This is only going to get better, and it’s essentially free, today. Automatic tricks with wordsĪutomatic captioning (in Premiere Pro, iOS, YouTube and Vimeo today) is a neat AI trick and a big time saver. If you’ve got a library of stock videos, tagging them could be a great way to help you find the right clips more easily.
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There’s even a Mac app called FCP VideoTag which can create Final Cut Pro keywords based on what it sees in the videos you feed it, which is a very clever trick indeed. Searching for “cars” on my own iPhone shows… mostly cars Photos on the Mac can do the same trick, and if you put videos in there, it’ll work on those too. This is down to AI-based categorization of your images that occurs entirely on your device, in the background, without violating your privacy. On an iPhone, head to the Photos app, then search for any word, like “chair” or “beach” and it will show you your own photos of those things.
Let’s start with something simple and useful that’s often overlooked. Those are just a few of the AI-based tools you might be using already, but there are a lot more out there that you’re probably not using. (While I haven’t reviewed this app yet myself, check out Oliver Peters’ look at it here.) In the land of color correction, FCP’s Color Balance and Match Color features are often unpredictable, while the more modern Colourlab AI grading solution (for all the major NLEs) seems to do a much better job. Photoshop has long offered many ways to up-rez an image, but the recently added Neural Filters include a Super Zoom feature, which invents new detail rather than letting things remain fuzzy. If a clip is too small, you can use simple upscaling in any NLE, or jump to more advanced AI-based upscaling with products like Video Enhance AI from Topaz Labs. For example, Final Cut Pro’s traditional Noise Removal feature can remove background noise from audio, but that older feature can’t compete with its more modern Voice Isolation sibling, based on machine learning. In truth, AI tech has been around for a while, but it’s only recently taken a more central role, and is doing a better job than traditional approaches. That’s how the world’s post offices automated the sorting of mail, and the same basic strategy is how we have OCR today, live on our phones in photos and videos we capture. To teach a computer to understand human handwriting, you could show it many pictures of handwritten characters, tell the computer which characters the images represent, and then use that trained neural net to identify new handwritten letters. Training a machine learning model is essentially showing a computer what you want by example, many times over, by providing it with a data set (inputs) and telling it what it means (outputs). Training a neural network, a prompt given to Stable Diffusion Yes, you could train a neural network, and you could use fuzzy logic for crude tasks, but today’s tech has upped the speed and scale of what’s possible by several orders of magnitude - here’s a technical primer. Today it’s often known as Machine Learning, or ML, and the latest chips from Apple and Intel both include modules designed to improve the speed of ML operations.īack at university, where I studied Information Technology, I majored in Artificial Intelligence and Artificial Life, but the pieces weren’t in place in the 1990s for a true revolution - “game-changing AI” was always a year away. Artificial Intelligence encompasses many areas: natural language processing, neural networks, expert systems, machine vision, and more. Let’s take a step back and look at some broad brushstrokes. Here, I’ll take you through the ideas behind the tech, along with specific examples of how modern, smart technology will change your post workflow tomorrow, or could today. While a variety of AI-based technologies have been improving steadily over the past several years, it’s the recent giant strides made in image generation that’s potentially groundbreaking for post production professionals.
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Siri doesn’t quite understand what you’re asking Google Translate can’t yet seamlessly bridge the gap between languages, and navigation software still doesn’t always offer an accurate route. Artificial Intelligence has promised so much for so long, and in many ways, it’s been less than perfect.
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