There are many concepts within the domain of artificial intelligence (AI), including machine learning (ML), deep learning, neural networks, natural learning processing (NLP), virtual reality (VR), Augmented Reality (AR), LSTM, and a few others. All of these deal with a vast amount of data in various shapes and forms - sound, image, video, electrical pulses, temperature, pH values, viscosity, biome markers, structured and unstructured data, and so on. Various techniques (like modeling) are used to process such data or a combination thereof using oversimplified as learning and scoring. AI is enabling facial recognition on your phone. Conversational AI is reducing the tactile footprint of human interaction, creating increased “naturalness” in ever increasing user surface area. Advancements in AI is transferring cognitive burden from user to the device. We are experiencing AI every day in some shape or form.
Edge computing puts low-level compute power in front of end devices, rather than sending all the data to a centralized hub. Instead of sending terabytes of data generated by an F1 car to a central server, each device (like tire pressure sensor) has small compute capacity and sends only relevant data to the next sensor or the central server. LSTM, on the other hand, handles large chunks of data by breaking into smaller relevant pieces with the ability to forget and pass-on.