It starts with a familiar intro, unmistakably the Weeknd's 2017 hit "Die for You." But as the first verse of the song begins, a different vocalist is heard: Michael Jackson. It's just one example of how artificial intelligence is seeping into the music industry. Surf YouTube or TikTok and you'll find many convincing AI-made covers. But there are also tools that can generate instrumentals from text, give people a starting beat or inspiration, and help them to edit tunes.

My commute to work every day is roughly one hour ( /- 15 minutes depending on the day). It's safe to say I cruise through A LOT of podcasts. The subjects I listen to range from True Crime, NFL Fantasy Football, Major League Baseball, and Data Science. This is my personal ranking/list of the best data science podcasts on SoundCloud, Apple Podcast, and Spotify. I found the descriptions of each podcast to be pretty true to what I would have written myself, which is why you won't see a whole lot of my own writing in the descriptions (why reinvent the wheel?).

The graph represents a network of 1,552 Twitter users whose tweets in the requested range contained "InsurTech", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 24 December 2021 at 13:11 UTC. The requested start date was Friday, 24 December 2021 at 01:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 6-day, 7-hour, 49-minute period from Friday, 17 December 2021 at 17:10 UTC to Friday, 24 December 2021 at 01:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

Previously, I developed a framework for identifying sellers of illicit narcotics advertising on Soundcloud.com. This framework scraped comments and identified comments that were advertising drugs through a simple keyword search. While this framework did well due to the similar structure of the comments, I wanted to try to improve this framework by using Latent Dirichlet Allocation. Latent Dirichlet Allocation (LDA) is a natural language processing (NLP) model for learning abstract topics of text, also known as topic modeling. LDA will cluster documents into topics, allowing us to classify comments and find comments that are advertising the sale of narcotics.

In 2014, music fans and critics began paying close attention to a mysterious group of artists who'd started releasing tracks online. They were part of PC Music, a loose electronic-music collective that functioned more like a conceptual-art project. Led by a young, inventive producer from London named A. G. Cook, PC Music, and its affiliates, rejected a dark, murky strain of underground electronic music that was beloved at the time. Instead, they latched onto the most exuberant and absurd elements of pop, making cutesy, theatrical songs that sounded a bit like children's music, but with an unsettling aftertaste. If mainstream pop is designed to make people feel as if they're on common ground with all of humanity, this music made listeners feel like they were in on a very specific joke.

At AWS, we love sharing our passion for technology and innovation, and AWS DeepComposer is no exception. This service is designed to help everyone learn about generative artificial intelligence (AI) through the language of music. You can use a sample melody, upload your own melody, or play a tune using the virtual or a real keyboard. Best of all, you don't have to write any code. But what exactly is generative AI, and why is it useful?

That is a really hard thing to do. So we banded together all our YouTube friends and started #teamtrees. Basically we partnered with Arbor Day and got them to agree that for every $1 we raise, they would plant 1 tree.

This is Algorunner coming at you with my very first YouTube video! In this video, we'll be going over some very basic functionality in the Tensorflow frame work. We build a Linear Regression Model optimized by Tensorflow's Gradient Decent optimizer to minimize the Mean Square Error. In this channel, I'll be focusing a lot on Artificial Intelligence theory and applications in the world of Machine Learning and Deep Learning. This is my first video so if you have any constructive criticism, please feel free to comment.

In our recent article on Indonesia's Big "Big Data" Problem, we looked at how technologies like blockchain and artificial intelligence (AI) are being used to uncover new sets of data that corporations can use to better understand the world's fourth biggest country by population. A consistent theme throughout the time we spent talking to local tech firms was that great potential was simply waiting to be unlocked, and that probably holds true at a smaller scale for many developed market corporations. For example, think about something like "data entry." The mere fact that you require a human to take data from FORM A and then manually input that data into FORM B means that your archaic business processes need changing. This is the equivalent of those backwards companies that ask you to fax them a form in order to request a change of address for your account.

Dez Blanchfield speaks with business leaders about artificial intelligence and deep learning adoption in the "From Here to AI" podcast series from IBM Power Systems. When you start to investigate artificial intelligence (AI), or branch out to buy a couple AI servers to tinker with for your organization, the process of implementing a full AI solution can seem daunting. With the help of four business executives and AI leaders and digital transformation expert and avid podcaster Dez Blanchfield, we set out to outline the natural progression of implementing AI in the data center. No matter what stage of the journey you are on, these podcasts should help you get "from here to AI." Below is a quick overview of each session. We've posted them as a series so you can binge-listen if you have the time, or you can tee them up separately to plug into the ones that interest you most.