Machine learning is one aspect of the AI portfolio of capability that has been with us in various forms for decades, so it's hardly a product of science fiction. It's widely used as a means of processing high volumes of customer data to provide a better service and hence increase profits. Yet things become more complex when the technology is brought into the public sector, where many decisions can greatly affect our lives. AI is often feared, particularly around removing the human touch that could lead to unfair judgements or decisions that could cause injury, death or even the complete destruction of humanity. If we think about medical diagnoses or the unfair denial of welfare for a citizen, it's apparent where the first two fears arise.

BREVARD COUNTY • MELBOURNE, FLORIDA -- Faculty and students from Florida Tech's Center for Advanced Data Analytics and Systems are working with a team from the U.S. Air Force Air Combat Command/Intelligence Data/Tech Futures Division and the Air Force Research Lab/Multi-Domain Sensing Autonomy Division to bring artificial intelligence and machine learning to COVID-19 planning and resource management. The goal of the Florida Tech work, which began in early April and could continue at least through the summer, is to strengthen the understanding of the effects COVID-19 has on Air Force missions and operations. "Our collaboration with Florida Tech has been critical to changing the way we think about data and present it to our commanders," said John Matyjas, ACC Chief Scientist and lead for their COVID Data Analytics Team. The CADAS team of Carlos Otero, Adrian M. Peter and Anthony O. Smith, supported by students Xavier Merino, David Elliott, Steven Wyatt, Benjamin Luchterhand, Evan Martino, Christopher Bonomi and David Nieves-Acaron, has developed capabilities to rapidly gain situational awareness and support the seamless integration of data-driven artificial intelligence (AI)/machine learning models for forecasting. "This task provides invaluable experience to our students while helping in the critical mission to better understand and utilize COVID-19-related data that ultimately can help the Air Force manage and move beyond this challenging situation," Otero said.

People in England can return to work if they can't work from home Restrictions to curb the spread of coronavirus are being eased slightly in England this week, but many have criticised the government for creating confusion with a new slogan telling people to "stay alert", which replaces previous advice to "stay at home." In a video message broadcast on Sunday evening, prime minister Boris Johnson announced the following changes to the government's policy in England, which are listed in full online and will come into effect from Wednesday 13 May: These new policies mean that social distancing rules in England are now different from the advice given to UK citizens in Scotland, Wales and Northern Ireland. Scotland's first minister Nicola Sturgeon said people should continue to "stay at home", and Northern Ireland's first minister Arlene Foster also rejected the new slogan. Some London Underground platforms were packed with passengers this morning following last night's announcement.

In new plans that offer a national reimagining of how to fight wildfires amid the risk of the coronavirus spreading through crews, it's not clear how officials will get the testing and equipment needed to keep firefighters safe in what's expected to be a difficult fire season. A U.S. group instead put together broad guidelines to consider when sending crews to blazes, with agencies and firefighting groups in different parts of the country able to tailor them to fit their needs. The wildfire season has largely begun, and states in the American West that have suffered catastrophic blazes in recent years could see higher-than-normal levels of wildfire because of drought. "This plan is intended to provide a higher-level framework of considerations and not specific operational procedures," the National Multi-Agency Coordination Group, made up of representatives from federal agencies who worked with state and local officials, wrote in each of the regional plans. "It is not written in terms of'how to' but instead provides considerations of'what,' 'why,' and'where.'"

The MITRE ATT&CK Framework provides a rich and actionable repository of adversarial tactics, techniques, and procedures (TTP). However, this information would be highly useful for attack diagnosis (i.e., forensics) and mitigation (i.e., intrusion response) if we can reliably construct technique associations that will enable predicting unobserved attack techniques based on observed ones. In this paper, we present our statistical machine learning analysis on APT and Software attack data reported by MITRE ATT&CK to infer the technique clustering that represents the significant correlation that can be used for technique prediction. Due to the complex multidimensional relationships between techniques, many of the traditional clustering methods could not obtain usable associations. Our approach, using hierarchical clustering for inferring attack technique associations with 95% confidence, provides statistically significant and explainable technique correlations. Our analysis discovers 98 different technique associations (i.e., clusters) for both APT and Software attacks. Our evaluation results show that 78% of the techniques associated by our algorithm exhibit significant mutual information that indicates reasonably high predictability.

This work introduces a new method to consider subjectivity and general context dependency in text analysis and uses as example the detection of emotions conveyed in text. The proposed method takes into account subjectivity using a computational version of the Framework Theory by Marvin Minsky (1974) leveraging on the Word2Vec approach to text vectorization by Mikolov et al. (2013), used to generate distributed representation of words based on the context where they appear. Our approach is based on three components: 1. a framework/"room" representing the point of view; 2. a benchmark representing the criteria for the analysis - in this case the emotion classification, from a study of human emotions by Robert Plutchik (1980); and 3. the document to be analyzed. By using similarity measure between words, we are able to extract the relative relevance of the elements in the benchmark - intensities of emotions in our case study - for the document to be analyzed. Our method provides a measure that take into account the point of view of the entity reading the document. This method could be applied to all the cases where evaluating subjectivity is relevant to understand the relative value or meaning of a text. Subjectivity can be not limited to human reactions, but it could be used to provide a text with an interpretation related to a given domain ("room"). To evaluate our method, we used a test case in the political domain.

Recent work in machine learning shows that deep neural networks can be used to solve a wide variety of inverse problems arising in computational imaging. We explore the central prevailing themes of this emerging area and present a taxonomy that can be used to categorize different problems and reconstruction methods. Our taxonomy is organized along two central axes: (1) whether or not a forward model is known and to what extent it is used in training and testing, and (2) whether or not the learning is supervised or unsupervised, i.e., whether or not the training relies on access to matched ground truth image and measurement pairs. We also discuss the trade-offs associated with these different reconstruction approaches, caveats and common failure modes, plus open problems and avenues for future work.

This article identifies a critical incompatibility between European notions of discrimination and existing statistical measures of fairness. First, we review the evidential requirements to bring a claim under EU non-discrimination law. Due to the disparate nature of algorithmic and human discrimination, the EU's current requirements are too contextual, reliant on intuition, and open to judicial interpretation to be automated. Second, we show how the legal protection offered by non-discrimination law is challenged when AI, not humans, discriminate. Humans discriminate due to negative attitudes (e.g. stereotypes, prejudice) and unintentional biases (e.g. organisational practices or internalised stereotypes) which can act as a signal to victims that discrimination has occurred. Finally, we examine how existing work on fairness in machine learning lines up with procedures for assessing cases under EU non-discrimination law. We propose "conditional demographic disparity" (CDD) as a standard baseline statistical measurement that aligns with the European Court of Justice's "gold standard." Establishing a standard set of statistical evidence for automated discrimination cases can help ensure consistent procedures for assessment, but not judicial interpretation, of cases involving AI and automated systems. Through this proposal for procedural regularity in the identification and assessment of automated discrimination, we clarify how to build considerations of fairness into automated systems as far as possible while still respecting and enabling the contextual approach to judicial interpretation practiced under EU non-discrimination law. N.B. Abridged abstract

Microsoft and Intel have partnered up in an effort to develop a new kind of malware detection. The project, called Static Malware-as-Image Network Analysis (STAMINA), is a joint effort by the tech giants to develop a software that sniffs out malicious code by converting it into greyscale images that can be assessed by utilizing deep-learning. Specifically, STAMINA converts one-dimensional malware bits into two-dimensional greyscale images and then'looks' at the images for patterns that may indicate specific types of malicious code using computer vision software designed to analyze images. One the image is assembled, STAMINA then resizes it into a smaller dimension to make it easier to view. This compressions, according to researchers helps avoid needing the software to assess billions of pixels - which would likely slow the process - and does not negatively affect its ability to identify malware.

Yeah, so the entire project is a coordinated effort by the White House Office of Science and Technology Policy. I think some time in early March a group at Georgetown, the Center for Security in Emerging Technology (CSET) reached out to us at Allen AI to help coordinate the release of this dataset, along with a couple of different organizations. You mentioned MSR (Microsoft Research), Chan Zuckerberg, Kaggle was also involved, and the National Library of Medicine, which is part of the NIH. So all these groups - we're going to come together to essentially create this dataset to help create text mining and information retrieval tools that could assist medical experts in understanding more of what was going on with the epidemic. For Allen AI, the way that we got involved is we had recently created a new pipeline to revamp our open research corpus.