An Executive’s Guide to Deep Learning
Last week’s OrboGraph Conference theme in Charlotte, NC was 2019: The Year of AI and Modernization, and the attendees were treated to a variety of presentations by and interactions with experts on various aspects of Artificial Intelligence (AI) and Machine Learning (ML) – – the Deep Learning topics that are of vital importance to any information-based industry that wants to thrive in the 21st century. We’ve previously noted how AI, for example, is becoming a powerful force in RCM.
For those who did not attend our conference and are still struggling to grasp the function and power of ML and AI, we offer some particularly useful and succinct information sources to help bring you up to speed. Obviously, this is a field into which the curious can be swallowed whole — there is a LOT of AI information available to absorb, and more being created every day — so the goal is to provide the “four corners” of the tech.
Start with An Executive’s Guide to Machine Learning, which gets into the concept of Machine Learning using a sports example:
This past spring, contenders for the US National Basketball Association championship relied on the analytics of Second Spectrum, a California machine-learning start-up. By digitizing the past few seasons’ games, it has created predictive models that allow a coach to distinguish between, as CEO Rajiv Maheswaran puts it, “a bad shooter who takes good shots and a good shooter who takes bad shots”—and to adjust his decisions accordingly.
Open Box Software explains the difference between simple automation and the deeper “cognition” of AI and ML. Where automation simply responds to binary information (“if this, then that”), AI/ML is deployed to actually bring its “experience” to each interaction:
AI and ML on the other hand aim to model their “choices” on the human mind, in other words, apply the concepts of intelligence, learning and cognition to decision making, instead of simply following the rules.
The Next Platform shows how generative adversarial networks (GANs) are used to actually aid in identifications and treatment of disease — an example of how AI is vital to the business and science of medical care.
And, finally, Data Driven Investor offers a succinct article explaining some of the differences between Machine Learning and Artificial Intelligence — they are not interchangeable — and why we should be wary of technologies that claim AI integration when they may indeed have only Machine Learning deployed.
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