Artificial Intelligence (AI) has been enjoying a major resurgence in recent months and for some seasoned professionals, who have been in the AI industry since the 1980s, it feels like dj vu all over again.

AI, being a loosely defined collection of techniques inspired by natural intelligence, does have a mystic aspect to it. After all, we do culturally assign positive value to all things smart, and so we naturally expect any system imbued with AI to be good, or it is not AI. When AI works, it is only doing what it is supposed to do, no matter how complex an algorithm being used to enable it, but when it fails to workeven if what was asked of it is impractical or out of scopeit is often not considered intelligent anymore. Just think of your personal assistant.

For these reasons, AI has typically gone through cycles of promise, leading to investment, and then under-delivery, due to the expectation problem noted above, which has inevitably led to a tapering off of the funding.

This time, however, the scale and scope of this surge in attention to AI is much larger than before. During the latter half of 2014, there was an injection of nearly half a billion dollars into the AI industry.

What are the drivers behind this?

For starters, the infrastructure speed, availability, and sheer scale has enabled bolder algorithms to tackle more ambitious problems. Not only is the hardware faster, sometimes augmented by specialized arrays of processors (e.g., GPUs), it is also available in the shape of cloud services. What used to be run in specialized labs with access to super computers can now be deployed to the cloud at a fraction of the cost and much more easily. This has democratized access to the necessary hardware platforms to run AI, enabling a proliferation of start-ups.

Furthermore, new emerging open source technologies, such as Hadoop, allow speedier development of scaled AI technologies applied to large and distributed data sets.

A combination of other events has helped AI gain the critical-mass necessary for it to become the center of attention for technology investment. Larger players are investing heavily in various AI technologies. These investments go beyond simple R&D extensions of existing products, and are often quite strategic in nature. Take for example, IBMs scale of investment in Watson, or Googles investment in driverless cars, Deep Learning (i.e., DeepMind), and even Quantum Computing, which promises to significantly improve on efficiency of machine learning algorithms.

On top of this, theres a more wide scale awareness of AI in the general population, thanks in no small part to the advent and relative success of natural language mobile personal assistants. Incidentally, the fact that Siri can be funny sometimes, which ironically is technically relatively simple to implement, does add to the impression that it is truly intelligent.

But theres more substance to this resurgence than the impression of intelligence that Siris jocularity gives its users. The recent advances in Machine Learning are truly groundbreaking. Artificial Neural Networks (deep learning computer systems that mimic the human brain) are now scaled to several tens of hidden layer nodes, increasing their abstraction power. They can be trained on tens of thousands of cores, speeding up the process of developing generalizing learning models. Other mainstream classification approaches, such as Random Forest classification, have been scaled to run on very large numbers of compute nodes, enabling the tackling of ever more ambitious problems on larger and larger data-sets (e.g., Wise.io).

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The AI Resurgence: Why Now?