The analysis of nonconvex matrix completion has recently attracted much attention in the community of machine learning thanks to its computational convenience. Existing analysis on this problem, however, usually relies on $\ell_{2,\infty}$ projection or regularization that involves unknown model parameters, although they are observed to be unnecessary in numerical simulations, see, e.g. Zheng and Lafferty [2016]. In this paper, we extend the analysis of the vanilla gradient descent for positive semidefinite matrix completion proposed in Ma et al. [2017] to the rectangular case, and more significantly, improve the required sampling complexity from $\widetilde{O}(r^3)$ to $\widetilde{O}(r^2)$. Our technical ideas and contributions are potentially useful in improving the leave-one-out analysis in other related problems.

A churn prediction system guides telecom service providers to reduce revenue loss. Development of a churn prediction system for a telecom industry is a challenging task, mainly due to size of the data, high dimensional features, and imbalanced distribution of the data. In this paper, we focus on a novel solution to the inherent problems of churn prediction, using the concept of Transfer Learning (TL) and Ensemble-based Meta-Classification. The proposed method TL-DeepE is applied in two stages. The first stage employs TL by fine tuning multiple pre-trained Deep Convolution Neural Networks (CNNs). Telecom datasets are in vector form, which is converted into 2D images because Deep CNNs have high learning capacity on images. In the second stage, predictions from these Deep CNNs are appended to the original feature vector and thus are used to build a final feature vector for the high-level Genetic Programming and AdaBoost based ensemble classifier. Thus, the experiments are conducted using various CNNs as base classifiers with the contribution of high-level GP-AdaBoost ensemble classifier, and the results achieved are as an average of the outcomes. By using 10-fold cross-validation, the performance of the proposed TL-DeepE system is compared with existing techniques, for two standard telecommunication datasets; Orange and Cell2cell. In experimental result, the prediction accuracy for Orange and Cell2cell datasets were as 75.4% and 68.2% and a score of the area under the curve as 0.83 and 0.74, respectively.

Recent years have seen remarkable progress of text generation in different contexts, such as the most common setting of generating text from scratch, and the emerging paradigm of retrieval-and-rewriting. Text infilling, which fills missing text portions of a sentence or paragraph, is also of numerous use in real life, yet is under-explored. Previous work has focused on restricted settings by either assuming single word per missing portion or limiting to a single missing portion to the end of the text. This paper studies the general task of text infilling, where the input text can have an arbitrary number of portions to be filled, each of which may require an arbitrary unknown number of tokens. We study various approaches for the task, including a self-attention model with segment-aware position encoding and bidirectional context modeling. We create extensive supervised data by masking out text with varying strategies. Experiments show the self-attention model greatly outperforms others, creating a strong baseline for future research.

Generative Adversarial Networks (GAN) receive great attentions recently due to its excellent performance in image generation, transformation, and super-resolution. However, GAN has rarely been studied and trained for classification, leading that the generated images may not be appropriate for classification. In this paper, we propose a novel Generative Adversarial Classifier (GAC) particularly for low-resolution Handwriting Character Recognition. Specifically, involving additionally a classifier in the training process of normal GANs, GAC is calibrated for learning suitable structures and restored characters images that benefits the classification. Experimental results show that our proposed method can achieve remarkable performance in handwriting characters 8x super-resolution, approximately 10% and 20% higher than the present state-of-the-art methods respectively on benchmark data CASIA-HWDB1.1 and MNIST.

It's a well-known fact that there was a lot of interspecies mingling back in the day. Modern humans have fragments of DNA from our ancient relatives, the Neanderthals and the Denisovans – and now, a third, previously unknown, mystery species. An international team of researchers have examined human DNA using deep learning algorithms to analyze genetic clues to human evolution for the very first time. The results are published in the journal Nature Communications. It's long been suspected that, further to the Neanderthals and Denisovans, people of Asian descent have a third ancestor that interbred with ancient humans.

Malcolm Fisher of Domino's Inc. learns about the Zivelo self-order kiosk from Mike Moon at the NRF Big Show. The merging of digital and physical retail continues to advance at a rapid pace, giving new life to an industry that many believed was headed for oblivion. The race to introduce interactive technologies in stores has unleashed a historic demand for self-service kiosks that was in full view at the NRF Big Show at Javits Center in New York City this week. Self-serve kiosks were dominant on the trade show floor, offering a range of technologies such as artificial intelligence, robotics, virtual reality, augmented reality, facial recognition, voice recognition, machine learning, advanced analytics, digital currency acceptance and more. The cashierless store concept, spearheaded in the past year by Amazon Go, has spawned scores of competitors, several of which were on display at NRF.

SAVING money and time is one thing artificial intelligence (AI) can do well, and American Express (AMEX) Global Business Travel (GBT) is using the technology to deliver savings every time clients book a hotel room. In an exclusive interview with Tech Wire Asia during a trip to Singapore, Chief Information and Technology Officer David Thompson spills the beans on the company's AI-based booking solution. As AMEX GBT books hotel rooms for clients, it follows travel policies handed down to them (preferred hotels, agreements, etc). However, as most hotel's inventory changes frequently, opportunities to move the traveler into a lower cost hotel room with the same amenities often arises. "This is where we found the opportunity to leverage AI. We trained AI to understand where the hotel room is and what the amenities are."

The world's biggest data dump has just hit the internet. And somewhere among the nearly a billion logins might well be yours. The trove of information – which is being referred to as Collection #1 – contains email addresses and passwords taken from a series of breaches from websites around the internet. It is now readily available, having been published online for conceivably anyone to download. The scale of the dump is unprecedented: it includes 800 million email addresses and passwords, many of which will have been re-used over the internet. Taken together, it is a powerful set of information for anyone who wants to attack people with it.

SeeTree, a Tel Aviv-based startup that uses drones and artificial intelligence to bring precision agriculture to their groves, today announced that it has raised an $11.5 million Series A funding round led by Hanaco Ventures, with participation from previous investors Canaan Partners Israel, Uri Levine and his investors group, iAngel and Mindset. This brings the company's total funding to $15 million. The idea behind the company, which also has offices in California and Brazil, is that in the past, drone-based precision agriculture hasn't really lived up to its promise and didn't work all that well for permanent crops like fruit trees. "In the past two decades, since the concept was born, the application of it, as well as measuring techniques, has seen limited success -- especially in the permanent-crop sector," said SeeTree CEO Israel Talpaz. "They failed to reach the full potential of precision agriculture as it is meant to be."

Artificial intelligence is not some futuristic dystopian concept years away from hitting society. AI is here already and it might not be what you think it is. When people think about AI, they think about Hollywood science fiction. Is that something on the horizon? I'm not sure in the future AI will become like a Hollywood movie because it's everyday life.