Some automakers are already planning to load their new models with Amazon's famous voice assistant, but you don't need to buy a new car to have Alexa as a road companion. Anker has launched a new plug-and-play Bluetooth smart charger at CES 2018 called Roav Viva, and it can give you access to the AI no matter what car you're driving -- as long as it supports Bluetooth 4.0 A2DP. The technology's been around since early 2010s, so chances are your vehicle comes equipped with it if you purchased it within the past few years. You can do anything you can usually do with Alexa through Roav Viva, such as checking for weather conditions or ordering a coffee. If you want to play music, you can ask Alexa to connect to iHeartRadio or Pandora.

The purpose of using batch-normalization is to keep the distribution of the vectors in a range where the ReLU is non-linear controlled automatically by the Beta and Gamma parameters (which are learnable). I am wondering if the same effect can be achieved by using scaling values for the activation function. Precisely, by multiplying the scaling values to the input of the activation non-linearity, we can stretch and squeeze it in the horizontal direction and by multiplying those values after the activation function, the same can be controlled in the vertical direction. Is there some prior work done on this concept that I can refer to? What are the subtleties involved in doing this compared to the traditional bn- relu non-linearity?

Today, it is no longer a question of adopting AI or not. Instead, ask yourself if you and your sales team are ready for the inevitable. Artificial intelligence for business is a reality. If your goal is to forge ahead and lead in your field, then you need to adapt to a workplace where AI plays a crucial role. As J.J. Kardwell, founder, and CEO of predictive marketing software company EverString put it: "Growth-focused sales organizations of every size and stage cannot afford to ignore the benefits of AI-assisted sales."

These are the best of times for entertainment content owners and distributors--but they are also very challenging times. There is more content--often great content--than ever before and also vastly more competition due to the rise of streaming services, as well as on-demand options. This presents a challenge for content owners and distributors: how to stand out from the crowd and help viewers find what they want. Awash in all that content--not just professionally produced long-form content, but also highly viral digital-first content--viewers have a hard time wading through it all. In fact, it would take a single viewer more than 5 million years to watch the amount of video that crosses global IP networks each month, according to a recent Cisco Systems report. That's why it's imperative for content owners and distributors to make it easy for viewers to search and discover their content.

One of the things I am trying to do this year is some more technical posts (following up on some issues I have noticed at the intersection between medicine and machine learning). This is the first in a little mini-series on performance testing. Medical research has a different way of doing things, being more cautious about making claims and a bit more rigorous in justifying them, both of which are useful ideas to apply more broadly in machine learning (particularly at the applied end). While performance testing is often considered basic knowledge, one of my supervisors/colleagues is a bit of a ROC expert so I hope I can pass on some new ways of looking at things that are interesting even for some of the more knowledgeable folks around here.

Singing voice separation attempts to separate the vocal and instrumental parts of a music recording, which is a fundamental problem in music information retrieval. Recent work on singing voice separation has shown that the low-rank representation and informed separation approaches are both able to improve separation quality. However, low-rank optimizations are computationally inefficient due to the use of singular value decompositions. Therefore, in this paper, we propose a new linear-time algorithm called informed group-sparse representation, and use it to separate the vocals from music using pitch annotations as side information. Experimental results on the iKala dataset confirm the efficacy of our approach, suggesting that the music accompaniment follows a group-sparse structure given a pre-trained instrumental dictionary. We also show how our work can be easily extended to accommodate multiple dictionaries using the DSD100 dataset.

IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. XX, MONTH 2016 1 Polar n -Complex and n -Bicomplex Singular V alue Decomposition and Principal Component Pursuit Tak-Shing T. Chan, Member, IEEE and Yi-Hsuan Y ang, Member, IEEE Abstract--Informed by recent work on tensor singular value decomposition and circulant algebra matrices, this paper presents a new theoretical bridge that unifies the hypercomplex and tensor-based approaches to singular value decomposition and robust principal component analysis. We begin our work by extending the principal component pursuit to Olariu's polar n - complex numbers as well as their bicomplex counterparts. In so doing, we have derived the polar n -complex and n -bicomplex proximity operators for both the 1-and trace-norm regularizers, which can be used by proximal optimization methods such as the alternating direction method of multipliers. Experimental results on two sets of audio data show that our algebraically-informed formulation outperforms tensor robust principal component analysis. We conclude with the message that an informed definition of the trace norm can bridge the gap between the hypercomplex and tensor-based approaches. Our approach can be seen as a general methodology for generating other principal component pursuit algorithms with proper algebraic structures. I NTRODUCTION T HE robust principal component analysis (RPCA) [1] has received a lot of attention lately in many application areas of signal processing [2]-[5]. Owing to the NPhardness of the above formulation, the principal component pursuit (PCP) [1] has been proposed to solve this relaxed problem instead [6]: min L, S ‖L ‖ λ‖S ‖ 1 s.t. X L S, (2) where ‖·‖ is the trace norm (sum of the singular values),‖·‖ 1 is the entrywise 1-norm, andλ can be set toc/ max(l,m) where c is a positive parameter [1], [2]. The trace norm and the 1-norm are the tightest convex relaxations of the rank and Manuscript received August 26, 2015; revised May 26, 2016 and July 16, 2016; accepted September 3, 2016. This work was supported by a grant from the Ministry of Science and Technology under the contract MOST102-2221-E-001-004-MY3 and the Academia Sinica Career Development Program. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Masahiro Y ukawa. The authors are with the Research Center for Information Technology Innovation, Academia Sinica, Taipei 11564, Taiwan (email: taksh-ingchan@citi.sinica.edu.tw;

I am both personally interested in Machine Learning, as well as for my PhD which is loosely related to ML, and as someone working on a startup in his spare time. I often come into situations where I need an expert in Machine Learning to push me into the right direction. But I often cannot disclose my problem publicly (e.g. because my PhD has a project sponsor). What are options for me to get a Machine Learning mentor (an actual expert ideally at the cross section of computer vision / image manipulation and Deep Learning)? How could I set up a basic agreement that I could ask some questions once in a while in exchange for some compensation (monetary or otherwise)?

AI is finding its way into.

Examine the current state of artificial intelligence and explore the future AI roadmap in this second installment of the exclusive AI series by Thomson Reuters.