Carrying out regular'pen-and-paper' exercises can reduce nausea during travel by more than 50 per cent, UK scientists claim. Cognitive training tasks, including identifying how patterns would appear on transparent paper when folded, help'train the brain' to reduce feelings of nausea in-transit, they say. Motion sickness, which creates a sensation of wooziness, can occur during car travel, at sea or even while using a virtual reality headset. But it's also an issue for passengers in self-driving cars, who are free to read, watch films and play video games thanks to the autonomous technology. Engaging in tasks before a journey was found to be effective at reducing motion sickness for passengers in both a driving simulator and on-the-road experiments, the experts found.

LAS VEGAS--It's become a common analogy for the use of predictive analysis in business technology: Wayne Gretzky became the best hockey player of his generation not because he skated to where the puck was, but because he skated to where the puck was going. Similarly, financial institutions are hoping to get ahead of the growing and seemingly insurmountable problem of payment card fraud not just by looking at who cyber-attackers are going after currently but who they are likely to defraud in the near future. At the Black Hat USA conference here last week, a pair of researchers -- one from Royal Bank of Canada and the other from a service provider that focuses on dark web intelligence -- presented on their joint effort to use machine learning, predictive analytics and transactional data together to get a handle on which cardholders might be the next victims of cyber-crime. With the vast stores of payment card, transactional, personal, demographic and historical fraud data to work from, it would seem that card-issuing banks already have a lot of information with which to work to help them determine the direction of fraudulent activity. The problem with having so much data is it is hard to find the right information at the right time.

She saw the window of opportunity and took it! A British man launched a GoFundMe campaign Tuesday asking viewers to help buy him a new window after his Tinder date got stuck in his old window while trying to retrieve her feces she discarded and had to be rescued by emergency officials. Liam Smyth, a student at the University of Bristol, wrote on the page that he had recently went on a first date with a fellow college student. He and his date had a lovely evening and went back to his residence for a "bottle of wine and a Scientology documentary." Smyth said his date went to the bathroom at one point but came out "with a panicked look in her eye."

Chatbots were supposed to be a big part of our AI-powered future, but they've mostly fallen flat. Scratch the surface of any online bot selling you takeout or flights abroad, and you'll usually find drop-down menus repackaged as questions. To get chatbots to the next level (and make them genuinely useful), they'll need to be given new skills -- like memory, and the ability to reason. Adding these new cognitive abilities is closer than you think. Facebook is one of the biggest players in this domain.

Trim touts itself as a "fast-learning, software-driven assistant that takes care of your money so you don't have to." Basically, Trim's A.I. looks through your finances (credit card statements, bank statements, etc.) and reminds you of all the recurring payments you're making. You can then send Trim a message through email or Facebook and begin an automated process to cancel those services--and save yourself a few extra bucks each month. Trim's A.I. can also challenge overdraft fees from your bank, which Trim co-founder/CEO Thomas Smyth says works nearly 50 percent of the time. Trim users eventually wanted to know if the A.I. could help with one of the most bothersome cancellations of all: internet from Comcast.

Ever wonder how much you spend each month on Uber? The newly released Trim chatbot will tell you in a text. Trim also helps you manage subscriptions, set up spending alerts and check your bank balance. But there is no app, only a bot that can be linked to SMS or Facebook. "Anything that you can do on your mobile banking app, you can do with trim with the exception of moving money," said CEO and co-founder Thomas Smyth.

Ever wonder how much you spend each month on Uber? The newly released Trim chatbot will tell you in a text. Trim also helps you manage subscriptions, set up spending alerts and check your bank balance. But there is no app, only a bot that can be linked to SMS or Facebook. "Anything that you can do on your mobile banking app, you can do with trim with the exception of moving money," said CEO and co-founder Thomas Smyth.

The task of machine condition monitoring is to detect machine failures at an early stage such that maintenance can be carried out in a timely manner. Most existing techniques are supervised approaches: they require user annotated training data to learn normal and faulty behaviors of a machine. However, such supervision can be difficult to acquire. In contrast, unsupervised methods don't need much human involvement, however, they face another challenge: how to model the generative (observation) process of sensor signals. We propose an unsupervised approach based on segmental hidden Markov models. Our method has a unifying observation model integrating three pieces of information that are complementary to each other. First, we model the signal as an explicit function over time, which describes its possible non-stationary trending patterns. Second, the stationary part of the signal is fit by an autoregressive model. Third, we introduce contextual information to break down the signal complexity such that the signal is modeled separately under different conditions. The advantages of the proposed model are demonstrated by tests on gas turbine, truck and honeybee datasets.

In case-based reasoning (CBR), problems are solved by retrieving prior cases and adapting their solutions to fit; learning occurs as new cases are stored. Controlling the growth of the case base is a fundamental problem, and research on case-base maintenance has developed methods for compacting case bases while maintaining system competence, primarily by competence-based deletion strategies assuming static case adaptation knowledge. This paper proposes adaptation-guided case-base maintenance (AGCBM), a case-base maintenance approach exploiting the ability to dynamically generate new adaptation knowledge from cases. In AGCBM, case retention decisions are based both on cases' value as base cases for solving problems and on their value for generating new adaptation rules. he paper illustrates the method for numerical prediction tasks (case-based regression) in which adaptation rules are generated automatically using the case difference heuristic. In comparisons of AGCBM to five alternative methods in four domains, for varying case base densities, AGCBM outperformed the alternatives in all domains, with greatest benefit at high compression.

We study methods to specify preferences among subsets of a set (a universe ). The methods we focus on are of two types. The first one assumes the universe comes with a preference relation on its elements and attempts to lift that relation to subsets of the universe. That approach has limited expressivity but results in orderings that capture interesting general preference principles. The second method consists of developing formalisms allowing the user to specify "atomic" improvements, and generating from them preferences on the powerset of the universe. We show that the particular formalism we propose is expressive enough to capture the lifted preference relations of the first approach, and generalizes propositional CP-nets. We discuss the importance of domain-independent methods for specifying preferences on sets for knowledge representation formalisms, selecting the formalism of argumentation frameworks as an illustrative example.