"What exactly is computer vision then? Computer vision is a research field working to equip computers with the ability to process and understand visual data, as sighted humans can. Human brains process the gigabytes of data passing through our eyes every second and translate that data into sight - that is, into discrete objects and entities we can recognise or understand. Similarly, computer vision aims to give computers the ability to understand what they are seeing, and act intelligently on that knowledge."
– Computer vision: Cheat Sheet. ZDNet.com (December 6, 2011), by Natasha Lomas.
We usually don't expect the image of a teacup to turn into a cat when we zoom out. But in the world of artificial intelligence research, strange things can happen. Researchers at Germany's Technische Universität Braunschweig have shown that carefully modifying the pixel values of digital photos can turn them into a completely different image when they are downscaled. What's concerning is the implications these modifications can have for AI algorithms. Malicious actors can use this image-scaling technique as a launchpad for adversarial attacks against machine learning models, the artificial intelligence algorithms used in computer vision tasks such as facial recognition and object detection.
Over the past few decades, software has been the engine of innovation for countless applications. From PCs to mobile phones, well-defined hardware platforms and instruction set architectures (ISA) have enabled many important advancements across vertical markets. The emergence of abundant-data computing is changing the software-hardware balance in a dramatic way. Diverse AI applications in facial recognition, virtual assistance, autonomous vehicles and more are sharing a common feature: They rely on hardware as the core enabler of innovation. Since 2017, the AI hardware market has grown 60-70% annually, and is projected to reach $65 billion by 2025.
Germany's Ibeo Automotive Systems, which specializes in lidar systems for autonomous driving, has signed a contract to provide China's Great Wall Motor Company (GWM) with its latest solid-state design. Ibeo said that it has commissioned key partner ZF Friedrichschafen – which in 2016 acquired a major stake in Ibeo – to produce the sensors and control unit for the "Level 3" system, which will provide partial autonomy. GWM has contracted one of its own subsidiaries to develop the system, which will be based around vertical cavity surface-emitting lasers (VCSELs) produced by Austria's AMS. Ibeo points out that, after signing a letter of intent in 2019, it has already been in pre-development with GWM for a year. Officially, the project started with the signing of an additional contract by the two parties last month.
In the first of a four-part series on FaceID, host Jennifer Strong explores the false arrest of Robert Williams by police in Detroit. The odd thing about Willliams's ordeal wasn't that police used face recognition to ID him--it's that the cops told him about it. There's no law saying they have to. The episode starts to unpack the complexities of this technology and introduces some thorny questions about its use. Credits: This episode was reported and produced by Jennifer Strong, Tate Ryan-Mosley and Emma Cillekens.
The use of facial recognition by police has come under a lot of scrutiny. In part three of our four-part series on FaceID, host Jennifer Strong takes you to Sin City, which actually has one of America's most buttoned-up policies on when cops can capture your likeness. She also finds out why celebrities like Woody Harrelson are playing a starring role in conversations about this technology. Credits: This episode was reported and produced by Jennifer Strong, Tate Ryan-Mosley and Emma Cillekens. We had help from Benji Rosen and Karen Hao.
Hosted by Dylan Doyle-Burke and Jessie J Smith, Radical AI is a podcast featuring the voices of the future in the field of artificial intelligence ethics. In this episode Jess and Dylan chat to Deb Raji about "IBM, Microsoft, and Amazon Disavow Facial Recognition Technology: What Do You Need to Know?". What does it mean that IBM, Microsoft, Amazon, and others have distanced themselves from developing facial recognition technology and providing facial recognition data to vendors? To answer these questions and more we welcome Deb Raji to the show. Deb is a tech fellow at the AI Now Institute Working on critical perspectives to evaluation practice in AI, conducting audits on deployed AI systems and facial recognition, and AI auditing policy.
Varjo, the Finnish startup that has developed a virtual and mixed reality headset capable of "human-eye resolution" for use in various enterprise applications, has closed a $51.7 million in Series C funding. Existing investors including Lifeline Ventures, Atomico, EQT Ventures and Volvo Cars Tech Fund have also followed on. It brings total raised by Varjo to around $100 million to date. The company is also announcing the appointment of Timo Toikkanen, who was previously president and COO of Varjo, as its new CEO. Co-founder and previous CEO, Niko Eiden, becomes CXO where he'll be tasked with continuing to drive the company's technology innovations and, notably, remains a board member.
This post is a practical example of Neural Style Transfer based on the paper A Neural Algorithm of Artistic Style (Gatys et al.). For this example we will use the pretained Arbitrary Image Stylization module which is available in TensorFlow Hub. We will work with Python and tensorflow 2.x. Neural style transfer is an optimization technique used to take two images--a content image and a style reference image (such as an artwork by a famous painter)--and blend them together so the output image looks like the content image, but "painted" in the style of the style reference image. This is implemented by optimizing the output image to match the content statistics of the content image and the style statistics of the style reference image.
Dave Ryan leads the Global Health & Life Sciences business unit at Intel that focuses on digital transformation from edge-to-cloud in order to make precision, value-based care a reality. His customers are the manufacturers who build life sciences instruments, medical equipment, clinical systems, compute appliances and devices used by research centers, hospitals, clinics, residential care settings and the home. Dave has served on the boards of Consumer Technology Association Health & Fitness Division, HIMSS' Personal Connected Health Alliance, the Global Coalition on Aging and the Alliance for Connected Care. What is Intel's Health & Life Sciences Business? Intel's Health & Life Sciences business helps customers create solutions in the areas of medical imaging, clinical systems, and lab and life sciences, enabling distributed, intelligent, and personalized care.