It is a known fact that the performance of optimization algorithms for NP-Hard problems vary from instance to instance. We observed the same trend when we comprehensively studied multi-objective evolutionary algorithms (MOEAs) on a six benchmark instances of discrete time-cost trade-off problem (DTCTP) in a construction project. In this paper, instead of using a single algorithm to solve DTCTP, we use a portfolio approach that takes multiple algorithms as its constituent. We proposed portfolio comprising of four MOEAs, Non-dominated Sorting Genetic Algorithm II (NSGA-II), the strength Pareto Evolutionary Algorithm II (SPEA-II), Pareto archive evolutionary strategy (PAES) and Niched Pareto Genetic Algorithm II (NPGA-II) to solve DTCTP. The result shows that the portfolio approach is computationally fast and qualitatively superior to its constituent algorithms for all benchmark instances. Moreover, portfolio approach provides an insight in selecting the best algorithm for all benchmark instances of DTCTP.

The Keras library provides a way to calculate and report on a suite of standard metrics when training deep learning models. In addition to offering standard metrics for classification and regression problems, Keras also allows you to define and report on your own custom metrics when training deep learning models. This is particularly useful if you want to keep track of a performance measure that better captures the skill of your model during training. In this tutorial, you will discover how to use the built-in metrics and how to define and use your own metrics when training deep learning models in Keras. Metrics and How to Use Custom Metrics for Deep Learning with Keras in Python Photo by Indi Samarajiva, some rights reserved.

Excellent customer service operations are essential to ensure customer retention. To help companies keep up with customers' growing expectations, SAP Service Ticket Intelligence automatically categorizes customer tickets, proposes solutions and prioritizes the tickets for customer service employees. There is consensus in the market that a compelling customer experience is critical to business success. Companies that align the business around their customers retain more of them, increase sales, and attract new business through positive word of mouth. With this in mind, it is not surprising that our SAP Center for Business Insight estimated that companies that offer best-in-class customer experience achieve year-over-year profit margins improvement 527% higher than their peers, and 359% greater company revenue growth.*

How can artificial neural nets help in understanding our brain's neural net? On the weekend of March 24–26, YCombinator-backed startup DeepGram hosted a deep learning hackathon. The weekend-long event included speakers and judges from Google Brain, NVIDIA, and Baidu. My colleague, Dr. Matt Rubashkin, also participated and you can read about his project here. I chose to work on one of the datasets suggested by DeepGram: EEG readings from a Stanford research project that predicted which category of images their test subjects were viewing using linear discriminant analysis.

People who played action video games that involve first-person shooters, such as Call of Duty and Medal of Honor, experienced shrinkage in a brain region called the hippocampus, according to a study published Tuesday in Molecular Psychiatry. That part of the brain is associated with spatial navigation, stress regulation and memory. Playing Super Mario games, in which the noble plumber strives to rescue a princess, had the opposite effect on the hippocampus, causing growth in it. Scientists have done dozens of studies looking to see if playing video games affects people's health and behavior. There's some evidence they may improve people's visual short-term memory and eye-hand coordination.

A musician improvises alongside A.I. Duet, software developed in part by Google's Magenta That's a question bordering on the philosophical, but artificial intelligence (AI) can certainly make music and artwork that people find pleasing. Last year, Google launched Magenta, a research project aimed at pushing the limits of what AI can do in the arts. Science spoke with Douglas Eck, the team's lead in San Francisco, California, about the past, present, and future of creative AI. This interview has been edited for brevity and clarity. Q: How does Magenta compose music?

A company can have the best technology in the world. It can have the strongest talent. It can have the coolest product ideas. But to train the algorithms that will deliver the intelligence to transform our cities, it needs data. Sign up to get Backchannel's weekly newsletter. That's why earlier this year, after leaving Microsoft the previous fall, legendary engineer Qi Lu headed to Beijing to become Baidu's chief operating officer. At his former job, he was, among other things, CEO Satya Nadella's top deputy in helping to lead the company's AI strategy. Clearly, he saw more opportunity across the Pacific: In China, 731 million people--nearly twice the entire population of the United States--are online.

The development of pilotless passenger aircraft could be worth 35 billion US dollars (£27 billion) to the aviation industry and cut fares for passengers, according to research. Analysis by investment bank UBS found that technology to enable remotely controlled planes carrying people and cargo could appear by 2025. Almost three-quarters of the economic benefit would be in airlines reducing the cost of employing pilots, the study found. Safety would also be boosted as the potential for pilot error or illness would be removed, the report said. In the US, passengers could benefit by air fares being cut by as much as 11 per cent, UBS said.

The same machine learning technology Google uses to anticipate the next word in a search query is in tests to advance predictive modeling in health care, and the tech giant is teaming up with three universities this year to research its ability to do so. Katherine Chou, head of product for Google's efforts in machine learning for health, has been working on using machine learning for preventive care since 2015, when health care systems reached out to Google for help. Part of the breakthrough now is that "our deep learning technology is finally mature enough to handle the unstructured data that you see in the records," she says. An example of unstructured data is pictures and notes from a physician or nurse that make up the majority of an electronic health record. Google's machine learning can now include those data in a predictive modeling algorithm.

Andrew Ng, one of the world's best-known artificial-intelligence experts, is launching an online effort to create millions more AI experts across a range of industries. Ng, an early pioneer in online learning, hopes his new deep-learning course on Coursera will train people to use the most powerful idea to have emerged in AI in recent years. AI experts have become some of the most sought-after and well-paid employees in today's tech economy. Deep learning involves teaching a machine to perform a complex task using large amounts of data along with a large simulated neural network. The technique has typically required deep technical knowledge and expertise to master (see "10 Breakthrough Technologies 2013: Deep Learning").