Algorithm selection (AS) deals with the automatic selection of an algorithm from a fixed set of candidate algorithms most suitable for a specific instance of an algorithmic problem class, where "suitability" often refers to an algorithm's runtime. Due to possibly extremely long runtimes of candidate algorithms, training data for algorithm selection models is usually generated under time constraints in the sense that not all algorithms are run to completion on all instances. Thus, training data usually comprises censored information, as the true runtime of algorithms timed out remains unknown. However, many standard AS approaches are not able to handle such information in a proper way. On the other side, survival analysis (SA) naturally supports censored data and offers appropriate ways to use such data for learning distributional models of algorithm runtime, as we demonstrate in this work. We leverage such models as a basis of a sophisticated decision-theoretic approach to algorithm selection, which we dub Run2Survive. Moreover, taking advantage of a framework of this kind, we advocate a risk-averse approach to algorithm selection, in which the avoidance of a timeout is given high priority. In an extensive experimental study with the standard benchmark ASlib, our approach is shown to be highly competitive and in many cases even superior to state-of-the-art AS approaches.

It's clear that 5G is not only going to revolutionise the way consumers connect with each other, but also how enterprises around the world will streamline their operations. With this in mind, says Andrew Burrell, head of Ultra Broadband & Analytics services, Nokia, service providers need to carefully consider how they are going to ensure they can provide and manage the necessary quality standards to meet this increased demand when upgrading their systems to enable 5G delivery. While consumers may be forgiving of some buffering while streaming a film, the effect of latency on enterprises could interrupt their day-to-day business and potentially significantly increase their costs. It is not enough for service providers to invest in the hardware in order to deliver reliable 5G to consumers and businesses, the real stand-out value – and profit – lies in intelligent, automated operations to protect their networks and assure service quality. With 5G, network slicing will be imperative for service providers.

Paris – Native Americans and Polynesians bridged vast expanses of open ocean around the year 1200 and mingled, leaving incontrovertible proof of their encounter in the DNA of present-day populations, scientists revealed Wednesday. Whether peoples from what is today Colombia or Ecuador drifted thousands of kilometers to tiny islands in the middle of the Pacific, or whether seafaring Polynesians sailed upwind to South America and then back again is still unknown. But what is certain, according to a study in Nature, is that the hook up took place hundreds of years before Europeans set foot in either region, and left individuals scattered across French Polynesia with signature traces of the New World in their DNA. "These findings change our understanding of one of the most unknown chapters in the history of our species' great continental expansions," senior author Andreas Moreno-Estrada, principal investigator at Mexico's National Laboratory of Genomics for biodiversity, said. Archeologists and historians have tussled for decades over whether Oceana islanders and native Americans crossed paths during the Middle Ages, and how, if they did, that contact might have unfolded.

Hi, I am José Luis. I have B.S., M.S. and Lic. in Physics, and currently I'm a Ph.D. Candidate in Physics at Uppsala University, Sweden. I have worked as a Research Engineer using Deep Reinforcement Learning to track multiple targets for autonomous vehicles at Veoneer. Additionally, I have taught thousands of students at Universities in Brazil and abroad. I work with Computer Simulations and I will share my experiences within programming across different fields.

WDG Automation’s software, which features artificial-intelligence capabilities, enables businesses to automate workplace tasks. The company, based in São José do Rio Preto, Brazil, has more than 600 prebuilt robotic process automation functions.

The Travelling Salesman Problem (TSP) is a challenging graph task in combinatorial optimization that requires reasoning about both local node neighborhoods and global graph structure. In this paper, we propose to use the novel Graph Learning Network (GLN), a generative approach, to approximately solve the TSP. GLN model learns directly the pattern of TSP instances as training dataset, encodes the graph properties, and merge the different node embeddings to output node-to-node an optimal tour directly or via graph search technique that validates the final tour. The preliminary results of the proposed novel approach proves its applicability to this challenging problem providing a low optimally gap with significant computation saving compared to the optimal solution.

First-price auctions have very recently swept the online advertising industry, replacing second-price auctions as the predominant auction mechanism on many platforms. This shift has brought forth important challenges for a bidder: how should one bid in a first-price auction, where unlike in second-price auctions, it is no longer optimal to bid one's private value truthfully and hard to know the others' bidding behaviors? In this paper, we take an online learning angle and address the fundamental problem of learning to bid in repeated first-price auctions, where both the bidder's private valuations and other bidders' bids can be arbitrary. We develop the first minimax optimal online bidding algorithm that achieves an $\widetilde{O}(\sqrt{T})$ regret when competing with the set of all Lipschitz bidding policies, a strong oracle that contains a rich set of bidding strategies. This novel algorithm is built on the insight that the presence of a good expert can be leveraged to improve performance, as well as an original hierarchical expert-chaining structure, both of which could be of independent interest in online learning. Further, by exploiting the product structure that exists in the problem, we modify this algorithm--in its vanilla form statistically optimal but computationally infeasible--to a computationally efficient and space efficient algorithm that also retains the same $\widetilde{O}(\sqrt{T})$ minimax optimal regret guarantee. Additionally, through an impossibility result, we highlight that one is unlikely to compete this favorably with a stronger oracle (than the considered Lipschitz bidding policies). Finally, we test our algorithm on three real-world first-price auction datasets obtained from Verizon Media and demonstrate our algorithm's superior performance compared to several existing bidding algorithms.

IBM (NYSE:IBM) has reached an agreement to buy WDG Automation, a robotic process automation (RPA) company. The Brazilian company produces software based on artificial intelligence (AI) that enhances access to intelligent automation using software robots. IBM expects to use this technology to improve its Cloud Pak offerings, beginning with Cloud Pak for automation. The current Cloud Pak technology offers AI-driven solutions for capabilities such as data capture, orchestrating workflow, monitoring and reporting, and decision management. The new RPA capabilities will help identify more opportunities for automation, enhance bot deployment, and streamline workflows.

The pandemic is proving to be the ultimate test for Singapore, the tiny city-state that has a reputation for reinventing itself during times of crises. Dismissed in the past as just a "little red dot" on the map, dwarfed by larger neighbors like Malaysia and Indonesia, and with no natural resources to speak of, Singapore has nonetheless transformed itself into one of the richest and most competitive economies in the world. As Singapore's leaders now grapple with what's turning out to be its worst slump since independence in 1965, the ruling party is looking to extend its mandate in Friday's election to help reinvent the economy once again. They're already positioning for a post-COVID-19 world with planned investment in health and biomedical sciences, climate change and artificial intelligence. Crises have been a catalyst for change in the past.

Global AI/Machine Learning market Size, Insights and Forecast 2020 to 2026 Latest Innovations & Application Analysis with the key players -GOOGLE, IBM, BAIDU, SOUNDHOUND, ZEBRA MEDICAL VISION, PRISMA, IRIS AI, PINTEREST, TRADEMARKVISION, DESCARTES LABS and Amazon, including Production, Price, Revenue, Cost, Application, Growth Rate, Import, Export, Capacity, Market Share and Technological Developments.The research report on AI/Machine Learning market provides a granular analysis of this business space and also assesses its various segmentations. Major aspects such as existing market size ad position in terms of volume and revenue estimations are detailed in the study.