Siraj Raval generates his own voice with AI using some cutting edge techniques.

This is a relatively new technology and people have started generating not just celebrity voices, but entire musical pieces as well. The technology to generate sounds, both voices & music, has been rapidly improving the past few years thanks to deep learning. In this episode, I’ll first demo some AI generated music. Then, i’ll explain the physics of a waveform and how DeepMind used waveform-based data to generate some pretty realistic sounds in 2016. At the end, I’ll describe the cutting edge of generative sound modeling, a paper released just 2 months ago called “MelNet”. Enjoy!

In this era of “Internet of Code”, data and metadata around open source projects are abundantly available.

Here’s an interesting talk by Microsoft Research on AI developing software itself.

While research in program synthesis is not new, deep learning systems that take advantage of large scale code as data is starting to show new promise in improving developer productivity. The availability of GPU machines and cloud-based distributed systems help build deeper networks and scale them to production systems. In addition to passive input from open repos, crowdsourcing software expertise and integrating this with software systems has shown positive results. AI promises assistance and automation in every aspect of software development from edit and build stage to test and deploy stage. What traditional compiler and run time systems did with rules and analyzers can be replaced with AI-driven algorithmic systems. The concept of Software 2.0 is being discussed where code appears as data and where traditional software development processes give way to AI-based systems. In this panel, we explore opportunities for research and technology to improve productivity in software engineering and how AI plays a role in it.

This episode of Data Exposed explores the new centralized experience brings together SQL Server offerings inside of Azure, including databases, managed instances, and SQL VMs.

We now have a centralized location for discovering, creating, and managing all of these resources as well as guidance to help you select and create the right resource for your needs.

ML.NET allows .NET developers to easily build and also consume machine learning models in their NET applications.

In this episode, Bri Achtman joins Rich to show off some really interesting scenarios that ML.NET and its family of tools enables. They talk about training models, AutoML, the ML.NET CLI, and even a Visual Studio Extension for training models!

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In this episode, learn how the Anomaly Detection service comes to your on-premises systems via containers.

By deploying the same API service close to your data in containers, now you don’t have to worry about situations when you have to keep the data on-premises to follow regulation, or to deal with network latency, or just want to reuse the same application powered-by Anomaly Detector across both the cloud and on-premise.

Azure Data Box Edge is as a server class target for Azure IoT Edge.

Sometimes people want to run more heavy-weight workloads via IoT Edge than fit on traditional gateways. Data Box Edge offers a server-class machine to do so.

During this episode of the IoT Show, get introduced the device capabilities and show a short demo of how simple it is to setup and configure from the Cloud.

Learn more HERE