Roughly a year ago, Scale and NuTonomy released a driverless data set called NuScenes that they claimed at the time surpassed corpora like KITTI, Baidu's ApolloScape, and the Udacity Self-Driving Car library in size, scale, and accuracy. Since then, new and more diverse corpora like the Waymo Open Dataset, the Ford Autonomous Vehicle Dataset, and Lyft's autonomous vehicle data set have emerged, but Motional -- whose CEO founded NuTonomy -- is looking to take back the crown with the release of an expanded NuScenes. Data sets like NuScenes can be used to improve the robustness of self-driving cars in environments from cities to back roads. The Rand Corporation estimates that autonomous cars will have to rack up 11 billion miles before we'll have reliable statistics on their safety, but as headwinds slow real-world testing, simulated miles have become the next best thing. This expansion of NuScenes includes NuScenes-lidarseg, which improves the semantic segmentation of 1,000 Singapore and Boston scenes, making it one of the largest publicly available lidar segmentation data sets.

Most of the buzz around artificial intelligence (AI) centers on autonomous vehicles, chatbots, digital-twin technology, robotics, and the use of AI-based'smart' systems to extract business insight out of large data sets. But AI and machine learning (ML) will one day play an important role down among the server racks in the guts of the enterprise data center. AI's potential to boost data-center efficiency – and by extension improve the business – falls into four main categories: Put it all together and the vision is that AI can help enterprises create highly automated, secure, self-healing data centers that require little human intervention and run at high levels of efficiency and resiliency. "AI automation can scale to interpret data at levels beyond human capacity, gleaning imperative insights needed for optimizing energy use, distributing workloads and maximizing efficiency to achieve higher data-center asset utilization," explains Said Tabet, distinguished engineer in the global CTO office at Dell Technologies. Of course, much like the promise of self-driving cars, the self-driving data center isn't here yet.

Size and coverage: This release contains data from 1,000 driving segments. Such continuous footage gives researchers the opportunity to develop models to track and predict the behavior of other road users. Diverse driving environments: This dataset covers dense urban and suburban environments across Phoenix, AZ, Kirkland, WA, Mountain View, CA and San Francisco, CA capturing a wide spectrum of driving conditions (day and night, dawn and dusk, sun and rain). High-resolution, 360 view: Each segment contains sensor data from five high-resolution Waymo lidars and five front-and-side-facing cameras. Dense labeling: The dataset includes lidar frames and images with vehicles, pedestrians, cyclists, and signage carefully labeled, capturing a total of 12 million 3D labels and 1.2 million 2D labels.

The self-driving car world is a secretive one, where software, hardware, and testing methods are jealously guarded (and occasionally spark a major lawsuit). But this week, we got a glimpse into what these developers have actually been up to, thanks first to a newly released batch of "disengagement" reports every autonomous vehicle outfit testing in California provides to the state at the end of each year. The disengagement data isn't too helpful, but the reports do reveal a serious spike in would-be AV testing, among other tidbits. More intel comes from SoftBank's latest move in this space, a nearly $1 billion investment in AV startup Nuro. In non-robo news, we get a tour of all the tools and tricks that keep Nascar races racy, and bid adieu to the 380, Airbus' freakishly large passenger jet.

Self-driving cars are making headlines every day; the future being envisioned as a car that runs itself, maintains itself, sends alerts when help is needed, and prevents accidents. While opinions of a self-driving car vary from excitement about simplifying the daily commute to "no way would I ever put total control in the hands of a machine," the concept gives rise to thoughts about self-driving data centers. What would they look like and how would they change IT as we know it? Reports indicate that enterprises are losing $21.8 million per year on average in downtime and 87 percent expect this to increase1. For organizations that are trying to manage and optimize increasingly complex hybrid IT environments that span mainframe and multi-cloud infrastructures, could evolving to a self-driven data center provide the keys to driving smarter, faster IT operations and preventing downtime?