The unprecedented interest, investment, and deployment of machine learning across many aspects of our lives in the past decade has come with a cost. Although there has been some movement towards moderating machine learning where it has been genuinely harmful, it's becoming increasingly clear that existing approaches suffer significant shortcomings. Nevertheless, there still exist new directions that hold potential for meaningfully addressing the harms of machine learning. In particular, new approaches to licensing the code and models that underlie these systems have the potential to create a meaningful impact on how they affect our world. This is Part I of a two-part essay.
These advances will create a network where almost every device can be simultaneously connected, enabling technologies not possible today. Governments and private entities are just beginning to invest in the technology, and projections suggest commercial availability around 2030. But given 6G's anticipated ubiquity and potential to change the landscape, we would be wise to begin learning about it now. Artificial intelligence ("AI") represents a new frontier in the global economy: Some estimates say it could contribute up to $15.7 trillion worldwide by 2030. Increases in computing power and innovations in computer science have fueled AI innovation.
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.
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.
Business units have started identifying processes they want to automate within the U.S. Patent and Trademark Office, now that its CIO is managing the infrastructure and licensing. The Robotic Process Automation Governance Team within the Office of the CIO handles configuration management and cybersecurity vetting to standardize the credentialing of bots, while business analysts pick the automations. Analysts need only fill out an RPA intake form, the first step of the governance process, which asks nine questions before calculating the necessary bot's complexity and expected time savings. "We've reached a point with our maturity where we're really encouraging different business units to come to the table with their own ideas for automation," said Jacob Feldman, program analyst at USPTO, during an ACT-IAC event Wednesday. "This is implementing a federated model of development."
In May 2015, The Simpsons voice actor Harry Shearer – who plays a number of key characters including, quite incredibly, both Mr Burns and Waylon Smithers – announced that he was leaving the show. By then, the animated series had been running for more than 25 years, and the pay of its vocal cast had risen from $30,000 an episode in 1998 to $400,000 an episode from 2008 onwards. But Fox, the producer of The Simpsons, was looking to cut costs – and was threatening to cancel the series unless the voice actors took a 30 per cent pay cut. Most of them agreed, but Shearer (who had been critical of the show's declining quality) refused to sign – after more than two decades, he wanted to break out of the golden handcuffs, and win back the freedom and the time to pursue his own work. Showrunner Al Jean said Shearer's iconic characters – who also include Principal Skinner, Ned Flanders and Otto Mann – would be recast.
This article was originally published by Industry Today on March 3, 2021, and is reproduced below in full with permission. With rapid changes, pressure to innovate, and acceleration of implementation of advanced technology across all stages of the supply chain over the past year, there are important intellectual property (IP) considerations that companies need to make to protect their inventions. Leading edge tech like Augmented and Virtual Reality, machine learning and Artificial Intelligence, and 3D printing have become integral to business success yet continue to cause confusion around how the technology should be patented. This article explores some of the nuances as they relate to the art of protecting the software that fuels the base technology of these advanced innovations and important considerations that need to be made in the current environment. Most machine learning (ML) and artificial intelligence (AI) innovations are generally based in computer software. While courts and the U.S. Patent and Trademark Office ("U.S. PTO") have established limits on the ability to patent computer software, it is still possible to obtain meaningful, broad, and valuable patent protection on computer software.
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