Model compression has emerged as an important area of research for deploying deep learning models on Internet-of-Things (IoT). However, for extremely memory-constrained scenarios, even the compressed models cannot fit within the memory of a single device and, as a result, must be distributed across multiple devices. This leads to a distributed inference paradigm in which memory and communication costs represent a major bottleneck. Yet, existing model compression techniques are not communication-aware. Therefore, we propose Network of Neural Networks (NoNN), a new distributed IoT learning paradigm that compresses a large pretrained 'teacher' deep network into several disjoint and highly-compressed 'student' modules, without loss of accuracy. Moreover, we propose a network science-based knowledge partitioning algorithm for the teacher model, and then train individual students on the resulting disjoint partitions. Extensive experimentation on five image classification datasets, for user-defined memory/performance budgets, show that NoNN achieves higher accuracy than several baselines and similar accuracy as the teacher model, while using minimal communication among students. Finally, as a case study, we deploy the proposed model for CIFAR-10 dataset on edge devices and demonstrate significant improvements in memory footprint (up to 24x), performance (up to 12x), and energy per node (up to 14x) compared to the large teacher model. We further show that for distributed inference on multiple edge devices, our proposed NoNN model results in up to 33x reduction in total latency w.r.t. a state-of-the-art model compression baseline.

In recent years, active subspace methods (ASMs) have become a popular means of performing subspace sensitivity analysis on black-box functions. Naively applied, however, ASMs require gradient evaluations of the target function. In the event of noisy, expensive, or stochastic simulators, evaluating gradients via finite differencing may be infeasible. In such cases, often a surrogate model is employed, on which finite differencing is performed. When the surrogate model is a Gaussian process, we show that the ASM estimator is available in closed form, rendering the finite-difference approximation unnecessary. We use our closed-form solution to develop acquisition functions focused on sequential learning tailored to sensitivity analysis on top of ASMs. We also show that the traditional ASM estimator may be viewed as a method of moments estimator for a certain class of Gaussian processes. We demonstrate how uncertainty on Gaussian process hyperparameters may be propagated to uncertainty on the sensitivity analysis, allowing model-based confidence intervals on the active subspace. Our methodological developments are illustrated on several examples.

On Wednesday, Treasury Secretary Steven Mnuchin declared that Google's work in China is not a security concern. As he told CNBC, "I don't see any area -- again the president and I did diligence on this issue -- and we're not aware of any areas where Google is working with the Chinese government in any way that raises concerns." Google's most disturbing Chinese initiatives involve the co-development of technology. Cooperation of this sort is so injurious to the United States that it should be criminalized, by emergency presidential order. Google, the Alphabet Inc. unit, also believes its projects in China are benign.

Cristina Might drew close to her son. He was listless and groggy after weeks of battling a puzzling illness that had filled his lungs with fluid and, hours earlier, stopped his breathing entirely. A code team had rushed to Buddy's bedside and jolted him back to life, but now the 11-year-old with the broad smile was gray, his eyes unable to focus. His mom leaned nearer still. It was time to say goodbye. But Cristina's words to her son, a brown-eyed boy who loved dolphins and his aquarium, offered no hint of her desperation: "I was telling him it was all going to be OK, that his fishies couldn't wait to see him again and that he had to hurry up and come home." Somehow, Buddy made it through that night this past May, allowing doctors at Children's Hospital of Alabama to insert a tube to drain his lungs. His illness had caused a frightening cascade of symptoms: a yellowish substance in his bones and a bulging abdomen, on top of the deluge of fluid.

It's 6 A.M., and the alarm clock is buzzing earlier than usual. It's not a malfunction: the smart clock scanned your schedule and adjusted because you've got that big presentation first thing in the morning. Your shower automatically turns on and warms to your preferred 103 F. The electric car is ready to go, charged by the solar panels or wind turbine on your roof. When you get home later, there's an unexpected package waiting, delivered by drone. You open it to find cold medicine.

Even in the first month of my governorship of this fine state, I began to have problems with the legislature, which belonged to the "other" political party. I had campaigned on the plan to transform the state capital, Columbville, into a Smart City, but my political party and the opposition wanted it to use different operating systems. Another problem was that we disagreed about what should be done with the three abandoned shopping centers, now that all our citizens bought their stuff online. That issue was tangled up with all the road improvements needed to keep the self-driving trucks and taxis from roaming the schoolyards, although that could have been worse if the kids were still attending classes rather than home-schooling online as most of them now did. I sent drafts of laws and budgets to the legislature, and they voted them down.

Spam! That's what Lorrie Faith Cranor and Brian LaMacchia exclaimed in the title of a popular call-to-action article that appeared 20 years ago in Communications.10 And yet, despite the tremendous efforts of the research community over the last two decades to mitigate this problem, the sense of urgency remains unchanged, as emerging technologies have brought new dangerous forms of digital spam under the spotlight. Furthermore, when spam is carried out with the intent to deceive or influence at scale, it can alter the very fabric of society and our behavior. In this article, I will briefly review the history of digital spam: starting from its quintessential incarnation, spam emails, to modern-days forms of spam affecting the Web and social media, the survey will close by depicting future risks associated with spam and abuse of new technologies, including artificial intelligence (AI), for example, digital humans. After providing a taxonomy of spam, and its most popular applications emerged throughout the last two decades, I will review technological and regulatory approaches proposed in the literature, and suggest some possible solutions to tackle this ubiquitous digital epidemic moving forward. An omni-comprehensive, universally acknowledged definition of digital spam is hard to formalize. Laws and regulation attempted to define particular forms of spam, for example, email (see 2003's Controlling the Assault of Non-Solicited Pornography and Marketing Act.) However, nowadays, spam occurs in a variety of forms, and across different techno-social systems. Each domain may warrant a slight different definition that suits what spam is in that precise context: some features of spam in a domain, for example, volume in mass spam campaigns, may not apply to others, for example, carefully targeted phishing operations.

British Prime Minister Boris Johnson delivered his first speech in the Parliament on Thursday, promising to unleash a "new golden age for the United Kingdom" and leaving the European Union by the Oct. 31 deadline. Johnson, who formally took office on Wednesday, made a fiery speech in the parliament in a bid to turbocharge the effort to execute Brexit "for the purpose of uniting and re-energizing our great United Kingdom and making this country the greatest place on earth." He said that despite the looming Oct. 31 deadline, there's time to renegotiate with the European bloc and remains optimistic that a new deal can be reached. "This is the first day of a new approach which will start with our departure from the European Union on October 31 ... beginning new golden age for the United Kingdom." "This is the first day of a new approach which will start with our departure from the European Union on October 31," Johnson said, adding that it will mark the beginning of a "new golden age for the United Kingdom." Johnson also assured EU citizens currently living in the country that they would have "absolute certainty" of their right to live and remain in the country after Brexit.

Gov. Andrew M. Cuomo created a commission Wednesday to guide New York in governing artificial intelligence in robots and examining how the burgeoning technology can impact the labor market, improve public services and industry, and look at how AI and robots "may be used in unlawful or unsafe ways." The commission will examine how other states have regulated artificial intelligence, and look at what revisions are needed to New York laws in how to regulate artificial intelligence to protect industry and residents. The 13-member commission will include five appointed by Cuomo, two by Senate Majority Leader Andrea Stewart-Cousins, two by Assembly Speaker Carl Heastie, one by Senate Republican leader John Flanagan, one by Assembly Republican leader Brian Kolb, and one each by the chancellors of the State University of New York and the City University of New York. Findings and recommendations from the new Artificial Intelligence, Robotics and Automation Commission will be reported to the governor and the Legislature for action. "Artificial intelligence and automation are already having a profound impact across many industries and their influence keeps growing, so it's critical that we do everything in our power to understand their capabilities and potential pitfalls," Cuomo said.

Raghav serves as Content Lead at Emerj, covering our major industry areas and conducting research. Raghav has a personal interest in robotics, and previously worked for research firms like Frost & Sullivan and Infiniti Research. AI has made some inroads in the cybersecurity sector and several AI vendors claim to have launched products that use AI to help safeguard against cyber threats. At Emerj, we've seen many cybersecurity vendors offering AI and machine learning-based products to help identify and deal with cyber threats. Even the Pentagon created the Joint Artificial Intelligence Center (JAIC) to upgrade to AI-enabled capabilities in their cybersecurity efforts.