Every tech vendor these days is quick to slap the AI label on products and services. Up until today, I thought Microsoft had done an admirable job in refraining from doing this with Windows. But the shark has been jumped as of March 7, the company's latest Windows Developer Day. Microsoft is telling developers that the next release of Windows 10, which we are still calling by its codename, "Redstone 4," will enable developers to "use AI to deliver more powerful and engaging experiences." Microsoft execs say there's now an AI platform in Windows 10 that enables developers to use "pre-trained machine learning in their apps on Windows 10 devices."
Microsoft has released the Embedded Learning Library, offering developers a pre-trained image recognition model for Raspberry Pi and other developer boards. The early preview of Embedded Learning Library (ELL), now available on GitHub, is part of Microsoft's effort to miniaturize its machine-learning software for a range of extremely low-powered chips on devices that aren't connected to the cloud. As the company explains in a blogpost, a team at the Microsoft Research lab is working on compressing its machine learning models to work on the Cortex-M0, an ARM processor no bigger than breadcrumb. The aim is to to push machine learning to devices that aren't connected to the internet, such as brain implants. Microsoft's new art feature for its Pix iPhone photo app uses AI on the device, but the plan is to enable it on much less powerful chips, such as a brain implant, which might need to work without a network connection.
The complexity of artificial intelligence (AI) machine learning requires specialized knowledge and experience. Microsoft hopes to change that. Today Microsoft provided a general preview of Lobe, a software application available at no charge that enables anyone to build machine learning models--no technical skills required. Recent trends such as decentralized cloud computing, adaptation of GPU for general computing, increasing availability of big data sets, and advances in deep learning, a subset of AI machine learning, has spurred a modern-day AI gold rush. Global investment in AI in just half a decade has soared across sectors and geographies.
Most of them sold as end-to-end platform for data science and machine learning where you can build and deploy models quickly and manage your ML workflows at scale. What all that today's AI platforms and applications are missing, beside of being over specialized, from video games to object, speech, face, or sentiment recognition, the real intelligence.
Microsoft CEO Satya Nadella keeps saying that Microsoft's Azure cloud platform makes it easier for firms to exploit machine learning (ML). But how far is this marketing message borne out by the services available on Azure? Azure's suite of machine-learning offerings is fairly comprehensive, targeting everything from companies seeking simple, on-demand services through to those looking to train their own models using in-house data scientists. Every platform-as-a-service (PaaS) machine learning-related product and service that Microsoft offers is part of the Cortana Intelligence Suite. This bundles Microsoft's analytics and ML-focused offerings with Microsoft cloud-based data stores, capable of holding the vast amount of data needed to train machine learning models.