K-nearest neighbor (kNN) search refers to the problem of finding K points closest toa given data point on a distance metric of interest. It is an important task in a wide range of applications, including similarity search in data mining [15,19], fast kernel methods in machine learning [17, 30, 38], nonparametric density estimation [5, 29, 31] and intrinsic dimension estimation [6, 26] in statistics, aswell as anomaly detection algorithms [2, 10, 37]. Numerous algorithms have been proposed for kNN search; the readers are referred to [35, 46] and references therein. Our interest is kNN search in emerging applications. Two 1 salient features of such applications are the expected scalability of the algorithms andtheir ability to handle data of high dimensionality. Additionally, such applications often desire more accurate kNN search. For example, robotic route planning [23] and face-based surveillance systems [34] require a high accuracy forthe robust execution of tasks. However, most existing work on kNN search [1, 4, 12, 15] have focused mainly on the fast computation and accuracy isofalessconcern.

In this tutorial, we will learn how to use deep learning to compose images in the style of another image (ever wish you could paint like Picasso or Van Gogh?). This is known as neural style transfer! This is a technique outlined in Leon A. Gatys' paper, A Neural Algorithm of Artistic Style, which is a great read, and you should definitely check it out. Neural style transfer is an optimization technique used to take three images, a content image, a style reference image (such as an artwork by a famous painter), and the input image you want to style -- and blend them together such that the input image is transformed to look like the content image, but "painted" in the style of the style image. For example, let's take an image of this turtle and Katsushika Hokusai's The Great Wave off Kanagawa: Now how would it look like if Hokusai decided to add the texture or style of his waves to the image of the turtle? Is this magic or just deep learning?

The report "Deep Learning Market: Global Report (2018 -2023)" provides market intelligence on the different segments based on type, application and geography. Market size and forecast (2018-2023) has been provided in terms of both, Value (USD) and Volume (KG) in the report. A detailed qualitative analysis of the factors responsible for driving and restraining growth of the Deep Learning and future market opportunities have also been discussed. The report covers the present scenario and the growth prospects of the Deep Learning market for 2018-2023. To calculate the market size, the report considers the revenue generated from the sales of the web conferencing and video conferencing, secondary resources and doing in-depth company share analysis of major Top players in the Deep Learning market: IBM Corporation, Qualcomm Technologies, Inc, Microsoft Corporation, Google Inc., General Vision Inc., Hewlett Packard Enterprise, Intel Corporation, Skymind, Baidu Inc., Nvidia Corporation, Sensory Inc.

Imagine a world in which you can scan your face to board a train, check into a hotel, order a meal at a café, or even track your food from farm to table. In China, all of this is already happening. Facial recognition became more pervasive this year after the Chinese government in December 2017 announced an ambitious plan to achieve greater face-reading accuracy by 2020. The country also plans to introduce a system that will identify any of its 1.3 billion citizens in just three seconds. Public and private enterprises have rushed to adopt the futuristic, artificial intelligence-powered technology, implementing facial-recognition systems in transportation networks, medical facilities, and law enforcement initiatives.

What an innocent, carefree year it must have been to spawn so bland a word of the year. It has a nice ring to it, especially when spoken with the Hokkaido lilt the women's curling team -- surprise bronze medalists -- gave it during February's Pyeongchang Winter Olympics. So what if all it means is, "That's right"? Let 2018, the Year of the Dog, end as it began -- on a positive note. Speaking of dogs and beginnings: Sony's robot dog Aibo was a big hit at January's Consumer Electronics Show in Las Vegas.

China, the world's biggest gaming market, stopped approving new video games in March amid a regulatory overhaul triggered by growing criticism of video games for being violent and allegations that they were causing myopia as well as addiction among young users.

If you work in the technology sector, you know by now that areas such as automation, big data and artificial intelligence are where the most lucrative opportunities exist. A new report by online skilling firm Simplilearn throws some light on this, listing the most high-paying job profiles, the total number of jobs in that space and the cities where demand for these is the most. 'The Future of IT Jobs In India' survey was conducted among 1000 learners, IT working professionals across metro cities in India to understand their views on digital transformation and how it is impacting their careers. "The salaries for some of the roles are dependent on the skills," Kashyap Dalal, Co-founder and Chief Business Office, Simplilearn told Moneycontrol. "Companies, especially in the IT sector, are no longer are into mass hiring. They are in fact looking at trained talent in areas like automation, data science and artificial intelligence," he added.

At 12 o'clock on the morning of December 28, 2018, Beijing time, under the witness of the online nodes and invited guests, the Bottos main network community was successfully activated, and the main network community was officially launched! This means that the Bottos project is launched and becomes a completely independent public blockchain platform, which can provide a solid foundation for the subsequent richer DApp development. Since the launch of the Bottos project in October 2016, the Bottos tech team has been relying on its strong business capabilities to create a new decentralized ecosystem of artificial intelligence. After the 2017 bull market and the 2018 market winter, many projects have disappeared, but the Bottos project has not lost the initial heart and has quietly stayed working. After 9 months of development and testing, the Bottos main network was officially launched, which means that the efficient circulation and real-time sharing of big data that artificial intelligence relies on is possible.

American inventor and billionaire Elon Musk believes that the first colonists of Mars may be artificial intelligence. On Thursday, the CEO of SpaceX said that the probability that the first resident of Mars would be artificial superintelligence. According to the Mask, the probability of this is reached in his calculations 30%. SpaceX is working on an ambitious schedule of sending two spaceships to Mars by 2022, paving the way for four more vehicles in 2024, two of which will be the first people on Mars. Musk said in November that the Mars colony can be formed in the next seven to ten years, which means that it may appear in 2025.

This work is concerned with the non-negative robust principal component analysis (PCA), where the goal is to recover the dominant non-negative principal component of a data matrix precisely, where a number of measurements could be grossly corrupted with sparse and arbitrary large noise. Most of the known techniques for solving the robust PCA rely on convex relaxation methods by lifting the problem to a higher dimension, which significantly increase the number of variables. As an alternative, the well-known Burer-Monteiro approach can be used to cast the robust PCA as a non-convex and non-smooth $\ell_1$ optimization problem with a significantly smaller number of variables. In this work, we show that the low-dimensional formulation of the symmetric and asymmetric positive robust PCA based on the Burer-Monteiro approach has benign landscape, i.e., 1) it does not have any spurious local solution, 2) has a unique global solution, and 3) its unique global solution coincides with the true components. An implication of this result is that simple local search algorithms are guaranteed to achieve a zero global optimality gap when directly applied to the low-dimensional formulation. Furthermore, we provide strong deterministic and statistical guarantees for the exact recovery of the true principal component. In particular, it is shown that a constant fraction of the measurements could be grossly corrupted and yet they would not create any spurious local solution.