AI News, Artificial intelligence: Cheat sheet artificial intelligence

This AI Perfectly Imitated Joe Rogan’s Voice And It’s Kinda Creepy

When it comes to artificial intelligence, we typically think about things like automation, image manipulation, machine learning, or even just science fiction.

And honestly, it’s a very convincing facsimile of Rogan’s voice.  In response to the deepfake, Rogan wrote on Instagram, “I just listened to an AI generated audio recording of me talking about chimp hockey teams and it’s terrifyingly accurate.

At this point I’ve long ago left enough content out there that they could basically have me saying anything they want, so my position is to shrug my shoulders and shake my head in awe, and just accept it.

The future is gonna be really f*cking weird, kids.” As deepfake technology improves, society will have to grapple with the implications of being able to fabricate convincing voice and video.

Joe Rogan’s comment regarding the future is truer than ever.  Dessa wrote of their unveiling on Medium, “It’s surreal for our engineers to be able to say they’ve legitimately created a life-like replica of Joe Rogan’s voice using AI.

Some cons included spammers or fraudsters impersonating a loved one to retrieve sensitive information, entrance into high-security areas, and political framing or propaganda.  However, pros include improved accessibility for those who use text-to-speech tools to communicate, for example those with Lou Gherig’s disease.

So in addition to raising awareness and acknowledging these issues, we also want to show this work as a way of starting a conversation on speech synthesis that must be had.” Fan reactions ranged from impressed to concerned.

Machine Learning Algorithm Cheat Sheet for Azure Machine Learning designer

The Azure Machine Learning Algorithm Cheat Sheet helps you choose the right algorithm for a predictive analytics model.

Azure Machine Learning has a large library of algorithms from the classification, recommender systems, clustering, anomaly detection, regression and text analytics families.

There are three main categories of machine learning: supervised learning, unsupervised learning, and reinforcement learning.

The goal of supervised learning is to study many labeled examples like these, and then to be able to make predictions about future data points.

For example, identifying new photos with the correct animal or assigning accurate sale prices to other used cars.

This can mean grouping it into clusters, as K-means does, or finding different ways of looking at complex data so that it appears simpler.

It is a common approach in robotics, where the set of sensor readings at one point in time is a data point, and the algorithm must choose the robot’s next action.

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