Boomy launches new AI music technology for creators
Boomy has launched as an AI-powered technology permitting anyone to create and share original music. It is the first platform that makes it possible for users to both create and distribute their songs across all major streaming and social channels, including Spotify, Apple Music, TikTok, YouTube, Instagram, and others. Creators who use the platform also earn 80% of the share of royalties every time their song is streamed.
The all-in-one platform leverages AI technologies to aid the creativity of music creators. But rather than AI creating the music, Boomy users collaborate with the technology to create, compose and edit songs. Allowing users to create completely original songs with their own compositions and even vocals.
"In this moment of incredible growth for the music industry, the vast majority of people are still left out of music-making. We've recognised that the time, education, and financial resources required to create original music lie at the root of many of the music industry's inequities," said Alex Mitchell, co-founder and CEO of Boomy. “With Boomy, we've used technology to break down all those barriers. Now, we're seeing people all over the world create instant songs with Boomy, release them, and even earn royalty share income. For the first time, musical expression is available to an entirely new type of creator and audience."
More than two million songs have already been created by Beta users and songs created on Boomy have been streamed millions of times.
What is Boomy and how does it work?
Boomy was founded in 2018 by Alex Mitchell and Matthew Cohen Santorelli, and expands on collaborative AI tools that have been used for decades by artists to generate lyrics, and mixing tools used by DJs and professional producers.
Boomy’s algorithms define the characteristics of different musical genres, such as Hip Hop, or Reggae, and also leverages ML to continuously improve song quality and personalisation as users create music. After a user creates a song, they can choose to release their music to more than 40 streaming platforms and begin earning a royalty share right away.
Facebook Develops AI to Crackdown on Deepfakes
In light of the large tidal wave of increasingly believable deepfake images and videos that have been hitting the feeds of every major social media and news outlet in recent years, global organisations have started to consider the risk factor behind them. While the majority of deepfakes are created purely for amusement, their increasing sophistication is leading to a very simple question: What happens when a deepfake is produced not for amusement, but for malicious intent on a grander scale?
Yesterday, Facebook revealed that it was also concerned by that very question and that it had decided to take a stand against deepfakes. In partnership with Michigan State University, the social media giant presented “a research method of detecting and attributing deepfakes that relies on reverse engineering from a single AI-generated image to the generative model used to produce it.”
The promise is that Facebook’s method will facilitate deepfake detection and tracing in real-world settings, where the deepfake image itself is often the only information detectors have to work with.
Why Reverse Engineering?
Right now, researchers identify deepfakes through two primary methods: detection, which distinguishes between real and deepfake images, and image attribution, which identifies whether the image was generated using one of the AI’s training models. But generative photo techniques have advanced in scale and sophistication over the past few years, and the old strategies are no longer sufficient.
First, there are only so many images presented in AI training. If the deepfake was generated by an unknown, alternative model, even artificial intelligence won’t be able to spot it—at least, until now. Reverse engineering, common practice in machine learning (ML), can uncover unique patterns left by the generating model, regardless of whether it was included in the AI’s training set. This helps discover coordinated deepfake attacks or other instances in which multiple deepfakes come from the same source.
How It Works
Before we could use deep learning to generate images, criminals and other ill-intentioned actors had a limited amount of options. Cameras only had so many tools at their disposal, and most researchers could easily identify certain makes and models. But deep learning has ushered in an age of endless options, and as a result, it’s grown increasingly difficult to identify deepfakes.
To counteract this, Facebook ran deepfakes through a fingerprint estimation network (FEN) to estimate some of their details. Fingerprints are essentially patterns left on an image due to manufacturing imperfections, and they help identify where the image came from. By evaluating the fingerprint magnitude, repetition frequency, and symmetrical frequency, Facebook then applied those constraints to predict the model’s hyperparameters.
What are hyperparameters? If you imagine a generative model as a car, hyperparameters are similar to the engine components: certain properties that distinguish your fancy automobile from others on the market. ‘Our reverse engineering technique is somewhat like recognising [the engine] components of a car based on how it sounds’, Facebook explained, ‘even if this is a new car we’ve never heard of before’.
What Did They Find?
‘On standard benchmarks, we get state-of-the-art results’, said Facebook research lead Tal Hassner. Facebook added that the fingerprint estimation network (FEN) method can be used for not only model parsing, but detection and image attribution. While this research is the first of its kind, making it difficult to assess the results, the future looks promising.
Facebook’s AI will introduce model parsing for real-world applications, increasing our understanding of deepfake detection. As cybersecurity attacks proliferate, and generative AI falls into the hands of those who would do us harm, this method could help the ‘good guys’ stay one step ahead. As Hassner explained: ‘This is a cat-and-mouse game, and it continues to be a cat-and-mouse game’.