Local • State • NationalGet Breaking NewsSign up now to get our FREE breaking news coverage delivered right to your inbox.
"}}}},{"node":{"postTitle":"Black Sesame, Chinese auto chip challenger to Nvidia, burns $140M a year","slug":"black-sesame-chinese-auto-chip-challenger-to-nvidia-burns-140m-a-year","publishDate":"2023-07-10 00:00:00","createdLocal":"2023-07-10 14:30:54.399771","feedName":"Autonomous Vehicle News","postSummary":{"childMarkdownRemark":{"html":"Founded by veterans of Bosch and OmniVision, Black Sesame is seen as one of the potential domestic players that can potentially replace the likes of Nvidia, Qualcomm and NXP Semiconductors in the auto chip space.\nThe question, then, is whether Black Sesame can continue to pour money into R&D until it becomes profitable.\nWhile Black Sesame plays a role in helping China achieve independence in auto chips, its own products rely heavily on access to the global supply chain.\nIncreasing lossesBlack Sesame’s revenue tripled from 53 million yuan ($7.33 million) to 165.4 million yuan between 2020 and 2022, but its losses grew to 1 billion yuan ($140 million) in 2022, a more than 200% increase from 293 million yuan in 2020.\nLike many companies in China’s critical industries, Black Sesame receives government grants and tax incentives because it operates in the field of automotive SoCs.
"}}}},{"node":{"postTitle":"Revolutionizing Transportation: The Rise of Self Driving Cars","slug":"revolutionizing-transportation-the-rise-of-self-driving-cars","publishDate":"2023-07-10 13:31:12+00:00","createdLocal":"2023-07-10 14:30:53.880290","feedName":"Autonomous Vehicle News","postSummary":{"childMarkdownRemark":{"html":"How self driving Cars WorkA self driving car operates without the need for human drivers.\nTesla is leading the charge regarding autonomous driving, producing one of the world’s first self driving cars to be used globally.\nFurthermore, if self driving cars prove to be as successful, many cars will be connected through GPS systems and tracking, but this greatly increases the risk of hacking.\nCan you buy self driving cars?\nAutonomous cars would have allowed vulnerable people in isolation to travel alone in self driving cars without the risk of getting ill.Self-driving cars are becoming increasingly popular and are being developed at an exponential rate.
"}}}},{"node":{"postTitle":"Area first responders help with developing vehicle alert safety system","slug":"area-first-responders-help-with-developing-vehicle-alert-safety-system","publishDate":"None","createdLocal":"2023-07-10 14:30:53.356165","feedName":"Connected Car","postSummary":{"childMarkdownRemark":{"html":"An email has been sent to with a link to confirm list signup.
"}}}},{"node":{"postTitle":"VW to test self-driving tech in retro-styled electric Microbuses","slug":"vw-to-test-self-driving-tech-in-retro-styled-electric-microbuses","publishDate":"None","createdLocal":"2023-07-10 14:30:52.682086","feedName":"Autonomous Vehicle News","postSummary":{"childMarkdownRemark":{"html":"You have permission to edit this article.
"}}}},{"node":{"postTitle":"Embracing Digital Disruption: The Future of the Automotive Aftermarket in a Post-COVID World","slug":"embracing-digital-disruption-the-future-of-the-automotive-aftermarket-in-a-post-covid-world","publishDate":"2023-07-10 12:21:31+00:00","createdLocal":"2023-07-10 14:30:52.476526","feedName":"Connected Car","postSummary":{"childMarkdownRemark":{"html":"Navigating the New Normal: Embracing Digital Disruption in the Automotive Aftermarket Post-COVIDThe automotive aftermarket industry is at the cusp of a significant transformation, driven by digital disruption and accelerated by the COVID-19 pandemic.\nDigital disruption in the automotive aftermarket is not just about online sales.\nHowever, embracing digital disruption is not without its challenges.\nIn conclusion, the future of the automotive aftermarket in a post-COVID world is digital.\nThis requires a strategic approach, involving investment in digital technologies, upskilling of the workforce, and a focus on cybersecurity and regulatory compliance.
"}}}},{"node":{"postTitle":"AI replicates anti-Blackness","slug":"ai-replicates-anti-blackness","publishDate":"2023-07-10 00:00:00","createdLocal":"2023-07-10 14:30:46.602853","feedName":"Image Recognition","postSummary":{"childMarkdownRemark":{"html":"JOY BUOLAMWINI has had an interesting experience in her research on artificial intelligence (AI) facial recognition technology.\nAs AI technology becomes more prominent in use across the world, the growing incidents of anti-Blackness being manifested among various AI systems dissolves the idea that technology is inherently neutral.\nIn the United States activists have raised concerns over wrongful arrests that have been attributed to faulty AI facial recognition.\nAI technology is becoming increasingly better at reasoning, learning, understanding language, recognising images and making independent decisions.\nCurrent AI systems are programmed by humans and often reflect the biases and prejudices of their creators.
"}}}},{"node":{"postTitle":"A deep convolutional neural network for efficient microglia detection","slug":"a-deep-convolutional-neural-network-for-efficient-microglia-detection","publishDate":"None","createdLocal":"2023-07-10 14:30:45.227702","feedName":"Image Recognition","postSummary":{"childMarkdownRemark":{"html":"Developing fully automated methods for counting microglia cells from immunohistological images with no user-defined parameters is a significant challenge in the field.\nDeep convolutional neural networks (DCNN)-based models are a way to overcome many shortcomings of manual or semi-automated methods in cell detection17.\nOur research aim was to develop a fully automated tool for microglia detection that would be more accurate, efficient, and faster than existing approaches.\nWe present an innovative algorithm for the automatic detection of microglia based on YOLOv334—a powerful DCNN platform that can be customised to deal with a range of object detection tasks (Supplementary Information).\nTo demonstrate the effectiveness of our microglia detection, we trained YOLOv3 using its general network architecture.
"}}}}]}},"pageContext":{"limit":8,"skip":3448,"homeNumPages":1077,"currentPage":432}}}