If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
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.
Throughout its history, Machine Learning (ML) has coexisted with Statistics uneasily, like an ex-boyfriend accidentally seated with the groom's family at a wedding reception: both uncertain where to lead the conversation, but painfully aware of the potential for awkwardness. This is caused in part by the fact that Machine Learning has adopted many of Statistics' methods, but was never intended to replace statistics, or even to have a statistical basis originally. Nevertheless, Statisticians and ML practitioners have often ended up working together, or working on similar tasks, and wondering what each was about. The question, "What's the difference between Machine Learning and Statistics?" has been asked now for decades. Machine Learning is largely a hybrid field, taking its inspiration and techniques from all manner of sources. It has changed directions throughout its history and often seemed like an enigma to those outside of it.1
Our reviews generally focus on products that will help enable or inform a secure lifestyle rather than more existential threats, but in the case of Life 3.0: Being human in the age of Artificial Intelligence, we made an exception. The potential implications of artificial intelligence (AI) for our future are simply too big too ignore – including many facets of security – and this book leads the reader through them beautifully. Following a slightly disturbing scene-setting prologue imagining an ultra-intelligent AI growing out of control, the author (Max Tegmark) gets straight down to business by defining what he means by Life 3.0. In short, Tegmark classifies life into three broad categories based on its capabilities. Life 1.0 is biological life that can only update its'hardware' (physical form) and'software' (learned abilities) through evolution.
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.
Our society relies heavily on digital devices and channels, and with that the concept of identity has quickly become the foundation of every customer interaction--particularly within the digital advertising ecosystem. In response to the emerging strategic importance of identity, Experian today announced a new innovative solution that uses the fusion of data and artificial intelligence, to help marketers connect Mobile Ad IDs (MAIDs) with digital and offline identity attributes to better understand their target audiences. Powered by Experian's vast data assets and identity platform, the new solution incorporates machine-learning algorithms, as well as deterministic and probabilistic techniques, to sift and connect billions of advanced identity signals and data elements, including MAIDs, from a wide variety of internal and external sources. The outcome of this process allows brand marketers to implement more effective analytics, audience segmentation and activation, and measurement capabilities. "Experian has always been a leader in identity resolution, helping brand marketers more accurately identify and understand customers, while also keeping customers at the heart of every marketing strategy," said Kevin Dean, Experian's president and general manager of Marketing Services, North America.
With the dawn of industrialization 4.0, there has been a tremendous shift in the technologies that we use to make our life better. From Artificial Intelligence to the Internet of Things, AR/VR and ML, the list doesn't seem to end. Of all these, one technology that has proven to be not only disruptive but also opportunistic is Machine Learning. Cumulative investments done in the field of machine learning is expected to rise to $58 billion by the end of 2021. Needless to state that the pace at which the industry is growing is day beyond expectations.
Artificial intelligence offers "unbelievable opportunities" in banking, according to HSBC's global head of digital, including help to cut costs. "There are unbelievable opportunities for artificial intelligence and machine learning in banks," Josh Bottomley said in a speech on Monday. "One of the reasons is actually a lot of the backends of banks are still about predicting, or preventing, or proscribing behaviour. "Unlike an airline, where you've still physically got to get a person from A to B, or a retailer, where usually there is a good that's there, the backend processes in banking are pretty much all data driven, they're all automatable, and they're very susceptible for machine learning. "There are some obvious use case and we're looking at those."