This story is part of Future Tense Fiction, a monthly series of short stories from Future Tense and Arizona State University's Center for Science and the Imagination about how technology and science will change our lives. Amanda sat at her desk, picking at the same $30 Little Gem salad she ordered daily, suffering a small burning sensation in her gut that was triggered either by acid reflux or the dying embers of her rapidly expiring conscience. Of course, it was standard procedure for her husband to demand that the security firm Dark Metal surveil potential new hires for any of his multibillion-dollar companies, but this was the first time Amanda had been involved in contracting the private intelligence agency herself. Seedlings is your venture, Reid had promised her, even though he'd named himself CEO. I want you to take the lead on this. Amanda was COO of Seedlings and reported to her husband, who dismissed Amanda's concerns about the legal ramifications of their actions. Worrying about the law was something poor people did, Reid insisted. Besides, she'd never seen Reid do anything that nefarious with this type of information. But Maggie Everett was the type of candidate that pleased Reid. Amanda had done her job, which was to find Maggie, and the people at Dark Metal had done theirs, which was to surveil her and create a comprehensive biographical profile. This seemed like overkill to Amanda. Maggie wasn't in the running to become a high-profile executive at one of Reid's billion-dollar firms. She was being interviewed to work at a preschool. Certainly, Seedlings differed from other private preschools--there was the possibility Maggie would be exposed to confidential information. But this was what NDAs were for. Unleashing a network of spies upon a poor teacher who would ultimately be responsible for 10 toddlers seemed like an absurd waste of resources. And this was just Phase 1. Phase 2 would have to wait until after Maggie was hired, of course. Amanda reopened Dark Metal's inch-thick dossier. The logline: Maggie was smart but stupid. Smart: She'd majored in English at Yale, then received an MFA in creative writing from Brown, and finally a master's in early childhood education from Columbia. Stupid: She'd accumulated $103,345 in student debt, which she'd never pay off unless she took a job somewhere like Seedlings.

For every problem you can think of, someone is out there pitching a solution that involves artificial intelligence. AI could help solve such intractable problems as climate change and dangerous work conditions, the technology's most eager boosters promise. It could even fix the much-maligned "Game of Thrones" finale, if you believe one of the industry's most powerful proponents and a featured speaker at this month's South by Southwest conference. "Imagine if you could ask your AI to make a new ending that goes a different way," said Greg Brockman, president and co-founder of OpenAI, the research group behind the conversation software ChatGPT and the image-generation module DALL-E. "Maybe even put yourself in there as a main character or something, having interactive experiences."

The humble broadcast-radio host, whether a disc jockey or interviewer or reporter, has been going through it for decades now. The 1996 Telecommunications Act fueled the consolidation of local stations, decimating their staffs. The explosion of online radio, music and video streaming, and podcasting have upended ratings for shows on public airwaves. Funding for public radio is notoriously unreliable. On top of all that, your local DJ was already on the losing end of the artificial-intelligence revolution. Before the A.I. hype from last year, and even before the COVID recession demolished media ad markets, broadcast networks were gutting on-air talent at the both the national and collegiate level to trim budgets and automate programming: syndicating well-known shows and brands, prerecording and prearranging late-night broadcasts, training a roboticized voice to fill in the space when needed.

Automated machine learning (AutoML) algorithms have grown in popularity due to their high performance and flexibility to adapt to different problems and data sets. With the increasing number of AutoML algorithms, deciding which would best suit a given problem becomes increasingly more work. Therefore, it is essential to use complex and challenging benchmarks which would be able to differentiate the AutoML algorithms from each other. This paper compares the performance of four different AutoML algorithms: Tree-based Pipeline Optimization Tool (TPOT), Auto-Sklearn, Auto-Sklearn 2, and H2O AutoML. We use the Diverse and Generative ML benchmark (DIGEN), a diverse set of synthetic datasets derived from generative functions designed to highlight the strengths and weaknesses of the performance of common machine learning algorithms. We confirm that AutoML can identify pipelines that perform well on all included datasets. Most AutoML algorithms performed similarly; however, there were some differences depending on the specific dataset and metric used.

Nearly any interview he does reveals as much. After four decades in Hollywood playing versions of the same fundamentally decent dude-in-crisis, he's learned to stay in his cyberpunk philosopher/surfing FBI agent/action hero lane. In person, he's pleasant and playful, but he also holds back, calibrating his remarks just so. Is this why we like him so much? We don't know who Keanu Reeves is, not really, but maybe we don't want to know. Or maybe this is all there is to know.

Open-domain conversational question answering can be viewed as two tasks: passage retrieval and conversational question answering, where the former relies on selecting candidate passages from a large corpus and the latter requires better understanding of a question with contexts to predict the answers. This paper proposes ConvADR-QA that leverages historical answers to boost retrieval performance and further achieves better answering performance. In our proposed framework, the retrievers use a teacher-student framework to reduce noises from previous turns. Our experiments on the benchmark dataset, OR-QuAC, demonstrate that our model outperforms existing baselines in both extractive and generative reader settings, well justifying the effectiveness of historical answers for open-domain conversational question answering.

Waymo, the robotaxi provider of Google's parent company Alphabet, said Wednesday that it will expand its ridehail service to Los Angeles. Waymo declined to say when fully autonomous car rides will be available to the public in the country's second largest metropolitan area. Waymo will begin with approximately a dozen vehicles in the coming months to lay the groundwork for operating a ridehail service by mapping the neighborhoods of Miracle Mile, Koreatown, Santa Monica, Westwood and West Hollywood. Mapping an area is a critical early step to operating Waymo's robotaxis, which rely on detailed maps, in addition to sensors, to help them navigate their surroundings. It offers robotaxi rides to the public in Chandler, Arizona and to its employees in San Francisco.

Dean Apostolopoulos is the man behind Uber's autonomous robots, but his goal of a driverless delivery is about more than food.

More Uber Eats customers would be getting their orders from vehicles with no delivery personnel in sight. That's because Uber has signed a 10-year partnership with Nuro to use its autonomous, electric vehicles to deliver food orders in the US. They'll start in Houston, Texas and Mountain View, California this fall before eventually expanding their service to the greater Bay Area. According to TechCrunch, customers won't get to choose and won't even know if their order is being delivered by a Nuro bot when they make their purchase. That also means they'll be charged the same rates for delivery regardless of what the delivery method is.

Evaluating change in ranked term importance in a growing corpus is a powerful tool for understanding changes in vocabulary usage. In this paper, we analyze a corpus of free-response answers where 33,993 LGBTQ Generation Z respondents from age 13 to 24 in the United States are asked to self-describe their sexual orientation. We observe that certain labels, such as bisexual, pansexual, and lesbian, remain equally important across age groups. The importance of other labels, such as homosexual, demisexual, and omnisexual, evolve across age groups. Although Generation Z is often stereotyped as homogenous, we observe noticeably different label usage when self-describing sexual orientation within it. We urge that interested parties must routinely survey the most important sexual orientation labels to their target audience and refresh their materials (such as demographic surveys) to reflect the constantly evolving LGBTQ community and create an inclusive environment.