This week's episode of Saturday Night Live, hosted by Jason Bateman, featured two different kinds of sketches that felt inevitable the moment they aired. First there was the cold open, which became obligatory the moment that Melissa Carone did her uncanny Cecily Strong impersonation at a Michigan hearing about voter fraud, necessitating a Cecily Strong impersonation of Melissa Carone. But this week also featured "Stu," an instant-classic Christmas sketch, which felt inevitable and surprising, because it's a perfect synthesis of the cast members' individual interests and strengths. Everyone is cast perfectly here. Meanwhile, Pete Davidson has been making goofy comedy rap videos since lockdown started, to somewhat mixed results. In "Stu," though, he's in top form lyrically and has reached deep within himself to find aspects of his role--a mentally ill young man who enjoys rap music, video games, and impulsive tattoos--that he could somehow relate to events in his own life.

We at Lionbridge have compiled a list of 14 movie datasets. The data on this list can be useful from a statistical learning perspective, because you can use them to master basic machine learning concepts, instead of relying on dry, esoteric datasets. Many of the datasets on this list contain data points such as the cast and crew members, script, run time, and reviews. You could use these movie datasets for machine learning projects in natural language processing, sentiment analysis, and more. IMDB Film Reviews Dataset: This dataset contains 50,000 movie reviews, and is already split equally into training and test sets for your machine learning model.

Artificial Intelligence Solutions Market is growing at a High CAGR during the forecast period 2021 to 2027.

Have you ever seen the movie The Lonely Guy? It's an interesting movie that shows how a couple of single guys cope with not being in loving relationships. It was a little bit silly, but that's how Martin's movies were at the time. It may be his best movie ever that most people probably never heard of. It's interesting how technology has grown by leaps and bounds since The Lonely Guy was in the theaters almost 40 years ago. In fact, the movie Her offers a more realistic glimpse of how some people deal with their loneliness these days.

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The Blendid kiosk uses an artificial intelligence and machine learning-powered system with a robotic arm, blenders, a refrigerator, and ingredient …

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Google Assistant received more natural-sounding voice models again in October thanks to updated natural language processing and prosody models which use machine learning to understand and stitch together sentences with better intonation, rhythm, and stress. Now, they seem to be applying that same technology to Google Play Books in order to turn any book into an audiobook, and I totally saw it coming. The upcoming feature seeks to give an auto-generated narrator's voice to text-only books to make them more accessible and immersive. Google Play is working with publishers in the U.S. and the UK for the rollout and will make the publisher tool to create auto-narrated audiobooks available in early 2021 with a beta available now. Once set up for auto-narration, a user can choose between different machine learning and AI-created narrator voices like a cowboy named Tex, and more.

In this work, we propose TediGAN, a novel framework for multi-modal image generation and manipulation with textual descriptions. The proposed method consists of three components: StyleGAN inversion module, visual-linguistic similarity learning, and instance-level optimization. The inversion module is to train an image encoder to map real images to the latent space of a well-trained StyleGAN. The visual-linguistic similarity is to learn the text-image matching by mapping the image and text into a common embedding space. The instance-level optimization is for identity preservation in manipulation. Our model can provide the lowest effect guarantee, and produce diverse and high-quality images with an unprecedented resolution at 1024. Using a control mechanism based on style-mixing, our TediGAN inherently supports image synthesis with multi-modal inputs, such as sketches or semantic labels with or without instance (text or real image) guidance. To facilitate text-guided multi-modal synthesis, we propose the Multi-Modal CelebA-HQ, a large-scale dataset consisting of real face images and corresponding semantic segmentation map, sketch, and textual descriptions. Extensive experiments on the introduced dataset demonstrate the superior performance of our proposed method. Code and data are available at https://github.com/weihaox/TediGAN.