New York City, NY: Dec 05, 2019 – Published via (Wired Release) – "MarketResearch.biz"

PFN to work with PyTorch and the open-source community to develop the framework and advance MN-Core processor support. Preferred Networks, Inc. (PFN, Head Office: Tokyo, President & CEO: Toru Nishikawa) today announced plans to incrementally transition its deep learning framework (a fundamental technology in research and development) from PFN's Chainer to PyTorch. Concurrently, PFN will collaborate with Facebook and the other contributors of the PyTorch community to actively participate in the development of PyTorch. With the latest major upgrade v7 released today, Chainer will move into a maintenance phase. PFN will provide documentation and a library to facilitate the migration to PyTorch for Chainer users.

Are you working with image data? This got me thinking -- what can we do if there are multiple object categories in an image? Making an image classification model was a good start, but I wanted to expand my horizons to take on a more challenging task -- building a multi-label image classification model! I didn't want to use toy datasets to build my model -- that is too generic. And then it struck me -- movie/TV series posters contain a variety of people. Could I build my own multi-label image classification model to predict the different genres just by looking at the poster?

When we give a machine values and it solves a calculation for us, that's simply computing. When we give a machine data and it learns from its experiences and then makes recommendations, that's artificial intelligence. So what happens when we give AI one of the most human of art forms: music? Quite a bit, as it turns out. AI uses machine learning models to produce new patterns and correlations based on the data it was trained from.

When Google introduced the Home Mini two years ago, it was playing catch-up to Amazon's Echo lineup. The Echo Dot, a tiny and affordable version of the larger Echo smart speaker, had been on sale for more than a year and a half, and Google was clearly responding to the Dot's popularity. The Home Mini bested the Dot in several ways, though, including a better speaker and more attractive design. Naturally, Amazon responded a year later with the third-generation Dot. It ditched the glossy black plastic and took inspiration from the Home Mini's fabric-covered exterior.

A documentary exploring how artificial intelligence is changing life as we know it -- from jobs to privacy to a growing rivalry between the U.S. and China. FRONTLINE investigates the promise and perils of AI and automation, tracing a new industrial revolution that will reshape and disrupt our world, and allow the emergence of a surveillance society. This journalism is made possible by viewers like you. Support your local PBS station here: http://www.pbs.org/donate Find us on the PBS Video App where there are more than 250 FRONTLINE documentaries available for you to watch any time: https://to.pbs.org/FLVideoApp

Younger generation just as uncomfortable as older generation with AI app being used to screen candidates. Despite a perceived comfort with most technology as compared to older generations, 89% of Americans age 18-34 say they would feel uncomfortable with an AI job interview app being used during the candidate screening process. Among those age 35 and older, the figure is nearly identical at 88%. Significantly more Americans age 65 who would feel uncomfortable with an artificial intelligence (AI) job interview app being used during the candidate screening process say it is because they prefer an in-person interview. Of those age 65 who say they would feel uncomfortable with an artificial intelligence job interview app being used during the candidate screening process, 64% say they would feel uncomfortable because they prefer an in-person interview.

Artificial Intelligence is becoming more and more important in everyday life, so here's what the Snapdragon 865 will bring to upcoming Android …

A wave of recent scholarship documenting the discriminatory harms of algorithmic systems has spurred widespread interest in algorithmic accountability and regulation. Yet effective accountability and regulation is stymied by a persistent lack of resources supporting public understanding of algorithms and artificial intelligence. Through interactions with a US-based civil rights organization and their coalition of community organizations, we identify a need for (i) heuristics that aid stakeholders in distinguishing between types of analytic and information systems in lay language, and (ii) risk assessment tools for such systems that begin by making algorithms more legible. The present work delivers a toolkit to achieve these aims. This paper both presents the Algorithmic Equity Toolkit (AEKit) Equity as an artifact, and details how our participatory process shaped its design. Our work fits within human-computer interaction scholarship as a demonstration of the value of HCI methods and approaches to problems in the area of algorithmic transparency and accountability.

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.