligent machine Deep Blue, which was capable of searching 200,000,000 moves per second.
It was a headline-grabbing demonstration of a machine crunching high volume data sets at high velocity to overcome the world’s best (human) chess player.
What’s more, it moved Artificial Intelligence back into the spotlight.
A new perspective
Perhaps more significantly, this helped shift attitudes to AI, igniting ideas and laying the foundations for today’s data-driven culture. For example, 8 out of 10 consumers in Singapore now welcome banking, insurance and retirement advice from a robot.
In Hong Kong, a recent KPMG report found ‘Banks are starting to use AI technology to gather social, economic and other relevant data to help create customised products and services. They can also utilise unstructured data to gain valuable insights into their customers’ behaviours and preferences’.
Earlier this year, a Japanese insurer hit the headlines when it announced plans to use AI to replace 30% of the workforce. The technology was reported to be used for calculating payouts, after ‘reading’ supporting medical evidence.
Over the next five years, AI will generate $19 billion in increased revenue in Australia. That’s according to an IDC report, which pinpoints 2018 as the landmark year for AI adoption in the region.
Change is the only constant
Previously, data scientists would have to work with samples of data. This limited approach left potential for errors. However, the advent of big data tools mean that scientists can now work with full data sets to fully understand patterns.
What’s more, the rise in visual data applications makes it possible to interpret the data and present it to a non-scientific audience. Which means business leaders can make strategic decisions based on the findings – without needing to be data experts. This has had a knock-on effect on data scientists, described as a ‘hot new career’ last year, now forced to evolve in the face of AI and machine learning. Indeed, some believe machine learning means that ‘the skill set of the data scientist will be rendered useless in 12–18 months’.
One thing that won’t be rendered useless is the data centre. After all, underpinning everything in AI is data. Take away access to data, and the AI revolution will grind to a halt. Of course, this won’t happen. As long as data centres continue evolving, to meet client demand for infrastructure that supports and enables AI.