Artificial intelligence is no longer just a machination of the science fiction genre of film and literature. Functional AI technology is being implemented in multiple ways throughout Western civilization to improve quality of life and general convenience as well as drive a variety of business strategies.
Whether you realize it or not, you already interact with some form of AI on a daily basis. Here are just a few examples of some of the best and most popular applications of AI in the marketplace today.
SIRI
Apple’s iconic automated personal assistant is an artificial intelligence built around a machine-learning system designed to adapt to the subtleties of natural language. Because there are so many different variances in pronunciation and enunciation of words, in order for the digital assistant to understand voice commands each sound must be analyzed, categorized, and compared to the ever-evolving database in real-time.
Machine learning allows Siri to constantly analyze and adapt to a user’s spoken language to increasingly improve its effectiveness at following commands. Software such as Siri relies on a solid MLOps strategy to manage its deployment and consistent operation.
AMAZON
Amazon is another company that leveraged a digital assistant AI with some similar base functions to Siri to turn their international conglomerate into a direct-to-customer portal. Amazon introduced Alexa as a voice service and Echo as a customizable, fully integrated machine learning suite designed to run web searches, manage personal schedules, control electronic appliances and controls around the home, and shop for a wide variety of goods.
Echo uses the Alexa voice service to engage with the natural language of the user, applying machine learning technology to increasingly understand the natural language of the user. It also has the ability to record and categorize user preferences to gauge what new features and services to offer as part of Amazon’s business marketing strategy.
TESLA
With Tesla, AI is moving into the automobile industry. Tesla is a pioneer in the field of predictive capabilities, automated safety systems, and self-driving technology. All of this functionality relies on a machine-learning core designed to receive input from a variety of sources, such as sensors, cameras, and GPS. Couple the ever-learning AI features with purely electric motors, and you have a truly next-gen automobile already in use around the world. Not only do Tesla cars adapt to data input from immediate surroundings but also connect through wireless networks to centralized servers for periodic software updates, bug fixes, and program enhancements.
NETFLIX
When you think of AI, you probably do not think about binge-watching your favorite TV show; however, Netflix is a prime example of machine learning software. Not only does Netflix house an impressive database of popular films and series, but they also sport an innovative predictive algorithm to offer new recommendations based on consumer data.
The algorithm used by Netflix determines what content best matches the patterns in user data to both make recommendations and hide content that is statistically unlikely to be enjoyed. What is more impressive is how this algorithm grows over time, developing keener capabilities to identify user preferences with each new user entering its database.
GOOGLE NEST
Google and AI go hand-in-hand, but one of the most powerful applications of AI implemented by Google so far is Nest, the learning thermostat. It works in conjunction with Amazon’s Alexa voice service, to offer full voice control functionality. While operating an automated script determined by behavioral algorithms designed to predictively learn the user’s temperature preferences. This allows Nest to preemptively adjust home or office temperatures throughout the day to suit your needs.
AI is rapidly transforming society, and that transformation is not slowing down. With AI finding increasingly more applications in day-to-day life, having a firm grasp on the core principle of machine learning is useful for both the casual user and the software manager for getting the most benefit out of this technology as it becomes available.
