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Artificial intelligence, machine learning major themes at second annual Goizueta Business Analytics Conference

Artificial intelligence, machine learning algorithms, natural language processing, the industrial internet of things and crime-fighting robots all converged at Goizueta’s second annual Business Analytics Conference and student showcase on May 3.

Capgemini Invent’s Chief of Intelligence and Vice President for Artificial Intelligence and Data Sciences Severence MacLaughlin kicked off the day with his energizing and well-received “AI for Business—An Evolution or an Extinction Event.” MacLaughlin explained, “AI is a collective term used to describe machines that can mimic the human mind, learning from experience and adjusting to new inputs.” Next, Rajkumar Bondugula, principal data scientist and senior director at Equifax, shared with the audience “How to Take Your Team Along on a High Performance Computing Journey.”  He reinforced the importance of explaining the business use case in common terms when training employees.  An audience member from one of the MSBA program’s Capstone partners indicated their company was facing similar challenges and appreciated hearing best practices.

Kadadi, who also attended the inaugural Goizueta Business Analytics conference, shared, “This year’s intentional focus on connecting and collaborating while delivering content was noticeable and even better than the first!” The event closed with a powerful combination: Joe Sutherland, head of data science at Search Discovery, discussed the misconceptions of AI, while Intel’s Director of Technology Muhammad Abozaed examined how ethical applications of AI will improve humankind.

Cisco Makes Networks Smarter With Artificial Intelligence, Machine Learning

“A few years ago, people would connect laptops, desktops and printers to the networks, applications would run in the data center and each device on the network would be managed, box by box,” Cisco senior vice president Sachin Gupta explained in an interview with Barron’s.

We need the network to operate as one system.” Cisco says adding AI and iterative machine learning to its network software will help IT departments cope with a data-management burden that grows every day even if their budgets don’t rise correspondingly.

The goal, Gupta says, is “a better user experience, better performance, better reliability, and lower operational cost—to get the best performance out of your network for your unique environment.” The new AI and machine learning features, which are included in subscriptions to the company’s DNA software, will be rolled out in August.

Machine Learning And Artificial Intelligence In Cybersecurity: Hype Versus Reality

In the last few years, we have witnessed a renaissance in machine learning (ML) and artificial intelligence (AI).

For example, a computer-vision ML model can be trained with millions of sample images that have been labeled by humans so that it can automatically classify objects in a new image.

In theory, if a machine has access to everything you currently know is bad, and everything you currently know is good, you can train it to find new malware and anomalies when they surface.

There is hype around the ability of new AI/ML-powered security endpoints that claim to “do it all.” But if you want to protect a user from cyber threats, you need to make sure all content the user accesses is scanned, and you have to keep the user’s system patched and up to date.

Sandboxing involves installing a suspicious file in a virtual machine sandbox that mimics the end user’s computer and then determining if the file is good or bad based on its observed behavior.

When a customer asks why a file was blocked, the answer cannot be “because our AI/ML said so.” Having security domain experts who understand what attributes or behaviors got triggered and who are able to analyze false positives/negatives is important — not just for understanding why a certain prediction was made, but to iteratively improve model prediction accuracy.

For malware, this means human experts classify each sample in the data set as good or bad, and feature-engineering is performed to determine what attributes of the malware are relevant to the prediction model prior to training.

Significant advances in AI/ML vision algorithms have resulted in the ability to apply techniques to detect fake websites designed to trick unsuspecting users.

AI/ML algorithms can also be used for detecting anomalous user behavior, learning a baseline of what a user normally does and flagging when there is a significant departure from the norm.

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