Machine learning systems have been around since the 1950s, so why are we suddenly seeing breakthroughs in so many diverse areas?
Three factors are at play: enormously increased data, significantly improved algorithms, and substantially more-powerful computer hardware.
In a recently published article entitled, “Why the Time is Now for AI and Machine Learning,” author and expert, Colm Maloney, writes:
“The world of digital transformation is growing at an exponential rate, and the focus of digitalization is rapidly shifting from one technology enabler to another,” adding that a couple of years back, it was all social, mobile, analytics, and cloud (SMAC).
“While these technologies are still significant enablers, they have become commonplace in digital strategies, which is driving other technologies to provide much-needed differentiation in a sea of competition and disruptors,” he writes.
The current hype is all Big Data, Internet of Things, blockchain, and artificial intelligence.
“Proof of this hype is, for example, Jamie Dimon of JP Morgan speaking against blockchain and Bitcoin, indicating the impact of these technologies on the banking industry. For another, the potential economic value enabled by these technologies is beyond impressive – one example of this is a recent report from Accenture called Why AI is the Future of Growth,” he writes.
Perhaps even more important is the potential of cloud-based AI and robotics to accelerate learning and diffusion. Consider a robot in one location that struggles with a task, such as recognizing an object. Once it has mastered that task, it will be able to upload that knowledge to the cloud and share it with other robots that use a compatible knowledge-representation system.
Maloney suggests that, in this way, robots, working independently, can effectively gather data from hundreds, thousands, and eventually millions of eyes and ears. By combining their information in a single system, they can learn vastly more rapidly and share their insights almost instantaneously.
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