(see Table 2).

The nation's top inventors and businesses rely heavily on the United States Patent and Trademark Office to issue patents for inventions and register trademarks for product and intellectual property identification. The agency has come to embrace increasingly more emerging and advanced technologies in recent years to meet its mission, and it is now also enduring a large-scale modernization. USPTO Chief Information Officer Jamie Holcombe is working to help make the agency's systems "better, cheaper and faster." Holcombe joined the agency at the request of Under Secretary of Commerce for Intellectual Property and Director Andrei Iancu, who immediately articulated his aims to "propel the USPTO into the next decade." "He wanted to ensure that all the up-to-date commercial tools were available to the USPTO examiners so that we could conduct our business in the most advanced way possible," Holcombe told Nextgov in the latest episode of Critical Update.

Computer vision is a field in computer science that falls under the umbrella of artificial intelligence (AI). Computer vision (CV) software developers strive to give computers the ability to process images in much the same way that humans do. They expect the computer will be able to identify objects, to make appropriate decisions based on what it "sees," and then to produce relevant outputs. Today, facial recognition software, autonomous vehicles, certain forms of surveillance, and gesture recognition are just a few examples of CV systems at work. Why is computer vision so complicated? Every parent can recall their child going through phases when "what's that?" became a recurring question.

Of course, there are a few useful AI projects happening here and there. In March, DeepMind announced that it had used a machine-learning technique called "free modelling" to detail the structures of six proteins associated with SARS-CoV-2, the coronavirus that causes the Covid-19 disease. Elsewhere, Israeli start-up Aidoc is using AI imaging to flag abnormalities in the lungs and a U.K. start-up founded by Viagra co-inventor David Brown is using AI to look for Covid-19 drug treatments. Verena Rieser, a computer science professor at Heriot-Watt University, pointed out that autonomous robots can be used to help disinfect hospitals and AI tutors can support parents with the burden of home schooling. She also said "AI companions" can help with self isolation, especially for the elderly.

Recently, I was walking in a little park in downtown Mountain View, right in the heart of Silicon Valley, when I saw something unexpected. They were little robots, well little "automated carts", really, with a flag on top and there were a few of them lined up near the library. Every now and then, one of them would leave the line and navigate the sidewalks and crosswalks of downtown. Being in what is essentially ground zero of Silicon Valley, I guess I shouldn't have been that surprised. When I moved to Mountain View in 2007, I remember being surprised at seeing Google's fleet of autonomous cars driving around (now called Waymo). This was a precursor to the boom in self-driving car companies that happened over the past decade, and which has yet not quite borne fruit.

From helping in the global fight against Covid-19 to driving cars and writing classical symphonies, artificial intelligence is rapidly reshaping the world we live in. But not everyone is comfortable with this new reality. The billionaire tech entrepreneur Elon Musk has referred to AI as the "biggest existential threat" of our time. With recent scientific studies testing the technology's ability to evolve on its own, every step in its development throws up new concerns as to who is in control and how it will affect the lives of ordinary people. Here are 9 important milestones in the history of AI and the ethical concerns that have long loomed over the field.

Facial-recognition algorithms from Los Angeles startup TrueFace are good enough that the US Air Force uses them to speed security checks at base entrances. But CEO Shaun Moore says he's facing a new question: How good is TrueFace's technology when people are wearing face masks? "It's something we don't know yet because it's not been deployed in that environment," Moore says. His engineers are testing their technology on masked faces and are hurriedly gathering images of masked faces to tune their machine-learning algorithms for pandemic times. Facial recognition has become more widespread and accurate in recent years, as an artificial intelligence technology called deep learning made computers much better at interpreting images.

The critic Raymond Williams once wrote that every historical period has its own "structure of feeling." How everything seemed in the nineteen-sixties, the way the Victorians understood one another, the chivalry of the Middle Ages, the world view of Tang-dynasty China: each period, Williams thought, had a distinct way of organizing basic human emotions into an overarching cultural system. Each had its own way of experiencing being alive. In mid-March, in a prior age, I spent a week rafting down the Grand Canyon. When I left for the trip, the United States was still beginning to grapple with the reality of the coronavirus pandemic.

The blogosphere is full of descriptions about how data science and "AI' is changing the world. In financial services, applications include personalized financial offers, fraud detection, risk assessment (e.g. These applications outlined are largely not new, nor are "AI" algorithms like neural networks. However, increasingly commoditized, flexible and cheaper hardware with readily available algorithms and APIs have lowered barriers to data-compute intensive approaches common to data science, making the use of "AI" algorithms much more straightforward. For practitioners, definitions are well understood. For those less familiar and curious, here are some quick definitions and introductions to baseline everyone. At their heart, data science workflows transform data, from heterogenous sources of information, through models and learning, to derive information from which "useful" decisions can be expedited. Decisions may be automated (e.g. an online search or a retail credit fraud check) or ...

As more artificial intelligence applications move to smartphones, deep learning models are getting smaller to allow apps to run faster and save battery power. Now, MIT researchers have a new and better way to compress models. It's so simple that they unveiled it in a tweet last month: Train the model, prune its weakest connections, retrain the model at its fast, early training rate, and repeat, until the model is as tiny as you want. "That's it," says Alex Renda, a PhD student at MIT. "The standard things people do to prune their models are crazy complicated." Renda discussed the technique when the International Conference of Learning Representations (ICLR) convened remotely this month.