The arc of innovation has reached an inflection point: technological change now threatens to overwhelm us. Discovery is unstoppable, but it must be shaped for good. We ourselves--not just market forces--must manage it. Ash Carter, former US Secretary of Defense, is the Director of Harvard Kennedy School's Belfer Center for Science and International Affairs and its project on Technology and Public Purpose. He is also an Innovation Fellow at MIT.

As a geologist and professor I speak and write rather cavalierly about eras and eons. One of the courses I routinely teach is "History of Earth and Life," a survey of the 4.5-billion-year saga of the entire planet--in a 10-week trimester. But as a human, and more specifically as a daughter, mother, and widow, I struggle like everyone else to look Time honestly in the face. That is, I admit to some time hypocrisy. The now risible "Y2K" crisis that threatened to cripple global computer systems and the world economy at the turn of the millennium was caused by programmers in the 1960s and '70s who apparently didn't really think the year 2000 would ever arrive.

Facebook is expanding its fake news spotting systems to include photos and videos as part of its ongoing battle to halt the spread of misinformation on its service. Following successful trials in France, India, and Mexico, the company said it will now roll-out the system in 17 countries worldwide in a bid to staunch what it has branded'misinformation in these new visual formats.' The Artificial Intelligence (AI) system feeds potentially fake content to human fact-checkers, who use visual verification techniques such as reverse image searching and analysing image metadata to check the veracity of photos and videos. Previously, the company's efforts to tackle misinformation had been focused on rooting out false articles and webpage links. Russian agents and other malicious groups seeking to influence democratic elections in the US and elsewhere have repeatedly used images and video.

You know when you're listening to a podcast interview and the guest says something and you literally smack your head, pause the podcast, and start tweeting? Happened to me last week. Andrew Yang, founder of "Venture for America" (and a long-shot candidate for president in 2020) said "Google recently demonstrated software that can do the job of an average call center worker … that's going to result in hundreds of thousands of jobs lost". Now, I grant some leniency for people outside our industry not getting some details right. But this thought is so wrong -- and, sadly, growing in popularity -- that it really needs correcting.

Google's secretive plans in China are attracting renewed scrutiny from privacy advocates. The tech giant is said to be building a prototype version of a censored Chinese search engine that links users' activity to their personal phone number, according to the Intercept. In doing so, it would be able to comply with the Chinese government's censorship requirements, increasing the chances that such a product would launch there in the future. A bipartisan group of 16 US lawmakers asked Google if it would comply with China's internet censorship and surveillance policies should it re-enter the search engine market there While China is home to the world's largest number of internet users, a 2015 report by US think tank Freedom House found that the country had the most restrictive online use policies of 65 nations it studied, ranking below Iran and Syria. But China has maintained that its various forms of web censorship are necessary for protecting its national security.

Three US Representatives have sent a letter to the Director of National Intelligence asking for a report on deepfake technology, how it could be used to harm the US and any countermeasures that can be taken to detect and deter nefarious use of the technology. While deepfakes gained notoriety when Reddit users began swapping celebrity faces onto porn stars, the potential for the technology's use in misinformation campaigns has generated a fair amount of concern. "Forged videos, images or audio could be used to target individuals for blackmail or for other nefarious purposes," the lawmakers said in their letter. The added, "Of greater concern for national security, they could also be used by foreign or domestic actors to spread misinformation. As deep fake technology becomes more advanced and more accessible, it could pose a threat to United States public discourse and national security, with broad and concerning implications for offensive active measures campaigns targeting the United States."

The General Data Protection Regulation (GDPR) is a European Union regulation that will replace the existing Data Protection Directive on 25 May 2018. The most significant change is a huge increase in the maximum fine that can be levied for breaches of the regulation. Yet fewer than half of UK companies are fully aware of GDPR - and a number of those who were preparing for it stopped doing so when the Brexit vote was announced. A last-minute rush to become compliant is therefore expected, and numerous companies are starting to offer advice, checklists and consultancy on how to comply with GDPR. In such an environment, artificial intelligence technologies ought to be able to assist by providing best advice; asking all and only the relevant questions; monitoring activities; and carrying out assessments. The paper considers four areas of GDPR compliance where rule based technologies and/or machine learning techniques may be relevant: - Following compliance checklists and codes of conduct; - Supporting risk assessments; - Complying with the new regulations regarding technologies that perform automatic profiling; - Complying with the new regulations concerning recognising and reporting breaches of security. It concludes that AI technology can support each of these four areas. The requirements that GDPR (or organisations that need to comply with GDPR) state for explanation and justification of reasoning imply that rule-based approaches are likely to be more helpful than machine learning approaches. However, there may be good business reasons to take a different approach in some circumstances.

Complex autonomous control systems are subjected to sensor failures, cyber-attacks, sensor noise, communication channel failures, etc. that introduce errors in the measurements. The corrupted information, if used for making decisions, can lead to degraded performance. We develop a framework for using adversarial deep reinforcement learning to design observer strategies that are robust to adversarial errors in information channels. We further show through simulation studies that the learned observation strategies perform remarkably well when the adversary's injected errors are bounded in some sense. We use neural network as function approximator in our studies with the understanding that any other suitable function approximating class can be used within our framework.

Early detection of cyber-attacks is crucial for a safe and reliable operation of the smart grid. In the literature, outlier detection schemes making sample-by-sample decisions and online detection schemes requiring perfect attack models have been proposed. In this paper, we formulate the online attack/anomaly detection problem as a partially observable Markov decision process (POMDP) problem and propose a universal robust online detection algorithm using the framework of model-free reinforcement learning (RL) for POMDPs. Numerical studies illustrate the effectiveness of the proposed RL-based algorithm in timely and accurate detection of cyber-attacks targeting the smart grid. A. Background and Related W ork The next generation power grid, i.e., the smart grid, relies on advanced control and communication technologies. This critical cyber infrastructure makes the smart grid vulnerable to hostile cyber-attacks [1]-[3]. Main objective of attackers is to damage/mislead the state estimation mechanism in the smart grid to cause wide-area power blackouts or to manipulate electricity market prices [4]. There are many types of cyber-attacks, among them false data injection (FDI), jamming, and denial of service (DoS) attacks are well known. FDI attacks add malicious fake data to meter measurements [5]-[8], jamming attacks corrupt meter measurements via additive noise [9], and DoS attacks block the access of system to meter measurements [8], [10], [11]. The smart grid is a complex network and any failure or anomaly in a part of the system may lead to huge damages on the overall system in a short period of time. Hence, early detection of cyber-attacks is critical for a timely and effective response. In this context, the framework of quickest change detection [12]-[15] is quite useful. In the quickest change detection problems, a change occurs in the sensing environment at an unknown time and the aim is to detect the change as soon as possible with the minimal level of false alarms based on the measurements that become available sequentially over time. After obtaining measurements at a given time, decision maker either declares a change or waits for the next time interval to have further measurements.

In the United States, heart disease is the leading cause of death for males and females, accounting for 610,000 deaths each year [1]. Physicians use Magnetic Resonance Imaging (MRI) scans to take images of the heart in order to non-invasively estimate its structural and functional parameters for cardiovascular diagnosis and disease management. The end-systolic volume (ESV) and end-diastolic volume (EDV) of the left ventricle (LV), and the ejection fraction (EF) are indicators of heart disease. These measures can be derived from the segmented contours of the LV; thus, consistent and accurate segmentation of the LV from MRI images are critical to the accuracy of the ESV, EDV, and EF, and to non-invasive cardiac disease detection. In this work, various image preprocessing techniques, model configurations using the U-Net deep learning architecture, postprocessing methods, and approaches for volume estimation are investigated. An end-to-end analytics pipeline with multiple stages is provided for automated LV segmentation and volume estimation. First, image data are reformatted and processed from DICOM and NIfTI formats to raw images in array format. Secondly, raw images are processed with multiple image preprocessing methods and cropped to include only the Region of Interest (ROI). Thirdly, preprocessed images are segmented using U-Net models. Lastly, post processing of segmented images to remove extra contours along with intelligent slice and frame selection are applied, followed by calculation of the ESV, EDV, and EF. This analytics pipeline is implemented and runs on a distributed computing environment with a GPU cluster at the San Diego Supercomputer Center at UCSD.