Heard the buzz about AI and Machine Learning? Here's the skinny.

The buzz around artificial intelligence is pretty dense. Here’s the buzz and the skinny to help you understand the truth about today’s AI landscape. Plus a few helpful points about major misconceptions to set your business on the right path toward success in the world of AI.
 
As with many new technologies, artificial intelligence has created a gold rush effect. All manner of products have been described as having been built with AI, to the point where the term has become a buzzword that has seemingly lost much of its meaning. Let’s get down to the skinny by breaking down the various forms of AI to understand what capabilities we really have available to us today.
 
At its simplest level, AI can be split into two categories:
 
WEAK AI or “narrow AI”

  • A collection of technologies that rely on algorithms and programmatic responses to simulate intelligence, generally focus on a specific task.  Turning on the lights with voice recognition is weak AI. Alexa may sound smart, but it doesn’t have any advanced understanding of language and can’t determine the meaning behind the words you speak.  To users, this can seem surprisingly intelligent — and voice recognition is far from a simple computing task — but in reality there is no actual “thinking” going on behind the scenes. In reality Alexa is simply following a pre-programmed series of actions.
 
STRONG AI or “true AI”
  • In contrast, strong AI is intended to think on its own.  To be aware of context and nuance making decisions that are not programmatic in nature but rather the result of a reasoned analysis.  In general, strong AI is designed to learn and adapt, to make a decision tomorrow that is better than the one it made today.
 
With this distinction in mind, what is Machine Learning?
 
  • Machine Learning is a specific type of AI put into practice, with the goal of giving a computing device access to some store of data and allowing it to learn from that data. Not all forms of AI are defined as machine learning. When Alexa turns on the lights, it doesn’t learn anything. It just waits to be told to turn the lights off.  In contrast, a ML system may be given a data feed:
  • Say temperature and tolerance information from sensors on a piece of manufacturing equipment; then asked to draw conclusions about the data feed. This may involve searching that data for trends, patterns, and anomalies, information that might not be obvious to a human observer.
  • A ML system may conclude that a machine needs to be repaired because it is about to fail or that it needs to be run at a lower speed. As the machine learning algorithm continues to learn from this data, it becomes progressively easier for it to generate additional insights down the line, and those insights become more accurate.
  • The bottom line: AI is not easy. AI is complicated. And people are throwing around buzz words conflating its meaning.  The skinny is it’s important to understand the distinctions so you know what you’re getting.
 
AI is not Magic Sauce with Sprinkles!
 
At its most fundamental level, the key to successfully building an AI outcome, regardless of the industry in which it’s deployed and its level of complexity, is training.  AI-enabled factory floor initiatives typically must gather several million gigabytes of data each week in order to have enough analytical data to make reasoned decisions about what might happen in the future. A spam filter must be trained on how to recognize a good email message from a bad one.  These are examples of training, and it’s not just a game of volume but one of quality, too. Successful AI algorithms must be trained on the right data sources.
 
If you tagged all the email messages from your spouse as spam, then tagged all the emails from Nigerian princes as not spam you’ll promptly see for yourself how quickly AI can go off the rails when it’s trained the wrong way.
The same is true in a more advanced industrial setting. If a sensor is miss calibrated and feeds inaccurate information to an algorithm tasked with monitoring equipment, all those gigabytes of data will end up being useless as the AI will use bad data in the course of reaching inaccurate conclusions.
 
My personal AI Buzz favorite:  Most companies don’t have the resources or need for AI/ML
 
Here’s the skinny on that one:  You don’t need a Ph.D. and there is no use case too small for smart technology.
  • It’s important to understand the difference between building an artificial intelligence solution from the ground up and implementing existing AI tools within your organization. The first of these is extremely difficult. The second is getting easier every day.  Small businesses limited in scope and scale can benefit from the lessons provided by AI and ML. In a small business you might task AI with testing and developing better social media ads, automating and improving customer service requests, or searching for patterns around when and why competitors are changing prices or product offerings. All of these AI-driven activities are readily accessible to even single-owner operations.
 
Another incorrect Buzz about AI: “AI and ML will replace me.”
 
McKinsey recently suggested that by 2030, 375 million workers — 14 percent of the global workforce — would need to “switch occupational categories” as machines become increasingly capable of doing work previously reserved for humans. In the shorter term, Gartner predicts that by 2020, 1.8 million jobs will be eliminated due to the increasing power of AI. The result has been a fairly breathless series of news reports that predict a full on Apocalypse.
The skinny on that one:
It requires humans to develop, deploy, manage and maintain it. That means jobs. In the same Gartner report, the firm predicts that those 1.8 million jobs lost will be offset by 2.3 million new jobs that will be created, for a net increase of 500,000 positions in 2020, and a net increase of 2 million jobs in 2025.
 
Last but not least; the final Buzz:
AI and ML are still science projects. A technology that’s decades away from making an impact and isn’t something that business leaders need to pay attention to today
 
The skinny:
More and more, leaders are finding that investments in AI and machine learning are paying off, and even pilot projects are turning in early, positive returns.

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