People routinely use electricity and think nothing of it. Artificial intelligence, machine learning and deep learning are likely going to be experienced the same way. Few people can explain how electricity works, but it already powers many parts of modern life.
Equally few are probably aware of how they already use machine learning in everyday life.
Amazon recommends books; Netflix suggests a film or TV show; your email app filters spam using machine learning. Machine learning underlies consumer interactions with Siri, Alexa, Google Assistant.
Personal assistant apps on smartphones use machine learning. Portrait mode cameras use machine learning.
Social media feeds use machine learning to customize your content. Video games sometimes incorporate machine learning to vary app responses based on player actions.
Machine learning helps Uber estimate how long a trip will take. ML also helps Uber estimate how much you are willing to pay for any particular trip. Riders are matched with drivers using machine learning.
Machine learning powers GMail’s auto-response features. LinkedIn uses machine learning to match jobs with jobseekers. Pinterest uses ML to classify photographs and visual images.
Your bank and credit and debit card providers use machine learning to monitor your accounts for fraudulent activity. ML also powers their reminder and alert systems.
Airliners use machine learning when autopilots are engaged. By some estimates, a typical flight on a Boeing 777 is on autopilot for all but 3.5 to seven minutes in the air.
Customer interaction software uses ML to answer questions and resolve problems at inbound call centers. Search engines use ML when you seek information.
Amazon makes product recommendations for you using ML. Spotify uses ML to make music recommendations. Online ads often are served up with the assistance of ML.
Google Maps uses ML to predict the fastest routes to a destination. Smart thermostats use ML to alter thermostat behavior based on how a user has acted in the past.
ML powers workforce analysis systems that make deductions from email and other written text. Machine learning increasingly can be used to assess organizational risk.
How AI in general, or machine and deep learning will work to enhance or create products and features are subjects likely of little immediate and compelling interest for most people, whose job responsibilities or life routines do not require any specific knowledge about AI.
AI, almost by definition, works in the background. People use computers, apps and smartphones, but few have any need to understand what happens at the component layer, inside computer rooms or servers. People do not need to understand particular coding languages to use software built with such languages.
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