AI

Suffolk County, NY, and Dynamic Infrastructure to maintain dozens of bridges through AI

Posted on by Techtime

The New York, Berlin and Tel Aviv based startup Dynamic Infrastructure is expanding its pilot project with the Public Works Department of New York’s Suffolk County using the world’s first deep-learning solution which allows bridge and tunnel owners and operators to obtain visual diagnosis of assets they manage in order to reduce direct and indirect maintenance costs. After the successful completion of a pilot involving one bridge, the parties have agreed to expand the use of the AI-based technology to 74 bridges in the county located on the eastern end of Long Island. Deployment of the technology is currently in process.

Dynamic Infrastructure is currently conducting projects in other states in the U.S. as well as in Germany, Switzerland, Greece, and Israel with private and public transportation bodies. The company’s clients operate a total of 30,000 assets, ranging from national, state, regional and municipal departments of transportation to Public-Private Partnerships (PPPs) and private companies.

“The latest project expansion aims to use our technology to cover the entire inventory by Q2 2021,” said Saar Dickman, Co-founder, and CEO of Dynamic Infrastructure. He added that Suffolk County is typical of the situation in the US at large, where data from the Federal Highway Administration deemed that approximately 30% of all bridges in the US were in fair or poor condition.

AI-based bridge maintenance

According to the American Society of Civil Engineers, which evaluates and publishes a report card on the U.S. infrastructure every four years, the country’s infrastructure was given a D+ grade and more than 56,000 bridges were classified as being “structurally deficient”.

The aim in the deployment in Suffolk County is to enable its Public Works Department to better coordinate and make the right decisions by prioritizing maintenance of its infrastructure assets. “The system allows any operator, inspector or maintenance engineer to have actionable intelligence at their fingertips in order to decide if, when and how the daily maintenance and  maintenance projects should be conducted, by supplying instant alerts about anomalies,” said Kevin Reigrut, member of Dynamic Infrastructure’s board of advisors and former executive director of the Maryland Transportation Authority.

The use of the novel technology translates into a huge annual savings for the Owners, and Operation and Maintenance engineers, and contractors. Dynamic Infrastructure’s AI-based, decision making, SaaS product continuously processes past and current inspection reports and visuals, identifying future maintenance risks and evolving defects. The proprietary technology provides live, cloud-based, risk analysis of any bridge or tunnel and automatically alerts when changes are detected in maintenance and operating conditions—before they develop into large-scale failures.

The platform creates a “visual medical record” for each asset, based on existing images taken from past and current inspection reports and interim inspections. The visual analysis is being done to any visual source, be it smartphones, drones, and laser scanning. The images are compared and serve as the basis for alerts on changes in maintenance conditions. They can be easily accessed through a simple browser and instantly shared with peers and contractors to speed maintenance workflows and improve budget expenditure.

Dynamic Infrastructure harnesses the power of AI to disrupt Operation & Maintenance of critical transportation assets. Founded by industry professionals with decades of operation and maintenance experience for PPPs and DOTs, Dynamic Infrastructure has become an industry leader and key driver of a data revolution in decision-making processes related to bridge and tunnel Operations & Maintenance. Headquartered in New York, NY, with offices in Germany and Israel, Dynamic Infrastructure maintains a close relationship with its clients.

AI-based Visual Assistance Tool for Technicians

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TechSee from Tel aviv announced the completion of $30 million equity investment round co-led by OurCrowd, Salesforce Ventures, and TELUS Ventures. Founded in 2015,  the company has raised $54 million in funding to date. TechSee has developed a Computer Vision AI and Augmented Reality solution to assist makers and technicians in the “unboxing” and the installation of electrical and electronics products.

TechSee’s AI platform can automatically identify components, ports, cables, LED indicators, and more to detect issues and suggest resolutions, contact center agents, and field technicians. Via a simple tap of a screen, customers use their smartphone camera to show the virtual technician exactly what they see in their physical environment.

Using Deep Learning, the software (virtual technician) identify the product and visually guide the customer through the unboxing process using a suite of augmented reality tools, including Augmenting guidance for specific components, Tracking consumer and device movements to allow interactive guidance, Providing step-by-step instructions through the installation process and testing to verify the device works properly.

“Our vision is to get rid of the User Manual”

“There has been a significant increase in demand for contactless customer service technologies propelled by COVID-19 and the acceleration of digital transformation projects,” said Eitan Cohen, CEO of TechSee. “Our Visual Automation technology is at the heart of it. Our vision is to get rid of the user manual and replace it with dynamic AR assistants.”

TechSee recently announced a commercial partnership with Verizon to address this issue by bringing visual assistance to customers. It also established commercial partnerships with Vodafone, Orange, Liberty Global, Accenture, Hitachi, and Lavazza. The need to enhance the user’s product unboxing experience has brought many brands to showcase their product unboxing process using video.

In fact, YouTube reports an increase in product unboxing video views of 57% in one year, and an increase in uploads of more than 50%. These videos have more than a billion views annually. Google Consumer Survey underscores these statistics, with 20% of consumers (1 in 5) reporting that they’ve watched an unboxing video.

Hailo Challenges Google and Intel

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AI chipmaker Hailo announced the launch of its M.2 and Mini PCIe high-performance AI acceleration modules for empowering edge devices. Integrating the Hailo-8 processor, the modules can be plugged into a variety of edge devices. The modules provides high performance Deep Learning-based applications to edge devices. Hailo’s AI acceleration modules seamlessly integrate into standard frameworks, such as TensorFlow and ONNX, which are both supported by its Dataflow Compiler.

Hailo announced that a comparison between the Hailo-8 average Frames Per Second (FPS) with competitors across multiple standard NN benchmarks shows that Hailo’s AI modules achieve a FPS rate 26x higher than Intel’s Myriad-X modules and 13x higher than Google’s Edge TPU modules. The Hailo-8 M.2 module (photo above) is already integrated into the next generation of Foxconn’s BOXiedge with no redesign required for the PCB.

“Manufacturers across industries understand how crucial it is to integrate AI capabilities into their edge devices,” said Orr Danon, CEO of Hailo. “Simply put, solutions without AI can no longer compete.” The Hailo-8 AI modules are already being integrated by select customers worldwide. More information on the Hailo-8 M.2 and Mini PCIe AI modules can be found here.

Hailo-8 vs. Intel Myriad-X(1) and Google Edge TPU(2) Performance across common Neural Network benchmarks

Foxconn and Hailo to Launch Edge AI Computer

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Foxconn has combined its edge computing solution, BOXiedge, with the Japanese Socionext parallel processor SynQuacer” SC2A11, and the Hailo-8 deep learning processor developed by Tel Aviv based chipmaker Hailo. The new device aimed to provide powerful AI processing solution for video analytics at the edge. The new BOXiedge is capable of processing and analyzing over 20 streaming camera input feeds in real-time, all at the edge, including image classification, detection, pose estimation, and other AI-powered applications.

This is a milestone win for Hailo, a chipmaker startup who was established in 2017 and had completed a $60 million financing round in March 2020, with the participation ABB Technology Ventures (VC arm of ABB) and NEC Corporation. The funding will be used to enter mass production of the company’s Hailo-8 Deep Learning chip during 2020. Today the company employs approximately 80 employees.

The company’s Hailo-8 processor is a dedicated Neural Networks processor aimed to implement inference functions on edge devices in Automotive and Industrial applications. Hailo-8 processor reaches up to 26 Tera Operations Per Second (TOPS) and 3 TOPS per Watt. It is comprised of four main components: an Image Signal Processor to improve the image arriving from the sensor before its transfer for processing by the neural network core, an H.264 encoder that handles the video stream, an ARM-M4 processor to manages the chip, and a unique Neural Network core.

Orr Danon, CEO and Co-Founder of Hailo
Orr Danon, CEO and Co-Founder of Hailo

“Our vision is to pave the way for next generation AI solutions,” said Gene Liu, VP of Semiconductor Subgroup at Foxconn Technology Group. “We recognize the great potential in adopting AI solutions for a multitude of applications, such as tumor detection and robotic navigation. This platform will positively impact rapidly evolving sectors including smart cities, smart medical, smart retail, and industrial IoT.”

Foxconn has already deployed several in-house developed AI solutions on different production lines, leading to a reduction of at least one third of the operating costs for appearance defect inspection projects. “We are thrilled to collaborate with two of the global leaders in AI solutions,” said Orr Danon, CEO and Co-Founder of Hailo. “A new generation of chips means a new generation of capabilities at the edge.”

AI Chipmaker Hailo Raised $60 Million

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The Tel Aviv based chipmaker startup Hailo, has successfully completed a a $60 million financing round with the participation of key strategic investors ABB Technology Ventures (VC arm of ABB) and NEC Corporation. The funding will be used to enter mass production of the company’s processor Hailo-8 Deep Learning chip during 2020. Today the company employs approximately 80 employees.

Since its inception in February 2017, Hailo had raised $88 million. Following the last investment round, it begins to recruit 30-40 new employees for its research and development and support department, as well as offshore stuff for new offices in Europe, Japan and the US, to be opened in 2020. The company’s Hailo-8 processor is a dedicated Neural Networks processor aimed to implement inference functions on edge devices in Automotive and Industrial applications.

“We look forward to combining Hailo’s solution with our cutting-edge industrial technology as an important piece of the puzzle to drive the digital transformation of industries,” said Kurt Kaltenegger, Head of ABB Technology Ventures. Hiroto Sugahara, General Manager of Corporate Technology Division, NEC Corporation, said that Hailo’s technology will help NEC,  “to dive  deeper into the intelligent video analytics market. We look forward to incorporating Hailo’s technology into our next generation edge-based products.”

CEO, Orr Danon: A novel architecture to enable fast and power saving implementation of Neural Networks
CEO, Orr Danon: A novel architecture to enable fast and power saving implementation of Neural Networks

The Company’s co-founder and CEO, Orr Danon, told Techtime Hailo-8 chip presents a novel architecture to enable fast and power saving implementation of Neural Networks. “We identified that in during the processing of inference, there are differences in the behavior of the different layers in the neural network. Our solution provides the exact resources needed in each layer.

“In contrast, our competitors, who use solutions such as GPU processors, allocate to each and every layer the same level of resources. Our development software learns the specific problem of each application, characterizes it, and give the chip instructions on how to manage the resources of each layer in an optimal method.”

According to Hailo, its processor reaches up to 26 Tera Operations Per Second (TOPS) and 3 TOPS per Watt. It will meet the strict ISO 26262 ASIL-B as well as the AEC Q 100 Grade 2 standards. Hailo-8 is comprised of four main components: an Image Signal Processor to improves the image arriving from the sensor before its transfer for processing by the neural network core, an H.264 encoder that handles the video stream, an ARM-M4 processor to manages the chip, and the neural network core itself.

This neural network is comprised of a flexible matrix of software-configurable processing, controls, computational resources and memory units. “Hailo’s Deep Learning chip is a real game changer in industries such as automotive, industry 4.0, robotics, smart cities, and more,” said Hailo Chairman Zohar Zisapel, “A new age of AI chips means a new age of computing capabilities at the edge.”

Intel Acquires AI Chipmaker Habana Labs for $2 Billion

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Photo above: Habana Labs chairman Avigdor Willenz

Intel Corporation announced that it has acquired Habana Labs, an Israel-based developer of programmable deep learning accelerators for approximately $2 billion. Habana will continue to be based in Israel where Intel also has a significant presence and long history of investment. Prior to this transaction, Intel Capital was an investor in Habana. Habana chairman Avigdor Willenz will serve as a senior adviser to Intel.

“Habana turbo-charges our AI offerings for the data center,” said Navin Shenoy, general manager of the Data Platforms Group at Intel. “This acquisition advances our AI strategy, which is to provide solutions to fit every performance need – from the intelligent edge to the data center. Our combined IP and expertise will deliver unmatched computing performance and efficiency for AI workloads in the data center.”

Intel expects that the fast-growing AI silicon market be greater than $25 billion by 2024, and within that, AI silicon in the data center is expected to be greater than $10 billion. In 2019, Intel expects to generate over $3.5 billion in AI-driven revenue, up more than 20 percent year-over-year.

Habana's Gaudi AI Training Processor
Habana’s Gaudi AI Training Processor

Based in Caesarea, Israel, Habana labs was established in 2016 with Willenz as its first investor, and has developed dedicated chips for Deep Learning Training and Inference. Its Goya AI Inference Processor, which is commercially available, has demonstrated excellent inference performance including throughput and real-time latency in a highly competitive power envelope.

The Gaudi AI Training Processor is currently sampling with select hyperscale customers. Large-node training systems based on Gaudi are expected to deliver up to a 4x increase in throughput versus systems built with the equivalent number of GPUs. It is produced in TSMC’s 16 manometer process.

The acquisition gives Habana access to Intel AI capabilities, including deep expertise in AI software, algorithms and research that will help Habana scale and accelerate. In November 2018, Intel Capital led a $75 million investment round in Habana Labs. “We have been fortunate to get to know and collaborate with Intel given its investment in Habana, and we’re thrilled to be officially joining the team,” said David Dahan, CEO of Habana.

Intel and Microsoft Promote Security Standard for AI

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Last week, Intel and Microsoft brought together nearly 100 security and Artificial Intelligence (AI) experts to discuss new standards for Homomorphic Encryption (HE), which is emerging as a leading method to protect privacy in machine learning and cloud computing. The HE standards workshop took place on Intel’s Santa Clara, California campus. Following the first meeting in October, 2018, Intel and Microsoft initiated the founding of the HomomorphicEncryption.org group.

As more data is collected and used to power AI systems, concerns about privacy are on the rise. Casimir Wierzynski from the office of the CTO of AI Products Group at Intel, said that Intel is collaborating with Microsoft Research and Duality Technologies on standardizing HE, “to unlock the power of AI while still protecting data privacy.”

Fully homomorphic encryption, or simply homomorphic encryption, refers to a class of encryption methods envisioned by Rivest, Adleman, and Dertouzos already in 1978, and first constructed by Craig Gentry in 2009. Homomorphic encryption differs from typical encryption methods in that it allows computation to be performed directly on encrypted data without requiring access to a secret key. The result of such a computation remains in encrypted form, and can at a later point be revealed by the owner of the secret key.

It allows AI computation on encrypted data, thus enabling data scientists and researchers to gain valuable insights without decrypting or exposing the underlying data or models. This is particularly useful in instances where data may be sensitive – such as with medical or financial data.  Homomorphic encryption also enables training models directly on encrypted data, without exposing its content. Such encryption would enable researchers to operate on data in a secure and private way, while still delivering insightful results.