In late 2017, AB InBev, the Belgian giant behind Budweiser and other beers, began adding a little artificial intelligence to its brewing recipe. Using data collected from a brewery in Newark, New Jersey, the company developed an AI algorithm to predict potential problems with the filtration process used to remove impurities from beer. Paul Silverman, who runs the New Jersey Beer Company, a small operation not far from the AB InBev brewery, says his team isn't even using computers, let alone AI. "We sit around tasting beer and thinking about what to make next," he says. The divide between the two breweries highlights the pace at which AI is being adopted by US companies. With so much hype around artificial intelligence, you might imagine that it's everywhere.
In the opening pages of Burn-In , an FBI agent conducts close-quarters surveillance of a suspected terrorist bomber in Washington, D.C. Simultaneously, in New Jersey, an elderly gentleman listens attentively to the enthusiastic technological prognostications of a world-famous computer scientist and mathematician from the back of a hallowed lecture hall at Princeton University. Moments later, he bludgeons the speaker to death with his cane. In this, their second novel, coauthors Peter Warren Singer and August Cole—both renowned technology and policy experts—come close to perfecting the genre of educational and informative techno-thriller. Like their first such collaboration ([ 1 ]), this latest entry portrays a world in which conventional aspects of domestic security and law enforcement—combating terrorism, managing protests and social upheavals, tracking a serial killer, providing a secure environment on college campuses—all occur within a transformative technological context that both enables and simultaneously disrupts these myriad objectives. As the narrative unfolds, a complex tapestry of emergent, disruptive technologies is revealed. Far from the fanciful inventions that typically populate science fiction, the systems described herein are currently available or under development for imminent deployment. The D.C. traffic congestion with which agent Lara Keegan and her partner have to contend, for example, is mostly composed of driverless vehicles, their complex operational algorithms engaged in competitive maneuvering for even the slightest comparative advantage. If the agents invoke the emergency override protocol granted to law enforcement personnel and cause the other vehicles to move aside, the surveillance drones buzzing overhead will immediately transmit this activity to the news outlets that operate them, alerting the terrorist to their presence. Keegan's field of vision, meanwhile, is networked into an operations command center via virtual reality glasses, which display real-time data on the suspect's location. These “viz glasses” continuously exchange data with other law enforcement personnel, while simultaneously performing facial scans of the surrounding crowds, subjecting each passerby to massive digital analysis. Once apprehended, despite his uncooperative silence, the suspect's identity is unmasked by a Tactical Autonomous Mobility System (TAMS), a military robot whose combat utility proved minimal and is now being tested for possible use in domestic law enforcement scenarios. Keegan, we learn, has been selected to field-test this robotic deep-learning technology system because of her prior experience managing the deployment and “force mix” of unmanned systems for the Marine Corps in Afghanistan. In technology circles, what she has been asked to undertake is known as a burn-in, a lengthy trial run of any new technological breakthrough, designed to push it to its limits of reliable functionality. The novel also contains ample instances of what the Defense Advanced Research Projects Agency (DARPA) and the National Science Foundation dub the ethical, legal, and social implications (ELSI) of technological development and diffusion. Just before his death, for example, the Princeton computer scientist boasts to his elderly guest how his use of Linux open-source software to develop complex machine-learning algorithms has made artificial intelligence (AI) universally available and affordable for every conceivable purpose. As his killer peels off an AI-designed silicon facial mask (manufactured on a 3D printer to confuse the university's AI-assisted security and surveillance system), he reveals himself to be a former DARPA engineer whose wife and son were tragically killed in a Metro crash caused by dangerous emergent behaviors in one of the scientist's AI-governed public transportation systems. This narrative thread, and many others throughout the book, illustrate what coauthor Peter Warren Singer identified in his widely acclaimed book Wired for War (published in 2009) as a key constituent of technological innovation and advance: “Anything that can go wrong, will—at the worst possible moment.” The aim of this work of fiction is not merely to engage and entertain but also to educate and inform readers about the vast array of automated and increasingly intelligent autonomous systems that are proliferating in availability and use. The authors provide detailed documentation of the actual features and current use of these systems, together with a companion educational guide to help instructors use the novel to teach about the profound depths of the robotic and AI revolution that is taking place all around us. 1. [↵]1. P. W. Singer, 2. A. Cole , Ghost Fleet: A Novel of the Next World War (Houghton Mifflin Harcourt, 2015). : #ref-1 : #xref-ref-1-1 "View reference 1 in text"
This article is contributed by Internet Creations, a professional services organization based in New Jersey, and a Salesforce partner with 11 support agent and sales apps on the AppExchange. During my career, I've learned the value of creating experiences that help keep stress levels down -- to anticipate what people may need before they have a problem. When the founder of Internet Creations asked me to take on the role of CEO shortly after COVID-19 hit, I knew this leadership approach would be more important than ever during the crisis. What fears might employees and customers have? How can we proactively solve them?
Fei-Fei Li is one of the people responsible for the current AI revolution and is now Co-Director of the Stanford Institute for Human-Centered Artificial Intelligence (HAI). In this episode, Fei-Fei talks about her early days running a New Jersey dry cleaner to finance her Princeton education; her creation of ImageNet, the world's first large labeled image data set, which allowed the validation of neural networks; and her latest work on ambient intelligence, which promises to transform elder care.
Humans may sometimes regard robots with apprehension or resentment over the increasing automation of labor, but the coronavirus pandemic is showing how the two can work together in new ways that might save lives during a crisis. Around the globe, robots and other technologies, like drones and telehealth devices, are being used in a variety of settings and capacities to assist in the COVID-19 response since there is a level of elevated risk for human workers. Automated devices have delivered meals to quarantined travelers in a Chinese hotel; enforced curfews in Tunisia; scanned visitors for fevers entering a South Korean hospital; monitored patients in a hard-hit Italian city; and tracked social distancing compliance from the skies in a number of cities around the world, including Elizabeth, New Jersey. Many of the technologies were available commercially prior to the coronavirus outbreak, said Texas A&M University professor Robin Murphy, who studies how robots can be deployed during disasters. But now, "they are being used 24/7 and adapted to fit the needs of those using them," Murphy added.
Dr. Andrew Toole is the Chief Economist at the U.S. Patent and Trademark Office (USPTO) and a Research Associate at the Centre for European Economic Research (ZEW). Dr. Toole joined the USPTO with experience in the private sector, academia, and government. While completing his PhD in economics at Michigan State University, Andrew Toole was a Senior Economist for Laurits R. Christensen Associates where he conducted studies on total factor productivity, cost and price analysis, and competitive strategy. In 1998, Dr. Toole went to Stanford University as a postdoctoral student before becoming a faculty member at Illinois State University and Rutgers University in New Jersey. As an academic researcher, Dr. Toole was asked to advise on science and technology policy issues for institutions such as the U.S. National Academies of Science, U.S. National Institutes of Health, and the U.S. Department of Agriculture (USDA).
If Hoan Ton-That is feeling the pressure, he isn't showing it. Over the last month, fears about facial recognition technology and police surveillance have intensified, all thanks to Ton-That's startup, Clearview AI. First came a front-page investigation in The New York Times, revealing Clearview has been working with law enforcement agencies to match photos of unknown faces to people's online images. Next, cease-and-desist letters rolled in from tech giants Twitter, Google and Facebook. Lawmakers made inquiries and New Jersey enacted a statewide ban on law enforcement using Clearview while it looks into the software.
In the first set the samples follow the origin probability density hp Z q and in the second the target density f p X q . The target density f p X q is considered unknown while hpZ q can either be known with the possibility to produce samples Z j every time it is necessary or unknown in which case we have a second fixed training set t Z ju . Our goal is to design a deterministic transformation GpZ q so that the data t Y ju produced by applying the transformation Y " Gp Z q onto t Z ju follow the target density f p Y q . Of course one may wonder whether the proposed problem enjoys any solution, namely, whether there indeed exists a transformation GpZ q capable of transforming Z into Y with the former following the origin density hp Z q and the latter the target density f pY q . The problem of transforming random vectors has been analyzed by (Box & Cox, 1964) where existence is shown under general conditions. Computing, however, the actual transformation is a completely different challenge with one of the possible solutions rely-1 Department of Computer Science, Rutgers University, New Brunswick, NJ, USA. 2 Department of Electrical and Computer Engineering, University of Patras, Patras, Greece.. Correspondence to: K. Basioti firstname.lastname@example.org
New Jersey's attorney general, Gurbir S. Grewal, has instructed prosecutors across the state to stop using Clearview AI, a private facial recognition software. Clearview AI's tools allow law enforcement officials to upload a photo of an unknown person they'd like to identify, and see a list of matches culled from a database of over 3 billion photos. The photos are taken from a variety of controversial sources, including Facebook, YouTube, Twitter, and even Venmo. New Jersey attorney general Gurbir S. Grewal told the state's prosecutor's to stop using Clearview AI, private facial recognition software that he worried might compromise the integrity of the state's investigations Clearview says that anyone can submit a request to the company to have a photo of them removed from its databases, but they must first present proof they own copyright to the photo. Grewal decided to issue the ban after seeing Clearview had used footage from a 2019 sting operation in New Jersey promoting its own services, something even he hadn't been aware of at the time.