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New USPTO Director, North Texas Experts Tackle Business of AI, Emerging Tech

Via | By Payton Potter – As artificial intelligence takes on bigger roles in business, the U.S. Patent Office, industry leaders, and academics work to understand the technology. United States Patent Office Director Andrei Iancu joined four artificial intelligence experts Friday morning in Dallas to jumpstart a discussion on AI between government, industry, and universities.

Iancu, during the invitation-only panel at SMU’s Dedman School of Law, took questions ranging from the USPTO’s role in patenting artificial intelligence to the future of the technology in business.

The panel consisted of Iancu, along with Dave Copps, founder of Brainspace; Evan Davies, president of Active Network; Romelia Flores, master inventor at IBM; and Gopal Gupta, computer science department head at the University of Texas at Dallas.

Iancu was appointed to the position of  Under Secretary of Commerce for Intellectual Property and Director of the United States Patent and Trademark Office by President Trump in February. Iancu spent 19 years as a partner at Irell & Manella LLP in Los Angeles, according to his LinkedIn profile.

Here are key takeaways from the panel.

What is Artificial Intelligence?

AI, the panelists agreed, is an innovative tool that uses learning and reasoning to solve problems. The technology can process data far faster than humans and solve problems in new ways. But AI doesn’t come without its issues.

Flores: “It’s all about the senses — being able to analyze someone’s tone, analyze someone’s decision-making processes, analyze what they’re seeing. It’s about the senses and how you do that more intelligently in all kinds of devices, and certainly in the future, even systems.”

Copps: “AI is the promise that machines can think and learn like we do — and perhaps go beyond that… AI has this amazing ability to crunch numbers and facts and make connections between massive amounts of information (millions of documents). A person still has capabilities that AI won’t have for a long time: to reason and make judgments and decisions from that data.”

Gupta: “If you want to really understand AI, you have to first understand what is meant by human intelligence. … Intelligent behavior essentially encompasses that we’re able to reason, and the other is equity to learn, among other skills.”

The Challenges of Patenting AI Technology

The U.S. Patent Office requires patent applicants to explain the method by which technology works. Artificial Intelligence is difficult to explain because it learns in a unique way, Copps said. Iancu agreed that a change of laws might be necessary as technology continues to evolve.

Copps: “Congress even came out and said all AI has to be explainable. I chuckled a little bit, because it’s at the point now where it’s a comfortable myth for us that AI learns the way we do. It doesn’t. I was talking to one of the developers of a translation system, and I asked how translation systems learn. He said, ‘We don’t really know.’”

Iancu: “How do you patent something when you don’t actually know how it does it? Under the patent system right now, the patent laws require disclosure, claiming how to do a method. If you’re just presenting the results — right now at least — theoretically under our laws you have to disclose how you do it. It might be the case that we need new laws to address that issue. It’s a very complex thing.”

Making The Most of Data with AI

Data is the lifeblood of AI. The technology analyzes data and identifies trends to make decisions and learn. Without sufficient data, AI can be unreliable, biased or even useless. Davies agreed, noting that mature AI technology, used correctly, can act as a database.

Copps: “For instance, with Google Translate, we took 12 years of human curation data, and we fed it to a learning system that read that information and learned it in 48 hours. From then on, it took that knowledge and kept growing smarter and smarter and smarter.”

Gupta: “You can do nontrivial things by trying to observe patterns from data and gleaning knowledge out of data. If you’ve got a huge amount of data, with machine learning you can [achieve] knowledge.”

Davis: “AI is really a category of maturing technology. As we look at things like Google’s doing, Microsoft’s doing, IBM’s doing, they’re taking those technologies and making them more like tools, like databases.”

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Source |  Dallas Innovates | Article written By Payton Potter 

Header Photo Via Brittany Fisher, USPTO


The UT Dallas Computer Science program is one of the largest Computer Science departments in the United States with over 2,800 bachelors-degree students, more than 1,000 master’s students, 190 Ph.D. students,  52 tenure-track faculty members, and 41 full-time senior lecturers, as of Fall 2018. With The University of Texas at Dallas’ unique history of starting as a graduate institution first, the CS Department is built on a legacy of valuing innovative research and providing advanced training for software engineers and computer scientists.

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