If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Have you noticed that the better you know someone, the easier it is to communicate with them? When we are particularly close, this can border on the telepathic as we start to anticipate what the other person is going to say and finish their sentences. Unconsciously, our brains are collecting, processing, storing, and recalling a huge range of verbal and nonverbal signals, then translating this learning and familiarity into actions. Of course, we're a long way from understanding – let alone replicating – the infinite complexities of the human brain. But in the simplest of terms, this is how machines can learn to interact with people.
The financial services market is one of the most data-driven industries in the world, yet it's bogged down by legacy CPU technologies that simply can't keep up with the task of querying and visualizing billions of records. In his session at 20th Cloud Expo, Karthik Lalithraj, a Principal Solutions Architect at Kinetica, discussed how the advent of advanced in-database analytics on the GPU makes it possible to run sophisticated data science workloads on the same database that is housing the rich information needed to drive trading decisions. With the unique multi-core architecture of the GPU, financial computations can be processed efficiently and quickly, making it ideal for financial services streaming datasets. He shared how several financial institutions' quantitative science groups are specifically using GPUs to accelerate analytics, deep learning/machine learning, and converging AI and BI. With over 18 years of software experience in a variety of roles and responsibilities, he takes a holistic view at software architecture with special emphasis on helping enterprise IT organizations improve their service availability, application performance and scale.
Machine learning and other forms of artificial intelligence will likely infiltrate all levels of the IT infrastructure stack, but some architectures will take to it more readily than others. And while it is tempting to view AI in terms of the changes it will bring to the data center, the more imminent and profound impact will be on the Internet of Things (IoT). Particularly on the edge, AI offers the only viable means of assessing and coordinating the data flows from massive numbers of devices – many of them imbued with their own levels of intelligence – to produce results that are both meaningful and timely. Much of the storage and processing of IoT workloads will take place on the edge, so it only makes sense that it will incorporate intelligence as a core asset. According to AutomatedBuildings.com founder Ken Sinclair, a new generation of edge controllers is poised to deliver on the promises of autonomy for everything from cars to washing machines and, yes, even buildings.
Because IoT devices are deployed in mission-critical environments more than ever before, it's increasingly imperative they be truly smart. In his session at @ThingsExpo, John Crupi, Vice President and Engineering System Architect at Greenwave Systems, will discuss how IoT artificial intelligence (AI) can be carried out via edge analytics and machine learning technologies that enable things to process event data at the source, learn patterns of behavior over time for taking independent action, and deliver more accurate results in real-time. This opens the door to limitless possibilities, enabling businesses to make better decisions with far less effort. Speaker Bio John Crupi is Vice President and Engineering System Architect at Greenwave Systems, where he guides development on the edge-based visual analytics and real-time pattern discovery environment AXON Predict. He has over 25 years of experience executing enterprise systems and advanced visual analytics solutions.