Artificial intelligence (AI) is right here, right now—and it’s changing our lives. The ever-present need for business optimization—combined with a long history of applied statistics, explosive growth in available data, and recent advances in cloud computing—has accelerated innovation and business transformation.
However, creating and implementing AI systems is tricky—many things to get right, many technology options, and many human and business considerations. This article covers the major functions and issues of AI at a high level, featuring Spotfire® analytics, which embodies all the major functions of AI—at scale in a single platform.
AI technologies
In recent years, we’ve been teaching machines ever more human tasks: speech recognition and in-home agents, image recognition, natural language processing, and chatbots that help customers troubleshoot their cable service. These AI techniques are firmly rooted in some areas, but it’s still difficult to get AI to function on-demand and get data science into operations. “The future is here, but it’s not evenly distributed,” as William Gibson says.
We take AI-driven systems for granted as part of daily life. For example, recommendation systems analyze our current behavior and purchase history to make personalized suggestions—Amazon for online purchases, Netflix for movie recommendations, and Spotify for music playlists.
More than this, the many real-life business applications that I see with Spotfire customers are a constant reminder of the value driven by AI and data science. AI helps all industries perform tasks otherwise not possible—such as managing financial risk, spotting fraudulent transactions, detecting and treating diseases, optimizing energy production, detecting anomalies in the manufacturing of computer chips, forecasting demand, engaging customers, and protecting the environment.
So, where did all this AI stuff come from all of a sudden?
Well, it turns out that the core ideas of AI are based on technologies built over years of mathematical statistics and computer science, that can now be run quickly and at scale. AI is algorithms—trained on historical data and managed in computer software pipelines, generating predictions that are actioned by business rules on event streams.
Machine learning and data science
The poster child for AI in recent years is machine learning (ML), especially the emergence of deep learning. Machine learning models are trained on historical data and predict from new observations. Broadly speaking, supervised learning models predict a target variable from other variables while unsupervised learning models identify patterns in data without focusing on a target. Classification models classify new observations into various categories like whether a credit card transaction is fraudulent or not, or if regression models can identify anomalies in IoT systems such as oil and gas production.
Extreme value is generated, and businesses are transformed, when ML models and AI apps are deployed into operations—making predictions on the data that flow through a business. While AI systems are generally developed to drive innovation and business transformation, we note that there is also the potential for unintended consequences. Indeed, with concepts such as hyper-personalization, people are now getting the news that they want to see, and some studies show falsehoods spread six times faster than truisms. As a practical matter, we are seeing regulations and ethical oversights driving “model fairness”—preventing discrimination against age, sex, race, and other communities by enforcing data and models that don’t discriminate.
AI on demand: Data science in operations
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