Thank you for Subscribing to CIO Applications Weekly Brief
Applying Machine Learning to Everyday Life
Machine learning impacts all of our lives daily. The use of navigation, online purchases, social media browsing, or streaming services is all impacted by machine learning in one way or another.
FREMONT, CA: A new wave of attention is being paid to machine learning, a subset of artificial intelligence. A resurgence in interest in big data is attributed to many factors, including powerful and affordable computational processing, increasing volumes of big data sets, and affordable data storage options.
Machine learning is teaching machines to recognize patterns in data and apply them to specific problems. Whenever new data is presented to machine learning models, they adapt independently to make sense of it. This is an important aspect of machine learning. Machines can make decisions and predict outcomes reliably and repeatedly without relying on human input.
Automated transportation: Organizing transportation has been revolutionized by services like Uber and Ola, which aggregate cabs. Machine learning is already being used to some extent by apps to book cabs. Personalized experiences provide you with a unique experience. Based on your transport history and patterns, they automatically detect your location and provide options for going home or to the office.
A machine learning algorithm makes more accurate ETA (Estimated Time Of Arrival) predictions based on historical trip data. Implementing machine learning in such apps has dramatically improved delivery and pickup accuracy.
Virtual personal assistance: A virtual personal assistant (VPA) is a software program that can perform tasks or provide services on your behalf. It does this by interpreting word-for-word input or commands. Natural language processing, speech-text conversion, and text-to-speech conversion are major machine learning applications in VPA.
Google translate: In Google's neural machine translation (GNMT), thousands of languages and dictionaries are translated using neural machine learning. Translations between texts are improved using natural language processing. Machine learning techniques such as POS tagging, NER (Named Entity Recognition), and chunking are some of the most commonly used ones.
Environmental protection: Unlike humans, machines can store and access incredible data. With big data, AI can one day identify trends and solve previously unsolvable
IBM's climate & sustainability program analyzes data from thousands of sensors and sources using artificial intelligence to find climate mitigation solutions. Planners can also simulate the effects of their products on the environment and run "what-if" scenarios.