There is plenty of talk about artificial intelligence in the enterprise, but a lot of it is not very practical. That's because enterprises aren't equipped with an army of data scientists to build and train new AI models. And it's not just the lack of qualified data scientists -- AI breakthroughs require massive amounts of relevant, annotated data. That doesn't mean however, there is no place for AI in your enterprise innovation strategy. Savvy CIOs are using in-market models and APIs by commercial and industry leaders to solve well-defined use cases, bringing immediate, measurable value to the organization.

Amazon Alexa might use the voice of friends and family who are no longer alive in a future update. Amazon mentioned the features at its re:MARS conference Wednesday as a way to "make memories last." After listening to someone's voice for less than a minute, Alexa would be able to simulate that voice when speaking. A video of the feature depicted a child who asked to have their grandmother read them a story, and Alexa affirmed before changing her voice, according to Sky News. It's not clear how far the feature is in development or when it could be rolled out to Alexa voice assistants.

Amazon's Alexa might soon replicate the voice of family members - even if they're dead. The capability, unveiled at Amazon's Re:Mars conference in Las Vegas, is in development and would allow the virtual assistant to mimic the voice of a specific person based on a less than a minute of provided recording. Rohit Prasad, senior vice president and head scientist for Alexa, said at the event Wednesday that the desire behind the feature was to build greater trust in the interactions users have with Alexa by putting more "human attributes of empathy and affect." "These attributes have become even more important during the ongoing pandemic when so many of us have lost ones that we love," Prasad said. "While AI can't eliminate that pain of loss, it can definitely make their memories last."

Do you miss the sound of a dead relative's voice? Well fear not: Amazon unveiled a new feature in the works for its virtual assistant Alexa that can read aloud in a deceased loved one's voice based on a short recording of the person. "While AI can't eliminate that pain of loss, it can definitely make their memories last," said Rohit Prasad, senior vice president and head scientist for Alexa, on Wednesday at Amazon's re:MARS conference in Las Vegas. In a video played at the event, an Amazon Echo Dot is asked: "Alexa, can Grandma finish reading me'The Wizard of Oz'?" "Instead of Alexa's voice reading the book, it's the kid's grandma's voice," Prasad said. "We had to learn to produce a high quality voice with less than a minute of recording."

Amazon plans to let people turn their dead loved ones' voices into digital assistants, with the company promising the ability to "make the memories last". The company is developing technology that will allow its Alexa digital assistant to mimic the voice of anyone it hears from less than a minute of provided audio, Rohit Prasad, its senior vice-president and head scientist, said on Wednesday. He added that during the coronavirus paramedic "so many of us have lost someone we love". While no timescale was given for the launch of the feature, the underlying technology has existed for several years. The company gave a demonstration where the reanimated voice of an older woman was used to read her grandson a bedtime story, after he asked Alexa: "Can grandma finish reading me the Wizard of Oz?" Prasad said: "The way we made it happen is by framing the problem as a voice conversion task and not a speech generation path."

Recommender systems personalize content by recommending items to users. Item recommendation algorithms are evaluated by metrics that compare the positions of truly relevant items among the recommended items. To speed up the computation of metrics, recent work often uses sampled metrics where only a smaller set of random items and the relevant items are ranked. This paper investigates such sampled metrics in more detail and shows that they are inconsistent with their exact counterpart, in the sense that they do not persist relative statements, for example, recommender A is better than B, not even in expectation. Moreover, the smaller the sample size, the less difference there is between metrics, and for very small sample size, all metrics collapse to the AUC metric. We show that it is possible to improve the quality of the sampled metrics by applying a correction, obtained by minimizing different criteria. We conclude with an empirical evaluation of the naive sampled metrics and their corrected variants. To summarize, our work suggests that sampling should be avoided for metric calculation, however if an experimental study needs to sample, the proposed corrections can improve the quality of the estimate. Recommender systems are a key technology in online platforms for personalizing the selection of items that are shown to a user. Examples include recommending which products to buy, which videos to watch or which songs to play. Recommendations are typically user-dependent and often context-dependent. A key operation of recommender systems is to retrieve a ranked list of the best items for a user in a particular context.

In Spain, the Madrid Metro uses AI to monitor its network and reduce energy consumption by 25%. In the U.S., a beverage company uses AI to drive sales by analyzing retailers and markets. In Europe, an energy company trains its engineers and managers in a digital twin factory powered by AI. In the Middle East, a telco's AI-powered virtual assistant speaks to 1.65 million customers every month in different Arab dialects and English. Undoubtedly, AI is in full adoption around the world, with all industries recognizing it as the next big thing in tech.

Amazon.com wants to give customers the chance to make Alexa, the company's voice assistant, sound just like their grandmother -- or anyone else. The online retailer is developing a system to let Alexa mimic any voice after hearing less than a minute of audio, said Rohit Prasad, an Amazon senior vice president, at a conference the company held in Las Vegas Wednesday. The goal is to "make the memories last" after "so many of us have lost someone we love" during the pandemic, Prasad said. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.

At Amazon's Re:Mars conference, Alexa's senior vice-president Rohit Prasad exhibited a startling new voice assistant capability: the supposed ability to mimic voices. So far, there's no timeline whatsoever as to when or if this feature will be released to the public. Stranger still, Amazon framed this copycatting ability as a way to commemorate lost loved ones. It played a demonstration video in which Alexa read to a child in the voice of his recently deceased grandmother. Prasad stressed that the company was seeking ways to make AI as personal as possible.

The intersection of learning to rank and choice modeling is an active area of research with applications in e-commerce, information retrieval and the social sciences. In some applications such as recommendation systems, the statistician is primarily interested in recovering the set of the top ranked items from a large pool of items as efficiently as possible using passively collected discrete choice data, i.e., the user picks one item from a set of multiple items. Motivated by this practical consideration, we propose the choice-based Borda count algorithm as a fast and accurate ranking algorithm for top $K$-recovery i.e., correctly identifying all of the top $K$ items. We show that the choice-based Borda count algorithm has optimal sample complexity for top-$K$ recovery under a broad class of random utility models. We prove that in the limit, the choice-based Borda count algorithm produces the same top-$K$ estimate as the commonly used Maximum Likelihood Estimate method but the former's speed and simplicity brings considerable advantages in practice. Experiments on both synthetic and real datasets show that the counting algorithm is competitive with commonly used ranking algorithms in terms of accuracy while being several orders of magnitude faster.