Tensor Recovery from Noisy and Multi-Level Quantized Measurements Ren Wang, Meng Wang, Jinjun Xiong Abstract --Higher-order tensors can represent scores in a rating system, frames in a video, and images of the same subject. In practice, the measurements are often highly quantized due to the sampling strategies or the quality of devices. Existing works on tensor recovery have focused on data losses and random noises. Only a few works consider tensor recovery from quantized measurements but are restricted to binary measurements. This paper, for the first time, addresses the problem of tensor recovery from multilevel quantized measurements. Leveraging the low-rank property of the tensor, this paper proposes a nonconvex optimization problem for tensor recovery. We provide a theoretical upper bound of the recovery error, which diminishes to zero when the sizes of dimensions increase to infinity. Our error bound significantly improves over the existing results in one-bit tensor recovery and quantized matrix recovery. A tensor-based alternating proximal gradient descent algorithm with a convergence guarantee is proposed to solve the nonconvex problem. Our recovery method can handle data losses and do not need the information of the quantization rule. The method is validated on synthetic data, image datasets, and music recommender datasets. I NTRODUCTION Many practical datasets are highly noisy and quantized, and recovering the actual values from quantized measurements finds applications in different domains.

The holidays are here, and if you haven't already started decorating, it's time to get going on creating an awesome display. Is the thought already stressing you out? You can simplify managing your amazing holiday light show by using smart plugs and other products that can be controlled remotely. Here are the five smart products you need to create an awesome holiday display. Control your holiday lights (or other festive decorations that plug into an outlet) from anywhere when you use an outdoor smart plug.

At a time when we seem to be busier than ever, productivity is the magic word. We're constantly trying to reach the holy productivity grail. Automated voice assistant technology can take us one step closer to offloading or effectively completing the'little' tasks, allowing us to refocus our efforts on the more creative, imaginative, explorative parts of life. There is clearly an appetite for the likes of Siri, Alexa, and Google Assistant but, equally, where there is demand there is often contempt.

Artificial intelligence will reach human levels by around 2029. Follow that out further to, say, 2045, we will have multiplied the intelligence, the human biological machine intelligence of our civilization a billion-fold.

Here's an interesting stat from the Pew Research Center: more than half of smart speaker owners in the US (54 percent) report saying "please" at least occasionally to their AI assistants, with one-in-five (19 percent) saying please frequently. Curiously, the question of AI politeness also breaks down along gender lines, with 62 percent of women reporting that they say "please" at least sometimes, versus 45 percent for men. One possible answer is that men are generally ruder to women, and this latter category now includes AI assistants coded as female. Experts have long noted that the design choices for AI bots could have misogynist effects by reinforcing gender stereotypes. "Because the speech of most voice assistants is female, it sends a signal that women are ... docile and eager-to-please helper," a report from the UN noted earlier this year.

The growth of ecommerce in the recent past can only be described as explosive and sweeping across the planet. According to a 2016 study, half of all dollars spent online in America belong to Amazon. And consider this, Recommendation Engines alone drive 35% of that revenue. But it is not ecommerce alone that's reaping the huge benefits that recommendation engines have to offer. Direct to device streaming services such as Netflix, Spotify among others, analyze user behavior almost to a micro moment level, then gather data surrounding similar users who are likely to buy the same items based on their browsing history, and provide that much needed nudge to move on to the next purchase on the platform.

In the last few years, numerous developments have led to a growing awareness of the maturity of Artificial Intelligence. Self-driving cars and personal assistants like Alexa and Siri are some of the consumer-facing technologies that have helped to fuel this awareness. This knowledge can also bring with it a certain dystopian fear about robots and technology "taking over". While we should always strive to be cautious with new technologies, our concerns should also be tempered by understanding the long curve of development that typically precedes these seemingly overnight maturings of technology.

In the last few years, numerous developments have led to a growing awareness of the maturity of Artificial Intelligence. Self-driving cars and personal assistants like Alexa and Siri are some of the consumer-facing technologies that have helped to fuel this awareness. This knowledge can also bring with it a certain dystopian fear about robots and technology "taking over". While we should always strive to be cautious with new technologies, our concerns should also be tempered by understanding the long curve of development that typically precedes these seemingly overnight maturings of technology. I've been reading Artificial Intelligence in Education, a 2019 publication by Wayne Holmes, Maya Bialik, and Charles Fadel, that explores implications of AI in the realm of teaching and learning.

And they're coming for your business -- with the power to build or destroy your ability to compete in the near future. "Those companies not considering investing and innovating will soon be outperformed by the new economy that runs on machine learning" Machine learning is already changing the world. As a key subset of artificial intelligence (AI), it enables computers to act and learn on their own, without being specifically programmed, by utilizing data and experience rather than being explicitly programmed. Self-driving cars, Netflix recommendations, and virtual personal assistants like Siri and Alexa are some of AI's best-known applications. One of the most immediate ways businesses use machine learning to improve their competitiveness is by automating back-office processes, the majority of which are high volume, rules-based functions that could seamlessly operate on a "lights out" basis, freeing up employees' time for achieving more strategic company objectives.

In early 2018, Abbott became the first in Indian pharmaceutical industry to deploy an AI assistant for its field force.