Since the beginning of cinema, artificial intelligence has been a recurring theme, enthralling (and frequently terrifying) audiences with the idea of sentient robots capable of matching humanity's distinctive qualities like consciousness and the capacity for emotion. Potential technical developments have been envisioned in various ways over the years. However, science fiction films have also raised issues about the moral, ethical, and societal effects of using technology like AI. The best artificial intelligence movies available to view in 2023 are listed in this article. So let's get started in any order: The novelist and screenwriter Alex Garland's first feature film, "Ex Machina," is a rare and much-welcome departure from that trend. It begins as an unsettling thriller about a young programmer (Domhnall Gleeson) who is drawn to a charismatic Dr. Frankenstein figure (Oscar Isaac).

Before becoming a devoted entrepreneur, I was a full-time actor appearing on TV and in film. From my experience, the marks of excellent performance, cinematography and entertainment were the ability to be absolutely convincing and creative. Creativity is the ability to find new solutions to problems or challenges. Creative people are innovative and able to see things differently from others, which helps them come up with new ideas or solutions. Creative thinking typically involves making connections between things that might not appear related at first glance.

When you browse Apple Books for your next audiobook, you might come across a few titles with a note that says they were "Narrated by Apple Books." That's because the tech giant has released a catalogue of titles that make use of its new AI-powered digital narration service. The company said the service uses the advanced speech synthesis technology it developed "to produce high-quality audiobooks from an ebook file." According to The Guardian, Apple approached independent publishers who may be interested in teaming up for the project's launch in recent months. Authors were reportedly told that the company behind the technology would shoulder the costs of turning their books into audiobooks and that they would be earning royalties.

Artificial Intelligence is officially here and it's doing everything from writing code to designing watches and even brewing beer. From the AI that makes watches to the AI that knows how to generate fake news, these are five of the weirdest ways people are now using Artificial Intelligence. READ MORE: This is why everyone's obsessed with the new'Nothing Phone' This timepiece is made by Nike in partnership with a clothing company named Jacquemus. However, the watch was actually designed by Artificial Intelligence. To be fair, it doesn't look half bad.

Apple's website says the feature is initially only available for romance and fiction books, where it lists two available digital voices: Madison and Jackson. The service is only available in English at present, and Apple is oddly specific about the genres of books its digital narrators are able to tackle. "Primary category must be romance or fiction (literary, historical, and women's fiction are eligible; mysteries and thrillers, and science fiction and fantasy are not currently supported)," its website reads.

For the first few seconds, the narrator of Kristen Ethridge's new romance audiobook, Shelter from the Storm, sounds like a human being. The voice is light and carefully enunciated, with the slow pacing of any audiobook narrator, as it begins: "There's a storm coming, and her name is Hope." "I know that sounds a little crazy," the breathy voice continues, grinding out the words. "That something so destructive could be labeled with such a peaceful name." It's the aural equivalent of watching the gears of a machine rotate under a surface of what looks like human skin.

The methods of automatic speech summarization are classified into two groups: supervised and unsupervised methods. Supervised methods are based on a set of features, while unsupervised methods perform summarization based on a set of rules. Latent Semantic Analysis (LSA) and Maximal Marginal Relevance (MMR) are considered the most important and well-known unsupervised methods in automatic speech summarization. This study set out to investigate the performance of two aforementioned unsupervised methods in transcriptions of Persian broadcast news summarization. The results show that in generic summarization, LSA outperforms MMR, and in query-based summarization, MMR outperforms LSA in broadcast news summarization.

Fashion-image editing represents a challenging computer vision task, where the goal is to incorporate selected apparel into a given input image. Most existing techniques, known as Virtual Try-On methods, deal with this task by first selecting an example image of the desired apparel and then transferring the clothing onto the target person. Conversely, in this paper, we consider editing fashion images with text descriptions. Such an approach has several advantages over example-based virtual try-on techniques, e.g.: (i) it does not require an image of the target fashion item, and (ii) it allows the expression of a wide variety of visual concepts through the use of natural language. Existing image-editing methods that work with language inputs are heavily constrained by their requirement for training sets with rich attribute annotations or they are only able to handle simple text descriptions. We address these constraints by proposing a novel text-conditioned editing model, called FICE (Fashion Image CLIP Editing), capable of handling a wide variety of diverse text descriptions to guide the editing procedure. Specifically with FICE, we augment the common GAN inversion process by including semantic, pose-related, and image-level constraints when generating images. We leverage the capabilities of the CLIP model to enforce the semantics, due to its impressive image-text association capabilities. We furthermore propose a latent-code regularization technique that provides the means to better control the fidelity of the synthesized images. We validate FICE through rigorous experiments on a combination of VITON images and Fashion-Gen text descriptions and in comparison with several state-of-the-art text-conditioned image editing approaches. Experimental results demonstrate FICE generates highly realistic fashion images and leads to stronger editing performance than existing competing approaches.

While GPT-2 generates sentences that are remarkably human-like, longer documents can ramble and do not follow human-like writing structure. We study the problem of imposing structure on long-range text. We propose a novel controlled text generation task, sequentially controlled text generation, and identify a dataset, NewsDiscourse as a starting point for this task. We develop a sequential controlled text generation pipeline with generation and editing. We test different degrees of structural awareness and show that, in general, more structural awareness results in higher control-accuracy, grammaticality, coherency and topicality, approaching human-level writing performance.

Apple has quietly launched a catalogue of books narrated by artificial intelligence in a move that may mark the beginning of the end for human narrators. The strategy marks an attempt to upend the lucrative and fast-growing audiobook market – but it also promises to intensify scrutiny over allegations of Apple's anti-competitive behaviour. The popularity of the audiobook market has exploded in recent years, with technology companies scrambling to gain a foothold. Sales last year jumped 25%, bringing in more than $1.5bn. Industry insiders believe the global market could be worth more than $35bn by 2030.