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5 Innovative uses of machine learning


5 Innovative uses of machine learning

5 Innovative uses of machine learning

Although its time horizon is unpredictable, artificial intelligence (AI) promises to fundamentally impact modern society, for better or worse. A subtype of artificial intelligence -- machine learning -- has received particular attention from experts for its potential impact on the world's most important industry.

Due to the hype that comes with it, a lot of talent and resources are entering the space.

Machine learning model is an application of artificial intelligence in which algorithms independently predict outcomes. In other words, these models can process large datasets, extract insights and make accurate predictions without much human intervention.

The accelerated development of this technology brings many value-producing implications, and many are poised to radically simplify the business world. Here are five of the most innovative use cases for machine learning. They'll enter your life -- at least your business life -- sooner than you think.

1. Widespread use of self-driving cars

The widespread adoption of autonomous vehicles represents a more efficient future of transportation. Early reports suggest that self-driving cars could reduce traffic-related deaths by as much as 90 percent.

While we may be a few years away from consumer production, at this point society's adoption of self-driving cars is inevitable. However, the timescale for adoption of the technology depends heavily on regulatory measures, which are often outside the tech world’s control.

The software engineers developing these self-driving "fleets of the future" rely heavily on machine learning techniques to power the algorithms that enable the vehicles to operate autonomously. These models effectively integrate data points from many different sensors - lidar (a measurement method that uses laser light), radar and cameras - to operate the vehicle. Over time, these deep learning algorithms become smarter, leading to safer driving.

2. A more efficient medical network

Despite being an important part of the economy, the healthcare industry operates on top of inefficient legacy infrastructure. A major focus is finding ways to optimize the system while preserving sensitive patient details.

Fortunately, we can apply innovative machine learning algorithms (that run without humans) to process large volumes of healthcare data without violating nondisclosure contracts. Additionally, we can use these models to better analyze and understand diagnoses, risk factors, and causality coefficients.

As Dr. Ed Corbett points out: "It is clear that machine learning has dropped another arrow in the quiver of clinical decision-making.

"Machine learning in medicine has been making headlines recently," said Corbett, a medical officer at Health Catalyst. 

3. Embedded retail management system

Over the past few years, international retail sales have consistently exceeded $20 trillion. This amazing p comes with a wealth of consumer behavioral data (demographics, trends and tastes) gathered from an infinite number of consumer shopping patterns and trend troughs.

However, many retail companies struggle to realize these valuable insights because the information often comes from disconnected data warehouses. Therefore, there is a huge opportunity to implement machine learning models that allow retailers to better understand their customers and provide a more personalized customer experience.

Using previously acquired data, machine learning models can predict everything from which product to recommend to when to give a discount. In particular, e-commerce retailers can incorporate digital behavioral patterns to optimize the entire user journey from the first point of contact to purchasing an item to follow-up.

4. Improve the content adjustment

Moderation of content is a major concern for social media platforms such as Facebook and Twitter, as they strive to provide accurate information to their audiences. As highlighted in the last election cycle, failing to properly overturn an election can have serious repercussions.

In response to a public outcry over "fake news," Facebook recently announced that it would hire 3,000 new employees dedicated to the platform's news feed content. However, this anxiety extends far beyond social media, as tech groups like Google are investing heavily in developing their own content monitoring teams to support their fast-growing market.

Emerging machine learning and AI platforms, such as the Orions system, are providing proprietary systems to "evolve and adapt the interaction between humans and AI" for tasks such as large-scale moderatingcontent.

Uniquely, these technologies are addressing the task of conditioning content using innovative tools and resources (for example, analyzing the context and content of each video frame) so employees can work more efficiently. This is an important advance because training machine learning to process video is notoriously difficult.

5. Advanced Cybersecurity

The cost of cybercrime damage is estimated to soar by more than $6 trillion annually in 2021. Experts predict that between 2017 and 2021, companies will spend more than $1 trillion on cybersecurity services to counteract this growing threat. Clearly, cybersecurity will continue to be a major priority for startups and large corporations alike.

Researchers are developing ingenious ways to implement machine learning models to detect fraud, prevent phishing, and defend against cyberattacks. Defense mechanisms systems are being trained using past data to quickly detect and prevent suspicious activity. Unlike humans, these algorithms can run 24/7 without running out.

As these machine learning models become more accessible to developers, they will start to gain a lot of acceptance from consumers and businesses. And, when that happens, it will be interesting to see which models.