As the capabilities of artificial intelligence and machine learning continue to grow, leaders need to provide the framework and reality check to truly harness the problem-solving possibilities.
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Artificial intelligence (AI) has seen surge of interest—and not a little hype—primarily due to the prevalence of larger and larger data sets, increases in computing power relative to cost and development of new theoretical understandings of how to more effectively parse the subtleties underlying our complex world. The last 50 years has seen computing power increase by a factor over 2 billion, with a significant portion of that exponential growth happening in the last decade. Along with increased computing has come increasing quantities of data. By 2025, humans are expected to generate 463 exabytes of data (463 million terabytes), each day.1 While the volume of information generated by an individual business is much smaller, the scale of available data is truly mind-boggling. At the same time that increased density of transistors enable the high performance of modern smart phones, they also create the capacity for every business to apply the latest advancements in AI to the data it generates. Despite such power, the vagaries of a global pandemic and related geopolitical, social and market events in 2020 helped remind leaders of nearly all organizations of the challenges that confront leaders in making informed decisions in the face of ambiguous data. Can AI and machine learning (ML) help improve our ability to navigate in the fog? Maybe. But the field is moving fast, its limitations are often misunderstood, and every day there seems to a new breakthrough that will change the playbook for how we interact with machines. (For instance, the recently developed GPT-3, a research project from OpenAI, is a natural language generator.2 Trained with nearly 175 billion parameters, it is able to generate sensible language that, in most instances, is indistinguishable from that written by a human.) My own role has traditionally been in technical leadership, connecting the needs of the business to the details of the implementation of technology. This article is meant to provide a guide for how leaders of an organization can harness advances in the field of AI, anticipate pitfalls along the way and help bridge the gap between the needs of the business and the ability of technology implementors to most effectively meet those needs.
Artificial intelligence (AI) as a discipline is focused on the creation of autonomous agents that are able to perceive their environment and take actions to maximize the chances of succeeding at their goals. Practically speaking, AI is the application of input data to a predictive model to optimize an outcome.
This definition has wide-ranging applicability, from the computer-controlled characters in a video game, to the choice of positions in a game of chess or Go, to the movements of an autonomous vehicle as it navigates roadways. At the inception of the field of AI, most models were selected and optimized by software developers, based on the goals of the particular software. Many of these models as applied in business were based in “expert systems,” a symbolic representation of the data connected with rules that were processed to make a decision.
Rather than construct large systems of interconnected rules to make a decision, another approach is to borrow from statistics and probability, and to allow the computer itself to infer the model based on a training dataset. Machine learning (ML) focuses on allowing computers to use data and a specified objective to build such models.
One of the best examples of this comes from the early application of ML to automatically filtering spam e-mail. The problem set: Marketers were developing methods for delivering spam faster than traditionally designed word and pattern recognition could match. Using a training set of data obtained from the e-mail recipients themselves, a Bayesian classifier (a type of ML program) could identify the unwanted messages for a given user with their own mail based upon that person’s filtering habits. The outcome: a reasonably personalized spam blocker that could evolve.
Applying AI in 2021
Big Circles, Little Circles
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