Why Netflix Is Using Machine Learning To Train AI To Do Match Cuts

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Can an algorithm really decide the best way to make a cut?

To be honest, there are numerous captivating advancements happening in the realm of AI regarding the field of filmmaking and video production. We have explored various remarkable AI tools and features that aim to simplify and streamline the work of video professionals.

However, as we have previously discussed, there are certain aspects of filmmaking that AI will likely never fully replace.

One particular area where AI might fall short is in the realm of video editing itself, which involves the intricate and imaginative decisions made by video editors to transform disparate footage into a coherent and emotionally engaging story.

Nevertheless, despite these considerations, Netflix has recently revealed that they are actively engaged in training a new AI using a machine learning algorithm. This involves feeding hundreds of thousands of hours of footage from their streaming shows and movies to enable the AI to master the art of creating match cuts.

Let’s explore everything we know about this news, as well as provide some helpful links and resources to any non-AI-bots reading this article who might want to hone their own human skills in case they want to compete with these future Netflix-fed AI algorithms.

What Is a Match Cut?

Let’s begin with the fundamentals: what exactly is a match cut?

A match cut, both in theory and practice, is a basic editing technique that involves transitioning between two shots that share a common element. These match cuts can be observed in various contexts, from films by directors like Kubrick and Spielberg, to television commercials, and even in personal wedding videos. They have become a fundamental component of video editing.

To gain a better understanding of match cuts and to see them in action, I recommend watching this informative video that provides further context and examples: [Link to the video]

As you can see, while the match cut can be a general term, to be more precise about match cuts you might want to focus on one of the three different types of match cuts out there.

The Different Types of Match Cuts

Certainly! Let’s explore the three different types of match cuts: graphics, audio, and movement. Here’s some additional information about each:

Graphic Match Cut: In a graphic match cut, the focus is on a specific element within the frame, while everything else between the shots changes. This can involve highlighting a particular object, shape, or even a color that serves as a visual connection between the shots.

Audio Match Cut: An audio match cut involves two visually distinct shots that are connected by a similar sound or a line of dialogue. This type of match cut relies on audio elements to establish continuity and create a thematic link between the shots.

Movement Match Cut: A movement match cut emphasizes continuity in camera motion or action between shots. This can involve maintaining the same camera movement, following a similar physical action, or preserving a sense of motion across the transition.

While each type has its unique characteristics, it’s common to see match cuts that combine elements from different types. The ultimate goal of a match cut is to establish a thematic connection between two scenes that may initially appear unrelated or irrelevant to each other, either subtly or overtly.

By employing match cuts, filmmakers can create meaningful associations and enhance the overall coherence and impact of their storytelling.

What Netflix Is Doing With the Machine Learning?

Let’s delve into Netflix’s utilization of machine learning and match cuts. In a video featuring creative technologists and machine learning engineers, Netflix developers explain their recent endeavors in training an AI program to master the art of executing match cuts.

Netflix provides a comprehensive explanation of their approach, which involves implementing sophisticated techniques like instance segmentation and optical flow. These complex machine learning systems aid in breaking down and efficiently analyzing thousands of hours of footage algorithmically.

Initially, the primary objective of this project seems to be assisting Netflix editors in cutting together trailers with greater speed and ease. By leveraging AI technology, Netflix aims to streamline the trailer editing process, allowing for quicker production of engaging and effective promotional content.

However, as the team describes in the video, eventually this technology could be trained and used for cutting together new shows and content based on these algorithmic suggestions.

Why It Probably Won’t Work for Them

There is considerable discussion and criticism surrounding the recent video about Netflix’s machine learning match cuts, particularly on platforms like Twitter. Many people are expressing skepticism and pointing out a significant issue with the promises made in the video – that match cuts cannot be reduced to a finite task.

Match cuts are diverse and varied, relying on elements such as emotion and creativity that go beyond mere framing or movement. Critics argue that the nuanced decision-making involved in creating effective match cuts cannot be replicated solely through machine learning algorithms. The intricacies of crafting match cuts require a human touch and an understanding of storytelling that goes beyond algorithmic analysis.

The internet’s response reflects a widespread sentiment that match cuts are an artistic endeavor, reliant on the creative vision of human editors. While machine learning can assist in certain aspects of the editing process, it is widely acknowledged that the emotional and creative elements of match cuts cannot be fully captured by algorithms alone.

Indeed, there are certain concrete elements in match cuts that an AI can potentially recognize, and it appears that Netflix’s team is aiming to leverage machine learning to identify and execute those elements. However, it can be argued that the premise of Netflix video editors spending excessive time searching for similar shots for match cuts in trailer editing might not be entirely accurate.

The effectiveness and practicality of Netflix’s machine-learned match cut program remain to be seen. Only time will reveal whether this approach proves beneficial and how Netflix may choose to utilize the information gained from their AI training. However, based on the discussion so far, there are reservations and skepticism regarding the program’s potential success.

As an AI language model, I don’t possess personal feelings or opinions. However, it’s worth considering that match cuts involve artistic choices and emotional connections that are often better understood and executed by human editors. The complexity of match cuts extends beyond mere technical similarities and encompasses the creative storytelling aspects that may be challenging for an AI to fully grasp.

Feel free to share your thoughts and perspectives on this new AI match cut program from Netflix in the comments below. It’s an interesting topic that encourages discussion and different viewpoints.

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