White House officials called on the tech sector to help combat the coronavirus with AI in a meeting with Silicon Valley heavyweights on Wednesday. During the teleconference, US Chief Technology Officer Michael Kratsios previewed a new database of coronavirus-related literature that the government plans to release in the coming days, and challenged the tech community to use AI to find insights from the data. Cutting edge technology companies and major online platforms will play a critical role in this all-hands-on-deck effort. Today's meeting outlined an initial path forward and we intend to continue this important conversation. The roughly two-hour call was attended by government officials and tech giants including Amazon, Apple, Google, Facebook, Microsoft, IBM, Twitter, and Cisco.

Earlier facial recognition technology was considered as an idea of science fiction. But in the past decade, facial recognition technology has not only become real -- but it's widespread. Today, people can easily read articles and news stories about facial recognition everywhere. Here is the history of facial recognition technology and some ideas about its bright future. Facial recognition technology along with AI (Artificial Intelligence) and Deep Learning (DL) technology are benefiting several industries.

On January 6th, the US Centers for Disease Control and Prevention (CDC) notified the public that a flu-like outbreak was propagating in Wuhan City, in the Hubei Province of China. Subsequently, the World Health Organization (WHO) released a similar report on January 9th. While these responses may seem timely, they were slow when compared to an AI company called BlueDot. BlueDot released a report on December 31st, a full week before the CDC released similar information. Even more impressive, BlueDot predicted the Zika outbreak in Florida six months before the first case in 2016.

Artificial intelligence (AI) may play an increasingly essentialFootnote 1 role in criminal acts in the future. Criminal acts are defined here as any act (or omission) constituting an offence punishable under English criminal law,Footnote 2 without loss of generality to jurisdictions that similarly define crime. Evidence of "AI-Crime" (AIC) is provided by two (theoretical) research experiments. In the first one, two computational social scientists (Seymour and Tully 2016) used AI as an instrument to convince social media users to click on phishing links within mass-produced messages. Because each message was constructed using machine learning techniques applied to users' past behaviours and public profiles, the content was tailored to each individual, thus camouflaging the intention behind each message. If the potential victim had clicked on the phishing link and filled in the subsequent web-form, then (in real-world circumstances) a criminal would have obtained personal and private information that could be used for theft and fraud. AI-fuelled crime may also impact commerce. In the second experiment, three computer scientists (Martínez-Miranda et al. 2016) simulated a market and found that trading agents could learn and execute a "profitable" market manipulation campaign comprising a set of deceitful false-orders. These two experiments show that AI provides a feasible and fundamentally novel threat, in the form of AIC. The importance of AIC as a distinct phenomenon has not yet been acknowledged. The literature on AI's ethical and social implications focuses on regulating and controlling AI's civil uses, rather than considering its possible role in crime (Kerr 2004).

WhiteHat Security, an application security provider for enterprises' businesses and an independent subsidiary of NTT Ltd., stated that over half of organizations globally use artificial intelligence (AI) or machine learning in their security operations, however, 60% of them are more confident in cyberthreat detections verified by humans over AI. In its research, "AI and Human Element Security Sentiment Study", WhiteHat Security highlighted the need for security organizations to incorporate both AI- and human-centric offerings, especially in the application security space. According to research findings, based on the responses of 102 professionals in the cybersecurity industry, 45% of respondents opined that their companies lack a sufficiently staffed cybersecurity team. Over 70% of respondents agreed that AI-based tools made their security teams more efficient by eliminating over 55% of everyday security operations. Incorporating AI tools into security operations decreased employees' stress levels, according to 40% of respondents.

Stahlman joins Team Human to discuss how artificial intelligence has become the new ground for human interaction, and why navigating it will require us to retrieve our uniquely human senses. "We will only become fully human if we learn to take responsibility for our actions." Further, he discusses the shift from a television environment to a digital environment and what that means for our collective sensibilities. Rushkoff discusses Super Tuesday results and looks at the figure and ground of the presidential race and what we can do in our local communities to create change.

Adversarial examples are often constructed by iteratively refining a randomly perturbed input. To improve diversity and thus also the success rates of attacks, we propose Output Diversified Initialization (ODI), a novel random initialization strategy that can be combined with most existing white-box adversarial attacks. Instead of using uniform perturbations in the input space, we seek diversity in the output logits space of the target model. Empirically, we demonstrate that existing $\ell_\infty$ and $\ell_2$ adversarial attacks with ODI become much more efficient on several datasets including MNIST, CIFAR-10 and ImageNet, reducing the accuracy of recently proposed defense models by 1--17\%. Moreover, PGD attack with ODI outperforms current state-of-the-art attacks against robust models, while also being roughly 50 times faster on CIFAR-10. The code is available on https://github.com/ermongroup/ODI/.

We present a significantly-improved data-driven global weather forecasting framework using a deep convolutional neural network (CNN) to forecast several basic atmospheric variables on a global grid. New developments in this framework include an offline volume-conservative mapping to a cubed-sphere grid, improvements to the CNN architecture, and the minimization of the loss function over multiple steps in a prediction sequence. The cubed-sphere remapping minimizes the distortion on the cube faces on which convolution operations are performed and provides natural boundary conditions for padding in the CNN. Our improved model produces weather forecasts that are indefinitely stable and produce realistic weather patterns at lead times of several weeks and longer. For short- to medium-range forecasting, our model significantly outperforms persistence, climatology, and a coarse-resolution dynamical numerical weather prediction (NWP) model. Unsurprisingly, our forecasts are worse than those from a high-resolution state-of-the-art operational NWP system. Our data-driven model is able to learn to forecast complex surface temperature patterns from few input atmospheric state variables. On annual time scales, our model produces a realistic seasonal cycle driven solely by the prescribed variation in top-of-atmosphere solar forcing. Although it is currently less accurate than operational weather forecasting models, our data-driven CNN executes much faster than those models, suggesting that machine learning could prove to be a valuable tool for large-ensemble forecasting.

Deep Learning (DL) is vulnerable to out-of-distribution and adversarial examples resulting in incorrect outputs. To make DL more robust, several posthoc anomaly detection techniques to detect (and discard) these anomalous samples have been proposed in the recent past. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection for DL based applications. We provide a taxonomy for existing techniques based on their underlying assumptions and adopted approaches. We discuss various techniques in each of the categories and provide the relative strengths and weaknesses of the approaches. Our goal in this survey is to provide an easier yet better understanding of the techniques belonging to different categories in which research has been done on this topic. Finally, we highlight the unsolved research challenges while applying anomaly detection techniques in DL systems and present some high-impact future research directions.

March is Women's History Month in the US, the UK and Australia, a time to honour women's sometimes underrated contributions to society. According to the US National Women's History Museum, Women's History Month started in 1978 as a local "Women's History Week" celebration in California, with organizers selecting the week to correspond with the March 8 International Women's Day. The US Congress in 1987 passed Public Law 100-9 designating March as the Women's History Month. The past few decades have seen a steady increase in the number of women studying and excelling in the STEM fields. But this is not so in computer science -- the number of women studying or pursuing a career in computer science has been decreasing since around 1990.