As we blast into 2017, marketers are wondering: what innovative technological developments will this year bring? How will these changes affect my strategy? What are my competitors doing, and more importantly, what are my customers expecting? These big, important questions require extensive research, if not a sixth sense for weeding out what's merely a fad, and what's as equally as groundbreaking as, say, the internet. To save you time, we've done the work for you.

A team of artificial intelligence researchers from Northwestern University have built a robot on CogSketch model that will mimic the understanding level of common human beings. This computational model of analogy is based on the structure mapping theory of Northwestern psychology professor Dedre Gentner and the same artificial intelligence platform was previously developed in Forbus' Laboratory. According to Ken Forbus this model has the ability to understand the world as adult Americans do with an accuracy of 75 percentages. He further added that the things those are difficult for humans to understand are also difficult to recognise by these robots; best proof that it is mimicking human cognition. However; it can solve complex visual problems citing as one of the hallmarks of human intelligence.

Chip giant Intel last week rolled out a new deep learning framework that runs as a Spark job atop Hadoop. Called BigDL, the open source software is designed to take advantage of hardware acceleration capabilities that Intel has built into its Xeon CPUs. BigDL, which Intel released on Github, is modeled after Torch, an open source deep learning framework used in scientific computing. Intel says the framework supports numeric computing via Tensor, as well as high level neural networks, and can be used to run prebuilt Caffe and Torch models on Spark. While many deep learning frameworks today leverage GPUs, Intel is taking a different route with BigDL, for obvious reasons.

When psychologist Dr. Paul Ekman visited the Fore tribe in the highlands of Papua New Guinea in 1967, he probably didn't imagine that his work would become the foundation for some of the latest developments in artificial intelligence (AI). After studying the tribe, which was still living in the preliterate state it had been in since the Stone Age, Ekman believed he had found the blueprint for a set of universal human emotions and related expressions that crossed cultures and were present in all humans. A decade later he created the Facial Action Coding System, a comprehensive tool for objectively measuring facial movement. Ekman's work has been used by the FBI and police departments to identify the seeds of violent behavior in nonverbal expressions of sentiment. He has also developed the online Atlas of Emotions at the behest of the Dalai Lama.

Photo taken on Jan. 17, 2017 shows Xuewei Qi, a leading researcher at the University of California, Riverside, is using vehicle connectivity and evolutionary algorithms to improve the efficiency of Plug-In Hybrid Electric Vehicles.

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This work was supported by grants from the following entities: Centre for Networked Biomedical Research into Neurodegenerative Diseases (CIBERNED, CB06/05/0066 to JDF), the Spanish Ministry of Economy and Competitiveness (grants SAF2009-09394 to JDF; Juan de la Cierva postdoctoral fellowship to RA; TIN2010-20900-C04-04 and the Cajal Blue Brain Project, Spanish partner of the Blue Brain Project initiative from EPFL, to JDF, PL and CB. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist.

Random forest is a highly versatile machine learning method with numerous applications ranging from marketing to healthcare and insurance. It can be used to model the impact of marketing on customer acquisition, retention, and churn or to predict disease risk and susceptibility in patients. Random forest is capable of regression and classification. It can handle a large number of features, and it's helpful for estimating which of your variables are important in the underlying data being modeled. Random forest is solid choice for nearly any prediction problem (even non-linear ones).

Many people, myself included, believe that AI will be the primary catalyst in transforming most industries over the next decade. The question that many people are asking is how to capture the alpha -- the active return on investment -- associated with artificial intelligence. In this article I will share my belief about where the market opportunities are for today's investors and entrepreneurs. The path that many startups are taking today is to provide the tools that firms will need to capitalize on the AI marketplace. Many of these startups are seeking an early exit -- and they aren't being disappointed.

Artificial intelligence has already begun to impact on our everyday lives and the technological revolution we are witnessing is set to transform the accounting profession – and the relationship between accountants and SMEs. That changing relationship will benefit accountants and businesses, heralding new ways of working and putting our service and knowledge at the heart of business development and strategy for the good of SMEs and the wider economy. There is no doubt that we are on a journey that is going in only one direction. Sophisticated accounting software and systems will change our role as artificial intelligence (AI), smart machine learning and increased automation takes over traditional accountancy roles such as bookkeeping and data entry. And the direction we are heading is already being well signposted.

Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain.