In the travelling thief problem (TTP), a thief undertakes a cyclic tour through a set of cities, and according to a picking plan, picks a subset of available items into a rented knapsack with limited capacity. The overall aim is to maximise profit while minimising renting cost. TTP combines two interdependent components: the travelling salesman problem (TSP) and the knapsack problem (KP). Existing TTP approaches typically solve the TSP and KP components in an interleaved fashion: the solution of one component is fixed while the solution of the other is changed. This indicates poor coordination between solving the two components, which may lead to poor quality TTP solutions. The 2-OPT heuristic is often used for solving the TSP component, which reverses a segment in the tour. Within the TTP context, 2-OPT does not take into account the picking plan, which can result in a lower objective value. This in turn can result in the tour modification to be rejected by a solver. To address this, we propose an extended form of 2-OPT in order to change the picking plan in coordination with modifying the tour. Items deemed as less profitable and picked in cities earlier in the reversed segment are replaced by items that tend to be equally or more profitable and not picked in cities later in the reversed segment. The picking plan is further changed through a modified form of the bit-flip search, where changes in the picking state are only permitted for boundary items, which are defined as lowest profitable picked items or highest profitable unpicked items. This restriction reduces the amount of time spent on the KP component, allowing more tours to be evaluated by the TSP component within a given time budget. The two modified heuristics form the basis of a new cooperative coordination solver, which is shown to outperform several state-of-the-art TTP solvers on a broad range of benchmark TTP instances.

Applied Marketing Science, a market research company that helps clients uncover new and impactful customer insights, has launched an innovative AI technology called ACE, or Automated Content Evaluator. Developed in collaboration with researchers at MIT, ACE uses convolutional neural networks – a type of supervised machine learning –to dramatically reduce the time and effort required to gather a comprehensive list of customer insights in a category. While many tools summarize key themes, keywords and sentiment in big data, this groundbreaking tool dives deeper. ACE identifies game changing insights that traditional market research methods like focus groups and interviews might overlook. NGMR, a professional networking group for market research practitioners dedicated to exploring and pursuing innovative market research techniques and technologies, has presented the annual NGMR Innovation Awards for over a decade.

Most users should sign in with their email address. If you originally registered with a username please use that to sign in. To purchase short term access, please sign in to your Oxford Academic account above. Don't already have an Oxford Academic account?

Between 2013-2017, 75% of residents in 16 communities in the SEARCH Study tested annually for HIV. In this population, we evaluated three strategies for using demographic factors to predict the one-year risk of HIV seroconversion: (1) membership in 1 known "Risk Group" (e.g., young woman or HIV-infected spouse); (2) a "Model-based" risk score constructed with logistic regression; (3) a "Machine Learning" risk score constructed with the Super Learner algorithm. We hypothesized Machine Learning would identify high-risk individuals more efficiently (fewer persons targeted for a fixed sensitivity) and with higher sensitivity (for a fixed number of persons targeted) than either other approach.

DUBLIN--(BUSINESS WIRE)--The "Cognitive Informatics Market by Technology, Solution (Smart Data, Self-Adaptive Software, Self-Correcting Infrastructure, Cognitive Analytics), Sector (Consumer, Enterprise, Industrial, Government), Industry Vertical, and Region 2019-2024" report has been added to ResearchAndMarkets.com's offering. This report assesses the cognitive informatics market including technologies, companies, strategies, and solutions. It includes analysis by industry sector and major industry verticals. It also evaluates the impact of 5G, edge computing, and IoT on the cognitive informatics market. All forecasts provide a market outlook from 2019 through 2024.

LONDON--(BUSINESS WIRE)--Quantzig, a global data analytics and advisory firm, that delivers actionable analytics solutions to resolve complex business problems has announced the completion of its latest article that explains why online food delivery companies bet big on AI and machine learning to drive performance. AI and machine learning today have broken the confines of sci-fi books and technology labs to become a key focal point for businesses across industries. The impact of AI and machine learning algorithms have grown tremendously over the past few years that barely a day passes by without newspaper articles, blog posts, and tweets about such advancements. Having said that, it's not very surprising that AI and machine learning in the food industry have played a crucial role in the rapid developments that have taken place over the past few years. Artificial intelligence and machine learning seem to be ubiquitous in the online food delivery market.

What happens when the Fourth Industrial Revolution collides with the need and desire to improve the state of the world? To be more specific: What impact will artificial intelligence (AI) have on the social sector? The answer depends on the reply to a bigger, deeper question: What ultimately does AI need to solve? The social sector may be defined as an ecosystem where resources are shared for the purpose of helping others rather than only for the benefit or profit of one person or a group. Actors in the sector are expected to ensure that people create and share resources equitably or fairly to the broadest extent possible.

The Stanford University Institute for Human-Centered Artificial Intelligence is calling for the U.S. government to make a $120 billion investment in the nation's AI ecosystem over the course of the next 10 years. The report calls efforts by the Trump administration, like the call for near $1 billion in U.S. non-defense research and development spending in 2020, "encouraging, but not nearly enough." The national AI vision report specifically calls for $2 billion in annual spending to support entrepreneurs and expand innovation, $3 billion on education, and $7 billion on interdisciplinary research to discover breakthrough advances in the field. The report was written by center directors John Etchemendy and Dr. Fei-Fei Li, and it calls underfunding of AI a threat to U.S. global leadership and a "national emergency in the making." Li is a leader at the Stanford Computer Vision Lab, creator of ImageNet, and until last year, served as chief AI scientist for Google Cloud.

In September 2018, I compared all the major deep learning frameworks in terms of demand, usage, and popularity in this article. TensorFlow was the undisputed heavyweight champion of deep learning frameworks. PyTorch was the young rookie with lots of buzz. How has the landscape changed for the leading deep learning frameworks in the past six months? To answer that question, I looked at the number of job listings on Indeed, Monster, LinkedIn, and SimplyHired.

I spend a disproportionate amount of time reading and talking to two somewhat niche groups of people in American politics: democratic socialists of the Sen. Bernie Sanders variety (or maybe a bit to the left of that), and left-libertarians from the Bay Area who are interested in effective altruism. These are both small groups, but they have social and intellectual influence bigger than their numbers. And while from a distance they look similar (I'm sure they both vote for Democrats in general elections, say), there's a big issue on which they part ways where collaboration could be productive: artificial intelligence safety. Effective altruists have, for complex sociological reasons I explored in a podcast episode, become very interested in AI as a potential "existential risk": a force that could, in extreme circumstances, wipe out humanity, just as nuclear war or asteroid strikes could. Kelsey Piper has a comprehensive Vox explainer of these arguments, and I take them seriously, but most friends to my left do not.