Software : Cloud Computing : General
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TELUS: Solving for workers’ safety with edge computing and 5G
Together with Google Cloud, we have been leveraging solutions with the power of MEC and 5G to develop a workers’ safety application in our Edmonton Data Center that enables on-premise video analytics cameras to screen manufacturing facilities and ensure compliance with safety requirements to operate heavy-duty machinery. The CCTV (closed-circuit television) cameras we used are cost-effective and easier to deploy than RTLS (real time location services) solutions that detect worker proximity and avoid collisions. This is a positive, proactive step to steadily improve workplace safety. For example, if a worker’s hand is close to a drill, that drill press will not bore holes in any surface until the video analytics camera detects that the worker’s hand has been removed from the safety zone area.
Introducing new Google Cloud manufacturing solutions: smart factories, smarter workers
The new manufacturing solutions from Google Cloud give manufacturing engineers and plant managers access to unified and contextualized data from across their disparate assets and processes.
Manufacturing Data Engine is the foundational cloud solution to process, contextualize and store factory data. The cloud platform can acquire data from any type of machine, supporting a wide range of data, from telemetry to image data, via a private, secure, and low cost connection between edge and cloud. With built-in data normalization and context-enrichment capabilities, it provides a common data model, with a factory-optimized data lakehouse for storage.
Manufacturing Connect is the factory edge platform co-developed with Litmus Automation that quickly connects with nearly any manufacturing asset via an extensive library of 250-plus machine protocols. It translates machine data into a digestible dataset and sends it to the Manufacturing Data Engine for processing, contextualization and storage. By supporting containerized workloads, it allows manufacturers to run low-latency data visualization, analytics and ML capabilities directly on the edge.
Price optimization notebook for apparel retail using Google Vertex AI
One of the key requirements of a price optimization system is an accurate forecasting model to quickly simulate demand response to price changes. Historically, developing a Machine Learning forecast model required a long timeline with heavy involvement from skilled specialists in data engineering, data science, and MLOps. The teams needed to perform a variety of tasks in feature engineering, model architecture selection, hyperparameter optimization, and then manage and monitor deployed models.
Vertex AI Forecast provides advanced AutoML workflow for time series forecasting which helps dramatically reduce the engineering and research effort required to develop accurate forecasting models. The service easily scales up to large datasets with over 100 million rows and 1000 columns, covering years of data for thousands of products with hundreds of possible demand drivers. Most importantly it produces highly accurate forecasts. The model scored in the top 2.5% of submissions in M5, the most recent global forecasting competition which used data from Walmart.
RightHand Robotics raises $23 million from Menlo Ventures, Google
RightHand Robotics, a leader in data-driven, autonomous robotic picking solutions for order fulfillment, announces today that it has secured $66 million in a Series C financing led by top growth investors: Safar Partners, a technology venture fund; Thomas H. Lee Partners L.P. (“THL”), a leading investor in automation and supply chain; and SoftBank Vision Fund 2, which is part of the SoftBank Group. Zebra Technologies, Epson and Global Brain also join this round, along with previous investors GV, F-Prime Capital, Menlo Ventures, Matrix Partners and Tony Fadell’s Future Shape. Previous rounds were led by Menlo Ventures and Playground Global.
RightHand Robotics intends to use the funds to accelerate product and business development, while scaling its global presence and partner network. The company will also expand its offices and invest in talent acquisition to support overall growth plans.
Robust Routing Using Electrical Flows
We view the road network as a graph, where intersections are nodes and roads are edges. Our method then models the graph as an electrical circuit by replacing the edges with resistors, whose resistances equal the road traversal time, and then connecting a battery to the origin and destination, which results in electrical current between those two points. In this analogy, the resistance models how time-consuming it is to traverse a segment. In this sense, long and congested segments have high resistances.
Improving PPA In Complex Designs With AI
The goal of chip design always has been to optimize power, performance, and area (PPA), but results can vary greatly even with the best tools and highly experienced engineering teams. AI works best in design when the problem is clearly defined in a way that AI can understand. So an IC designer must first see if there is a problem that can be tied to a system’s ability to adapt to, learn, and generalize knowledge/rules, and then apply these knowledge/rules to an unfamiliar scenario.
Can Robots Follow Instructions for New Tasks?
The results of this research show that simple imitation learning approaches can be scaled in a way that enables zero-shot generalization to new tasks. That is, it shows one of the first indications of robots being able to successfully carry out behaviors that were not in the training data. Interestingly, language embeddings pre-trained on ungrounded language corpora make for excellent task conditioners. We demonstrated that natural language models can not only provide a flexible input interface to robots, but that pretrained language representations actually confer new generalization capabilities to the downstream policy, such as composing unseen object pairs together.
In the course of building this system, we confirmed that periodic human interventions are a simple but important technique for achieving good performance. While there is a substantial amount of work to be done in the future, we believe that the zero-shot generalization capabilities of BC-Z are an important advancement towards increasing the generality of robotic learning systems and allowing people to command robots. We have released the teleoperated demonstrations used to train the policy in this paper, which we hope will provide researchers with a valuable resource for future multi-task robotic learning research.
Inside X’s Mission to Make Robots Boring
It’s research by Everyday Robots, a project of X, Alphabet’s self-styled “moonshot factory.” The cafe testing ground is one of dozens on the Google campus in Mountain View, California, where a small percentage of the company’s massive workforce has now returned to work. The project hopes to make robots useful, operating in the wild instead of controlled environments like factories. After years of development, Everyday Robots is finally sending its robots into the world—or at least out of the X headquarters building—to do actual work.
Chip floorplanning with deep reinforcement learning
AWS, Google, Microsoft apply expertise in data, software to manufacturing
As manufacturing becomes digitized, Google’s methodologies that were developed for the consumer market are becoming relevant for industry, said Wee, who previously worked in the semiconductor industry as an industrial engineer. “We believe we’re at a point in time where these technologies—primarily the analytics and AI area—that have been very difficult to use for the typical industrial engineer are becoming so easy to use on the shop floor,” he said. “That’s where we believe our competitive differentiation lies.”
Meanwhile, Ford is also selectively favoring human brain power over software to analyze data and turning more and more to in-house coders than applications vendors. “The solution will be dependent upon the application,” Mikula said. “Sometimes it will be software, and sometimes it’ll be a data analyst who crunches the data sources. We would like to move to solutions that are more autonomous and driven by machine learning and artificial intelligence. The goal is to be less reliant on purchased SaaS.”
Intrinsic is working to unlock the creative and economic potential of industrial robotics for millions more businesses, entrepreneurs, and developers. We’re developing software tools designed to make industrial robots (which are used to make everything from solar panels to cars) easier to use, less costly and more flexible, so that more people can use them to make new products, businesses and services.
Visual Inspection AI: a purpose-built solution for faster, more accurate quality control
The Google Cloud Visual Inspection AI solution automates visual inspection tasks using a set of AI and computer vision technologies that enable manufacturers to transform quality control processes by automatically detecting product defects.
We built Visual Inspection AI to meet the needs of quality, test, manufacturing, and process engineers who are experts in their domain, but not in AI. By combining ease of use with a focus on priority uses cases, customers are realizing significant benefits compared to general purpose machine learning (ML) approaches.
Toward Generalized Sim-to-Real Transfer for Robot Learning
A limitation for their use in sim-to-real transfer, however, is that because GANs translate images at the pixel-level, multi-pixel features or structures that are necessary for robot task learning may be arbitrarily modified or even removed.
To address the above limitation, and in collaboration with the Everyday Robot Project at X, we introduce two works, RL-CycleGAN and RetinaGAN, that train GANs with robot-specific consistencies — so that they do not arbitrarily modify visual features that are specifically necessary for robot task learning — and thus bridge the visual discrepancy between sim and real.
Learning to Manipulate Deformable Objects
In “Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networks,” to appear at ICRA 2021, we release an open-source simulated benchmark, called DeformableRavens, with the goal of accelerating research into deformable object manipulation. DeformableRavens features 12 tasks that involve manipulating cables, fabrics, and bags and includes a set of model architectures for manipulating deformable objects towards desired goal configurations, specified with images. These architectures enable a robot to rearrange cables to match a target shape, to smooth a fabric to a target zone, and to insert an item in a bag. To our knowledge, this is the first simulator that includes a task in which a robot must use a bag to contain other items, which presents key challenges in enabling a robot to learn more complex relative spatial relations.
Google Cloud and Seagate: Transforming hard-disk drive maintenance with predictive ML
At Google Cloud, we know first-hand how critical it is to manage HDDs in operations and preemptively identify potential failures. We are responsible for running some of the largest data centers in the world—any misses in identifying these failures at the right time can potentially cause serious outages across our many products and services. In the past, when a disk was flagged for a problem, the main option was to repair the problem on site using software. But this procedure was expensive and time-consuming. It required draining the data from the drive, isolating the drive, running diagnostics, and then re-introducing it to traffic.
That’s why we teamed up with Seagate, our HDD original equipment manufacturer (OEM) partner for Google’s data centers, to find a way to predict frequent HDD problems. Together, we developed a machine learning (ML) system, built on top of Google Cloud, to forecast the probability of a recurring failing disk—a disk that fails or has experienced three or more problems in 30 days.
Multi-Task Robotic Reinforcement Learning at Scale
For general-purpose robots to be most useful, they would need to be able to perform a range of tasks, such as cleaning, maintenance and delivery. But training even a single task (e.g., grasping) using offline reinforcement learning (RL), a trial and error learning method where the agent uses training previously collected data, can take thousands of robot-hours, in addition to the significant engineering needed to enable autonomous operation of a large-scale robotic system. Thus, the computational costs of building general-purpose everyday robots using current robot learning methods becomes prohibitive as the number of tasks grows.
Way beyond AlphaZero: Berkeley and Google work shows robotics may be the deepest machine learning of all
With no well-specified rewards and state transitions that take place in a myriad of ways, training a robot via reinforcement learning represents perhaps the most complex arena for machine learning.
Rearranging the Visual World
Transporter Nets use a novel approach to 3D spatial understanding that avoids reliance on object-centric representations, making them general for vision-based manipulation but far more sample efficient than benchmarked end-to-end alternatives. As a consequence, they are fast and practical to train on real robots. We are also releasing an accompanying open-source implementation of Transporter Nets together with Ravens, our new simulated benchmark suite of ten vision-based manipulation tasks.
Edge-Inference Architectures Proliferate
What makes one AI system better than another depends on a lot of different factors, including some that aren’t entirely clear.
The new offerings exhibit a wide range of structure, technology, and optimization goals. All must be gentle on power, but some target wired devices while others target battery-powered devices, giving different power/performance targets. While no single architecture is expected to solve every problem, the industry is in a phase of proliferation, not consolidation. It will be a while before the dust settles on the preferred architectures.
RightHand Robotics raises $23 million from Menlo Ventures, Google
With its reinforced bank account, Somerville, Mass.-based RightHand plans to expand its business and technical teams and broaden its suite of product applications, the firm said. “The funds will be used to support our growth and in hiring people as fast as we effectively can,” Martinelli said. “We’re getting follow-on orders and we need to support those orders and extend the product line, both for projects in the U.S. and in Europe and Japan.”
Google Glass Didn't Disappear. You Can Find It On The Factory Floor
With Google Glass, she scans the serial number on the part she’s working on. This brings up manuals, photos or videos she may need. She can tap the side of headset or say “OK Glass” and use voice commands to leave notes for the next shift worker.
Peggy Gullick, business process improvement director with AGCO, says the addition of Google Glass has been “a total game changer.” Quality checks are now 20 percent faster, she says, and it’s also helpful for on-the-job training of new employees. Before this, workers used tablets.
Augmented Reality Is Already Improving Worker Performance
The video below, for example, shows a side-by-side time-lapse comparison of a GE technician wiring a wind turbine’s control box using the company’s current process, and then doing the same task while guided by line-of-sight instructions overlaid on the job by an AR headset. The device improved the worker’s performance by 34% on first use.
There’s been concern about machines replacing human workers, and certainly this is happening for some jobs. But the experience at General Electric and other industrial firms shows that, for many jobs, combinations of humans and machines outperform either working alone. Wearable augmented reality devices are especially powerful, as they deliver the right information at the right moment and in the ideal format, directly in workers’ line of sight, while leaving workers’ hands free so they can work without interruption. This dramatically reduces the time needed to complete a job because workers needn’t stop what they’re doing to flip through a paper manual or engage with a device or workstation. It also reduces errors because the AR display provides explicit guidance overlaid on the work being done, delivered on demand. Workers need only follow the detailed instructions directly in front of them in order to move through a sequence of steps to completion. If they encounter problems, they can launch training videos or connect by video with remote experts to share what they see through their smart glasses and get real-time assistance.