Gender balance in AI conference line-ups has been noticeably poor. But women in AI exist and their work is multifaceted and extraordinary. Artificial intelligence (AI) is the hot ticket in tech at the moment. Unfortunately, too many events and conferences delving into the subject, and its related disciplines of machine learning (ML) and data science, feature few women – if at all – among their speakers. To prove it doesn't have to be like this, and as a follow-on from International Women's Day, we at Silicon Republic have found 20 terrific women in AI, ML and data science who we think should be turning up in speaker line-ups across the country (and around the world).

A Dutch startup has teamed up with Google Glass to create a set of AI-powered spectacles that help blind and visually-impaired people to see. The glasses extract visual information from images of people, belongings, and public transport, and then speaks about them out loud. It can read text from books, name friends by analyzing their faces, and describe surroundings such as train signs and street hazards. The wearer could use the glasses to read a recipe from a cookery book, get to the grocery store, find ingredients on the shelves, and then return home to prepare the dish. Check out how it works in the video below.

Today at MODEX 2020, KNAPP, a leading supplier of intralogistics systems, and Covariant, a leading AI Robotics company, announced a partnership to deploy and bring to market advanced AI Robotics solutions. KNAPP and Covariant have already developed several solutions together. The Pick-It-Easy Robot powered by Covariant AI, is designed for high performance single-piece picking applications and is currently operating in production at several customer sites in North America and Europe, including at Obeta, a German electrical supply wholesaler outside Berlin. "Deploying AI Robotics solutions in customer environments is extremely challenging" "Artificial intelligence will be a defining feature of the warehouse of the future, impacting all aspects of operations and fundamentally changing how business is done," said Jusuf Buzimkic, SVP Engineering at KNAPP. "We looked at every solution on the market, and Covariant was the clear winner. Our partnership with Covariant will enable us to deliver cutting-edge artificial intelligence technology to our customers, providing a major leg up in an increasingly competitive world."

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This is an updated version. The Godfathers of AI and 2018 ACM Turing Award winners Geoffrey Hinton, Yann LeCun, and Yoshua Bengio shared a stage in New York on Sunday night at an event organized by the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020). The trio of researchers have made deep neural networks a critical component of computing, and in individual talks and a panel discussion they discussed their views on current challenges facing deep learning and where it should be heading. Introduced in the mid 1980s, deep learning gained traction in the AI community the early 2000s. The year 2012 saw the publication of the CVPR paper Multi-column Deep Neural Networks for Image Classification, which showed how max-pooling CNNs on GPUs could dramatically improve performance on many vision benchmarks; while a similar system introduced months later by Hinton and a University of Toronto team won the large-scale ImageNet competition by a significant margin over shallow machine learning methods. These events are regarded by many as the beginning of a deep learning revolution that has transformed AI.

Artificial Intelligence (AI) is a new exciting opportunity to better protect European consumers. As the European Commission calls for a human-centric AI, we see exciting developments which put the welfare of users and citizens at the heart of new AI tools. During this CERRE Breakfast Conversation, a new promising application to screen the compliance of consumer contracts with EU law will be presented. Policy-makers and stakeholders will discuss how to reinforce EU consumer protection policy with AI. This event will bring together key actors from the digital sector, regulatory authorities, policy-makers and academic experts for a morning of stimulating discussions.

Bringing a new medical treatment to market is a slow, laborious process -- and for a good reason: patient safety is the top priority. But when recruiting patients to test promising treatments in clinical trials, the faster the better. "Many people in medicine have ideas of how to improve healthcare," said Wout Brusselaers, CEO of Pasadena, Calif.-based startup Deep 6 AI. "What's stopping them is being able to demonstrate that their new process or new drug works, and is safe and effective on real patients. For that, they need the clinical trial process."

Google today announced the launch of TensorFlow Quantum, bringing together machine learning and quantum computing initiatives at the company. The framework can construct quantum datasets, prototype hybrid quantum and classic machine learning models, support quantum circuit simulators, and train discriminative and generative quantum models. Last fall, Google said it achieved quantum supremacy with the debut of a newly engineered solution. The release of TensorFlow Quantum follows the launch of Azure Quantum and progress by companies like Honeywell. Creating quantum models is made possible with standard Keras functions and by providing quantum circuit simulators and quantum computing primitives compatible with existing TensorFlow APIs, according to a Google AI blog.

NexOptic Technology Corp., reports that it has made significant advancements to its cutting-edge artificial intelligence (AI) imaging solution. NexOptic's Advanced Low Light Imaging Solution (ALLIS) provides immediate solutions to problems that have plagued the imaging industry for decades. NexOptic's engineered AI drastically reduces image noise common to all imaging systems while improving performance in low light conditions. This is accomplished with NexOptic's expanding suite of patent-pending, deep learning algorithms. Some of the key benefits of ALLIS include: improved low-light performance; dramatic reduction in image noise; improved downstream applications (computational imaging, facial recognition); enhanced long-range image stabilization; major reduction in file sizes.

Single subject prediction of brain disorders in neuroimaging: promises and pitfalls.