The United States Patent and Trademark Office (USPTO) today released a report titled "Public Views on Artificial Intelligence and Intellectual Property Policy." The new report represents the agency's firm commitment to keeping pace with this rapidly changing and critical technology in order to accelerate American innovation. "On February 11, 2019, President Trump signed Executive Order 13859 announcing the American Artificial Intelligence Initiative, our nation's strategy on artificial intelligence," said U.S. Secretary of Commerce Wilbur Ross. "As artificial intelligence technologies continue to advance, the United States will not cede leadership in global innovation. The Department of Commerce recognizes the importance of harnessing American ingenuity to advance and protect our economic security." "The USPTO has long been committed to ensuring our nation maintains its leadership in all areas of innovation, especially in emerging technologies such as artificial intelligence," said Andrei Iancu, Under Secretary of Commerce for Intellectual Property and Director of the USPTO.
All Arm trademarks featured in this course are registered or unregistered trademarks of Arm Limited (or its subsidiaries) in the US or elsewhere. Welcome to the Deep Learning From Ground Up on ARM Processors course. We are going to embark on a very exciting journey together. We are going to learn how to build deep neural networks from scratch on our microcontrollers. We shall begin by learning the basics of deep learning with practical code showing each of the basic building blocks that end up making a giant deep neural network.
It's not surprising that we've experienced an explosion in artificial intelligence (AI) patent activity over the past several years. As recently as 2016, the United States Patent & Trademark Office (USPTO) issued less than 1,000 AI-related patents. As this explosion has occurred, so have interesting questions concerning patentability, inventorship, ownership, and disclosure issues. To address these (and other) concerns, the USPTO launched its Artificial Intelligence Initiative in 2019, engaging the innovation community and experts to determine whether AI required any changes to the U.S. Patent system. In response to requests for public comments on these topics, the USPTO received comments from 43 organizations, ranging from domestic and international patent/IP bar associations to companies such as Ford Motor Co. and Merck, and also from 55 individuals.
We fuel our ambitions with our hard work and persistence every day to make our lives easier and convenient. Spiderman is truly a visionary when he says "with great power, comes great responsibility". Machine Learning is one such power that boosts our convenience from Spotify's suggestions based on our previous playlists to filtering spam and phishing emails. Though ML is an ingenious gift of advanced technology to us, it always remains in the ring succumbed by notorious malware and attacks. Every business develops with the Trust of its customers and investors.
A new patent granted to Microsoft by the United States Patent and Trademark Office (USPTO) reveals that the company is working on conversational agents that mirror users' conversational style and/or facial expressions. The patent - Linguistic Style Matching Agent – was granted to Microsoft on September 3, 2020, and credits Daniel J McDuff, Kael R. Rowan, Mary P Czerwinski, Deepali Aneja, and Rens Hoegen as inventors. With advances in speech recognition and generative dialogue models, conversational interfaces like chatbots and virtual agents are becoming increasingly popular. While such natural language interactions have led to an evolution in human-computer interactions, the communication is mostly monotonic and constrained. These conversations, therefore, end up being only transactional and are not very natural.
The United States Patent and Trademark Office recently ruled that only flesh and blood humans can be granted patents, not artificial intelligence beings, thus ensuring that a Skynet scenario will play out (you didn't think I'd talk about AI without a Skynet reference, did you? Not it faces a lawsuit over its decision. What set this in motion is the filing of two patent applications in July of last year by Stephen Thaler, a physicist and AI researcher, on behalf of an AI "creative engine" called DABUS. One of the patents relates to an adjustable food container and the other one has to do with an emergency flashlight. On both applications, Thaler listed DABUS as the inventor.
MIT SMR Connections is the custom content creation unit within MIT Sloan Management Review. In this Q&A, Michelle K. Lee, vice president of the Amazon Web Services (AWS) Machine Learning Solutions Lab, shares real-world examples of machine learning in action, describes four key implementation challenges, and offers other advice. This conversation has been condensed and edited for clarity, length, and editorial style. Q: Can you provide an overview of how artificial intelligence (AI) and machine learning (ML) are driving digital transformation? Lee: AI and machine learning went from being aspirational technology to mainstream extremely fast.
In the U.S., we don't expect or allow government officials – including judges--to be speech police. Courts are allowed to restrain speech only in the rarest circumstances, subject to strict limitations. So we were troubled to learn that a judge in Missouri has issued an order stifling the speech of a small company that's chosen to speak out about a patent troll lawsuit that was filed against it. Mycroft AI, a company with nine employees that makes open-source voice technology, published a blog post on February 5 describing how it had been threatened by a patent troll called Voice Tech Corporation. It simply owns patents, which it acquired through more than a decade of one-party argumentation with the U.S. Patent Office.
At Zetane (Trademark) we're looking for passionate contributors who want to commit to a startup and make a contribution to our mission, "to drive the democratization, access and explainability of AI in industry and facilitate collaboration between subject matter experts and AI specialists." Zetane Systems is a Montreal-region software technology company specializing in commercial applications of artificial intelligence (AI). Zetane's AI development engine is proprietary software that provides a visual, intuitive and collaborative environment for AI teams in businesses to build AI solutions and products based on machine learning models. We aim to make the development of AI solutions more accessible to the various stakeholders in businesses and promote abilities to explain the inner workings of complex neural networks to address "black box" concerns, increase stakeholder buy-in, reduce time to market and mitigate risk prior to deployment. In addition to selling powerful software, the company also provides a full suite of consulting services to facilitate the adoption of AI solutions in diverse industries.
Whether to give rights to artificial intelligence (AI) and robots has been a sensitive topic since the European Parliament proposed advanced robots could be granted "electronic personalities." Numerous scholars who favor or disfavor its feasibility have participated in the debate. This paper presents an experiment (N=1270) that 1) collects online users' first impressions of 11 possible rights that could be granted to autonomous electronic agents of the future and 2) examines whether debunking common misconceptions on the proposal modifies one's stance toward the issue. The results indicate that even though online users mainly disfavor AI and robot rights, they are supportive of protecting electronic agents from cruelty (i.e., favor the right against cruel treatment). Furthermore, people's perceptions became more positive when given information about rights-bearing non-human entities or myth-refuting statements. The style used to introduce AI and robot rights significantly affected how the participants perceived the proposal, similar to the way metaphors function in creating laws. For robustness, we repeated the experiment over a more representative sample of U.S. residents (N=164) and found that perceptions gathered from online users and those by the general population are similar.