Thank you for joining us on "The cloud hub: From cloud chaos to clarity." John Domingue, computer science professor with the Knowledge Media Institute at Open University, shares how evolving AI technology is democratizing education by allowing the customization of more than 300 courses at the largest university in the UK.

AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner Is Lifelong #machinelearning, a paradigm for continuous learning? Real ML is Lifelong ML. Real ML is about lifelong and constant learning with the memory (knowledge base). Human beings always retain and accumulate the knowledge learned in the past and use it in future learning. Over time we learn more and become more knowledgeable, and more effective at learning.

Sign in to report inappropriate content. Hava Siegelmann, Microsystems Technology Office Program Manager DARPA, gives a keynote at the Human-Level AI Conference in Prague in August 2018. The conference combined three major conferences AGI, BICA, and NeSy and was organized by AI research and development company GoodAI.

Among the consequences of big data is a wealth of relevant minutiae that can be used to train machine learning and other models. That often translates into processing-intensive steps required to train models to perform a specific task. In response, technology startups and government-funded university researchers are promoting a new machine intelligence approach called "lifelong" machine learning as a way to accelerate model training. The latest entrant is Neurala, which this week took the wraps off its lifelong deep neural network technology. The Boston-based startup claims its AI approach can slash training time from upwards of 15 hours to as little as 20 seconds by utilizing "incremental learning."

As AI and machine learning systems advance, real-world applications are exposing limitations such as the inability to adapt to situations beyond the narrow tasks those systems were trained to perform. Indeed, some observers note that AI researchers still do not understand the mechanisms by which automated systems learn. As AI researchers grapple with these unknowns, the Defense Advanced Research Projects Agency (DARPA) launched a program last year aimed at advancing "lifelong machine learning." This week, the research agency announced funding for three university research teams that will tackle various components of the adaptability problem. Those efforts augment initial research at Columbia University focused on reproducing neural networks.