While some of this spectacular failure rate can be blamed on the relative newness of the technology, which often translates into a lack of skills with the specialized hardware and software necessary to deploy ML, that's only part of the story.

Imagine the added safety if semis are equipped with road marking recognition technology that can adjust a vehicle's steering if a tired operator fails to do so. Imagine a world where image recognition becomes more available and more efficient for organizations needing to harness the technology, helping to catch more bad guys. At the Embedded World 2019 Exhibition & Conference, February 26-28 in Nuremberg, Western Digital, in collaboration with Mipsology, will demonstrate an impressive, innovative new prototype in Booth #3A-429. Western Digital's small form factor (SFF) accelerator offers a highly optimized platform for inference at the edge of the data center, with performance and power optimized for the most common mid-range networks workloads. Able to quickly and dynamically process multiple in-bound neural networks at once, it has the potential to improve the intelligence and analytics of a wide range of deep learning and neural network-enabled applications, including smart surveillance and image classification and recognition.

With the mandate of fostering an ecosystem of applied machine learning startups, Google on Wednesday revealed the first four companies to join its Launchpad Studio and said this initial track is aimed squarely at healthcare and biotech. It--s no secret that Google and rivals Amazon, Apple, IBM and Microsoft are eyeing the $2.7 trillion healthcare market as fertile ground for technological disruption--though it appears Google is the first of the titans to formally establish a program for working with startups specific to the industry. The first four startups, Augmedix, BrainQ, Byteflies and Cytovale, get what Google deftly described as --equity-free support,-- and access to Google mentors, community engagement as well as datasets and testing environments for prototyping, as examples. Augmedix is working to minimize the time doctors spend on a computer during patient visits by leveraging Google Glass to automate scribing and collect audio, video and written notes then use natural language processing to help clinicians make sense of that information. Cantor described BrainQ as a research project concentrating on taking advances in neural networks and applying machine learning to signal processing to develop customized treatment protocols for people who cannot walk anymore, whether because of a stroke, spinal or brain injuries.

If reality TV isn't your thing, our new startup series may change your mind. No, you won't find any housemate drama here, but you'll get a taste of the startup life--the good, the bad, and the ugly--and a behind-the-scenes look into Microsoft's accelerators. Last month, we announced the 10 startups invited to join our Machine Learning Accelerator in Seattle. Now, we want to give you the opportunity to get to know them better. We've given each startup a GoPro camera to document their journey in the accelerator, and we've set up a "confessional booth" where team members can share all the trials and tribulations inherent to being an entrepreneur--and, of course, all the rewards, too.