They were mechanical marvels of technology that could perform many impressive functions within and unto themselves, but artificial intelligence (AI), machine learning, true driver personalization, and external data exchange capabilities were still conceptual. Its value will be judged by how elegantly it understands and communicates with its users using speech and natural language, while accessing and delivering a world of information from a wide range of "expert" sources to instantly and/or proactively deliver the right answer, content, or action. Similarly, the automotive assistant, while highly capable itself, delivers the best experience for users by intelligently coordinating all pieces of the connected world ecosystem. Taken together, rapid advances in AI interoperability, personalization, and contextualization will allow automotive assistants to significantly enhance car mobility for drivers and passengers.
Meeting these requirements is somewhat problematic through the current centralized, cloud-based model powering IoT systems, but can be made possible through fog computing, a decentralized architectural pattern that brings computing resources and application services closer to the edge, the most logical and efficient spot in the continuum between the data source and the cloud. Fog computing reduces the amount of data that is transferred to the cloud for processing and analysis, while also improving security, a major concern in the IoT industry. IoT nodes are closer to the action, but for the moment, they do not have the computing and storage resources to perform analytics and machine learning tasks. An example is Cisco's recent acquisition of IoT analytics company ParStream and IoT platform provider Jasper, which will enable the network giant to embed better computing capabilities into its networking gear and grab a bigger share of the enterprise IoT market, where fog computing is most crucial.
How do you satisfy the "one button" trick to help solve the menu and button configuration dilemma on the vehicle dashboard? One reason I chose to move into the automotive group at SAS is their proven approaches and experience with applying machine learning techniques to help these situations. Driverless vehicles, connected cars, e-hailing, car sharing, and other innovative offerings are reshaping our industry. In the case of getting the dashboard to work intuitively, conveniently and effectively with the driver, machine learning techniques are a wise choice.
The Internet of Things (IoT) and Machine Learning are two of the hottest technologies of our time. At first glance, I really like some of the ideas that are being proposed by the combination of Internet of Things and Machine Learning: smart light bulbs that know when to turn themselves on and off; smart kettles that will make sure you'll have freshly made coffee at the exact time you want it without having thought about it; smart fridges that will do the grocery for you; smart locks that will recognize you and unlock with the tap of a phone. I don't have to worry about leaving the door unlocked because my smart lock will automatically lock the door when it senses that the house is empty. Combined with the power of the fast evolving VR technology, IoT will enable us to travel to distant locations, feel things, meet people and do a lot more without ever setting foot outside our homes.