By Thibault Dousson – If you were around in the 1980s when the first PCs appeared, you’ll remember how we marvelled at the fact that devices that previously filled entire rooms now sat on our desks.
For the next three decades or so, we saw impressive evolutions of that original concept, yet, there was little in terms of real revolution.
But then, about 10 years ago, revolution kicked in. We started to see the convergence of smart phones and laptops. People had more devices to interact with. New software in the form of apps transformed what we could do, and reconfigured our expectations of what technology was capable of.
Workplaces introduced bring-your-own-device (BYOD) options, which accelerated the notion of choosing what was right for you – based on everything from price and power, to operating system and processor speed. This is in stark contrast to the PC of 30 years ago, when your only options were between two brands and two price points.
We are now in a time where the way in which technology interacts with itself and humans is transforming how we touch, choose, and use technology. It is the era of personalised computing.
This is a captivating shift because it brings us a step closer to a long-imagined ideal: computing that is all around us. Technology is now helpful, omnipresent, and integrated with our everyday lives. It is moving beyond traditional form factors and morphing into what suits the individual’s lifestyle and personal taste. Laptops, tablets, smartphones, and desktops are increasingly indistinguishable.
With technology now more powerful than ever, personalised computing is evolving even more as artificial intelligence (AI) develops rapidly.
The ABCD needed for the development of AI
In order to further accelerate the development of AI, and therefore continue to push the boundaries of personalised computing, we need a simple ABCD – algorithm (A), business (B), computing power (C) and data (D).
The development history of algorithms, from logistic regression, ANN, SVM, HMM, and expert system to deep learning are all of vital importance to the development of AI.
As for computing power, it now seems almost unlimited (as we now have GPU and FPGA) with the former being mainly used for servers and workstations and the latter for embedded systems. Moreover, people across the industry are working on ASIC, which is, however, not yet mature.
The explosion of data we have had in the last five years was unimaginable 20 years ago. Ninety percent of the current data was created in the past two years with 1,7Mb of data created per second per person – and we’ve only used a very small amount of all this data.
A, C and D are extremely important for the development of AI. But for AI to impact upon people’s lives, we need to combine it with B, or specific industries. For example, when you come across an interesting tree or flower while walking in Kirstenbosch Gardens, you may want to know what it is and learn more about it. By joining the insights of botanical experts with AI, we could help people do that.
In the future, AI will continue to profoundly change industries and people’s lifestyles.
To further advance the development of AI and make personalised computing even more unique to each individual, we still need to make a greater effort in the fields of algorithm, business, computing power, and data.
The algorithm is like the engine of a car, business is the steering wheel of the car, computing power drives the car forward and data is the fuel.
Thibault Dousson is the GM of Lenovo South Africa & SADC