Since I was a young girl studying computer science and engineering in India, I've always been interested in exploring how science and technology can help solve real-world problems. In my IBM Research career that spans 20 years and counting, I've indulged my passion for problem-solving by building technologies that help businesses and organizations of all shapes and sizes overcome their challenges. I try to instill this fascination for problem-solving in my 10-year-old daughter, who recently attended a robotics summer camp. When she came home frustrated from the camp one day, she asked me, "Why should I care about artificial intelligence? What does A.I. have to do with me?"

Neural networks are a rapidly developing paradigm for information processing based loosely on how neurons in the brain processes information. A neural network consists of multiple layers of node, where each node performs a unit of computation, and passes the result onto the next node. Multiple nodes can pass inputs to a single node, and vice-versa. The neural network also contains a set of weights, which can be refined over time as the network learns from sample data. The weights are used to describe and refine the connection strengths between nodes.

TensorFlow is a framework created by Google for creating Deep Learning models. Deep Learning is a category of machine learning models that use multi-layer neural networks. The idea of deep learning has been around since 1943 when neurophysiologist Warren McCulloch and mathematician Walter Pitts wrote a paper on how neurons might work and they modeled a simple neural network using electrical circuits. Many, many developments have occurred since then. These highly accurate mathematical models are extremely computationally expensive.

Rana El Kaliouby is cofounder and CEO of Affectiva, the pioneer in emotion-aware technology--the next frontier of artificial intelligence. Rana invented the company's award-winning emotion recognition technology, built on an emotion AI science platform that uses deep learning and the world's largest emotion data repository of nearly 4 million faces analyzed from 75 countries, amounting to more than 40 billion emotion data points. Prior to founding Affectiva, as a research scientist at MIT Media Lab, Rana spearheaded the application of emotion technology in a variety of fields, including mental health and autism research. Her work has appeared in numerous publications including the New Yorker, Wired, Forbes, Fast Company, the Wall Street Journal, the New York Times, CNN, CBS, Time magazine, Fortune, and Reddit. A TED speaker, Rana was recognized by Entrepreneur as one of the seven most powerful women to watch In 2014, inducted into the Women in Engineering Hall of Fame, recognized as a 2012 Technology Review top 35 innovators under 35, listed on Ad Age's 40 under 40, and given Smithsonian magazine's 2015 American Ingenuity Award for Technology.

Artificial intelligence and machine learning are ushering in the rise of smart machines that will be able to carry out many of the complex cognitive tasks that once seemed exclusive to middle class work in the knowledge economy. Already, doctors are using deeper learning to help diagnose illnesses, entry-level lawyers are finding themselves out-analyzed by machines that can harvest case history faster than any human, artificial intelligence is writing news stories and robots are staffing restaurants. This is just the beginning: one Oxford University study suggests that as many as 47% of current middle-class American jobs could get displaced or change significantly over the next two decades due to automation.

The industry has always invested in technology and has also not been averse to adopting innovations in technology. However, the demand, from both the customers and the business leaders, has been consistently outpacing the resources available to execute all the various initiatives within the bank. Chief information officers (CIOs) have always had to prioritize and select where they will invest their resources. The initiatives that enhance revenue generation opportunities and those which improve customer experience have always taken precedence over those that address operational efficiencies. So as they expand and scale their operations, inefficiencies start building up in the operational processes.

Three years earlier, Apple had been the first major tech company to integrate a smart assistant into its operating system. Siri was the company's adaptation of a standalone app it had purchased, along with the team that created it, in 2010. Initial reviews were ecstatic, but over the next few months and years, users became impatient with its shortcomings. All too often, it erroneously interpreted commands. So Apple moved Siri voice recognition to a neural-net based system for US users on that late July day (it went worldwide on August 15, 2014.)

When I enrolled in Computer Science in 1995, Data Science didn't exist yet, but a lot of the algorithms we are still using already did. And this is not just because of the return of the neural networks, but also because probably not that much has fundamentally changed since back then. At least it feels to me this way. Which is funny considering that starting this year or so AI seems to finally have gone mainstream. But as I have learned these past years, it only feels like a long time ago if you haven't been there yourself.

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