Large language models have achieved remarkable success in various tasks. However, it is challenging for them to learn new tasks incrementally due to catastrophic forgetting. Existing approaches rely on experience replay, optimization constraints, or task differentiation, which encounter strict limitations in real-world scenarios. To address these issues, we propose Joint Flashback Adaptation. We first introduce flashbacks -- a limited number of prompts from old tasks -- when adapting to new tasks and constrain the deviations of the model outputs compared to the original one. We then interpolate latent tasks between flashbacks and new tasks to enable jointly learning relevant latent tasks, new tasks, and flashbacks, alleviating data sparsity in flashbacks and facilitating knowledge sharing for smooth adaptation. Our method requires only a limited number of flashbacks without access to the replay data and is task-agnostic. We conduct extensive experiments on state-of-the-art large language models across 1000+ instruction-following tasks, arithmetic reasoning tasks, and general reasoning tasks. The results demonstrate the superior performance of our method in improving generalization on new tasks and reducing forgetting in old tasks.

Psychological trauma can manifest following various distressing events and is captured in diverse online contexts. However, studies traditionally focus on a single aspect of trauma, often neglecting the transferability of findings across different scenarios. We address this gap by training language models with progressing complexity on trauma-related datasets, including genocide-related court data, a Reddit dataset on post-traumatic stress disorder (PTSD), counseling conversations, and Incel forum posts. Our results show that the fine-tuned RoBERTa model excels in predicting traumatic events across domains, slightly outperforming large language models like GPT-4. Additionally, SLALOM-feature scores and conceptual explanations effectively differentiate and cluster trauma-related language, highlighting different trauma aspects and identifying sexual abuse and experiences related to death as a common traumatic event across all datasets. This transferability is crucial as it allows for the development of tools to enhance trauma detection and intervention in diverse populations and settings.

Fox News host Greg Gutfeld gives his take on Deadspin laying off all of its staff on'Gutfeld!' I love every one of you. All right, let's get started. Dylan Mulvaney is trying to switch from transgender influencer to stand-up comic. After the Budweiser fiasco, you got to admit, that takes balls.

In Federated Learning (FL), forgetting, or the loss of knowledge across rounds, hampers algorithm convergence, particularly in the presence of severe data heterogeneity among clients. This study explores the nuances of this issue, emphasizing the critical role of forgetting in FL's inefficient learning within heterogeneous data contexts. Knowledge loss occurs in both client-local updates and server-side aggregation steps; addressing one without the other fails to mitigate forgetting. We introduce a metric to measure forgetting granularly, ensuring distinct recognition amid new knowledge acquisition. Leveraging these insights, we propose Flashback, an FL algorithm with a dynamic distillation approach that is used to regularize the local models, and effectively aggregate their knowledge. Across different benchmarks, Flashback outperforms other methods, mitigates forgetting, and achieves faster round-to-target-accuracy, by converging in 6 to 16 rounds.

Fox News contributor Joe Concha joins "Fox & Friends First" to discuss Elon Musk's warning that artificial intelligence could threaten elections and his concerns on the declining birth rate. Long before Elon Musk and Apple co-founder Steve Wozniak signed a letter warning that artificial intelligence poses "profound risks" to humanity, British theoretical physicist Stephen Hawking had been sounding the alarm on the rapidly evolving technology. "The development of full artificial intelligence could spell the end of the human race," Hawking told the BBC in an interview in 2014. Hawking, who suffered from amyotrophic lateral sclerosis (ALS) for more than 55 years, died in 2018 at the age of 76. Though he had critical remarks on AI, he also used a very basic form of the technology in order to communicate due to his disease, which weakens muscles and required Hawking to use a wheelchair.

In this paper we describe a system for generating textual narrations of what happened in a simulation-based serious game, focusing on the use of focalization (telling the story from the perspective of one of the characters) and flashbacks to give the player insights into the internal state of non-player characters.

The 2000 essay by Joy, then chief scientist at Sun Microsystems, was called "Why the future doesn't need us." In it, he argued that new forms of technology were potentially dangerous and could render humans obsolete. It sparked an ardent debate. One impassioned reaction was from Richard Wallace, a chatbot expert who created ALICE, the inspiration for Spike Jonze's Her, and who now works at Pandorabots. I asked Andrew Moore, dean of computer science at Carnegie Mellon University and a former VP of engineering at Google, for his thoughts on the 18-year-old comment.

Get YouTube without the ads. Want to watch this again later? Sign in to add this video to a playlist. Report Need to report the video? Sign in to report inappropriate content.

Dave Allen once said that men know they're getting older when they watch Sunset Boulevard and realise they find Gloria Swanson quite attractive. Similarly, a certain generation will sense the grim reaper's presence now that Angelina Jolie is no longer the screen face of Lara Croft, because the mantle has passed to Alicia Vikander. This Lara is notably more serious and sensitive, and unlike Angelina, or the figure in the 90s video game – or indeed Karen Gillan in the new Jumanji movie – she doesn't have to wear cute shorts or revealing clothes, which is fair enough. But she does an awful lot of very pathetic and borderline creepy daddy-daughter pining for that all-important man in her life. This guy is forever smiling wisely in soft focus flashback in the grounds of Croft Manor, which has the same totemic importance as Wayne Manor, always kissing the demure infant Lara's forehead or indeed brushing his fingers with his lips and touching her forehead (eeuuww) prior to going off on one of his dangerous ethnological adventures.

Altered Carbon, a maximalist cyberpunk series arriving on Netflix this Friday, is the story of Takeshi Kovacs, a half-Japanese, half-Slavic fighting machine who, after being unconscious for 250 years--more on the logistics shortly--is revived in the body of a white cop. This is a particularly complicated version of whitewashing, the Hollywood habit of casting white actors in historically nonwhite roles, insofar as Altered Carbon is based on a novel by Richard K. Morgan, in which an Asian man is stuck in the body of a white man and not happy about it. "I stared into a fragmented mirror at the face I was wearing as if it had committed a crime against me," Kovacs says in the book, after seeing his new visage for the first time. Altered Carbon is not Ghost in the Shell, the boondoggle of a film in which a (cybernetic) Asian character was played by Scarlett Johansson. In flashbacks, Kovacs is played by the Asian actor Will Yun Lee, and in future seasons the character may be played by a nonwhite actor.