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

Lesbians on dating and hookup apps aren't looking for men, but that's what platforms like Bumble and Tinder are serving them. On today's show, Madison and Rachelle speak to some queer women who've had this problem and what sorts of issues it creates. Then they discuss the women-focused apps that've tried to fill that space, and why it's so difficult to find safe queer dates online. This podcast is produced by Daniel Schroeder, Madison Malone Kircher, and Rachelle Hampton.

Machine learning is the key technology to the future of business. AI-driven software has already helped companies improve efficiency, customer relations, and increase sales. Researchers estimate that machine learning has the potential to add $2.6 trillion in value to the marketing and sales industry by 2020, as well as another $2 trillion to manufacturing and logistics fields. According to the International Data Corporation, machine learning spending will amount to $77.6 Billion by 2022. Companies of all sizes collaborate with Python development outsourcing companies to source data scientists and create custom data analytics software.

Recently, one critical issue looms large in the field of recommender systems -- there are no effective benchmarks for rigorous evaluation -- which consequently leads to unreproducible evaluation and unfair comparison. We, therefore, conduct studies from the perspectives of practical theory and experiments, aiming at benchmarking recommendation for rigorous evaluation. Regarding the theoretical study, a series of hyper-factors affecting recommendation performance throughout the whole evaluation chain are systematically summarized and analyzed via an exhaustive review on 141 papers published at eight top-tier conferences within 2017-2020. We then classify them into model-independent and model-dependent hyper-factors, and different modes of rigorous evaluation are defined and discussed in-depth accordingly. For the experimental study, we release DaisyRec 2.0 library by integrating these hyper-factors to perform rigorous evaluation, whereby a holistic empirical study is conducted to unveil the impacts of different hyper-factors on recommendation performance. Supported by the theoretical and experimental studies, we finally create benchmarks for rigorous evaluation by proposing standardized procedures and providing performance of ten state-of-the-arts across six evaluation metrics on six datasets as a reference for later study. Overall, our work sheds light on the issues in recommendation evaluation, provides potential solutions for rigorous evaluation, and lays foundation for further investigation.