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- 🤖 The End of Music?
Good morning and welcome to the latest edition of neonpulse!
Today’s issue is about two things: the ability to detect hit songs with 97% accuracy, and the ablity to detect deepfakes with 99.6% accuracy. Let’s get started!
The End of Music?
AI's applications continue to grow every single day, and now it’s starting to be really good at reading our minds. The newest development? It can predict we’ll like something or not.
Researchers can now predict hit songs using a machine learning model that analyzes neural responses, achieving an impressive 97% accuracy rate. The innovative approach is called 'neuroforecasting'.
To develop the neuroforecasting method, participants listened to a set of 24 songs while their neurophysiological responses were monitored. By applying machine learning to these responses, the researchers could predict hit songs with remarkable accuracy, surpassing the previous 50% accuracy rate of traditional methods.
The study revealed that the neurophysiological responses to the first minute of songs played a crucial role in determining their potential popularity, with an 82% success rate in predicting hits.
Although the research has some limitations, such as the relatively small number of songs analyzed and moderately diverse participant demographics, the researchers believe this method could be applicable beyond song identification, potentially extending to predicting hits in movies and TV shows.
The neuroforecasting approach holds promise for the future, where wearable neuroscience technologies could send tailored entertainment options to individuals based on their neurophysiology. Instead of overwhelming listeners with numerous choices, this method could offer them a more curated selection of music that they are likely to enjoy.
This breakthrough in neuroforecasting potentially opens up new possibilities for basically every form of entertainment. But the question is: will a hit still be a hit if we know it’s supposed to be?
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Detecting Deepfakes with 99.6% Accuracy
Last week, we discussed how spotting deepfakes is becoming increasingly difficult, which is dangerous. But guess what? There's a recent breakthrough that could change things.
LinkedIn and UC Berkeley have collaborated on a study to improve the detection of deepfakes. Their new method can accurately identify artificially generated profile pictures 99.6% of the time, with only a 1% misidentification rate for genuine pictures.
There are two types of forensic methods used to investigate this issue.
The hypothesis-based methods can detect abnormalities in synthetic faces by learning noticeable semantic outliers. However, learning-capable synthesis engines already possess these features, making it challenging to identify deepfakes.
On the other hand, data-driven methods, like machine learning, can differentiate between natural and computer-generated faces. However, when presented with unfamiliar images, trained systems may struggle with classification.
They used five distinct synthesis engines to create 41,500 synthetic faces and supplemented the training with 100,000 real LinkedIn profile pictures.
While the progress in detecting deepfake profile pictures is undoubtedly positive, it's important to remember that there are much more complex forms of deepfakes beyond simple headshots. But, this development undeniably marks the beginning of advancements that can protect us from the dangers of deepfakes while embracing their creative potential.
Cool AI Tools
🔗 Puzzicle: Turn YouTube videos into lessons for students.
🔗 Image Creator for PS: Free AI-powered Photoshop plugin.
🔗 CustomGPT: Let’s anyone build their own ChatGPT plugins in just minutes.
And now your moment of zen
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More: The Simpsons by Rembrandt.
That’s all for today folks!
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