Contact-tracing is an essential tool in order to mitigate the impact of pandemic such as the COVID-19. In order to achieve efficient and scalable contact-tracing in real time, digital devices can play an important role. While a lot of attention has been paid to analyzing the privacy and ethical risks of the associated mobile applications, so far much less research has been devoted to optimizing their performance and assessing their impact on the mitigation of the epidemic. We develop Bayesian inference methods to estimate the risk that an individual is infected. This inference is based on the list of his recent contacts and their own risk levels, as well as personal information such as results of tests or presence of syndromes. We propose to use probabilistic risk estimation in order to optimize testing and quarantining strategies for the control of an epidemic. Our results show that in some range of epidemic spreading (typically when the manual tracing of all contacts of infected people becomes practically impossible, but before the fraction of infected people reaches the scale where a lockdown becomes unavoidable), this inference of individuals at risk could be an efficient way to mitigate the epidemic. Our approaches translate into fully distributed algorithms that only require communication between individuals who have recently been in contact. Such communication may be encrypted and anonymized and thus compatible with privacy preserving standards. We conclude that probabilistic risk estimation is capable to enhance performance of digital contact tracing and should be considered in the currently developed mobile applications. Identifying, calling, testing, and if needed quarantining the recent contacts of an individual who has just been tested positive is the standard route for limiting the transmission of a highly contagious virus.

The chief of Iran's paramilitary Revolutionary Guard threatened Saturday to go after everyone who had a role in a top general's January killing during a U.S. drone strike in Iraq. The guard's website quoted Gen. Hossein Salami as saying, "Mr. Our revenge for martyrdom of our great general is obvious, serious and real." U.S. President Donald Trump warned this week that Washington would harshly respond to any Iranian attempts to take revenge for the death of Gen. Qassem Soleimani, tweeting that "if they hit us in any way, any form, written instructions already done we're going to hit them 1000 times harder." The president's warning came in response to a report that Iran was plotting to assassinate the U.S. ambassador to South Africa in retaliation for Soleimani's killing at Baghdad's airport at the beginning of the year.

It's been three months since OpenAI launched an API underpinned by cutting-edge language model GPT-3, and it continues to be the subject of fascination within the AI community and beyond. Portland State University computer science professor Melanie Mitchell found evidence that GPT-3 can make primitive analogies, and Columbia University's Raphaël Millière asked GPT-3 to compose a response to the philosophical essays written about it. But as the U.S. presidential election nears, there's growing concern among academics that tools like GPT-3 could be co-opted by malicious actors to foment discord by spreading misinformation, disinformation, and outright lies. In a paper published by the Middlebury Institute of International Studies' Center on Terrorism, Extremism, and Counterterrorism (CTEC), the coauthors find that GPT-3's strength in generating "informational," "influential" text could be leveraged to "radicalize individuals into violent far-right extremist ideologies and behaviors." Bots are increasingly being used around the world to sow the seeds of unrest, either through the spread of misinformation or the amplification of controversial points of view.

The White House's recent decision to allow the sale of advanced weapons systems to the United Arab Emirates highlights the deliberate shift in US policy towards the UAE after it signed "normalisation" accords with Israel. Why would the UAE want American drones as it already has dozens of Chinese armed unmanned aerial vehicles (UAVs) in its inventory? And why has the United States now agreed to these sales, overcoming its traditional reticence to sell sophisticated weapons to other countries? Chinese armed drones have made a significant effect on the battlefields across the Middle East and North Africa. They have been used to assassinate Houthi rebel leaders in Yemen, kill ISIL-affiliated fighters in the Sinai, and for a time help Khalifa Haftar dominate the battlespace in Libya.

AI will take your job, AI can sort out even the messiest data, AI will take over the world, AI is new. AI has been touted in the recent past, with it comes myths that often lead to misunderstanding of the technology. Eliezer Yudkowsk says that "By far the greatest danger of Artificial Intelligence is that people conclude too early that they understand it." In this article, we take a look at the top myths about Artificial intelligence we get to see what is true and what is not. "understanding is much deeper than knowledge there are very many people who know artificial intelligence but very few understand AI" AI dates back to the 19th century when an English mathematician and writer, Lady Ada Lovelace predicted that "a machine might compose elaborate and scientific pieces of music of any degree of complexity or extent" this was later advanced in the 1940s when a Bombe machine was created by Alan Turing.

Machine Learning Africa events are created to help business and technology executives cut through the hype, and learn how they can use AI to create competitive advantage, drive new business opportunities and foster innovation. Every organization is looking at how they can strive in the 4th industrial revolution, therefore AI, machine learning, and similar technologies need to be demystified. Target Audience includes C-Level Executives and business leaders from the public and private sector who are passionate about technology and innovation.

In Neural Architecture Search (NAS), Differentiable ARchiTecture Search (DARTS) has recently attracted much attention due to its high efficiency. It defines an over-parameterized network with mixed edges each of which represents all operator candidates, and jointly optimizes the weights of the network and its architecture in an alternating way. However, this process prefers a model whose weights converge faster than the others, and such a model with fastest convergence often leads to overfitting. Accordingly the resulting model cannot always be well-generalized. To overcome this problem, we propose Minimum Stable Rank DARTS (MSR-DARTS), which aims to find a model with the best generalization error by replacing the architecture optimization with the selection process using the minimum stable rank criterion. Specifically, a convolution operator is represented by a matrix and our method chooses the one whose stable rank is the smallest. We evaluate MSR-DARTS on CIFAR-10 and ImageNet dataset. It achieves an error rate of 2.92% with only 1.7M parameters within 0.5 GPU-days on CIFAR-10, and a top-1 error rate of 24.0% on ImageNet. Our MSR-DARTS directly optimizes an ImageNet model with only 2.6 GPU days while it is often impractical for existing NAS methods to directly optimize a large model such as ImageNet models and hence a proxy dataset such as CIFAR-10 is often utilized.

Proof by induction is a long-standing challenge in Computer Science. Induction tactics of proof assistants facilitate proof by induction, but rely on humans to manually specify how to apply induction. In this paper, we present SeLFiE, a domain-specific language to encode experienced users' expertise on how to apply the induct tactic in Isabelle/HOL: when we apply an induction heuristic written in SeLFiE to an inductive problem and arguments to the induct tactic, the SeLFiE interpreter examines both the syntactic structure of the problem and semantics of the relevant constants to judge whether the arguments to the induct tactic are plausible according to the heuristic. Then, we present semantic_induct, an automatic tool to recommend how to apply the induct tactic. Given an inductive problem, semantic_induct produces candidate arguments to the induct tactic and selects promising ones using heuristics written in SeLFiE. Our evaluation based on 254 inductive problems from nine problem domains show that semantic_induct achieved 15.7 percentage points of improvements in coincidence rates for the three most promising recommendations while achieving 43% of reduction in the median value for the execution time when compared to an existing tool, smart_induct.

When an apparently indecipherable manuscript from a lost language turns up, AI can help. But first, how is a language born and how does it die (or get lost)? We really don't know how human language was born; theories abound but all we know for sure is that it is unique. In a 2017 paper at BMC Biology, evolutionary biologist Mark Pagel states flatly, "Human language is unique among all forms of animal communication." Most ape sign language, for example, is concerned with requests for food.

When the elephant arrived in the night, on the hunt for sugarcane, Uthorn Kanthong was waiting for him. Like many of his neighbors, the 69-year-old Thai farmer had taken to staying in his fields into the late hours, to try and scare off elephants that came to snack on his crop. He usually returned home by midnight. But that night in 2018, he didn't come back. Worried, his daughter sent out family and friends to look for him.