Using AI to create the album lost to music’s mental health crisis.
As long as there’s been popular music, musicians and crews have struggled with mental health at a rate far exceeding the general adult population. And this issue hasn’t just been ignored. It’s been romanticized, by things like the 27 Club—a group of musicians whose lives were all lost at just 27 years old.
To show the world what’s been lost to this mental health crisis, we’ve used artificial intelligence to create the album the 27 Club never had the chance to. Through this album, we’re encouraging more music industry insiders to get the mental health support they need, so they can continue making the music we all love for years to come.
Because even AI will never replace the real thing. Read More
Daily Archives: April 2, 2021
If Your Company Uses AI, It Needs an Institutional Review Board
Conversations around AI and ethics may have started as a preoccupation of activists and academics, but now — prompted by the increasing frequency of headlines of biased algorithms, black box models, and privacy violations — boards, C-suites, and data and AI leaders have realized it’s an issue for which they need a strategic approach.
A solution is hiding in plain sight. Other industries have already found ways to deal with complex ethical quandaries quickly, effectively, and in a way that can be easily replicated. Instead of trying to reinvent this process, companies need to adopt and customize one of health care’s greatest inventions: the Institutional Review Board, or IRB. Read More
Ensemble Learning: Bagging & Boosting
How to combine weak learners to build a stronger learner to reduce bias and variance in your ML model
The bias and variance tradeoff is one of the key concerns when working with machine learning algorithms. Fortunately there are some Ensemble Learning based techniques that machine learning practitioners can take advantage of in order to tackle the bias and variance tradeoff, these techniques are bagging and boosting. So, in this blog we are going to explain how bagging and boosting works, what theirs components are and how you can implement them in your ML problem. Read More
Google starts trialing its FLoC cookie alternative in Chrome
Google today announced that it is rolling out Federated Learning of Cohorts (FLoC), a crucial part of its Privacy Sandbox project for Chrome, as a developer origin trial.
FLoC is meant to be an alternative to the kind of cookies that advertising technology companies use today to track you across the web. Instead of a personally identifiable cookie, FLoC runs locally and analyzes your browsing behavior to group you into a cohort of like-minded people with similar interests (and doesn’t share your browsing history with Google). That cohort is specific enough to allow advertisers to do their thing and show you relevant ads, but without being so specific as to allow marketers to identify you personally.
This “interest-based advertising,” as Google likes to call it, allows you to hide within the crowd of users with similar interests. All the browser displays is a cohort ID and all your browsing history and other data stay locally. Read More
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