Acoustically engineered to produce exceptional frequency response for an enhanced listening experience. Sweat proof, portable and lightweight headset can stay in your ears comfortably. Allowing you to control the volume, answer or end calls, control the playback of music and video with click of button and without taking your phone out.

Online kernel learning (OKL) is a flexible framework to approach prediction problems, since the large approximation space provided by reproducing kernel Hilbert spaces can contain an accurate function for the problem. Nonetheless, optimizing over this space is computationally expensive. Not only first order methods accumulate $\O(\sqrt{T})$ more loss than the optimal function, but the curse of kernelization results in a $\O(t)$ per step complexity. Second-order methods get closer to the optimum much faster, suffering only $\O(\log(T))$ regret, but second-order updates are even more expensive, with a $\O(t 2)$ per-step cost. Existing approximate OKL methods try to reduce this complexity either by limiting the Support Vectors (SV) introduced in the predictor, or by avoiding the kernelization process altogether using embedding.

Many online platforms act as intermediaries between a seller and a set of buyers. Examples of such settings include online retailers (such as Ebay) selling items on behalf of sellers to buyers, or advertising exchanges (such as AdX) selling pageviews on behalf of publishers to advertisers. In such settings, revenue sharing is a central part of running such a marketplace for the intermediary, and fixed-percentage revenue sharing schemes are often used to split the revenue among the platform and the sellers. In particular, such revenue sharing schemes require the platform to (i) take at most a constant fraction \alpha of the revenue from auctions and (ii) pay the seller at least the seller declared opportunity cost c for each item sold. A straightforward way to satisfy the constraints is to set a reserve price at c / (1 - \alpha) for each item, but it is not the optimal solution on maximizing the profit of the intermediary.

Artificial intelligence (AI), otherwise known as machine learning, is slowly reshaping retail from optimizing back-end supply chain operations to in-store execution. It is also impacting marketing, customer service engagement and anti-fraud activities, according to a report from New York-based information technology industry analyst firm 451 Research. While AI is far from the mainstream, researchers said plenty of retailers are experimenting with how machine learning can be applied in many areas of retail. The report states retailers won't be the only ones needing to adapt to the disruption of machine learning as customers will also face changes in how they view and experience shopping. For AI to work to its full potential, researchers said customers will need to be comfortable with increased data sharing if they want to benefit from personalized shopping experiences via machine learning.

Billie Eilish has fielded countless interview questions during her meteoric rise to fame, but this week was the first time that they've been asked by a bot. Fresh from sweeping the boards at the 2020 Grammy Awards, the 18-year-old songstress sat down with the AI interviewer in a video for Vogue -- and the bot proved a more original interlocutor than many of its human rivals. Its abstract questions provoked some surprising insights into the singer's mind. Viewers learnt that Eilish used to dream of working at Jamba Juice or Trader Joe's, and once wore a wig out to dinner to avoid attracting attention -- but she doesn't want to go back to being anonymous. "How much of the world is out of date?" the AI asked.

We propose Sketched Online Newton (SON), an online second order learning algorithm that enjoys substantially improved regret guarantees for ill-conditioned data. SON is an enhanced version of the Online Newton Step, which, via sketching techniques enjoys a running time linear in the dimension and sketch size. We further develop sparse forms of the sketching methods (such as Oja's rule), making the computation linear in the sparsity of features. Together, the algorithm eliminates all computational obstacles in previous second order online learning approaches. Papers published at the Neural Information Processing Systems Conference.

"There's this folklore mythology around if Amazon launches a business in a certain area, it means that all the other businesses in those areas are not going to be as successful," Jassy said at the Goldman Sachs Technology and Internet Conference in San Francisco yesterday. "I just haven't seen it." There are only two significant industries that Amazon has "disrupted," according to Jassy: retail with Amazon.com, and technology infrastructure with AWS. His remarks come as federal and state regulators are conducting antitrust probes to determine whether Amazon and other technology giants stifle competition and innovation. "In both cases, they were models that were pretty antiquated, and customers weren't so happy with those models, and somebody was going to end up reinventing them," Jassy said.

A clerk pick an item for a customer order at the Amazon Prime warehouse in New York. Amazon Empire director James Jacoby describes the pace of work within the company's warehouses as "incredibly grueling." A clerk pick an item for a customer order at the Amazon Prime warehouse in New York. Amazon Empire director James Jacoby describes the pace of work within the company's warehouses as "incredibly grueling." Amazon founder Jeff Bezos is now the richest man in the world, with an empire that stretches from Hollywood to Whole Foods -- and even into outer space.

Amazon Personalize is a machine learning service which enables you to personalize your website, app, ads, emails, and more, with custom machine learning models which can be created in Amazon Personalize, with no prior machine learning experience. AWS is pleased to announce that Amazon Personalize now supports ten times more item attributes for modeling in Personalize. Previously, you could use up to five item attributes while building an ML model in Amazon Personalize. This limit is now 50 attributes. You can now use more information about your items, for example, category, brand, price, duration, size, author, year of release etc., to increase the relevance of recommendations.

As the world grapples with artificial intelligence (AI) and debates its impact on security and privacy, many industries have already embraced this promising technology. The customer service space has become particularly intrigued by its capability, relying on AI to power text- and voice-based chatbots to field any number of questions or complaints. This has enabled the likes of Whole Foods, eBay and Burberry to field many phone and web inquiries before involving a human customer service agent. It's hard to imagine a world that would turn out differently, particularly as AI infiltrates a growing number of sectors. From airports to grocery stores, the spread of AI has only just begun.