I have monitoring system watching for bandwidth, connections and connections rates from multiple firewalls, which is stream of counters with interval 5 min. My current system create baseline from data for last 4 weeks and compare current value with baseline. It is ok but it either give me lots of false alerts or too slow to react without additional triggers. Is there anything better available today? Some system I can feed data in that will learn patterns and identify outages in real time.

Abstract: Model efficiency has become increasingly important in computer vision. In this paper, we systematically study various neural network architecture design choices for object detection and propose several key optimizations to improve efficiency. First, we propose a weighted bi- directional feature pyramid network (BiFPN), which allows easy and fast multi- scale feature fusion; Second, we propose a compound scaling method that uniformly scales the resolution, depth, and width for all backbone, feature network, and box/class prediction networks at the same time. Based on these optimizations, we have developed a new family of object detectors, called EfficientDet, which consistently achieve an order-of-magnitude better efficiency than prior art across a wide spectrum of resource constraints. In particular, without bells and whistles, our EfficientDet-D7 achieves stateof- the-art 51.0 mAP on COCO dataset with 52M parameters and 326B FLOPS1, being 4x smaller and using 9.3x fewer FLOPS yet still more accurate ( 0.3% mAP) than the best previous detector.

"It will be a hub for machine learning education on campus so people can learn the basics of algorithmic machine learning or more advanced things," …

Machine learning models can identify intricate correlations between enormous numbers of data points.

Say what you will about Kim Kardashian--at least she's a human. The next generation of the famous-for-being-famous are being engineered from scratch. They're synthetic stars--algorithmically generated characters who have millions of Instagram followers, show up in glossy magazines, and have songs on Spotify. She models for the likes of Prada and Calvin Klein, her first single came out last year, and she has sponsorship deals with companies like Samsung. Among her pals: Bermuda, a rule-breaking bad girl who models and touts brands, and Blawko, an L.A.-based Gen-Zer who likes fast cars and Absolut vodka, and who is never seen without his trademark scarf covering his nose and mouth.

We live in a data society. Journalists are becoming data analysts and data curators, and computation is an essential tool for reporting. Data and computation reshape the way a reporter sees the world and composes a story. They also control the operation of the information ecosystem she sends her journalism into, influencing where it finds audiences and generates discussion. So every reporting beat is now a data beat, and computation is an essential tool for investigation. But digitization is affected by inequities, leaving gaps that often reflect the very disparities reporters seek to illustrate. Computation is creating new systems of power and inequality in the world. We rely on journalists, the "explainers of last resort"[1], to hold these new constellations of power to account. We report on computation, not just with computation. While a term with considerable history and mystery, artificial intelligence (AI) represents the most recent bundling of data and computation to optimize business decisions, automate tasks, and, from the point of view of a reporter, learn about the world. The relationship between a journalist and AI is not unlike the process of developing sources or cultivating fixers. As with human sources, artificial intelligences may be knowledgeable, but they are not free of subjectivetivity in their design -- they also need to be contextualized and qualified. Ethical questions of introducing AI in journalism abound. But since AI has once again captured the public imagination, it is hard to have a clear-eyed discussion about the issues involved with journalism's call to both report on and with these new computational tools. And so our article will alternate a discussion of issues facing the profession today with a "slant narrative" -- indicated because these sections are in italics. The slant narrative starts with the 1964 World's Fair and a partnership between IBM and The New York Times, winds through commentary by Joseph Weizenbaum, a famed figure in AI research in the 1960s, and ends in 1983 with the shuttering of one of the most ambitious information delivery systems of the time. The simplicity of the role of computation in the slant narrative will help us better understand our contemporary situation with AI. But we begin our article with context for the use of data and computation in journalism -- a short, and certainly incomplete, history before we settle into the rhythm of alternating narratives. Reporters depend on data, and through computation they make sense of that data. This reliance is not new. Joseph Pulitzer listed a series of topics that should be taught to aspiring journalists in his 1904 article "The College of Journalism."

Can AI teach itself the laws of physics? Will classical computers soon be replaced by deep neural networks? Sure looks like it, if you've been following the news, which lately has been filled with headlines like, "A neural net solves the three-body problem 100 million times faster: Machine learning provides an entirely new way to tackle one of the classic problems of applied mathematics," and "Who needs Copernicus if you have machine learning?". The latter was described by another journalist, in an article called "AI Teaches Itself Laws of Physics," as a "monumental moment in both AI and physics," which "could be critical in solving quantum mechanics problems." The trouble is, the authors have given no compelling reason to think that they could actually do this.

Prior to becoming a full-time musician, Grimes learned how to use the production software Logic for her neuroscience studies at Montreal's McGill University. The Vancouver native brought her unique perspective to Sean Carroll's Mindscape podcast, where she spoke about artificial intelligence's growing capacity to create music. "I feel like we're in the end of art, human art." said Grimes, who is now going by the name c in reference to the speed of light. "Once there's actual AGI (Artificial General Intelligence), it's gonna be so much better at making art than us… Once AI can totally master science and art, which could happen in the next 10 years, probably more like 20 or 30 years." She also predicted that AI will reach a point when it will be building and creating art for itself.

In an era of data driven digital transformation, a customer driven business strategy is essential for success. In the motion picture industry, movie exhibitors must compete to win share of consumers entertainment time (and wallet) against digital entertainment alternatives offered by mammoth sized, digital focused, competitors like Netflix, Amazon and Disney [1]. Customer loyalty, point-of-sale and digital payment p latforms produce rich insights that can leveraged to inform business operations and automate the decision-making pr ocess, effectively enabling movie exhibitors to compete using analytics and artificial intelligence. This study presen ts a new, customer driven, quantitative approach to movie scheduling that can be utilized by movie exhibitors to increase attendance and market share. The role of the exhibitor is to show films that are produced by movie st udios (see [2] for more details on the roles of the stakeholders in the movie industry). Exhibitors do not have d ecision making authority over the movies that are produced by the studios.