AIR5: Five Pillars of Artificial Intelligence Research

Currently, many (if not most) of the innovations in AI are driven by ML techniques centered around the use of so-called The original inspiration of artificial intelligence (AI) was to deep neural network (DNN) models [2]. The design of DNNs build autonomous systems capable of demonstrating humanlike is loosely based on the complex biological neural network that intelligence. Likewise, the related field of computational makes up a human brain - which (unsurprisingly) has drawn intelligence (CI) emerged in an attempt to artificially recreate significant interest among CI researchers as a dominant source the consummate learning and problem-solving ability depicted of intelligence in the natural world. However, DNNs are often by various natural phenomena - including the workings of the criticized for being highly opaque. It has been widely biological brain. However, in the present-day, the combined acknowledged that although these models can frequently attain effects of (i) the relatively easy access to massive and growing remarkable prediction accuracies, their layered nonlinear volumes of data, (ii) rapid increase in computational power, structure makes it exceeding difficult (if not impossible) to and (iii) the steady improvements in data-driven machine unambiguously interpret why a certain set of inputs leads to a learning (ML) algorithms [1, 2], have played a major role in particular output / prediction / decision. As a result, at least at helping modern AI systems vastly surpass humanly achievable present, these models have come to be viewed mainly as performance across a variety of applications.