Hardly a day goes by without someone talking about Artificial Intelligence (AI), and apps like ChatGPT , DALL-E , Flawless , Midjourney , Google Bard , or other generative AI technologies are severe threats to original film and TV content creation. Every major news outlet has done stories on AI's potential detriment to the industry.

Spherex's patented Spherexgreenlight™ AI/ML content analysis technology provides frame-level cultural feedback to content creators, distributors, and platforms so that titles can be adequately prepared for distribution to any country worldwide without regulatory or brand risk. Creators can use this technology at any stage of production to be alerted to events within the title that audiences or regulators may find objectionable. They can then decide how to address those issues before public release.

Spherex, a global entertainment technology and data company, will demonstrate Spherexgreenlight™, which is its revolutionary expert-in-the-loop artificial intelligence (AI) and machine learning (ML) solution, at Pepcom’s Digital Experience! at CES.

Few technologies instill intense fear or promise in the public’s mind than Artificial Intelligence (AI) and Machine Learning (ML). From personalized shoppers to writing press releases to robots performing physical warehouse tasks to video content analysis to “Terminator’s” Skynet and video deep fakes , AI/ML can impact industry and society positively and negatively.   AI/ML applications are already affecting the Media and Entertainment (M&E) industry. Spherex greenlight ™ uses an AI/ML platform to analyze video content for regulatory and age rating compliance. Flawless.ai uses it to digitally edit content to sync language translations to facial expressions and lip-movement. Respeecher uses AI to create speech that’s indistinguishable from the original speaker.   Volumes have been written about AI since the first workshop in 1956 established the field. A discussion of the systems AI/ML runs on would overwhelm most people, but understanding what makes them function is much more comprehensible and relatable.   It’s Complicated Each time we learn something new or are exposed to new information, we’re processing “data.” Our brain uses those data to learn how to speak, think, and interact with others. The more data we’re exposed to, the more we know.   Think about describing the task of using a hammer to drive a nail. As simple as it sounds, for a computer to recognize, understand and accurately identify what’s happening requires many types of “data” humans take for granted. For example:   1.   Why would you use a hammer and nails? 2.   Are there different kinds and sizes of hammers and nails? 3.   How do you hold the hammer? 4.   How do you use a nail? 5.   Where do you hit the nail with the hammer?   Computers can’t know any of this, so every component and step must be described in detail. “Showing” a photo of a hammer and nail is insufficient because computers can’t know if you want them to recognize the image or the objects in the picture. What if a roof with shingles, people, trees, a toolbox, and the sky is visible? How is the computer supposed to differentiate between these other objects if it doesn’t know what they are, their relationship to the hammer and nail, or the significance of the other things?   The point is, you have to teach the computer. This process gets complicated quickly and shows how hard it is for AI/ML systems to recognize simple objects or tasks. Consider how complex it becomes when teaching the system about the context and nuance of artifacts in a movie scene.   Those who build AI systems understand these challenges and recognize the difficulty of collecting, analyzing, and processing the mountains of data required to make their systems work.   Data, Data and More Data No company or organization owns or maintains all the data necessary to make AI/ML systems function. It is a global and collaborative undertaking. Universities and corporations understand the challenge, so they open-source their datasets to help seed the industry and invite others to contribute to their improvement. Google , YouTube , IBM , Kaggle, and hundreds of others have contributed trillions of individual records covering hundreds of data types that comprise AI’s comprehensive knowledge base.   Spherex harnessed its near decade of cultural and rating expertise and its catalog of 25M film and TV titles as a knowledge base and augmented it with public and private-source AI datasets to build its award-winning Spherex greenlight ™ content-rating solution. It is the M&E industry’s first AI/ML-based system that automates rating and cultural event detection and extraction for any form of video content. This patented technology scales to enable any regulator or company to rate quantities ranging from single titles to complete catalogs of thousands of titles efficiently and correctly.   In future posts, we’ll provide additional peeks into how these data are used in M&E to improve content creation, post-production, and distribution workflows. The best AI/ML applications are the ones that help you do your job while saving time and money and reducing risk.   A lot has changed since we wrote about AI in M&E in 2021. Technology and science have improved dramatically since then, and 2023 looks to be another year of significant advancement—especially with Spherex products and services.

Spherex has been approved as the national classification tool and a new mechanism to regulate film content in Australia.

A new classification tool will help online streaming services speed up the time it takes to assess new movies and TV shows from up to 20 days to 24 hours.

The Albanese Government, the federal executive government of Australia, announced it has approved the use of the Spherex Classification Tool as a new mechanism of safeguarding and regulating content in Australia under the Classification (Publications, Films and Computer Games) Act 1995.

How Spherex technologies assess your content's cultural fit for global audiencesHave you ever eaten something unique at a restaurant and wondered, and maybe even asked, what the chef did that made it so good?Of course, you have.