AI, What is it and why MUST we care?

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There is a huge buzz right now about data science, machine learning and artificial intelligence or AI, but what is it really and why do we care? Is it the latest hype, and this too will pass, or does it really have legs? And why are so many companies buying the tools, building data lakes, and hiring the consultants? So is AI real?

First, a definition. Artificial intelligence, data science, machine learning, just what is it? Data science is the superset of the data processing spectrum, including Artificial Intelligence, Machine Learning and Deep Learning. John Foreman, Chief data scientist for Mailchimp, defines data science as “the transformation of data using mathematics and statistics, into valuable insights, decisions, and products.”  Simple really.  Apply advanced statistics and math to find patterns, trends and make predictions. I like this definition because it is business based. It clearly indicates the intrinsic merit of data but only when applied through lenses to derive insights and business outcomes.

While data science covers the whole spectrum of data processing, we still need to define AI, Machine Learning and Deep Learning. UC Berkeley defined the parts of data science in these segments.  Webster’s dictionary defines artificial intelligence as, “a branch of computer science dealing with the simulation of intelligent behavior in computers”. You use AI every day when you speak to Cortana, Siri, or Google Now as a personal assistant on your phone. AI is in play when we say things like, “Hey Cortana, text my son - call your mom”, And Cortana looks up my son, his phone number, and sends the text.

In 1959 (yes, 1959!), Arthur Samuel defined “machine learning as the construction of algorithms that can learn from and make predictions on data”.  Machine learning is everywhere now. It is used in the financial sector to detect credit card fraud, Facebook uses it to rank ads and news feed impressions, and in retail, it is used for customer segmentation by buying behavior patterns. 

Techopedia has the simplest definition of deep learning. “Deep learning is a specific approach used for building and training neural networks, which are considered highly promising decision-making nodes. An algorithm is considered to be deep if the input data is passed through a series of nonlinearities or nonlinear transformations before it becomes output”. Yes, this is the simplest definition! As an example, language translation services (Spanish to English, for example) have made phenomenal improvements due to deep learning.

So why do you care? AI, machine learning, deep learning….so what? AI will change the world because of its speed and power. According to SAS, AI automates repetitive learning, and more importantly delivers discovery and insights through the analysis of vast amounts of disparate data (data from Facebook, email, video cameras and core systems (e.g. ERP and CRM systems).  AI is real, and growing at a very fast rate. According to Deloitte, the compound annual growth rate of AI is over 36% per year.

Artificial intelligence is being used in every industry, from health care (e.g. personal health care assistants, predicting patients who are at risk for hospital readmission), retail (e.g. BOTs for customer service, personalized offers, optimizing pricing and inventory), manufacturing (e.g. predictive maintenance for manufacturing equipment), to sports (e.g. player selection, optimizing team performance, play calling). For a simple example of the use of AI, watch Moneyball, where statistical analysis (data science) is used to take a losing baseball team with a small budget, to a championship team (still with a small budget). 

So where will you apply AI to transform your business?