'Our characters are cybernetic or larger than life,' said Zak London, the founder of Gossip Goblin. 'We adapt to the limits of AI acting.' 'Our characters are cybernetic or larger than life,' said Zak London, the founder of Gossip Goblin. 'We adapt to the limits of AI acting.' 'There are no rules': spotlight on Gossip Goblin as AI film-making enters new era Defying criticisms of'slop' and'theft', the growing culture of AI-powered creativity is attracting interest from Hollywood In a former hemstitching workshop where artisans sewed pleats for Stockholm's 19th-century bourgeoisie, a distinctly 21st-century craft is taking root: AI film-making. One day last week, an actor, director and composer squeezed into a tiny studio booth to record a voiceover for their next AI release. But this had a distinctly homespun feel, the little team fussing over a monologue by a poetic Scottish gorilla inhabiting a transhumanist cyberpunk universe.

Adult content creators are having their performances used without consent. This is just one way that AI now threatens their rights and livelihoods. When Jennifer got a job doing research for a nonprofit in 2023, she ran her new professional headshot through a facial recognition program. She wanted to see if the tech would pull up the porn videos she'd made more than 10 years before, when she was in her early 20s. It did in fact return some of that content, and also something alarming that she'd never seen before: one of her old videos, but with someone else's face on her body. "At first, I thought it was just a different person," says Jennifer, who is being identified by a pseudonym to protect her privacy. But then she recognized a distinctly garish background from a video she'd shot around 2013, and she realized: "Somebody used me in a deepfake."

AI performances and screenplays won't be eligible for Oscars AI performances and screenplays won't be eligible for Oscars Sorry, Val Kilmer fans, but the late actor's Oscar ship has officially sailed. On Friday, reported that AI-generated acting and writing won't be eligible for Academy Awards. The new rules from the Academy of Motion Picture Arts and Sciences will take effect beginning with next year's presentation, scheduled for March 2027. The Academy's updated rules state that while filmmakers can use AI tools, synthetic performers can't win any awards. Ditto for AI-written screenplays, which must be human-authored.

Musk, who co-founded the company that created ChatGPT with Altman, wants more than $130 billion in damages in a lawsuit that could shakeup the artificial intelligence landscape. The BBC's Lily Jamali explains why the two tech giants are facing off in court. How much screen time is too much for under fives? Some major retailers and independent stores have introduced AI body scans, CCTV or facial recognition equipment to identify crimes like shoplifting. What does TikTok's deal mean for America's users?

Previous work [2, 1] has observed that standard training and greedy decoding usually cause models to generate consecutive repetitive texts. These consecutive repetitive texts are redundant and do not convey new information, which is avoided in human language. There are three types of consecutive repetitions: word-level, phrase-level and sentence-level. The phrase-level means that a phrase consisting of several words is repeated consecutively. The sentence in our paper refers to a sequence split by '.!?' is repeated consecutively 2. We calculate the ratio of consecutive repetition in a sequence x as follows.

Although actor-critic methods have been successful in practice, their theoretical analyses have several limitations. Specifically, existing theoretical work either sidesteps the exploration problem by making strong assumptions or analyzes impractical methods with complicated algorithmic modifications. Moreover, the actor-critic methods analyzed for linear MDPs often employ natural policy gradient and construct "implicit" policies without explicit parameterization. Such policies are computationally expensive to sample from, making the environment interactions inefficient. To that end, we focus on the finite-horizon linear MDPs and propose an optimistic actor-critic framework that uses parametric log-linear policies. In particular, we introduce a tractable $\textit{logit-matching}$ regression objective for the actor. For the critic, we use approximate Thompson sampling via Langevin Monte Carlo to obtain optimistic value estimates. We prove that the resulting algorithm achieves $\widetilde{\mathcal{O}}(ε^{-4})$ and $\widetilde{\mathcal{O}}(ε^{-2})$ sample complexity in the on-policy and off-policy setting, respectively. Our results match prior theoretical work in achieving the state-of-the-art sample complexity, while our algorithm is more aligned with practice.

Opponent modeling is a longstanding research topic aimed at enhancing decision-making by modeling information about opponents in multi-agent environments. However, existing approaches often face challenges such as having difficulty generalizing to unknown opponent policies and conducting unstable performance. To tackle these challenges, we propose a novel approach based on in-context learning and decision-time search named Opponent Modeling with In-context Search (OMIS). OMIS leverages in-context learning-based pretraining to train a Transformer model for decision-making. It consists of three in-context components: an actor learning best responses to opponent policies, an opponent imitator mimicking opponent actions, and a critic estimating state values. When testing in an environment that features unknown non-stationary opponent agents, OMIS uses pretrained in-context components for decision-time search to refine the actor's policy. Theoretically, we prove that under reasonable assumptions, OMIS without search converges in opponent policy recognition and has good generalization properties; with search, OMIS provides improvement guarantees, exhibiting performance stability. Empirically, in competitive, cooperative, and mixed environments, OMIS demonstrates more effective and stable adaptation to opponents than other approaches. See our project website at https://sites.google.com/view/nips2024-omis.

We present temporally abstract actor-critic (TAAC), a simple but effective off-policy RL algorithm that incorporates closed-loop temporal abstraction into the actor-critic framework. TAAC adds a second-stage binary policy to choose between the previous action and a new action output by an actor. Crucially, its act-or-repeat decision hinges on the actually sampled action instead of the expected behavior of the actor. This post-acting switching scheme let the overall policy make more informed decisions. TAAC has two important features: a) persistent exploration, and b) a new compare-through Q operator for multi-step TD backup, specially tailored to the action repetition scenario. We demonstrate TAAC's advantages over several strong baselines across 14 continuous control tasks. Our surprising finding reveals that while achieving top performance, TAAC is able to mine a significant number of repeated actions with the trained policy even on continuous tasks whose problem structures on the surface seem to repel action repetition. This suggests that aside from encouraging persistent exploration, action repetition can find its place in a good policy behavior. Code is available at https://github.com/hnyu/taac.