Artificial intelligence (AI) is set to transform many aspects of our lives, including our home and health. AI is already widely used in internet searches, and home devices with speech recognition, but in the near future we will see AI become even more widespread. This will have significant repercussions as AI performs many tasks that until now could only be undertaken by humans. AI will remove human intervention from much of the picture. This will particularly affect intellectual property law.
When Michelle K. Lee, '88, SM '89, was sworn in as the director of the U.S. Patent and Trademark Agency in 2015, she saw an opportunity. The agency was a bit behind on digital transformation and adopting things like cloud computing and artificial intelligence, but the organization had mountains of data -- like more than 10 million patents the office has issued since opening in 1802, and 600,000 patent applications received each year. Lee led a project to use data and analytics to modernize the agency, such as implementing AI solutions to improve patent searches and the speed and quality of patents issued. By gathering data about how patent examiners make decisions, and determining outlying behavior, the office could also pinpoint areas in which examiners would benefit from targeted training. "If the U.S. Patent and Trademark Office, a 200-plus-year-old governmental agency, has a machine learning opportunity, so too does every organization," Lee said during a presentation at EmTech Digital, hosted by MIT Technology Review.
Arm said Tuesday that ray tracing and variable rate shading will migrate from the PC to Arm-powered smartphones and tablets as part of Armv9, the next-generation CPU architecture that the company expects will power the next decade of Arm devices. Chips based upon the v9 architecture will be released in 2021, providing an estimated 30-percent improvement in performance over the next two Arm chip generations and the devices that run them. Arm's v9 will also add SVE2, new AI-specific instructions that will probably be used for the AI image processing used on smartphones, such as portrait mode. Arm v9 will also include what Arm is calling Realms, a hardware container of sorts specifically designed to protect virtual machines and secure applications. As an intellectual-property licensing company, Arm enjoys a unique position in the computing industry.
What does a chair from furniture manufacturer Kartell have in common with a rocket engine by the software powerhouse Hyperganic? They were both created by generative design -- in other words, made by AI. But it's a far cry from simple CAD design, using algorithms created by AI to generate a first set of designs for a product based on certain input parameters. It will then continue to refine these designs with each iteration until the final product materialises. Combined with industrial 3D printing, the result is a technically superior product that weighs less, has better functional features and is often less prone to wear and tear.
Two key components for using ML responsibly provide a prudent "start here" for organizations: model explainability and data transparency. The inability to explain why a model arrived at a particular result presents a level of risk in nearly every industry. In some areas, like healthcare, the stakes are particularly high when a model could be presenting a recommendation for patient care. In financial services, regulators may need to know why a lender is making a loan. Data transparency can ensure there is no unfair or unintended bias in the training data sets used to build the model, which can lead to disparate impact for protected classes – and consumers have what is increasingly a legally protected right to know how their data is being used.
But how can organizations developing ML models enforce explainability and transparency standards when doing so might mean sharing with the public the very features, data sets, and model frameworks that represent that organization's proprietary intellectual property (IP)? Given machine learning's complexity and interdisciplinary nature, executives should employ a wide variety of approaches to manage the associated risks, which include building risk management into model development and applying holistic risk frameworks that leverage and adapt principles used in managing other types of enterprise risk. Whereas standard technical documentation is created to help practitioners implement a model, documentation focused on explainability and transparency informs consumers, regulators, and others about why and how a model or data set is being used. Such documentation includes a high-level overview of the model itself, including: its intended purpose, performance, and provenance; information about the training data set and training process; known issues or tradeoffs with the model; identified risk mitigation strategies; and any other information that can help contextualize the technology. Similarly, model documentation can become the proxy for sharing the model and its features and data sets with the world as opposed to sharing the actual "cookie recipe."
I am regularly asked to summarize my many posts. I thought it would be a good idea to publish on this blog, every Monday, some of the most relevant articles that I have already shared with you on my social networks. Today I will share some of the most relevant articles about Artificial Intelligence and in what form you can find it in today's life. I will also comment on the articles. The purpose of the Association for the Advancement of Artificial Intelligence, according to its bylaws, is twofold.
Otologic Technologies, Inc., a Wisconsin-based health-tech startup developing an artificial intelligence (AI) system to improve treatment of ear disease, announced the issuance of US Patent No. 10,932,662, "System and Method of Otoscopy Image Analysis to Diagnose Ear Pathology." The patent explains a novel artificial intelligence system to help doctors better diagnose ear disease. "One of the biggest challenges in diagnosing ear disease is the difficult nature of an ear exam," said Aaron Moberly, MD, associate professor of otolaryngology at The Ohio State University and one of the inventors of the technology. "Even experienced doctors can have trouble with a live ear exam, as patients are usually uncomfortable and the view can be obstructed. In 2015, Dr. Moberly began an ongoing collaboration with Metin Gurcan, PhD, an artificial intelligence (AI) expert at The Ohio State University.