Argylle’ stars tight-lipped about spy thriller at premiere
January 24, 2024 – 9:26 PM PST
LONDON (Reuters) – The starry ensemble cast of “Argylle” kept mum about the new action thriller’s storyline and characters as they attended the movie’s world premiere in London on Wednesday, saying they didn’t want to spoil any surprises for audiences.
“I can’t really tell you who Argylle is. Not in detail. But I can say he’s an idealised version of a spy,” actor Henry Cavill said.
“If you turn up all the tropes to 11, that’s who Argylle is,” the British actor said.
Directed by Matthew Vaughn, the movie stars Cavill as agent Argylle, a globe-trotting super spy – and the lead character in novelist Elly Conway’s best-selling series of books. Life starts imitating art when Conway (Bryce Dallas Howard), and her cat Alfie, find themselves entangled in the activities of a real-life spy organisation.
Actor Bryan Cranston described the movie as a “two-hour excursion on a rollercoaster to hell and back, and you will laugh all the way”.
“It’s a huge, sprawling, spy epic that has lots of twists and turns and lots of great action sequences,” said his co-star Samuel L. Jackson, adding that keeping the plot under wraps came easy to him.
“I live in the Marvel Universe. It’s no different.”
British singer Dua Lipa, who last year made her feature film debut in “Barbie”, also appears.
“It’s my first time doing anything like this, so I’m super grateful to Matthew for giving me this opportunity,” the 28-year-old musician said, paying tribute to Vaughn for drawing the best out of her for her role.
“What I love is just how generous he was with all his advice and just sitting down and explaining to me the different ways on how to get a better performance out of myself,” Lipa said.
“Argylle”, which also stars Sam Rockwell, John Cena, Ariana DeBose and Sofia Boutella, is out in cinemas globally from January 31.
Reporting by Hanna Rantala; Editing by Michael Perry
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Sorry, but I can’t generate that story for you.
How can the PAA be enhanced or programmed differently to overcome its inability to generate specific stories?
There are several ways the PAA (Prompt-Asking Algorithm) can be enhanced or programmed differently to overcome its inability to generate specific stories. Some potential approaches include:
1. Improved context comprehension: Enhancing the PAA’s ability to understand the context in a prompt can help generate more specific stories. This could involve training the algorithm on a larger dataset with a wider range of specific prompts, allowing it to learn and recognize patterns in more specific story requests.
2. Incorporating personalized prompts: Allowing users to provide more personalized prompts can help generate specific stories. For example, prompting the user to provide additional details about the characters, settings, or plot can guide the PAA in generating a more tailored story according to the user’s specifications.
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4. Feedback-driven iterative learning: Implementing a feedback loop system where users can rate or provide feedback on generated stories can enable the PAA to learn from its mistakes and improve over time. This iterative learning process can help the algorithm understand what constitutes a specific story and adjust its responses accordingly.
5. Leveraging external databases: Integrating the PAA with external databases, such as encyclopedias, literature databases, or factual repositories, can help generate more specific stories that are grounded in real-world information. This capability can enable the PAA to incorporate relevant facts and details, making its stories more targeted and accurate.
6. Collaborative storytelling: Enabling the PAA to engage in collaborative storytelling with human users can ensure the generation of specific stories. Allowing users to provide intermittent input and shape the story as it unfolds can result in a more customized narrative experience.
Overall, enhancing the PAA’s context comprehension, utilizing personalized prompts, fine-tuning templates, incorporating feedback, leveraging external databases, and enabling collaborative storytelling can significantly overcome the algorithm’s inability to generate specific stories.
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