Arm aims to take machine learning to mainstream and low-end devices with the launch of its new neural processing units (NPUs). The company is unveiling the Ethos-N57 and Ethos-N37 NPUs, which it will license to chipmakers who can integrate it into their products. The idea is to extend the range of Arm machine learning (ML) processors to enable artificial intelligence (AI) applications in mainstream devices. The company also unveiled the Mali-G57 graphics processing unit (GPU). This is the first mainstream Valhall architecture-based GPU, delivering 1.3 times better performance over previous generations.

Check out the full schedule at the Strata Data Conference in New York, September 11-13, 2018. Interest in machine learning (ML) has been growing steadily, and many companies and organizations are aware of the potential impact these tools and technologies can have on their underlying operations and processes. The reality is that we are still in the early phases of adoption, and a majority of companies have yet to deploy ML across their operations. In this post, I'll describe how one can go from "exploration and evaluation" to actual "implementation" of ML technologies. Along the way, I'll highlight key sections of the upcoming Strata Data conference in New York this September.

The demand for talent that can work on artificial intelligence and its applications is set to spike as it becomes pervasive in industries ranging from automotive to retail. Tech giants are fighting over AI talent, often paying anywhere between $300,000- $500,000 for their skills. It's still early days but in Bengaluru, which led the charge during the outsourcing boom, some early moves are being made to ready talent for the fourth industrial revolution-- one where machines and software meld into one. Can India supply that talent? Last week we wrote about how the country is moving to address the AI talent supply gap.

Amper music uses a proprietary mix of methods to model the fundamentals of music and replicate them. The process can involve neural networks, the types of deep learning models that are fueling much of today's AI renaissance. But, just as often, the team uses other methods that were not specified in the interview. Ingraham said neural networks are good at divining some tasks in the creative process, but not others. This is because they often obscure larger context in favor of specific probabilities.

H2O.ai and Nvidia today announced that they have partnered to take machine learning and deep learning algorithms to the enterprise through deals with Nvidia's graphics processing units (GPUs). Mountain View, Calif.-based H20.ai has created AI software that enables customers to train machine learning and deep learning models up to 75 times faster than conventional central processing unit (CPU) solutions. The company made the announcement at Nvidia's GPU Tech event in San Jose, Calif. H2O.ai will offer its machine learning algorithms in a newly minted GPU-edition and its Deep Water product on Nvidia GPUs. In addition, H2O.ai's platform will now be optimized for the Nvidia's DGX-1 AI processor.

Google made its Cloud Machine Learning platform, which is used by Google Photos, Translate, and Inbox, available to developers today. During the announcement at NEXT 2016, Google's Cloud Platform conference, Alphabet Chairman Eric Schmidt called machine learning "what's next," according to TechCrunch. Machine learning powers features like the Google app's speech recognition and the smart reply feature in the Inbox app, and Google sees the technology as the future of computing. "Cloud Machine Learning will take machine learning mainstream, giving data scientists and developers a way to build a new class of intelligent applications," Fausto Ibarra, Google's director of product management wrote in a blog post. "It provides access to the same technologies that power Google Now, Google Photos, and voice recognition in Google Search as easy to use REST APIs."