WASHINGTON – President Donald Trump invited Iranian Foreign Minister Mohammad Javad Zarif to the White House last month at the height of tensions between the two countries, The New Yorker magazine reported. The invitation, extended by Sen. Rand Paul with permission from the president, was turned down for now, The New Yorker reported Friday. Zarif said it was up to Tehran to decide on accepting it. Neither the White House nor the State Department responded to requests for comment on the report, which quoted U.S. and Iranian sources and what the magazine called a well-placed diplomat. Zarif told the magazine he would not want a White House meeting that yielded just a photo op and a two page statement afterwards, The New Yorker said.

TAIWAN-BASED technology firm CyCraft, which provides artificial intelligence (AI) solutions to enhance cybersecurity of businesses, on Friday announced a US$5.65 million fundraise in a Series B round. In total, CyCraft's total funding now amounts to US$8.15 million. Backers of this investment include Singapore-based investment company Pavilion Capital, and CID Group, an investment company based in Taiwan. Funds raised will propel the startup's research efforts in enhancing its AI technology and support market expansion in Asia. The firm has also formally launched SecOps Platform, an AI-driven platform which offers three variations of automated cybersecurity solutions.

During the presidential elections in Russia last year, a candidate named "Alice" ran for president. She ran her campaign using a slogan like "the president who knows you best" and she did receive a couple thousand votes. To be correct, "Alice" was not a she, but an artificial intelligence (AI) system. Alice's campaign page is still up. "Alice" is not the only AI system to run for public office.

April last year, a medical device powered by artificial intelligence (AI) received approval from the US Food and Drug Administration (USFDA), marking a historic moment in healthcare globally. The IDx-DR, a software algorithm that uses AI to analyse images of the eye using a camera, achieved an 87.4% accuracy rate while detecting'more than mild' diabetic retinopathy, a condition where high blood sugar levels damage the blood vessels in the retina. For IT services firms, which are already developing AI and machine language (ML) tools for other uses and industries, extending AI and ML capabilities to healthcare is a fairly non-complex process, and comes with a large upside. Rather than doing it entirely on their own though, these companies are partnering hospital chains and niche players in the field to acquire the required domain expertise. For instance, Japanese technology firm NTT DATA Services tied up with Pune's Deenanath Mangeshkar Hospital last year to use an AI-based solution to diagnose emphysema, a chronic condition of the lungs.

By the time his play "R.U.R." (which stands for "Rossum's Universal Robots") premiered in Prague in 1921, Karel Čapek was a well-known Czech intellectual. Like many of his peers, he was appalled by the carnage wrought by the mechanical and chemical weapons that marked World War I as a departure from previous combat. He was also deeply skeptical of the utopian notions of science and technology. "The product of the human brain has escaped the control of human hands," Čapek told the London Saturday Review following the play's premiere. "This is the comedy of science." In that same interview, Čapek reflected on the origin of one of the play's characters: The old inventor, Mr. Rossum (whose name translated into English signifies "Mr.

Creating an autonomous system to make scientific discoveries at a Nobel Prize level by 2050. With the system, AI would formulate hypotheses from enormous amounts of existing experimental data, and robots would conduct experiments to prove them. Achieving artificial hibernation technology by 2050, to help extend healthy human life spans.

--Recreating cyber-attack alert data with a high level of fidelity is challenging due to the intricate interaction between features, non-homogeneity of alerts, and potential for rare yet critical samples. Generative Adversarial Networks (GANs) have been shown to effectively learn complex data distributions with the intent of creating increasingly realistic data. This paper presents the application of GANs to cyber-attack alert data and shows that GANs not only successfully learn to generate realistic alerts, but also reveal feature dependencies within alerts. This is accomplished by reviewing the intersection of histograms for varying alert-feature combinations between the ground truth and generated datsets. Traditional statistical metrics, such as conditional and joint entropy, are also employed to verify the accuracy of these dependencies. Finally, it is shown that a Mutual Information constraint on the network can be used to increase the generation of low probability, critical, alert values. By mapping alerts to a set of attack stages it is shown that the output of these low probability alerts has a direct contextual meaning for Cyber Security analysts. Overall, this work provides the basis for generating new cyber intrusion alerts and provides evidence that synthesized alerts emulate critical dependencies from the source dataset. I NTRODUCTION Classifying, predicting, and generating cyber-attack alert data provides a unique set of challenges due to imbalance and a lack of homogeneity in alert datasets. Furthering these challenges critical exploits in a network are often rare and difficult to identify. Despite this is has been shown that alert data can be used to identify anomalous traffic [1] [2] [3], network vulnerabilities [4], and bad actor behavior profiling [5]. However, to fully realize the potential of cyber-attack alert data, a means to acquire more data and analyze critical dependencies within alerts is needed. This work seeks to provide solutions to these challenges by showing that deep learning models are able to recreate cyber-attack alert data when given representative real world data. This includes a means for driving better coverage of the feature domain in model outputs, allowing more rare but critical events to be synthesized.

Abstract--Recently, NLP models have been shown to be susceptible to adversarial attacks. In this paper, we explore adve rsarial attacks on neural machine translation (NMT) systems. Given a sentence in the source language, the goal of the proposed att ack is to change multiple words while ensuring that the predicte d translation remains unchanged. In order to choose the word from the source vocabulary, we propose a soft-attention bas ed technique. The experiments are conducted on two language pa irs: English-German (en-de) and English-French (en-fr) and two state-of-the-art NMT systems: BLSTM-based encoder-decod er with attention and Transformer . The proposed soft-attenti on based technique outperforms existing methods like HotFlip by a significant margin for all the conducted experiments The res ults demonstrate that state-of-the-art NMT systems are unable t o capture the semantics of the source language.

The White House Office of Management and Budget is promoting robotic process automation (RPA) as a technology that can help agencies minimize the time employees spend doing low-value, repetitive work. In a memorandum on "shifting from low-value to high-value work" released Monday, Director Mick Mulvaney delivers three directives to executive agencies. "Each year, Federal employees devote tens of thousands of hours to low-value compliance activities from rules and requirements that have built up over decades," the memo reads. In fact, shifting from low-value to high-value work is Cross-Agency Priority (CAP) goal number six. Per the recent memorandum, to operationalize this goal, agencies must designate a point of contact to coordinate "burden-reduction initiatives" and provide semi-annual updates on progress.

IDx, a Coralville-based med technology firm, is giving the world a glimpse into what eye health care might look like in the future. IDx-DR, an autonomous artificial intelligence diagnostic system developed by Dr. Michael Abramoff to detect diabetic retinopathy, gained approval in 2018 from the U.S. Food and Drug Administration. The technology is to be used in health care settings patients already visit, such as their primary care physician's office, to catch the disease before it causes permanent damage. "The early detection of this disease is important. We're reducing the burden on patients by making it easier to access testing," said Ben Clark, president and chief operating officer of IDx. "Diabetic retinopathy is the leading cause of preventable blindness in the United States," he said.