Fintech is a lucrative yet quite saturated market. In order to stay competitive, businesses should keep track of the emerging trends and be able to capitalize on them before their competitors do. Artificial intelligence is currently among the most promising fintech trends. Leading financial brands such as Capital One, MasterCard, as well as hundreds of startups have set the pace for the adoption of virtual financial advisors. If you want to stay ahead of your competition or simply explore the opportunities for AI in fintech, then this article is for you.

Do you ever feel overwhelmed by an unending stream of information? It can seem like a barrage of new email and text messages demands constant attention, and there are also phone calls to pick up, articles to read, and knocks on the door to answer. Putting these pieces together to keep track of what is important can be a real challenge. The same information overload is a concern in many computational settings. Telecommunications companies, for example, want to keep track of the activity on their networks, to identify overall network health and spot anomalies or changes in behavior. Yet, the scale of events occurring is huge: many millions of network events per hour, per network element. While new technologies allow the scale and granularity of events being monitored to increase by orders of magnitude, the capacity of computing elements (processors, memory, and disks) to make sense of these is barely increasing. Even on a small scale, the amount of information may be too large to store in an impoverished setting (say, an embedded device) or to keep conveniently in fast storage. In response to this challenge, the model of streaming data processing has grown in popularity. The aim is no longer to capture, store, and index every minute event, but rather to process each observation quickly in order to create a summary of the current state. Following its processing, an event is dropped and is no longer accessible. The summary that is retained is often referred to as a sketch of the data. Coping with the vast scale of information means making compromises: The description of the world is approximate rather than exact; the nature of queries to be answered must be decided in advance rather than after the fact; and some questions are now insoluble. The ability to process vast quantities of data at blinding speeds with modest resources, however, can more than make up for these limitations.

As excited as we are about the forthcoming generation of social home robots (including Jibo, Kuri, and many others), it's hard to ignore the fact that most of them look somewhat similar. They tend to feature lots of shiny white and black plasticky roundness. That's for admittedly very good reasons, but it comes at the cost of both uniqueness and visual and tactile personality. Guy Hoffman, who is well known for the fascinating creativity of his robot designs, has been working on a completely new kind of social robot in a collaboration between his lab at Cornell and Google ZOO's creative technology team in APAC. The robot is called Blossom, and we'd describe it for you, except that it's designed to be handmade out of warm natural materials like wool and wood so that every single one is a little bit different.

This paper considers applications of trajectory data in transportation, and makes two primary contributions. First, it provides a comprehensive literature review detailing ways in which trajectory data has been used for transportation systems analysis, distilling existing research into the following six areas: demand estimation, modeling human behavior, designing public transit, measuring and predicting traffic performance, quantifying environmental impact, and safety analysis. Additionally, it presents innovative applications of trajectory data for the state of Maryland, employing visualization and machine learning techniques to extract value from 20 million GPS traces. These visual analytics will be implemented in the Regional Integrated Transportation Information System (RITIS), which provides free data sharing and visual analytics tools to help transportation agencies attain situational awareness, evaluate performance, and share insights with the public.

After a couple of AI winters and periods of false hope over the past four decades, rapid advances in data storage and computer processing power have dramatically changed the game in recent years. Artificial intelligence is the study of agents that perceive the world around them, form plans, and make decisions to achieve their goals. Meanwhile, we're continuing to make foundational advances towards human-level artificial general intelligence (AGI), also known as strong AI. The definition of an AGI is an artificial intelligence that can successfully perform any intellectual task that a human being can, including learning, planning and decision-making under uncertainty, communicating in natural language, making jokes, manipulating people, trading stocks, or… reprogramming itself.

Person re-identification (Re-ID) aims at matching images of the same person across disjoint camera views, which is a challenging problem in multimedia analysis, multimedia editing and content-based media retrieval communities. The major challenge lies in how to preserve similarity of the same person across video footages with large appearance variations, while discriminating different individuals. To address this problem, conventional methods usually consider the pairwise similarity between persons by only measuring the point to point (P2P) distance. In this paper, we propose to use deep learning technique to model a novel set to set (S2S) distance, in which the underline objective focuses on preserving the compactness of intra-class samples for each camera view, while maximizing the margin between the intra-class set and inter-class set. The S2S distance metric is consisted of three terms, namely the class-identity term, the relative distance term and the regularization term. The class-identity term keeps the intra-class samples within each camera view gathering together, the relative distance term maximizes the distance between the intra-class class set and inter-class set across different camera views, and the regularization term smoothness the parameters of deep convolutional neural network (CNN). As a result, the final learned deep model can effectively find out the matched target to the probe object among various candidates in the video gallery by learning discriminative and stable feature representations. Using the CUHK01, CUHK03, PRID2011 and Market1501 benchmark datasets, we extensively conducted comparative evaluations to demonstrate the advantages of our method over the state-of-the-art approaches.

Deep multi-task learning with low level tasks supervised at lower layers.

Natural-language-facilitated human-robot cooperation (NLC) refers to using natural language (NL) to facilitate interactive information sharing and task executions with a common goal constraint between robots and humans. Recently, NLC research has received increasing attention. Typical NLC scenarios include robotic daily assistance, robotic health caregiving, intelligent manufacturing, autonomous navigation, and robot social accompany. However, a thorough review, that can reveal latest methodologies to use NL to facilitate human-robot cooperation, is missing. In this review, a comprehensive summary about methodologies for NLC is presented. NLC research includes three main research focuses: NL instruction understanding, NL-based execution plan generation, and knowledge-world mapping. In-depth analyses on theoretical methods, applications, and model advantages and disadvantages are made. Based on our paper review and perspective, potential research directions of NLC are summarized.

If you are involved in the UK Robotics and Autonomous Systems (RAS) sector, we'd love to hear from you. Please fill in this survey. In it is set an'open door' challenge to industry to come to the Government with proposals to transform and upgrade their sector through'Sector Deals'. Businesses rather than the Government are being encouraged to identify what companies need in order to enhance their competitiveness as a sector. This is not about the Government providing additional funding; rather, it is an open call to business to organise behind strong leadership, like the automotive and aerospace sectors, to address shared challenges and opportunities.

This survey explores Procedural Content Generation via Machine Learning (PCGML), defined as the generation of game content using machine learning models trained on existing content. As the importance of PCG for game development increases, researchers explore new avenues for generating high-quality content with or without human involvement; this paper addresses the relatively new paradigm of using machine learning (in contrast with search-based, solver-based, and constructive methods). We focus on what is most often considered functional game content such as platformer levels, game maps, interactive fiction stories, and cards in collectible card games, as opposed to cosmetic content such as sprites and sound effects. In addition to using PCG for autonomous generation, co-creativity, mixed-initiative design, and compression, PCGML is suited for repair, critique, and content analysis because of its focus on modeling existing content. We discuss various data sources and representations that affect the resulting generated content. Multiple PCGML methods are covered, including neural networks, long short-term memory (LSTM) networks, autoencoders, and deep convolutional networks; Markov models, $n$-grams, and multi-dimensional Markov chains; clustering; and matrix factorization. Finally, we discuss open problems in the application of PCGML, including learning from small datasets, lack of training data, multi-layered learning, style-transfer, parameter tuning, and PCG as a game mechanic.