The AI Revolution HAS begun!

Some curmudgeons are arguing Artificial Intelligence (AI) is a bastardized term and the hype is distracting. People are arguing that we don’t have the freethinking, sci-fiesque AI or, as some people refer to it, Artificial General Intelligence (AGI). I say so what. Those of us in the AI business aren’t delusional and know that AI, aka Machine Learning (ML), is a fine weapon to bring into a software application to make it more sophisticated. It took me a while to understand that is all we are doing and that still is super important and valuable to organizations. I might admit I was initially mystified by “AI”, but at the end of the day (today anyway), it’s a bunch of math, code, data (for training – more on that later), and algorithms that either classify (organize data so it’s more valuable for making predictions) or triage (make decisions on which branch to send a task – automation or those pesky humans). Don’t make light of the ability to classify and triage at this level of complexity. We are seeing powerful applications of ML that are making dramatic impacts on numerous parts of organizations. We have published an ebook that goes into more detail and an ML Workflow to shed more light on embracing ML at a high level.

The math and coding needed to embrace AI are straightforward (for those skilled in the art). Access to relatively inexpensive compute power is certainly plentiful and also not a roadblock. The hard parts are 1) getting, grooming, and labeling data for the algorithms to use to learn how to accomplish new tasks, and 2) building accurate algorithms that use the state-of-the-art techniques and current, reliable libraries. Some of the same curmudgeons alluded to above are saying things like “machines can’t learn to be human-like by pattern matching from strings of labeled data”. I happen to agree, but again so what? We will see a natural progression toward AI that is more human-like. In the meantime, there is a lot we can do with what we currently have. High-quality, groomed, labeled strings of data pipelined in for training is a fine way to teach models to learn (ML models that is) new tasks – rule-driven or even unsupervised. Albeit a narrowly focused task, but still a task the machines can perform better and/or significantly cheaper than humans.

AI had some false starts over the years, but I can attest to the fact there are real budgets for and real initiatives surrounding AI. And not just at the big boys anymore. Amazon, Apple, Google, Netflix, Facebook, Microsoft, IBM, etc., have made great use of machine learning over the past decade or so. But recently, with major contributions to the open source, AI has been democratized. It is now possible for a boutique consulting firm like to help companies employ AI as an extension to their existing data management, computer science, and statistical/analytics practices. Staged adoption is the key. Come in with eyes wide open and know that there are nuances that need to be “tuned”, but you will see an impact and it will likely be orders of magnitude better than your current methods. And don’t expect Gideon or Skynet.

Ever since Geoffrey Hinton, Ilya Sutskever, and Alex Krizhevsky from the University of Toronto won the 2012 ImageNet competition with a deep convolutional neural network that beat the 2nd place team by ~41%, the “industry” has been paying attention. Academia has continuously supported furthering the AI cause over the years, but most companies and governments were leery of it as miserable failures had been the reputation… Then, in 2015, Microsoft won ImageNet with a model that surpassed human-level performance. With these milestones and benchmarks, adoption of machine learning has exploded over the past few years.

So, while we may not have AI with human-level intelligence (another way of saying it is that the machines can’t reason on their own), we do have AI that can imitate (i.e. replace) well-defined, domain-specific capabilities that were historically human-powered tasks. AI is proliferating marketing, advertising, sales, network and cyber security, business processes, and equipment maintenance, and will continue to work its way into new areas to augment or replace humans. Insurance companies are getting lift for the claims adjudication process, chatbots are supporting customer service, dialogue agents are selling products and services, sentiment models are driving portfolio management, radiological images are being assessed, cars are driving themselves, etc. These real-world examples coupled with the maturity of organizational readiness we are seeing when it comes to data management and engineering, is, in my opinion, evidence that the AI revolution has begun.