Things Made Possible by the Machine Learning Revolution

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One of the hottest areas of technology today, machine learning promises to have a profound impact on our world and the way we live. Perhaps most exciting is the fact that we have reached the point where it is much more than just hype, as evidenced by the massive amount of investment from the corporate world in talent and resources dedicated to this space.

Usually described as a subset of artificial intelligence, machine learning involves computers using large amounts of data to teach themselves and improve their performance of certain tasks. Already, we are seeing machine learning applied in a variety of areas, from transportation to healthcare, retail and cybersecurity, among others.

Self-driving Cars

Probably the most high-profile example machine of learning becoming reality is in the autonomous vehicles space. While widespread adoption is still several years away, self-driving cars have been in development for some time, and are currently in test pilots in 50 cities around the world – with about half of them in the U.S. The algorithms that allow these vehicles to operate autonomously are heavily reliant upon machine learning technologies. As these algorithms learn and become “smarter,” driver safety is increased. Some experts believe autonomous vehicles could reduce traffic fatalities by up to 90 percent.

Increased Patient Privacy and Smarter Healthcare

The healthcare industry has long been criticized for its use of outdated and inefficient legacy technologies. One of the challenges that has held the industry back is the difficulty in optimizing its systems while still maintaining and protecting sensitive patient records. But without the need for human interaction, machine learning algorithms can process large sets of healthcare data while avoiding the risk of confidential contracts being breached. At the same time, machine learning algorithms can be used to make faster and more accurate diagnoses and to better understand and analyze potential health risk factors based on age, genetic history and socio-economic status.

A Better Shopping Experience

The recent boom in the retail industry has been well publicized. Over the past few years, it has consistently generated more than $20 trillion in sales annually. A major driver in this tremendous growth is the increasing strength of online sales, which comes with a huge amount of data being compiled on consumer behavior, shopping trends and tendencies. But because much of this data is housed in disconnected warehouses, many retailers have not been able to take full advantage of these valuable insights.

That is beginning to change, however, as machine learning models are being implemented that use previously acquired data to predict such things as which products to recommend and the optimal time to offer discounts. As that happens, retailers will gain a much better understanding of their customers’ behavior and be able to provide an even more personalized online shopping experience. These are likely significant factors in estimates placing the global retail market at close to $32 billion by 2023.

Fighting Cybercrime

Another area driving adoption of machine learning technologies has to do with something we all read a lot about these days and have increasingly experienced firsthand: cybersecurity threats. Not only is cybercrime on the rise, but the severity of the attacks is increasing. By 2021, worldwide damages from cybercrime – including fraud, theft of personal and financial data, and mass disruptions of corporate and governmental systems – are expected to reach $6 trillion. For companies, the repercussions to reputation, data and bottom line are almost immeasurable. The situation has gotten so bad that employers have been unable to hire enough cybersecurity workers to combat the problem.

Worse still, cybercriminals are becoming increasingly more advanced and sophisticated in their methods, meaning no business or device is safe from an attack. As a result, companies are expected to spend a staggering $124 billion on cybersecurity in 2019, a predicted increase of almost nine percent over the previous year.

The good news is, researchers are implementing machine learning models in clever ways to defend against cyberattacks and detect fraud, using past data to quickly spot and protect against suspicious activity. In a recent survey by 451 Research, almost half of respondents indicated that they have deployed or plan to deploy machine learning in their organizations in the next 12 months.

With the ability to learn based on experience and use that knowledge to inform their behavior when confronted with similar issues in the future, today’s cybersecurity applications have major advantages over older, more passive versions. And, unlike humans, the algorithms these applications use are able to continuously run 24 hours a day, seven days a week. That said, humans will still be needed to help identify the policies, procedures, processes and countermeasures to keep an organization safe.

Machine learning is not the silver bullet that will vanquish cybercrime altogether, as threat actors are also employing machine learning methods in their attacks, but it represents a significant step forward in combating the threat. The good guys just have to remain diligent in ensuring their learning models evolve quickly to keep up with the latest trends.

The machine learning revolution is well underway, and the things it will make possible will change our world in ways we can only begin to imagine. These examples barely scratch the surface. The most fascinating use cases are likely things we have not yet conceived.

About the Author

Joseph Feiman, PhD, is the Chief Strategy Officer at WhiteHat Security, a leading application security provider. Feiman is responsible for WhiteHat’s overarching business strategy and vision, to further its success in empowering secure development and operations. Previously, Feiman worked for Gartner as Research Vice President and Fellow. During his tenure at Gartner, he served as a trusted resource for security executives and professionals across the globe, co-founding the application security market category. Prior to joining WhiteHat Security, Feiman was chief innovation officer at application security vendor Veracode, helping the company to reach its peak.

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