The U.S. government may be planning for a quantum computing workforce (the government passed a bill to foster an active quantum computing industry in September 2018), yet this hasn't prevented the security community from regarding quantum computing being seen as an'emerging threat' together with certain forms of artificial intelligence. The study was commissioned by the U.S. Government Accountability Office, in a white paper titled "Long-Range Emerging Threats Facing the United States As Identified by Federal Agencies." Here Federal agencies identified 26 long-term threats within four categories, which were: Adversaries' Political and Military Advancements--e.g., China's increasing ability to match the U.S. military's strength. Dual-Use Technologies--e.g., self-driving cars might be developed for private use, but militaries can use them too. Within this, the future of "dual-use technologies" took center stage, according to TechCrunch.

Binary data matrices can represent many types of data such as social networks, votes or gene expression. In some cases, the analysis of binary matrices can be tackled with nonnegative matrix factorization (NMF), where the observed data matrix is approximated by the product of two smaller nonnegative matrices. In this context, probabilistic NMF assumes a generative model where the data is usually Bernoulli-distributed. Often, a link function is used to map the factorization to the $[0,1]$ range, ensuring a valid Bernoulli mean parameter. However, link functions have the potential disadvantage to lead to uninterpretable models. Mean-parameterized NMF, on the contrary, overcomes this problem. We propose a unified framework for Bayesian mean-parameterized nonnegative binary matrix factorization models (NBMF). We analyze three models which correspond to three possible constraints that respect the mean-parametrization without the need for link functions. Furthermore, we derive a novel collapsed Gibbs sampler and a collapsed variational algorithm to infer the posterior distribution of the factors. Next, we extend the proposed models to a nonparametric setting where the number of used latent dimensions is automatically driven by the observed data. We analyze the performance of our NBMF methods in multiple datasets for different tasks such as dictionary learning and prediction of missing data. Experiments show that our methods provide similar or superior results than the state of the art, while automatically detecting the number of relevant components.

We provide a methodology, resilient feature engineering, for creating adversarially resilient classifiers. According to existing work, adversarial attacks identify weakly correlated or non-predictive features learned by the classifier during training and design the adversarial noise to utilize these features. Therefore, highly predictive features should be used first during classification in order to determine the set of possible output labels. Our methodology focuses the problem of designing resilient classifiers into a problem of designing resilient feature extractors for these highly predictive features. We provide two theorems, which support our methodology. The Serial Composition Resilience and Parallel Composition Resilience theorems show that the output of adversarially resilient feature extractors can be combined to create an equally resilient classifier. Based on our theoretical results, we outline the design of an adversarially resilient classifier.

We regret to report there's more bad news to share about Facebook and how it's protecting your privacy. Some of the highlights from earlier this week include an incredible tech demo, tech gift ideas that cost less than $50 and hands-on with Tesla's latest Autopilot system. As columnist Violet Blue explains, "anyone holding even the barest minimum of cybersecurity knowledge could've figured out in minutes that Butina's interest in cybersecurity was minimal. While she spied on and infiltrated the Republican party, she also was a research assistant at American University and co-authored a paper titled "Cybersecurity Knowledge Networks." Read it if you want to see what achingly fake, buzzword bingo looks like." Oops!'Overpowered' Infinity Blade removed from'Fortnite' Epic admitted it "messed up" with the weapon and has already sent the Infinity Blade into Fortnite's vault.

The manufacturing sector is heavily influenced by artificial intelligence-based technologies with the extraordinary increases in computational power and data volumes. It has been reported that 35% of US manufacturers are currently collecting data from sensors for manufacturing processes enhancement. Nevertheless, many are still struggling to achieve the 'Industry 4.0', which aims to achieve nearly 50% reduction in maintenance cost and total machine downtime by proper health management. For increasing productivity and reducing operating costs, a central challenge lies in the detection of faults or wearing parts in machining operations. Here we propose a data-driven, end-to-end framework for monitoring of manufacturing systems. This framework, derived from deep learning techniques, evaluates fused sensory measurements to detect and even predict faults and wearing conditions. This work exploits the predictive power of deep learning to extract hidden degradation features from noisy data. We demonstrate the proposed framework on several representative experimental manufacturing datasets drawn from a wide variety of applications, ranging from mechanical to electrical systems. Results reveal that the framework performs well in all benchmark applications examined and can be applied in diverse contexts, indicating its potential for use as a critical corner stone in smart manufacturing.

Higher-order motif structures and multi-vertex interactions are becoming increasingly important in studies that aim to improve our understanding of functionalities and evolution patterns of networks. To elucidate the role of higher-order structures in community detection problems over complex networks, we introduce the notion of a Superimposed Stochastic Block Model (SupSBM). The model is based on a random graph framework in which certain higher-order structures or subgraphs are generated through an independent hyperedge generation process, and are then replaced with graphs that are superimposed with directed or undirected edges generated by an inhomogeneous random graph model. Consequently, the model introduces controlled dependencies between edges which allow for capturing more realistic network phenomena, namely strong local clustering in a sparse network, short average path length, and community structure. We proceed to rigorously analyze the performance of a number of recently proposed higher-order spectral clustering methods on the SupSBM. In particular, we prove non-asymptotic upper bounds on the misclustering error of spectral community detection for a SupSBM setting in which triangles or 3-uniform hyperedges are superimposed with undirected edges. As part of our analysis, we also derive new bounds on the misclustering error of higher-order spectral clustering methods for the standard SBM and the 3-uniform hypergraph SBM. Furthermore, for a non-uniform hypergraph SBM model in which one directly observes both edges and 3-uniform hyperedges, we obtain a criterion that describes when to perform spectral clustering based on edges and when on hyperedges, based on a function of hyperedge density and observation quality.

Amazon's use of facial recognition has sparked fears of an authoritarian future resembling that described by George Orwell. Privacy advocates and campaigners have said Amazon using facial recognition in its smart doorbells could provide the perfect tool for extreme surveillance. The campaigners called the technology'nightmarish' and'disturbing'. Amazon bought the US-based firm Ring earlier this year. The doorbell company has previously filed for a patent to use facial recognition in its products.

The AI Index 2018 report is out, and if you're interested in AI enough to read this newsletter, you really should read the report through for yourself. Maybe it's the nerdy thing you do when lounging with family this holiday season, or something you take in during a long walk or travel, but it's worth a look since it's one of very few attempts to collate a comprehensive look at the amalgamation that is the AI industry. See last year's newsletter on the annual report for a recap. It doesn't hurt that leaders from the most advanced organizations in this space, including OpenAI, MIT, and SRI International, played a role in putting it together. Corporate AI papers saw a 73 percent increase.

Artificial intelligence is already beginning to spiral out of our control, a new report from top researchers warns. Not so much in a Skynet kind of sense, but more in a'technology companies and governments are already using AI in ways that amp up surveillance and further marginalize vulnerable populations' kind of way. On Thursday, the AI Now Institute, which is affiliated with New York University and is home to top AI researchers with Google and Microsoft, released a report detailing, essentially, the state of AI in 2018, and the raft of disconcerting trends unfolding in the field. What we broadly define as AI--machine learning, automated systems, etc.--is currently being developed faster than our regulatory system is prepared to handle, the report says. And it threatens to consolidate power in the tech companies and oppressive governments that deploy AI while rendering just about everyone else more vulnerable to its biases, capacities for surveillance, and myriad dysfunctions.

This post has been updated since it was originally published. The AI-hype would have you believing that we'll soon be enslaved by super-intelligent beings or hunted by killer robots. Before building that Soviet-era bunker to survive the AIpocalypse, consider more immediate issues which are already affecting society today. According to 23 AI experts Martin Ford interviewed for his new book, Architects Of Intelligence, the real imminent AI-threats relate to politics, security, privacy, and the weaponization of AI. To understand how these problems affect society today, it's helpful to see them from the perspective of leaders which have helped shape the current AI revolution.