Zoom has just announced that its "Zoom for Home" video conferencing platform is coming to Amazon's Echo Show line, Google's smart displays plus Facebook's Portal devices. Zoom will arrive on Portal in September, while it's slated to be available on the Echo Show (beginning with the Echo Show 8) and Google-powered displays before the end of the year. The company only launched its Zoom for Home initiative a month ago, when it announced a partnership with third-party manufacturers to create a line of "Zoom for Home" hardware. The first Zoom for Home product was the DTEN ME, and was designed for business professionals. By expanding to existing devices like the Echo Show, Google-powered displays (that includes the Nest Hub Max as well as other Assistant-powered displays like the Lenovo Smart Display) and the Portal however, Zoom is widening its footprint into the consumer space as well.

After frustrating stay-at-home workers and parents looking for an easy way to connect their kids to Zoom because it wasn't available, the world's most popular video meeting application is finally coming to Amazon, Google and Facebook video display units. The Echo Show, Google Nest Hub Max and Facebook Portal were originally released as a way for folks to engage in video chatting and home entertainment without the bother of turning on the computer, phone or TV. That there would be a coronavirus that sent Zoom usage up 10x in less than a year wasn't foreseen, nor the need to have a dedicated video display that could handle one-touch setup for the meetings and classes, without the bother of phones and computers. Fun:How to make video meetings more like in-person experience? But now, Zoom is finally coming to the devices, first to Portal, in September, the latest version of the Amazon Echo Show (version 8) and Google's Nest Hub Max, but not until later this year.

Typical SVD is less effective when data is not enough. How to leverage attention model to solve the cold start problem? Although SVD provides a satisfactory solution to recommendation system, it is less effective when the new items did not accumulate enough data. In this article, I will show you how to leverage the attention mechanism to solve the cold start problem in recommendation system. Attention Mechanism has a long history of applications and recently introduced to solve problems in NLP.

Artificial intelligence (AI) is the most disruptive innovation of our lifetime. Its adoption has grown 60 percent in the last year, according to an April 2020 report by Narrative Science. The report's authors say the technology is having a "significant and imminent impact on everything from company strategy, to business operations, to job functions." So what are some of AI's implications in the new normal, one in which American entrepreneurs find themselves saving cash, working from home and wearing masks everywhere they go? Currently, for entrepreneurs, the most popular AI-powered solutions deal with predictive analytics (24 percent), machine learning (21 percent), language processing (14 percent) and voice recognition and response (14 percent), according to the same Narrative Science report.

Voice tech is another application of Natural Language Processing and voice seems to be getting adopted quicker than any other major technology. Led by Amazon's Alexa, smart speakers' install base is expected to reach 200 million units worldwide by 2020. I have compiled a list of voice tech startups which are innovative in nature and are already in the market. This list is a starting point and probably far from exhaustive and is not sorted in any order. I will come up with another post with more startups added, in future. SoundHound develops voice-enabled AI and conversational intelligence technologies.

When robots were first discovered in the year 1495 by Leonardo Da Vinci, the world would not have that they would become an integral part of human life. However, with the advancement in technology, robotics has seen innovation and intervention that was earlier not possible. At present, Humanoid robots present a vast range of scope to the daily mundane task. Alexa, Chatbots in Financial spaces, and Siri are some of the examples where robots exhibited assistance to humans in a mundane task. Be it playing a song or assuring emotional support, especially during COVID 19, the humanoid robots halved the task conducted by humans.

We introduce Auto-Surprise, an Automated Recommender System library. Auto-Surprise is an extension of the Surprise recommender system library and eases the algorithm selection and configuration process. Compared to out-of-the-box Surprise library, Auto-Surprise performs better when evaluated with MovieLens, Book Crossing and Jester Datasets. It may also result in the selection of an algorithm with significantly lower runtime. Compared to Surprise's grid search, Auto-Surprise performs equally well or slightly better in terms of RMSE, and is notably faster in finding the optimum hyperparameters.

As IoT smart home devices such as Amazon Alexas continue to grow in popularity, they provide us the opportunity to connect and interact with the internet in a variety of ways. Despite their popularity and the benefits, they bring to the user, they also pose a significant security risk if not properly secured. This is because hackers are acutely aware about the lack of basic security measures some .... [Read More ]

Career advisor systems are essentially recommender systems in the space of job searching and career advice. They provide recommendations to candidates with possible career paths and to employers with possible candidates for a job opening. In this post we will outline the required capabilities of such systems, and highlight the challenges that need to be overcome in order to construct a working system. Questions like "What is a career advisor system?", "What is it capable of doing?", "Why do we need them?" etc are answered in this article. We also discuss our recent work (presented at AAAI-IAAI [1]) which describes how we proposed to solve this problem.

Writing is a complex non-linear process that begins with a mental model of intent, and progresses through an outline of ideas, to words on paper (and their subsequent refinement). Despite past research in understanding writing, Web-scale consumer and enterprise collaborative digital writing environments are yet to greatly benefit from intelligent systems that understand the stages of document evolution, providing opportune assistance based on authors' situated actions and context. In this paper, we present three studies that explore temporal stages of document authoring. We first survey information workers at a large technology company about their writing habits and preferences, concluding that writers do in fact conceptually progress through several distinct phases while authoring documents. We also explore, qualitatively, how writing stages are linked to document lifespan. We supplement these qualitative findings with an analysis of the longitudinal user interaction logs of a popular digital writing platform over several million documents. Finally, as a first step towards facilitating an intelligent digital writing assistant, we conduct a preliminary investigation into the utility of user interaction log data for predicting the temporal stage of a document. Our results support the benefit of tools tailored to writing stages, identify primary tasks associated with these stages, and show that it is possible to predict stages from anonymous interaction logs. Together, these results argue for the benefit and feasibility of more tailored digital writing assistance.