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.

The term "artificial intelligence" was coined in 1956 by John McCarthy, one of the founders of the field. He defined it as "the science and engineering of making intelligent machines, especially intelligent computer programs." In practical terms, AI involves using computers to learn from data, recognize patterns, and make decisions. The goal is to create systems that can solve problems or otherwise improve human endeavors. AI has been used for decades in a wide range of applications, from personal assistants and medical diagnosis to stock trading and military operations.

Let's delve into the machine learning benefits and drawbacks. Many job titles are included in machine learning, including business managers, data scientists, and DevOps engineers. A good grasp of the machine learning lifecycle will assist you in correctly allocating resources and determining where you stand in it. Don't worry; machine learning benefits will reward you greatly for this effort. We have a comprehensive article for you to look at the history of machine learning before you start. We hear the term "Machine Learning" a lot these days, especially after all the buzz about Big Data.

Item recommendation algorithms rank the items in a catalogue from the most relevant to the least relevant ones for a given context (for example, query) provided in input. Such algorithms are a key component of our daily interactions with digital systems, and their diffusion in the society will only increase in the foreseeable future. Given the diffusion of recommendation systems, their comparison is a crucial endeavor. Item recommendation algorithms are usually compared using some metric (for example, average precision) that depends on the position of the truly relevant items in the ranking, produced by the algorithm, of all the items in a catalogue. The experimental evaluation and comparison of algorithms is far from easy.

Polestar promised Apple CarPlay support for its EVs two years ago, and it's finally delivering. As The Verge explains, the car brand has released an over-the-air update for the Polestar 2 that makes CarPlay available on the Android Automotive-based sedan. If you'd rather use Apple Maps instead of Google Maps or prefer to talk to Siri in lieu of Google Assistant, you now have that choice as long as you connect your iPhone. The most fun part about driving a Polestar is driving it, but there's more to it. Our latest over-the-air update for the Polestar 2 comes with Apple CarPlay, allowing Polestar owners with an iPhone to change music, use apps, and communicate through Siri or the infotainment system pic.twitter.com/mulkjIUR6D