We hear the terms artificial intelligence, machine learning, and deep learning used interchangeably, but there are differences, and they impact how we use big data analytics in operations management. Artificial intelligence (AI) has become an umbrella term for anytime a machine can make decisions that imitate human behavior through logic, think Alexa and Siri. Machine learning refers to instances where humans teach the machine. The machine is always adding new information and updating its decisions. Nest thermostats are an example of machine learning. The Nest can be voice-controlled by Alexa and learns from data input to predict and adjust the temperature. Deep learning is a subset of machine learning characterized by layers of human-like neural networks. Together AI, machine learning, and deep learning are optimizing business operations and fueling autonomous vehicle systems.
Machine learning in Business Operations
Machine learning is increasing the speed and accuracy of decision making which in turn increases productivity and provides data to the supply chain. For example, retailers such as North Face, Third Love, and Thread use a virtual assistant (AI) to gather information about a customer’s specific size and fit preferences along with brands already in their closets. Thread specializes in men’s clothing and has customers upload a current photo. The machine learns the customer preferences and best fit and then suggests appropriate clothing options. The machine continues to learn with every new customer query or input of new data such as their next order or a return. This mix of AI and machine learning are improving business operations by providing customers with a personalized shopping experience and providing data for the supply chain, improving real-time decision making.
Popular streaming services are an excellent example of how machine learning uses big data analytics in operations management. For example, when you click a thumbs-up rating on a Netflix for a particular show- you are inputting personal data. Netflix's automated machine learning recommends other shows and movies based on your data. The input of data from humans, not only fuels your individual Netflix interface, its aggregated with all Netflix user data and informs the company's decisions on investments in original series such as "Orange Is the New Black" and “Stranger Things.”
Machine Learning and Autonomous Vehicles
AI, machine learning, and deep learning are all part of what drives autonomous vehicles. The combination of these systems makes informed decisions like human drivers to master tasks such as lane changing, braking to avoid a collision, and making turns. Auto-makers use a combination of tools to teach cars to see, identify, and dodge objects as human drivers do when driving. Sensors, cameras, and radar systems spot lane lines, speed signs, traffic lights, other vehicles, and objects.
Lidars are another tool in the autonomous vehicle toolbox. You may see lidars spinning on top of most self-driving car models. This is a light detection and ranging system that sends out millions of laser beams every second measuring how long it takes for the laser to bounce back. The data is continuously fed into the machine as it builds a 3D map to base decisions.
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