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Developers generally exhibit a strong affinity (usually paired with an equally strong hatred) for certain frameworks, libraries, and tools. But which ones do they love, dread, and want the most? Stack Overflow, as part of its enormous, annual Developers Survey, asked that very question, and the answers provide some interesting insights into how developers work. Some 65,000 developers responded to the survey, and the sheer size of that sample makes these breakdowns a bit more interesting to parse. For example, although game developers might have strong opinions about Unreal Engine and Unity 3D (which placed high on the following lists), those aren't used at all by the bulk of developers concerned with A.I. and machine learning, who have strong feelings about TensorFlow that many other developers might not share.
CV is a nascent market but it contains a plethora of both big technology companies and disruptors. Technology players with large sets of visual data are leading the pack in CV, with Chinese and US tech giants dominating each segment of the value chain. Google has been at the forefront of CV applications since 2012. Over the years the company has hired several ML experts. In 2014 it acquired the deep learning start-up DeepMind. Google's biggest asset is its wealth of customer data provided by their search business and YouTube.
This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. Twenty years ago, the people interested in artificial intelligence research were mostly confined in universities and non-profit AI labs. AI research projects were mostly long-term engagements that spanned across several years--or even decades-- and the goal was to serve science and expand human knowledge. But in the past decade, thanks to advances in deep learning and artificial neural networks, the AI industry has undergone a dramatic change. Today, AI has found its way into many practical applications.
Himax Technologies, Inc., a leading supplier and fabless manufacturer of display drivers and other semiconductor products, announced the launch of WiseEye WE-I Plus HX6537-A solution that supports Google's TensorFlow Lite for Microcontrollers. In this collaboration, Himax is providing the HX6537-A processor with NN (neural network) based SDK (Software Development Kit) for developers to generate deep learning inferences running on TensorFlow Lite for Microcontrollers kernel to boost overall system AI performance. With support to TensorFlow Lite for Microcontrollers, developers are able to take advantage of the WE-I Plus platform as well as the integrated ecosystem from TensorFlow Lite for Microcontrollers to develop their NN based edge AI applications targeted for Notebook, TV, Home Appliance, Battery Camera and IP Surveillance edge computing markets. The benefits of the Himax HX6537-A processor are driven by three unique features. The HX6537-A processor adopts a programmable DSP that runs at 400MHz with power-efficient and multi-level power schemes that incorporate CDM, HOG and JPEG hardware accelerators for real-time motion detection, object detection and image processing.
That year, numerous experienced computer chip designers set out on their own to design novel kinds of parts to improve the performance of artificial intelligence. It's taken a few years, but the world is finally seeing what those young hopefuls have been working on. The new chips coming out suggest, as ZDNet has reported in past, that AI is totally changing the nature of computing. It also suggests that changes in computing are going to have an effect on how artificial intelligence programs, such as deep learning neural networks, are designed. Case in point, startup Tenstorrent, founded in 2016 and headquartered in Toronto, Canada, on Thursday unveiled its first chip, "Grayskull," at a microprocessor conference run by the legendary computer chip analysis firm The Linley Group.
TOKYO, June 30, 2020 /PRNewswire-PRWeb/ -- About Cyneural While cyber-attack defenses generally respond by detecting specific patterns of "signatures" that indicate malicious access, complex or unknown attacks that utilize AI or BOTs can be difficult to detect or can result in false positives. This is why cyber-attack defenses also need to take advantage of technology with flexibility such as AI. Against this backdrop, Cyber Security Cloud developed its own attack detection AI engine, Cyneural, in August 2019. "Cyneural" uses a feature extraction engine that utilizes the knowledge cultivated through CSC's research on web access and various attack methods. It builds multiple types of training models to help detect not only common attacks but also unknown cyber-attacks and false positives at a higher speed. About Cyneural being used in Shadankun and WafCharm Since the development of Cyneural, CSC has been operating it by utilizing the large amount of data that they have.
Yesterday, AIM published an article on how difficult it is for the small labs and individual researchers to persevere in the high compute, high-cost industry of deep learning. Today, the policymakers of the US have introduced a new bill that will ensure deep learning is affordable for all. The National AI Research Resource Task Force Act was introduced in the House by Representatives Anna G. Eshoo (D-CA) and her colleagues. This bill was met with unanimous support from the top universities and companies, which are engaged in artificial intelligence (AI) research. Some of the well-known supporters include Stanford University, Princeton University, UCLA, Carnegie Mellon University, Johns Hopkins University, OpenAI, Mozilla, Google, Amazon Web Services, Microsoft, IBM and NVIDIA amongst others.
Whether or not your organisation suffers a cyber attack has long been considered a case of'when, not if', with cyber attacks having a huge impact on organisations. In 2018, 2.8 billion consumer data records were exposed in 342 breaches, ranging from credential stuffing to ransomware, at an estimated cost of more than $654bn. In 2019, this had increased to an exposure of 4.1 billion records. While the use of artificial intelligence (AI) and machine learning as a primary offensive tool in cyber attacks is not yet mainstream, its use and capabilities are growing and becoming more sophisticated. In time, cyber criminals will, inevitably, take advantage of AI, and such a move will increase threats to digital security and increase the volume and sophistication of cyber attacks.
With the rise of autonomous vehicles, smart video surveillance, facial detection and various people counting applications, fast and accurate object detection systems are rising in demand. These systems involve not only recognizing and classifying every object in an image, but localizing each one by drawing the appropriate bounding box around it. This makes object detection a significantly harder task than its traditional computer vision predecessor, image classification.