Ever posted or left a comment on Reddit? Your words will soon be used to train an artificial intelligence companies' models, according to Bloomberg. The website signed a deal that's "worth about 60 million on an annualized basis" earlier this year, it reportedly told potential investors ahead of its expected initial public offering (IPO). Bloomberg didn't name the "large AI company" that's paying Reddit millions for access to its content, but their agreement could apparently serve as a model for future contracts, which could mean more multi-million deals for the firm. Reddit first announced that it was going to start charging companies for API access in April last year.

Rachelle Hampton and Candice Lim catch up on the latest stories churning the Taylor Swift media machine, from her lawyers sending a cease and desist letter to a college student, to her possibly leading a groundbreaking case against AI deepfakes. Then, they break down the backlash surrounding Emily Mariko, who was criticized by her followers for selling out -- and shelling out -- a tote bag. This podcast is produced by Se'era Spragley Ricks, Daisy Rosario, Candice Lim and Rachelle Hampton.

OpenAI has unveiled its latest artificial intelligence system, a program called Sora that can transform text descriptions into photorealistic videos. The video generation model is spurring excitement about advancing AI technology, along with growing concerns over how artificial deepfake videos worsen misinformation and disinformation during a pivotal election year worldwide. The Sora AI model can currently create videos up to 60 seconds long using either text instructions alone or text combined with an image. One demonstration video starts with a text prompt that describes how "a stylish woman walks down a Tokyo street filled with warm glowing neon and animated city signage". Other examples include a dog frolicking in the snow, vehicles driving along roads and more fantastical scenarios such as sharks swimming in midair between city skyscrapers.

Previous in-context learning (ICL) research has focused on tasks such as classification, machine translation, text2table, etc., while studies on whether ICL can improve human-like dialogue generation are scarce. Our work fills this gap by systematically investigating the ICL capabilities of large language models (LLMs) in persona-based dialogue generation, conducting extensive experiments on high-quality real human Chinese dialogue datasets. From experimental results, we draw three conclusions: 1) adjusting prompt instructions is the most direct, effective, and economical way to improve generation quality; 2) randomly retrieving demonstrations (demos) achieves the best results, possibly due to the greater diversity and the amount of effective information; counter-intuitively, retrieving demos with a context identical to the query performs the worst; 3) even when we destroy the multi-turn associations and single-turn semantics in the demos, increasing the number of demos still improves dialogue performance, proving that LLMs can learn from corrupted dialogue demos. Previous explanations of the ICL mechanism, such as $n$-gram induction head, cannot fully account for this phenomenon.

For the past couple of weeks, I've been making a home video on my phone, using Apple's iMovie software. The idea is to weave together clips of my family that I've taken during the month of February; I plan to keep working on it until March. So far, the movie shows my five-month-old daughter cooing and waving her arms; my five-year-old son chasing me with a snowball; and a visit to the spooky, run-down amusement park in our town, among other things. I thought of my movie while absorbing the announcement, yesterday, of Sora, an astonishing new text-to-video system from OpenAI, the makers of ChatGPT. Sora can take prompts from users and produce detailed, inventive, and photorealistic one-minute-long videos.

Yesterday afternoon, OpenAI teased Sora, a video-generation model that promises to convert written text prompts into highly realistic videos. Footage released by the company depicts such examples as "a Shiba Inu dog wearing a beret and black turtleneck" and "in an ornate, historical hall, a massive tidal wave peaks and begins to crash." The excitement from the press has been reminiscent of the buzz surrounding the image creator DALL-E or ChatGPT in 2022: Sora is described as "eye-popping," "world-changing," and "breathtaking, yet terrifying." The imagery is genuinely impressive. At a glance, one example of an animated "fluffy monster" looks better than Shrek; an "extreme close up" of a woman's eye, complete with a reflection of the scene in front of her, is startlingly lifelike.

Have you ever wanted to know what two golden retrievers podcasting on top of a mountain might look like? Or perhaps watch a bicycle race on the ocean with different animals riding the bicycles? OpenAI's latest generative artificial intelligence offering, Sora, can generate breathtakingly realistic videos that are up to a minute long from text prompts. OpenAI CEO Sam Altman announced the model's creation on X on Thursday. Sora is not yet available to the public. For now, OpenAI is only granting access to red teamers--individuals employed to look for issues--who will assess potential risks associated with the model's release, as well as a limited number of "visual artists, designers, and filmmakers to gain feedback on how to advance the model to be most helpful for creative professionals," according to a blog post.

Tesla CEO Elon Musk stands in front of the damaged Cybertruck after it fails a demonstration of its durability.Ringo H.W. Chiu / AP At a live delivery event this November, where Elon Musk awkwardly opened the door for about a dozen new Cybertruck owners, he told the world: "The apocalypse can come along any moment, and here at Tesla, we have the best in apocalypse technology." Then he showed a video of the vehicle being pummeled by a machine gun, quipping, "If you're ever in an argument with another car, you will win." And then he sold a bunch of Cybertrucks. Two million have been preordered--and 500 delivered--for over 60,000 a pop. Some soon proved that they couldn't survive a test drive, let alone a ride with Mad Max.

A quarter of US adults regularly get their news from YouTube. Yet, despite the massive political content available on the platform, to date no classifier has been proposed to identify the political leaning of YouTube videos. To fill this gap, we propose a novel classifier based on Bert -- a language model from Google -- to classify YouTube videos merely based on their titles into six categories, namely: Far Left, Left, Center, Anti-Woke, Right, and Far Right. We used a public dataset of 10 million YouTube video titles (under various categories) to train and validate the proposed classifier. We compare the classifier against several alternatives that we trained on the same dataset, revealing that our classifier achieves the highest accuracy (75%) and the highest F1 score (77%). To further validate the classification performance, we collect videos from YouTube channels of numerous prominent news agencies, such as Fox News and New York Times, which have widely known political leanings, and apply our classifier to their video titles. For the vast majority of cases, the predicted political leaning matches that of the news agency.

Several parameter-efficient fine-tuning methods based on adapters have been proposed as a streamlined approach to incorporate not only a single specialized knowledge into existing Pre-Trained Language Models (PLMs) but also multiple of them at once. Recent works such as AdapterSoup propose to mix not all but only a selective sub-set of domain-specific adapters during inference via model weight averaging to optimize performance on novel, unseen domains with excellent computational efficiency. However, the essential generalizability of this emerging weight-space adapter mixing mechanism on unseen, in-domain examples remains unexplored. Thus, in this study, we conduct a comprehensive analysis to elucidate the generalizability of domain-specific adapter mixtures in in-domain evaluation. We also provide investigations into the inner workings of the mixture of domain-specific adapters by analyzing their weight signs, yielding critical analysis on the negative correlation between their fraction of weight sign difference and their mixtures' generalizability. All source code will be published.