On 19 February 2020, the European Commission published a white paper on the use of artificial intelligence ("AI") in the EU (the "White Paper"). The White Paper forms part of the Commission President, Ursula Von der Leyen's, digital strategy, one of the key pillars of her administration's five year tenure, recognising that the EU has fallen behind the US and China with respect to the strategic deployment of AI. To tackle this problem, the Commission proposes a common EU approach to'speed up the uptake' of AI in the EU, whilst also tackling the human and ethical implications of AI's fast growing use in the EU, including the possible downsides of its use, such as opaque decision making and hidden, embedded gender and racial discrimination. In order to achieve a common EU approach to AI, and to create "trustworthy" AI that can rival developments in the US and China, the Commission proposes the creation of a regulatory framework for AI. Under the regulatory framework, AI applications deemed'high-risk' will be distinguished from'non high-risk' AI applications.

Chris Mattmann is the Deputy Chief Technology and Innovation Officer at NASA Jet Propulsion Lab, where he has been recognised as JPL's first Principal Scientist in the area of Data Science. Chris has applied TensorFlow to challenges he's faced at NASA, including building an implementation of Google's Show & Tell algorithm for image captioning using TensorFlow. He was involved in the Mars rover landing mission, where he was working in a planetary data system engineering node, helping to build a data management framework called object-oriented data technology to support capturing, processing and sharing of data for NASA's scientific archives. He contributes to open source as a former Director at the Apache Software Foundation, and teaches graduate courses at USC in Content Detection and Analysis, and in Search Engines and Information Retrieval. In this episode, Chris opens the show discussing his interest in data.

These are unprecedented times, at least by information age standards. Much of the U.S. economy has ground to a halt, and social norms about our data and our privacy have been thrown out the window throughout much of the world. Moreover, things seem likely to keep changing until a vaccine or effective treatment for COVID-19 becomes available. All this change could wreak havoc on artificial intelligence (AI) systems. Garbage in, garbage out still holds in 2020. The most common types of AI systems are still only as good as their training data.

Jose-Marie Griffiths is a commissioner on the National Security Commission on Artificial ... [ ] Intelligence. Jose-Marie Griffiths was born and raised in London where she earned a Bachelor's degree in Physics, a PhD in Information Science and a Post Doctorate in Computer Science and Statistics. She has taught at University of California, Berkeley, done research for various US government agencies and is now president of South Dakota's Dakota State University. She was named a commissioner on the National Security Commission on Artificial Intelligence in 2019. At the commission, Dr. Griffiths heads a line of effort focused on raising understanding of AI in the federal government and streamlining the government's hiring practices to make it easier to bring young AI practitioners into national security roles.

As tensions and tech rivalry between the U.S. and China intensify, artificial intelligence is taking center stage. During the recent Tortoise Global AI Summit, panelists discussed the increasingly fraught relationship between these global superpowers, whose rivalry had shown signs of bitterness even before President Trump launched a trade war. While this competition extends across a wide range of technologies, the panelists agreed AI has increasingly become a focal point, thanks to the essential role many predict it will play in the coming decades. And not only is the race for AI supremacy pitting China against the U.S., it is forcing every other country to reassess their place in this technological duel. "We're seeing a technology competition in the context of a worsening relationship between the world's two great powers," said John Sawers, former head of the U.K.'s MI6 spy agency.

Emergency managers receive communication training about the importance of being 'first, right and credible', and taking into account the psychology of their audience and their particular reasoning under stress and risk. But we believe that citizens should be similarly trained about how to deal with risk communication. In particular, such messages necessarily carry a part of uncertainty since most natural risks are difficult to accurately forecast ahead of time. Yet, citizens should keep trusting the emergency communicators even after they made forecasting errors in the past. We have designed a serious game called Vigiflood, based on a real case study of flash floods hitting the South West of France in October 2018. In this game, the user changes perspective by taking the role of an emergency communicator, having to set the level of vigilance to alert the population, based on uncertain clues. Our hypothesis is that this change of perspective can improve the player's awareness and response to future flood vigilance announcements. We evaluated this game through an online survey where people were asked to answer a questionnaire about flood risk awareness and behavioural intentions before and after playing the game, in order to assess its impact.

Many environments currently employ machine learning models for data processing and analytics that were built using a limited number of training data points. Once deployed, the models are exposed to significant amounts of previously-unseen data, not all of which is representative of the original, limited training data. However, updating these deployed models can be difficult due to logistical, bandwidth, time, hardware, and/or data sensitivity constraints. We propose a framework, Self-Updating Models with Error Remediation (SUMER), in which a deployed model updates itself as new data becomes available. SUMER uses techniques from semi-supervised learning and noise remediation to iteratively retrain a deployed model using intelligently-chosen predictions from the model as the labels for new training iterations. A key component of SUMER is the notion of error remediation as self-labeled data can be susceptible to the propagation of errors. We investigate the use of SUMER across various data sets and iterations. We find that self-updating models (SUMs) generally perform better than models that do not attempt to self-update when presented with additional previously-unseen data. This performance gap is accentuated in cases where there is only limited amounts of initial training data. We also find that the performance of SUMER is generally better than the performance of SUMs, demonstrating a benefit in applying error remediation. Consequently, SUMER can autonomously enhance the operational capabilities of existing data processing systems by intelligently updating models in dynamic environments.

Tracking users' activities on the World Wide Web (WWW) allows researchers to analyze each user's internet behavior as time passes and for the amount of time spent on a particular domain. This analysis can be used in research design, as researchers may access to their participant's behaviors while browsing the web. Web search behavior has been a subject of interest because of its real-world applications in marketing, digital advertisement, and identifying potential threats online. In this paper, we present an image-processing based method to extract domains which are visited by a participant over multiple browsers during a lab session. This method could provide another way to collect users' activities during an online session given that the session recorder collected the data.

A preliminary test in only eight volunteers suggests the first coronavirus vaccine to be tested in people seems to be safe and can stimulate an immune response against the virus. Antibodies generated by the volunteers were able to stop the virus from replicating in human cells in the laboratory and the levels of antibodies in their blood were similar to those previously detected in recovered covid-19 patients. Tal Zaks of Moderna, the US firm developing the vaccine, said that if the next stages go well, it could be widely available by the end of this year or early next year. The US stock market was up sharply today following the announcement. However, it remains to be seen if such speedy testing and manufacturing of a vaccine is really possible – no vaccine has ever been produced in less than five years. Meanwhile, a trial of another vaccine, developed by researchers at the University of Oxford found it wasn't able to stop six rhesus macaque monkeys from becoming infected with the ...

The US Air Force (USAF) has launched a competition to design the artificially intelligent software, called Skyborg, that would control its planned fleet of loyal wingman unmanned air vehicles (UAV). The service intends to grant indefinite delivery/indefinite quantity contracts worth $400 million per awardee to develop the software and related hardware, it says in a request for proposals released on 15 May. The USAF is looking for technical and cost proposals from companies by 15 June 2020 and intends to award multiple companies contracts, though it may award just one contract or no contracts, based on proposals. Skyborg would be artificially intelligent software used to control the flight path, weapons and sensors of large numbers of UAVs. Automating flight control, in particular via artificial intelligence, is seen as necessary to allow a single person, perhaps a backseat pilot in a fighter aircraft, to command multiple UAVs at once.