Can a computer write a great novel or a script for a movie? Artificial intelligence (AI) is manna from heaven for sci-fi writers. We've seen a sentient computer called HAL wreak quiet havoc in 2001: A Space Odyssey. We've watched a robot girl's will to survive in 2015's Ex Machina. Most recently we've seen an AI-meets-the-wild-west scenario in TV series Westworld.

As we start 2018 here at Cray, we believe artificial intelligence and, more specifically, deep learning will continue to dominate the emerging technology landscape conversation (and I know that some might argue block chain technologies have already surpassed AI, but that's another conversation). Since we have several subject matter experts at Cray, I thought it might be a good idea to reach out to the Cray AI team and ask them, "What is a trend for AI or deep learning you expect in 2018?" While 10x may be hard to repeat in the next year only based on software modifications, newer algorithms will provide a similar speed up for GANs, LSTMS, RNNs and so forth, and together will guide the requirements for hardware acceleration and scale-up into 2019. To quote Aaron, "This is the #1 most important thing for those in HPC to realize -- machine learning is coming for traditional science domains outside of analytics and informatics. Machine learning with neural networks is coming to the physical sciences at scale, and this means high-performance computing. You need a net of size N 2 trained on M data items to find cats in 2D images, but you need a net of size N 3 with M 2 data items to find the binding of drug molecules to proteins. As ML goes into the physical sciences with 3D data, these nets are going to'blow up' in terms of compute resources needed. These will be true HPC applications requiring true supercomputer scale."

Australian-based TrademarkVision developed a deep learning-based reverse visual search platform that protects your brand by identifying similar trademarks from around the world. Simply upload your image to the platform, and their image recognition technology will compare it against other trademarked logos – making it much easier to identify IP infringements than the previous time-consuming and costly text-based search process. "Our technology not only makes it easy for an entrepreneur with a new design to ensure it is unique, but also enables the largest of companies to monitor for infringement," explains Cameron Mitchell, the Chief Operations Officer of TrademarkVision. The young startup has already integrated their technology with the intellectual property departments in the EU, Australia, Chile and more. Most recently, they launched a visual search for industrial designs.

Artificial intelligence has been shaping our world since the 1970s, or even before. It started in 1950 when a handful of pioneers from the nascent field of computer science started asking whether computers could be made to "think." Nowadays, most of the AI shown on TV and media is harmful and dangerous for our population (i.e. Still, far from that futuristic scenario, I am going to discuss some of the real applications that AI has and what is at the core of this new machine intelligence. AI is nothing but intelligence thrust into machines.

In this paper we present a broad overview of the last 40 years of research on cognitive architectures. Although the number of existing architectures is nearing several hundred, most of the existing surveys do not reflect this growth and focus on a handful of well-established architectures. Thus, in this survey we wanted to shift the focus towards a more inclusive and high-level overview of the research on cognitive architectures. Our final set of 84 architectures includes 49 that are still actively developed, and borrow from a diverse set of disciplines, spanning areas from psychoanalysis to neuroscience. To keep the length of this paper within reasonable limits we discuss only the core cognitive abilities, such as perception, attention mechanisms, action selection, memory, learning and reasoning. In order to assess the breadth of practical applications of cognitive architectures we gathered information on over 900 practical projects implemented using the cognitive architectures in our list. We use various visualization techniques to highlight overall trends in the development of the field. In addition to summarizing the current state-of-the-art in the cognitive architecture research, this survey describes a variety of methods and ideas that have been tried and their relative success in modeling human cognitive abilities, as well as which aspects of cognitive behavior need more research with respect to their mechanistic counterparts and thus can further inform how cognitive science might progress.

Abstract--Over the past decade, multivariate time series classification has been receiving a lot of attention. We propose augmenting the existing univariate time series classification models, LSTM-FCN and ALSTM-FCN with a squeeze and excitation block to further improve performance. Our proposed models outperform most of the state of the art models while requiring minimum preprocessing. The proposed models work efficiently on various complex multivariate time series classification tasks such as activity recognition or action recognition. Furthermore, the proposed models are highly efficient at test time and small enough to deploy on memory constrained systems. Time series data is used in various fields of studies, ranging from weather readings to psychological signals [1, 2]. A time series is a sequence of data points in a time domain, typically in a uniform interval [3]. There is a significant increase of time series data being collected by sensors [4].

Traditionally, most complex intelligence architectures are extremely non-convex, which could not be well performed by convex optimization. However, this paper decomposes complex structures into three types of nodes: operators, algorithms and functions. Iteratively, propagating from node to node along edge, we prove that "regarding the tree-structured neural graph, it is nearly convex in each variable, when the other variables are fixed." In fact, the non-convex properties stem from circles and functions, which could be transformed to be convex with our proposed \textit{\textbf{scale mechanism}}. Experimentally, we justify our theoretical analysis by two practical applications.

In recent years, deep learning has completely revolutionized the fields of computer vision, speech recognition and natural language processing. Despite breakthroughs in all three fields, one common barrier for training neural networks to solve real-world problems remains the amount of labeled training data that is required to train a model. In some domains, like video understanding, gathering real world data can be prohibitively expensive and time consuming in the absence of innovative solutions. At TwentyBN, we solved this problem by building an in-house data factory for generating high-quality videos for neural networks to learn about the real world. We instruct crowd workers to record short video clips based on carefully predefined and highly specific descriptions.

All sectors are trying to explore this new world, to see which business processes could be improved by using AI, chatbots, deep learning or voice recognition while others are creating new services.

Deep learning and neural networks may have benefited from the huge quantities of data and computing power, but they won't take us all the way to artificial general intelligence, according to a recent academic assessment. Gary Marcus, ex-director of Uber's AI labs and a psychology professor at the University of New York, argues that there are numerous challenges to deep learning systems that broadly fall into a series of categories. The first one is data. It's arguably the most important ingredient to any deep learning system and current models are too hungry for it. Machines require huge troves of labelled data to learn how to perform a certain task well.