The last half decade has ushered in the era of humans interacting with technology through speech, with Amazon's Alexa, Apple's Siri, and Google's AI rapidly becoming ubiquitous elements of the human experience. But, while the migration from typing to voice has brought great convenience for some folks (and improved safety, in the case of people utilizing technology while driving), it has not delivered on its potential for the people who might otherwise stand to benefit the most from it: those of us with disabilities. For people with Down Syndrome, for example, voice-based control of technology offers the promise of increased independence – and even of some new, potentially life-saving products. Yet, for this particular group of people, today's voice-recognizing AIs pose serious problems, as a result of a combination of 3 factors: To address this issue, and as a step forward towards ensuring that people with health conditions that cause AIs to be unable to understand them are able to utilize modern technology, Google is partnering with the Canadian Down Syndrome Society; via an effort called Project Understood, Google hopes to obtain recordings of people with Down Syndrome reading simple phrases, and to use those recordings to help train its AI to understand the speech patterns common to those with Down Syndrome. This effort is an extension of Google's own Project Euphonia, which seeks to improve computers' abilities to understand diverse speech patterns including impaired speech, and, which, earlier this year, began an effort to train AIs to recognize communication from people with the neuro-degenerative condition ALS, commonly known as Lou Gehrig's Disease. So far, 300 people have contributed recordings to Project Understood – and, of course, the more people who do "lend their voices" to the effort, the better the AI will likely become at understanding people with Down Syndrome.

Cyber Monday was the biggest shopping day in Amazon's history. Turns out it was also a day for Amazon to promote its own brands over all others. On Monday, asking an Amazon Echo for the "best Cyber Monday deals" returned five straight responses from smart assistant Alexa that promoted products owned by Amazon: Amazon acquired Blink, a maker of security cameras, in 2017; it purchased Ring, the maker of video doorbells, in 2018. An Amazon spokesperson told Recode that after those first five deals, Amazon did promote items made by other companies, and that Alexa-enabled non-Echo devices returned deals in their first five responses that included non-Amazon-owned items. But the initial Alexa responses are just another sign of Amazon's aggressiveness in promoting its own brands, whether they are gadgets like the Echo family of smart speaker products and Ring doorbells or apparel lines like Goodthreads.

Match Group, the largest dating app conglomerate in the US, doesn't perform background checks on any of its apps' free users. A ProPublica report today highlights a few incidents in which registered sex offenders went on dates with women who had no idea they were talking to a convicted criminal. These men then raped the women on their dates, leaving the women to report them to the police and to the apps' moderators. These women expected their dating apps to protect them, or at least vet users, only to discover that Match has little to no insight on who's using their apps. The piece walks through individual attacks and argues that the apps have no real case for not vetting their users.

Disclaimer: There are statements about potential uses of AI within this post that some people may find disturbing. When the formal study of AI was conceived back in the post-WW2 era it's potential was heralded as a boon for mankind and viewed in a almost exclusively positive light. What could be bad about that? Well, beyond science fiction's killer robots and ghosts in the machine stories, AI still has much of that positivity surrounding it as we enter into the era of autonomous vehicles, functional digital assistants, and recommendation engines that actually make good recommendations. But is the rise of functional AI actually hiding another more sinister development, one which has the potential to do serious harm to those who are on the wrong side of the algorithm?

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.

Fusion Informatics builds a team of eCommerce virtual assistants to our clients that are exclusive to their company. We also hired and trained 1 x customer support representatives to help answer hundreds of emails that our clients receive per day in his ecommerce business. We provided with all support and training documents for their company, we then trained their staff members and within a few weeks, the staff had answered the message without the support of the client or Fusion Informatics. Finally, we successfully plant a Virtual Assistant and provided solutions for our customers who benefit from an increase in sales of 30% in the first month itself.

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.