Scientists at the California NanoSystems Institute at UCLA have developed a new technique for identifying cancer cells in blood samples faster and more accurately than the current standard methods. In one common approach to testing for cancer, doctors add biochemicals to blood samples. Those biochemicals attach biological "labels" to the cancer cells, and those labels enable instruments to detect and identify them. However, the biochemicals can damage the cells and render the samples unusable for future analyses. There are other current techniques that don't use labeling but can be inaccurate because they identify cancer cells based only on one physical characteristic.

Optimization provides a valuable framework for thinking about, formulating, and solving many problems in machine learning. Since specialized techniques for the quadratic programming problem arising in support vector classification were developed in the 1990s, there has been more and more cross-fertilization between optimization and machine learning, with the large size and computational demands of machine learning applications driving much recent algorithmic research in optimization. This tutorial reviews the major computational paradigms in machine learning that are amenable to optimization algorithms, then discusses the algorithmic tools that are being brought to bear on such applications. We focus particularly on such algorithmic tools of recent interest as stochastic and incremental gradient methods, online optimization, augmented Lagrangian methods, and the various tools that have been applied recently in sparse and regularized optimization.

A lot of people think that gophers go into hibernation during the winter months but that's not the case. Inference and learning on large graphical models, i.e. large systems of simple probabilistic units linked by a complex network of ... Following the Future Architecture platform's call for ideas that generated a full 291 ideas by 524 authors from 39 countries ... Future Architecture is the first pan-European platform of architecture museums, festivals and producers, bringing ideas on the future of cities and architecture closer to the wider public. From 18 - 20 February MAO organized Future Architecture Matchmaking Conference where candidates selected by the platform members and the public presented their projects. The workshop brings together researchers from the fields of machine learning and statistical physics in order to discuss the new challenges originating from dynamical data. It provides a forum for exploring possible synergies between the inference and learning approaches developed for the various models.

Hewlett Packard Enterprise is expanding into cloud services to help companies build data-rich mobile and enterprise applications and analyze data such as photos, audio clips and comments on social media. Haven OnDemand, which runs on computers operated by Microsoft's Azure cloud-computing, gives users access to sophisticated techniques such as machine learning without the need to maintain a data center or develop the underlying technology. HPE first pioneered this effort in December 2014 with the beta launch of HPE Haven OnDemand. Today, HPE Haven OnDemand has more than 12,750 registered developers who currently generate millions of API calls per week, and have provided feedback to improve and refine the offering. "The software industry is on the cusp of a new era of breakthroughs, driven by machine learning that will power data-driven applications across all facets of life," said Kamal Dutta, Managing Director - Software, Asia Growth Countries, Hewlett Packard Enterprise.

Extreme developments in the field of artificial intelligence have become an integral part of the technology industry. AI has helped the world reach a point where continuous improvements have resulted in making life easier for humans. "A chatbot is an artificially intelligent software program that uses natural language processing to simulate intelligent conversation with end users via auditory or textual methods".Chat Bots date as far back as 1960's and this was the time when the computer world idealized such machines which could be used to impersonate humans in real-time, sufficiently well that the reviewer was unable to distinguish, whether the conversation was between real humans themselves or between the program and a real human. Joseph Weizenbaum's, a professor at MIT developed a program named ELIZA, published in 1966. ELIZA operated in a unique way, as it focused on the recognition of cue words or phrases in the input.

The crowdsourcing platform combines human insights with machine learning techniques to untangle and promote wider analytics use of unstructured data. To improve accuracy, the company's "Reputation Engine" applied machine-learning techniques to rate each individual's performance by domain. The resulting combination of human insights and machine learning can then be used to organize unstructured data into "clean," labeled data. Spare5 asserted limitations in current data quality tools leave much unstructured data unused.

As unstructured data continues to pile up, different approaches and platforms are emerging to help businesses make greater analytical use of what otherwise might amount to clutter. Among the latest approaches is an "intelligent crowdsourcing platform" from Spare5, a Seattle-based startup specializing in, among other things, data "cleanup." Spare5's proprietary platform unveiled Wednesday (April 13) incorporates a crowdsourcing approach that leverages the experience of domain specialists to perform "custom micro-tasks." Once "filtered for quality," the resulting tasks can be used for applications ranging from training artificial intelligence models and improving searches to augmenting directories. The crowdsourcing platform combines human insights with machine learning techniques to untangle and promote wider analytics use of unstructured data.

"We cannot predict the future," says BMW Group design chief Adrian van Hooydonk, "but we can take a stance. That's what our company feels is very important, to try and shape the future before it shapes you." And the future, says van Hooydonk, is a lot closer than many think. He is convinced the next paradigm for the automobile and individual mobility will be shaped by technologies that will become commonplace in the next 10 years. So what does the BMW Vision Next 100, the first of four advanced concepts to be unveiled this year as BMW celebrates its centenary, reveal about a 100-year-old automaker's idea of the future?

Microsoft is the world's most valuable company, with a 261 billion market cap. And an IBM computer named Deep Blue defeats Garry Kasparov, reigning world chess champion and, at the time, the highest-ranked chess player to have ever lived. Though it was not the first time man has lost to machine, it is perhaps the most prominent, highly publicized by IBM and widely covered by the global media. It was viewed as a milestone for AI, the true arrival of computer intelligence. The world celebrated the achievement of technology -- or offered doomsday predictions of a robot revolution.

Microsoft researchers have come up with a novel way to have computers tell stories about what's happening in multiple photographs using artificial intelligence (AI). Today the company is publishing an academic paper describing the technology, which could one day power services that are especially useful to the visually impaired, as well as the photos, captions, and "stories" developed in the research. The work is significant because it goes well beyond just identifying objects in images, or even videos, in order to generate captions. "It's still hard to evaluate, but minimally you want to get the most important things in a dimension. With storytelling, a lot more that comes in is about what the background is and what sort of stuff might have been happening around the event," Microsoft researcher Margaret Mitchell told VentureBeat in an interview.