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Machine Learning Is Creating a Global Shift Featured

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Artificial intelligence (AI) and Machine Learning (ML) are now considered some of the most groundbreaking inventions since the internet. We used to watch AI in movies and science fiction books, but it has now become a reality that we interact with in our daily lives. AI has become so famous to the extent that almost all technologies being invented today have some element of AI or machine learning. ML, a subset of AI, is an implementation of some abilities of humans. It enables people to carry out certain tasks by collaborating with smart software.

Here is how machine learning is creating a global shift:

  • Self-driving cars and automation of transportation

Self-driving cars and automated transportation has caught the attention of automakers in recent years. It is a battle-ground among companies such as Tesla, Toyota and other tech companies such as Google and Baidu. Commercial airplanes are using a combination of GPS, computer systems and sensors to track a plane's position during flight. Self-driving vehicles are among the key adopters of ML. These cars use ML on the road to avoid obstacles and process information regarding traffic patterns and rules. AI-powered cars are now considered safe than human-driven cars.

  • Personalized digital media

Social media has become a destination of choice for marketers and advertisers. For this reason, social media companies now use ML to help advertisers make decisions based on the preferences of their target market. From feeds that we see to social media notifications we receive, ML is used to curate the content that is shown to the customer. Machine learning examines our past behavior, web searches and interactions and customizes content based on what we like to improve our experiences. An example of such is YouTube, Facebook and Spotify, all of which use recommendation engines for their respective customers.

  • Healthcare

Machine learning is being leveraged in healthcare to enhance patient diagnosis. They can predict patient’s health issues based on their history and other factors. ML algorithms are now used to detect tumors in radiology scans and detect cancerous cells. Furthermore, computers help doctors classify images from scans and detect cancer more accurately than human professionals. Machine learning algorithms are also aiding researchers in looking for treatment for certain diseases in a faster and efficient manner. In general, hospitals will soon put the well-being of their patients in the hands of AI. Some hospitals are currently using machine learning to treat patients to reduce accidents and reduce the cases of hospital-related illnesses.

  • Banking

Machine learning is changing the banking industry. With the growing number of people using banks, it would be difficult for employees to go through transactions of each one of them daily. Using location data and customer purchase patterns, machine learning can help banks and financial institutions offering credit services to identify fraud right on time. The machine learning-based anomaly detection algorithms monitor customer transaction requests and alert users of potential cases of fraud.  With this, banks can arrest the situation before it gets out of hand.

  • Smarthome and home security

Machine learning is becoming the best home security alternative for home security. Many homeowners look for AI-integrated cameras and alarm systems to guard their property and homes. These high-tech systems use facial recognition technology software and machine learning to build a catalogue of frequent home visitors and instantaneously detect uninvited guests. The newest smart home systems can even track your kids and notify you when they are at home. They can also call emergency services autonomously in case of any issue. With digital assistants like Alexa and Amazon Echo, different aspects of smart homes can be voice-controlled.

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Scott Koegler

Scott Koegler is Executive Editor for PMG360. He is a technology writer and editor with 20+ years experience delivering high value content to readers and publishers. 

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