In the third season of "The Comeback," Kudrow has brought back her character Valerie Cherish, which had its roots at the Groundlings. A visitor to Stage 24 on the Warner Bros. lot, in Burbank, last November could be forgiven for thinking that the television show being filmed there was a sitcom called "How's That?!" The parking spaces outside were marked with "How's That?!" signs. Inside, director's chairs with the "How's That?!" logo were arranged around video monitors. The set--a New England bed-and-breakfast, with kitschy floral wallpaper--was surrounded by sitcom cameras and buzzing crew members wearing headsets. A studio audience filed into the bleachers, and a warmup comic urged them to "shake those funny bones." Then, with mounting gusto, he introduced the star of "How's That?!": "Here she is . . . the one and only . . . the living legend . . . She emerged to applause, in a potter's smock, wavy red hair under a bandanna, looking like a cross between Lucy Ricardo and Mrs. Garrett ...

Thing is, I've been trying to find a moment to write down what happened to Bennett and me for a while now, but the demands of my audience rarely abate. I've hardly time to jot down a grocery list, let alone compose a personal chronicle. Bennett says I'm practically the Charles (as in Dickens) of scribblers devoted to mining the rich vein of a certain underappreciated sitcom of the nineteen-eighties, but I will leave that for history to judge. Besides, what does Bennett know? Just before he got that way, I was in Amok Mocha, where I like to sip cold brew and do my "C: FB" conjuring, and I struck up a conversation with a young woman who confessed to being a creative-writing student. She told me that in her workshop they talk about the "occasion" of the story. Why is the narrator telling this tale now? What pressures or conditions have coalesced to move a person to speak? I feigned ignorance of the concept, though I'd heard it often in my own writing classes long ago. Instead, I told her that, if the installment I was presently crafting flowed from any occasion, it was this: Charles is anxious about the imminent disintegration of the universe via the ever-increasing tug of dark matter. Moreover, he's ticked off that his best buddy, Buddy, doesn't seem perturbed by the prospect. "How imminent?" the woman said, and sipped her Balkan, a new offering at Amok. When I informed her that he was the titular hero of "Charles in Charge," the most criminally uncelebrated television program of the Reagan era, the woman pursed her lips. "We all write fan fiction," I told her. "Some of us are just more honest about it." The young woman gathered up her belongings, moved to another table. Did she think I was being facetious? Still, if there is an occasion for the story I'm relating now, it's a bit nearer on the space-time continuum. My best buddy, Bennett, is in a vegetative state induced by an anoxic brain injury, and, if he doesn't wake up soon and vouch for me, I could be kicked out of our apartment.

Fans of the 1980's-era sitcom A Different World were initially thrilled to hear that the Cosby Show spinoff was making the jump to Netflix, with all six seasons presented in HD quality. Then they started watching, and noticed there was, indeed, something very different about how A Different World looks on streaming. The text on signs looks strangely garbled. Faces in the background appear squished, sometimes even a tad monstrous. The image as a whole looks like an animated watercolor painting. Everything looks… well, weird, bordering on grotesque.

Diffusion models (DMs) are a class of generative models that allow sampling from a distribution learned over a training set. When applied to solving inverse imaging problems (IPs), the reverse sampling steps of DMs are typically modified to approximately sample from a measurement-conditioned distribution in the image space. However, these modifications may be unsuitable for certain settings (such as in the presence of measurement noise) and non-linear tasks, as they often struggle to correct errors from earlier sampling steps and generally require a large number of optimization and/or sampling steps. To address these challenges, we state three conditions for achieving measurement-consistent diffusion trajectories. Building on these conditions, we propose a new optimization-based sampling method that not only enforces the standard data manifold measurement consistency and forward diffusion consistency, as seen in previous studies, but also incorporates backward diffusion consistency that maintains a diffusion trajectory by optimizing over the input of the pre-trained model at every sampling step. By enforcing these conditions, either implicitly or explicitly, our sampler requires significantly fewer reverse steps. Therefore, we refer to our accelerated method as Step-wise Triple-Consistent Sampling (SITCOM). Compared to existing state-of-the-art baseline methods, under different levels of measurement noise, our extensive experiments across five linear and three non-linear image restoration tasks demonstrate that SITCOM achieves competitive or superior results in terms of standard image similarity metrics while requiring a significantly reduced run-time across all considered tasks.

Understanding the non-literal meaning of an utterance is critical for large language models (LLMs) to become human-like social communicators. In this work, we introduce SwordsmanImp, the first Chinese multi-turn-dialogue-based dataset aimed at conversational implicature, sourced from dialogues in the Chinese sitcom $\textit{My Own Swordsman}$. It includes 200 carefully handcrafted questions, all annotated on which Gricean maxims have been violated. We test eight close-source and open-source LLMs under two tasks: a multiple-choice question task and an implicature explanation task. Our results show that GPT-4 attains human-level accuracy (94%) on multiple-choice questions. CausalLM demonstrates a 78.5% accuracy following GPT-4. Other models, including GPT-3.5 and several open-source models, demonstrate a lower accuracy ranging from 20% to 60% on multiple-choice questions. Human raters were asked to rate the explanation of the implicatures generated by LLMs on their reasonability, logic and fluency. While all models generate largely fluent and self-consistent text, their explanations score low on reasonability except for GPT-4, suggesting that most LLMs cannot produce satisfactory explanations of the implicatures in the conversation. Moreover, we find LLMs' performance does not vary significantly by Gricean maxims, suggesting that LLMs do not seem to process implicatures derived from different maxims differently. Our data and code are available at https://github.com/sjtu-compling/llm-pragmatics.

Whether we're living in the age of Peak TV or Trough TV, one thing is clear: There's too much TV. Thankfully, not every show has to be watched in its entirety. One of the best things about television is its serialized nature, the continuous thread that strings viewers along from one episode to the next. It's a cliché that prestige television is the new novel precisely because of the way that many dramas develop their characters and plots over many hours of storytelling. But an older virtue of TV is its brevity--the way a scenario can be introduced and resolved within the space of an hour, or half that--and some of the best episodes are less like chapters in a long-running novel than like short stories or short films. There's been no shortage of debate about this question, but for our purposes, we're defining it simply as an episode that stands up on its own, whether or not you've seen the rest of the show. Some are "bottle episodes," which typically confine a small cast to one location to save money. Some are "departure episodes," in which a show abandons its usual format or style to suddenly become, say, silent, animated, a musical, or about a minor character it was never about before. But not all bottle episodes and departure episodes are stand-alones, and vice versa. It's for this reason that you won't find Breaking Bad's celebrated "Fly" on this list: It may be a bottle episode, but it doesn't stand alone, because the best thing about it--how the housefly is a metaphor for everything else going on in the series--is comprehensible only to those who have watched the show. These are English-language selections, and, out of fairness, we have limited ourselves to one episode per series, although some shows are full of stellar contenders. Use these picks--arranged in chronological order, with an admitted bias toward our most recent, and best, era of television--to populate your streaming queue with a feast of bite-sized morsels, each of which could double as either a snackable introduction to a new show or a satisfying meal in itself. If movies made Alfred Hitchcock a name, TV made him a brand. The master of suspense embraced the burgeoning medium in 1955 with Alfred Hitchcock Presents (later renamed The Alfred Hitchcock Hour), an anthology series whose entries began and ended the same way: the titular celebrity providing context to a unique half-hour thriller, typically an adaption of a short story by an esteemed author (John Cheever, Ray Bradbury, many others).

Jerry Stiller, a comedy legend in his own way, has worked in the entertainment world for many years. Though he has starred in a number of movies and television shows, his performances as Arthur Spooner in "The King of Queens" and Frank Costanza in the classic sitcom "Seinfeld" have made him possibly the most well-known actor in the world. Do you know anything about his early career? With his wife, Anne Meara, he started his career as a member of the comedic team Stiller and Meara. In the 1950s and 1960s, the couple performed stand-up comedy and made appearances on a number of television programs, including "The Ed Sullivan Show."

Artificial intelligence's take on a classic sitcom is more than a load of "yada yada yada." "Nothing, Forever" is an AI-generated, "Seinfeld" like show on streaming platform Twitch that's set to never stop broadcasting. The 24/7 show, which has been streaming since December, has grown in popularity over the past week as thousands have tuned in to watch the adventures of animated characters Larry Feinberg, Fred Kastopolous, Yvonne Torres and Zoltan Kalker. As of Saturday morning, "Nothing, Forever" had over 131,000 Twitch followers. The show plays out in a similar fashion to the TV classic: It includes stand-up sequences, laugh tracks and conversations among AI friends similar to Jerry, Elaine, George and Kramer inside of an apartment.

Since the hit sitcom Seinfeld went off the air in 1998 after nine seasons, the show's devoted followers have long mused about an alternate reality: What if the original "show about nothing" had never ended? Now they've gotten what they wished for--well, sort of. In mid-December, a never-ending AI-generated reboot, aptly named Nothing, Forever, launched on the streaming platform Twitch. You could, anyway, until earlier this week, when forever abruptly ended--or was at least briefly interrupted, and in just about the most fitting way imaginable: by the AI scriptwriter devolving into bigotry. Nothing, Forever is powered by Davinci, the newest publicly available version of OpenAI's GPT-3 language model--a close relative of ChatGPT--and although that technology is impressive, the show, in most respects, is not.

"So, I was at the store the other day, and as I'm checking out, the cashier asks me if I have any coupons, and I say, 'No coupon problem!'" recalls a pixelated, barely three-dimensional figure that vaguely resembles Jerry Seinfeld. "So I'm walking down the street, and this guy comes up to me and says, 'Hey, how's it going?' and I say, 'It's going coupon!'" An automated laugh track plays, but the joke doesn't make sense. Then again, it doesn't have to make sense. "Nothing, Forever" is a never-ending, AI-generated spoof of "Seinfeld," the show about nothing.