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Luminar's CFO Aims to Conserve Cash as Company Begins Commercial Production

WSJ.com: WSJD - Technology

The transaction provided Luminar with the infusion of capital it needed to begin producing lidar sensors that use lasers to measure distances and classify objects for self-driving vehicles at a commercial scale, according to Chief Financial Officer Tom Fennimore. As a public company, however, Luminar must be mindful of how it spends the cash, he added. Luminar has positioned itself in recent years to benefit from the expected rise of autonomous vehicles. It has announced partnerships with car makers including Volvo Cars, which is owned by China's Zhejiang Geely Holding Group, Daimler AG's trucks business and SAIC Motor Corp. Ltd. to incorporate its sensor technology into self-driving vehicle designs. The Morning Ledger provides daily news and insights on corporate finance from the CFO Journal team.


CloudCommerce Uses Artificial Intelligence to Deliver Winning Solution for Energy in Focus

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SAN ANTONIO, June 22, 2021 (GLOBE NEWSWIRE) -- CloudCommerce, Inc. (CLWD), a technology driven provider of digital advertising solutions, today announced that SWARM, the Company's AI-driven advertising solution, reduced media costs by more than 60% for Energy in Focus, a web based platform that showcases diverse information on energy in California. Based on the first-round results, the client has committed to a second round. Energy in Focus turned to CloudCommerce to better understand which creative initiatives would be best for their different audiences, such as b2b partners and its public advocacy audience. SWARM analyzed the top 5 previous posts from Facebook and used artificial intelligence to develop creative variations which ran on other media platforms. The result: the cost was reduced by more than 60%.


Global Artificial Intelligence in Banking Market

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Brooklyn, New York, June 22, 2021 (GLOBE NEWSWIRE) -- According to a new market research report published by Global Market Estimates, the Global Artificial Intelligence in Banking Market is projected to grow at a CAGR value of 24.5% during the forecast period of 2021 to 2026. The rising launch of advanced technologies such as AI-based core banking software for retail and commercial banks, also with increasing demand for hassle-free online and mobile banking services, and the increasing trend of offering customer-centric services will drive the market from 2021 to 2026. Browse 151 Market Data Tables and 111 Figures spread through 181 Pages and in-depth TOC on "Global Artificial Intelligence in Banking Market - Forecast to 2026"


Lidar company Quanergy to go public via $1.4B SPAC deal – TechCrunch

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Quanergy Systems, the Sunnyvale, California-based lidar company, said Tuesday it has agreed to merge with special purpose acquisition fund CITIC Capital Acquisition Corp., a Chinese blank-check firm affiliated with the country's largest state-owned investment conglomerate. The deal, which puts an implied valuation on Quanergy at $1.4 billion, is expected to close in the second half of 2021. After closing, the transaction will inject the lidar company with around $278 million in pro forma net cash, including $40 million in private investment in public equity (PIPE) funding. Lidar is an essential component of most autonomous driving systems -- the notable exception being Tesla's stack, which is attempting to develop a pure vision-based system to support its pursuit of automated driving (Tesla vehicles are not autonomous today and have what is considered a Level 2 advanced driver assistance system). Quanergy is a developer of solid state silicon lidar units, which pulses a low-power laser through an optical phased array to measure the distance and shape of objects.


Artificial Intelligence in Banking Market - Forecast to 2026

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Press release - Global Market Estimates Research & Consultants - Artificial Intelligence in Banking Market - Forecast to 2026 - published on openPR.com


TechSee's AI can recognize devices and guide users through setup

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TechSee, which describes itself as an "intelligent visual assistance" company, today announced the launch of Eve Cortex, a platform that teaches itself to recognize thousands of products, models, parts, and components by ingesting only a handful of data points. TechSee claims that by leveraging a combination of AI and synthetic data, Cortex can train itself in a matter of hours, providing end users with step-by-step visual guidance via an augmented reality (AR) overlay. The AR market is estimated to grow from $10.7 billion in 2019 to $72.7 billion by 2024, according to a recent Markets and Markets report. At least a portion of that growth has been driven by field service applications; technicians are faced with the challenging task of working on equipment with varying technical specifications, often in confined or hard-to-reach spaces. With AR apps, they could have all of the information they need displayed in front of them while keeping their hands free to work.


Updates to Azure Arc-enabled Machine Learning

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Azure Machine Learning (AML) team is excited to announce the availability of Azure Arc-enabled Machine Learning (ML) public preview release. All customers of Azure Arc-enabled Kubernetes now can deploy AzureML extension release and bring AML to, and the edge using Kubernetes on their hardware of choice. The design for Azure Arc-enabled ML helps IT Operators leverage native Kubernetes concepts such as namespace, node selector, and resources requests/limits for ML compute utilization and optimization. By letting the IT operator manage ML compute setup, Azure Arc-enabled ML creates a seamless AML experience for data scientists who do not need to learn or use Kubernetes directly. Data scientists now can focus on models and work with tools such as Azure Machine Learning AML Studio, AML 2.0 CLI, AML Python SDK, productivity tools like Jupyter notebook, and ML frameworks like TensorFlow and PyTorch.


Announcing the winners of the Sample-Efficient Sequential Bayesian Decision Making request for proposals - Facebook Research

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In February 2021, Facebook launched a request for proposals (RFP) on sample-efficient sequential Bayesian decision-making. View RFP In a Q&A about the RFP, Core Data Science researchers said they are keen to learn more about all the great research that is going on in the area of Bayesian optimization. Eytan Bakshy and Max Balandat, members of the team behind the RFP, also spoke about sharing a number of really interesting real-world use cases that they hope can help inspire additional applied research and increase interest and research activity into sample-efficient sequential Bayesian decision-making. The team reviewed 89 high-quality proposals and are pleased to announce the two winning proposals below, as well as the 10 finalists. Thank you to everyone who took the time to submit a proposal, and congratulations to the winners.


Global Artificial Intelligence in Medical Imaging Market To Hit $1,579.33 Million by 2028

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Data Bridge Market Research published a new report, titled, "Artificial intelligence in medical imaging Market". The report offers an extensive analysis of key growth strategies, drivers, opportunities, key segments, and competitive landscape. This study is a helpful source of information for market players, investors, VPs, stakeholders, and new entrants to gain a thorough understanding of the industry and determine steps to be taken to gain a competitive advantage. Businesses can bring about an absolute knowhow of general market conditions and tendencies with the information and data covered in the large scale Artificial intelligence in medical imaging market survey report. To get knowledge of all the above things, this market report is made transparent, wide-ranging and supreme in quality.


Ping An Makes Breakthrough in Artificial Intelligence-Driven Drug Research

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Research by Ping An Healthcare Technology Research Institute and Tsinghua University has led to a promising deep learning framework for drug discovery, announced Ping An Insurance (Group) Company of China, Ltd. (hereafter "Ping An" or the "Group", HKEX: 2318; SSE: 601318). The findings were published in "An effective self-supervised framework for learning expressive molecular global representations to drug discovery" in Briefings in Bioinformatics, a peer-reviewed bioinformatics journal. It marks a major technology breakthrough for the Group in the field of AI-driven pharmaceutical research. Drug discovery can take 10 to 15 years from invention to market. It can take a large number of experiments, with significant costs and high failure rates.