Data scientists are clever, they often come armed with PhDs, and these days, while data sits in the cloud, they often command salaries that reside above the clouds. Will it always be like that? What about the future of data scientists? Demand for data scientists going one way and that is up, but in parallel with this, increasingly more tasks, previously seen as the preserve of data scientists, may be carried out by others, or indeed, automated.
AI developing an understanding of semantics is the next step in its evolution. AI’s true power to revolutionise industries and determine key business insights, lies in its ability to read text and understand the semantics (or relationship between words) to help organisations further mitigate risk and uncover liabilities. In turn this creates massive value in natural language processing. So, in the story of AI enabling understanding of semantics, what is the next step in its evolution and when are we likely to reach this milestone?
Artificial intelligence or AI, the phrase/acronym gets banded about an awful lot. In fact, many experts in AI don’t even like the term, they much prefer to use the words machine and learning, or ML. Delve deeper, and you come across many more terms, neural networks, deep learning, natural language processing and random forests. The trouble is, to a lot of people, the phrase artificial intelligence conjures up images of machines, ruling the world. The reality is very far from this vision of science fiction.
The explosion of data causes problems for the way in which we currently process data. Edge computing helps solve this, by providing computational power, in the form of local computing power, such as smart phones, on the edge. Why do we need Edge computing? What is it? What are the advantages? The five pillars to edge computing provide the answers. Edge computing, by taking advantage of hardware that has already been funded, can overcome many of those disadvantages without necessarily losing the flexibility of the cloud.
You could say there is too much AI hype. It is not a contentious thing to say, it just is. Maybe it would help if we re-defined it. Instead is saying AI means artificial intelligence, maybe we should return to an earlier definition, algorithmic intelligence, instead. Artificial intelligence is the application of algorithmic computation to large data sets. To see through the hype, just remember that AI could just as easily mean algorithmic intelligence.
Now the technology cycle has thrown up edge computing but the shift is still in its infancy. The shift was partly created by the Internet of Things — the massive explosion in devices, all with their own processing power. This processing power was often under-utilised. The swing in the technology cycle to edge computing has also been driven by privacy concerns — the ability to store personal data on individuals’ own devices, rather than somewhere in the cloud.
AI needs to be applied such that it augments us, not compete with us, is long. Yet the supply of reports warning that AI threatens jobs doesn’t seem to have an end. On the other hand, a new report looking at a technology called Swarm AI may provide a much more benign fix. Swarm AI can take individual humans, whom one would like to think are more intelligent than shiners, and create something truly insightful.