Artificial Intelligence is when a machine mimics the cognitive functions that humans associate with other human minds, such as learning and problem solving, reasoning, problem solving, knowledge representation, social intelligence and general intelligence. The central problems of AI include reasoning, knowledge, planning, learning, natural language processing perception and the ability to move and manipulate objects. Approaches include statistical methods, computational intelligence, soft computing and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics. AI platform is defined as some sort of hardware architecture or software framework (including application frameworks), that allows software to run.
AI has become the next major battleground in a wide range of software and service markets, including aspects of enterprise resource planning, Clearley explains. Packaged software and service providers should outline how they'll be using AI to add business value in new versions in the form of advanced analytics, intelligent processes and advanced user experiences. Intelligent things are physical things that go beyond the execution of rigid programming models to exploit AI to deliver advanced behaviors and interact more naturally with their surroundings and with people, Clearley explains. While conversational interfaces are changing how people control the digital world, virtual reality, augmented reality and mixed reality are changing the way that people perceive and interact with the digital world.
Today's post is from Sean McClure. Sean is the Director of Data Science at Space-Time Insight, a leading provider of advanced analytics software for organizations looking to leverage machine learning for their business applications. Having worked across diverse industries, and alongside many talented professionals, Sean has seen the blend of approaches required to convert raw data successfully into real world value. Sean's passion is working with cross-discipline teams to build the next generation of adaptive, data-driven applications. It would hardly be an overstatement to say we live in a software economy.
As machine learning makes its way into more applications--leveraging everything from sensor data to consumer information repositories--pressure for hardware and software engineers to familiarize themselves with the technology grows. Because this type of control algorithm differs in key ways from those based on traditional logic, the learning curve may be steeper for some designers. Nevertheless, it's time for all engineers to understand how this technology changes the design process and what tools and practices help with its implementation. One of the best ways to understand machine learning is to consider how it differs from conventional control mechanisms. Traditional programming uses Boolean logic's true and false rules to define a program's behavior, building the application via a series of defined steps, where the rules making up the program ensure what action happens next. Machine learning takes a different approach, built on inductive reasoning.
Despite incredible recent advances in machine learning, building machine learning applications remains prohibitively time-consuming and expensive for all but the best-trained, best-funded engineering organizations. This expense comes not from a need for new and improved statistical models but instead from a lack of systems and tools for supporting end-to-end machine learning application development, from data preparation and labeling to productionization and monitoring. In this document, we outline opportunities for infrastructure supporting usable, end-to-end machine learning applications in the context of the nascent DAWN (Data Analytics for What's Next) project at Stanford.