For months now I have been in the thrall of Pokémon Trading Card Pocket. It's a devilishly slick blend of card-collecting and pared-down battling that has had me obediently opening the app on my phone at least twice a day since it launched. The virtual cards are beautifully done; the rare art cards especially, with their pastoral scenes of Pokémon in their natural habitats. I have spent many hours on the battles, too, honing decks and chasing win streaks to earn myself victory emblems. I got most of my friends into it, anticipating the day when its makers at DeNa would finally enable trading so I could fill the last couple of holes in my collection.

While reading through the list of Grammy nominees earlier I came across quite a surprise. There, competing for record of the year alongside the likes of Beyoncé's Texas Hold'Em and Chappell Roan's Good Luck Babe, was Now and Then by The Beatles. So, here's the story of how The Beatles got nominated for two Grammys -- they also snagged a best rock performance nod -- 50 years after formally breaking up. It starts with a demo John Lennon recorded in the 1970s that was given to Paul McCartney, Ringo Starr and George Harrison for inclusion on the The Beatles Anthology, released in 1995. While other tracks like Free as a Bird and Real Love made it on, technology wasn't advanced enough to separate Lennon's vocals and piano without reducing the recording's quality. But, last year McCartney and Starr used modern machine learning technology to pull Lennon's vocals for a new track.

Text summarization models have typically focused on optimizing aspects of quality such as fluency, relevance, and coherence, particularly in the context of news articles. However, summarization models are increasingly being used to summarize diverse sources of text, such as social media data, that encompass a wide demographic user base. It is thus crucial to assess not only the quality of the generated summaries, but also the extent to which they can fairly represent the opinions of diverse social groups. Position bias, a long-known issue in news summarization, has received limited attention in the context of social multi-document summarization. We deeply investigate this phenomenon by analyzing the effect of group ordering in input documents when summarizing tweets from three distinct linguistic communities: African-American English, Hispanic-aligned Language, and White-aligned Language. Our empirical analysis shows that although the textual quality of the summaries remains consistent regardless of the input document order, in terms of fairness, the results vary significantly depending on how the dialect groups are presented in the input data. Our results suggest that position bias manifests differently in social multi-document summarization, severely impacting the fairness of summarization models.

We address the problem of scaling up the production of media content, including commentary and personalized news stories, for large-scale sports and music events worldwide. Our approach relies on generative AI models to transform a large volume of multimodal data (e.g., videos, articles, real-time scoring feeds, statistics, and fact sheets) into coherent and fluent text. Based on this approach, we introduce, for the first time, an AI commentary system, which was deployed to produce automated narrations for highlight packages at the 2023 US Open, Wimbledon, and Masters tournaments. In the same vein, our solution was extended to create personalized content for ESPN Fantasy Football and stories about music artists for the Grammy awards. These applications were built using a common software architecture achieved a 15x speed improvement with an average Rouge-L of 82.00 and perplexity of 6.6. Our work was successfully deployed at the aforementioned events, supporting 90 million fans around the world with 8 billion page views, continuously pushing the bounds on what is possible at the intersection of sports, entertainment, and AI.

The person behind an AI-generated song that went viral earlier this year has submitted the track for Grammy Awards consideration. The Recording Academy has stated that such works aren't eligible for certain gongs. However, Ghostwriter, the pseudonymous person behind "Heart on My Sleeve," has submitted the track in the best rap song and song of the year categories, according to Variety. Both of those are songwriting honors. The Academy has suggested it's open to rewarding tracks that are mostly written by a human, even if the actual recording is largely AI-generated.

Since the days of Alan Turing, music created by computational models has been inextricably bound up with technological process. But now that tools like Voicify.AI are going viral on TikTok and the Recording Academy has updated their rules to allow music created with AI tools to be eligible for Grammy consideration, is it already too late to consider the musicians whose work will be made obsolete by AI? This podcast is produced by Se'era Spragley Ricks, Daisy Rosario, Candice Lim and Rachelle Hampton.

Amazon has announced the dates for its next annual shopping event. Prime Day 2023 will be on July 11th and 12th this year, beginning at 12AM PT/ 3AM ET on Tuesday, July 11th, and concluding at the end of Wednesday, July 12th. Prime Day isn't necessarily a perk of Amazon's subscription service, like access to Prime Video content, but most deals on Amazon during the two-day event are exclusively available to Prime members. The cost of Prime has increased quite a bit since its launch in 2005, and even in the past few years. An annual membership will set you back $139 right now, $20 more than its previous price.

It looks like Fake Drake won't be taking home a Grammy. Recording Academy CEO Harvey Mason Jr. said this week that although the organization will consider music with limited AI-generated voices or instrumentation for award recognition, it will only honor songs written and performed "mostly by a human." "At this point, we are going to allow AI music and content to be submitted, but the Grammys will only be allowed to go to human creators who have contributed creatively in the appropriate categories," Mason said in an interview with Grammy.com. "If there's an AI voice singing the song or AI instrumentation, we'll consider it. But in a songwriting-based category, it has to have been written mostly by a human. Same goes for performance categories – only a human performer can be considered for a Grammy. If AI did the songwriting or created the music, that's a different consideration. But the Grammy will go to human creators at this point."

Grammy-winning artists are already gamers. Jon Batiste, the jazz musician and bandleader for "The Late Show," told The Post in 2019 that the scores and soundtracks in video games first inspired him to become a musician. In April, Batiste took home five Grammys, including one for album of the year. He told The Post his favorite game is "Final Fantasy VII" from Square Enix, which boasts a four-hour score with melodic interludes and climactic, operatic tracks for the boss battles.

Take a look at how people can build software to handle low-level tasks and massive calculations and what it means for both machine and human workloads. Social media can send up an early warning sign of illness, and data analysis can predict how it will spread. Social media can make or break food trends, says Signals Analytics Trends change faster than ever and within minutes or hours, something can go viral. Data privacy is an increasing concern for companies and individuals. Jan. 28 was Data Privacy Day, … Google CEO Sundar Pichai and other AI researchers say limited regulation is needed to protect people from irresponsible use cases.