Technology has become so advanced that, today, there's an app for almost anything, from children's education, to home improvement, to health monitoring, to workplace productivity. Gathering critical data to determine the best action to apply to specific situations has become integral in people's daily lives. Because of technology, critical decisions are now mostly based on scientific data. This makes every action more precise and error-free, especially in the business world. By using artificial intelligence and machine learning, industries can better cope with their consumers' demands.

AI & Machine Learning Applications in the Real World According to the latest trends of AI-based solutions, there is hardly any decisive sector or industry that does not rely on smart algorithms and automation to perform highly advanced tasks that would be impossible for most humans. Many companies use Machine Learning and Artificial Intelligence to identify and sort through the best possible candidates for a position. With a few Machine Learning courses that are specially designed for regular people, without advanced technical knowledge, it's easy to understand why there are so many applications of advanced technologies in the real world. Luckily, this situation can now be avoided by training machine learning algorithms to take over the task. According to a case study performed at Canada's largest bookstore chain (Indigo), the use of AI and machine learning algorithms to screen job candidates and decide who to hire has led to an increase in overall productivity.

See things in your data that no one else can see – and make the right decisions! Due to modern technology and the internet, the amount of available data grows substantially from day to day. And they also know that seeing the patterns in the data gives them an edge on increasingly competitive markets. Proper understanding and training in Machine Learning and Statistical Modeling will give you the power to identify those patterns. This can make you an invaluable asset for your company/institution and can boost your career!

Machine learning is headed for a major growth spurt. After ticking past the $1 billion mark in 2016, the machine learning market is expected to hit $40 billion by 2025, according to a new report by Research and Markets. Of course, the first challenge of machine learning is identifying a use case. Not sure where to start? To make the most of this explosive technology, consider how today's top companies, ranging in industry from retail to hardware to media, are using it:

Day-by-day Machine Learning is becoming popular among industries, and business owners have finally started to believe that it can bring a colossal change in their business's efficiency. Many experts say that Machine Learning is a breakthrough as big as the internet and the personal computer. Since the past decade, people have started to learn about ML and understand what it can bring to the table when implemented in their business. ML is not a completely flourished technology, but people are giving it a chance because it can provide a huge boost to their businesses. Still, a large number of audiences are not aware of what Machine Learning actually is even though they have been using it for the past 5 years in the form of Siri, Cortona, Google Maps, and even the recommendations while shopping online.

"We see a really deep and pressing need for guidelines and for an actual framework to measure risk for machine learning," says Andrew Burt, chief privacy officer at Immuta and one of the paper's authors. In the paper, released Tuesday, they offer some guidance to companies thinking about these issues. Among their suggestions, inspired in part by a 2011 Federal Reserve document on handling financial model risk, is that companies set up three "lines of defense" in handling artificial intelligence risk. Those should include data scientists and other experts defining exact assumptions and goals around a project; a second team of data and legal experts who work as "validators" and review assumptions, methods, documentation, and information on underlying data quality; and a regular third line of defense involving reviews of the overall assumptions around the model and how they're working out. FPF & Immuta – How can we govern a technology its creators can't fully explain?

Caterpillar, a company that manufactures marine power systems, uses IoT and machine learning to uncover patterns in equipment and device data. In one example, Caterpillar identified that fuel meter readings were correlated with the amount of power used by on-board refrigerated containers. They use that data to optimize operating parameters by modifying generator output. The resulting savings was $30 per hour, or $650,000 over a year, for 50 ships.

When a car company issues a recall, it's typically on dealerships to reach out to affected customers. But since vehicles can change hands, leaving records out of date, dealers aren't always able to provide drivers with this at times vital information. One company that addresses this issue is Recall Masters, founded by programmer Chris Miller. Recall Masters, which employs 20 people and even a lobbyist in Washington, D.C., collects data from more than 50 different sources, then utilizes machine learning to analyze it. The startup can then determine if a vehicle qualifies for a recall and who its current owner is -- even if it has been resold multiple times -- by poring over billions of transactions, according to Miller.

If Gabe Otte '11 hadn't had a Cornell advisor who steered him down a more challenging path and hadn't had some chance conversations with Nobel Prize-winning chemist Roald Hoffman, he might be squirreled away in a lab somewhere. Instead, he's the CEO of Freenome, a start-up just awarded 5.5 million in venture capital for its product, a data-driven blood test that can detect various types of cancers in their earliest stages and recommend the best treatments. Otte came to Cornell planning to study computer science, but a freshman-year advisor encouraged him to choose another major. "I had been coding and programming since I was nine years old," Otte said, so he elected to study chemistry and computational biology, using his knack for computer science to do his homework. "I fell in love with chemistry when I took organic chemistry," he said, adding that he developed his own computer program to do computations related to the synthesis of molecules.