Nines, a Palo Alto-based teleradiology startup, received FDA 510(k) clearance for a system that can detect and triage two serious conditions from CT scans. The company's NinesAI system uses machine learning to identify potential cases of intercranial hemorrhage and mass effect, both of which are associated with strokes. The system then flags those cases for expedited review by radiologists. For both of these conditions, every hour counts toward a patient's survival. "This has been a long time coming. A lot of hard work went into it," CEO David Stavens said in a phone interview.

The worldwide fight against COVID-19 continues to challenge AI experts. The Pope issued a challenge for AI experts to develop an "ethical algorithm" that would ensure fairness; Some new AI research shows how people are feeling about the virus. Other researchers are experimenting with the use of sound to detect the virus. Shortly before the Vatican closed due to the virus, members of the Pontifical Academy for Life, which researches bioethics and Catholic moral theology, worked on getting a commitment from AI developers to write an "ethical" algorithm in each AI system, according to an account in SSPX.news, the communication agency of the Society of St. Pius, based in Paris. "Following the example of electricity, AI is not necessary to perform a specific action, it is rather intended to change the way, the mode with which we carry out our daily actions," stated Fr.

In 2004, a few unmanned vehicles showed up at the starting gate of the lengthy course across the Mojave Desert -- this was the inaugural DARPA Grand Challenge. It signified the beginning of the technological race to develop a practical self-driving car, which sparked a global movement that continues even today. The networking community too embarked on a similar journey to provide production-ready, economically feasible, Self-Driving Networks. Self-Driving Networks are autonomous networks that use Artificial Intelligence (AI) and Machine Learning (ML) to program independently and carry out prescribed intentions while eliminating complex programming and management tasks required today to run the networks. In view of this, the proliferation of data breaches and cyberattacks in today's networking environment has also increased, leading to extensive repercussions across businesses.

To enhance existing cybersecurity systems and practices, organizations can apply AI at three levels. For some time, researchers have focused on AI's potential to stop cyberintruders. In 2014, the US Defense Advanced Research Projects Agency announced its first DARPA Cyber Grand Challenge, a competition in which professional hackers and information security researchers develop automated systems that can figure out security flaws and develop and deploy solutions in real time. While it is still early days, the future of cybersecurity will likely benefit from more AI-enabled prevention and protection systems that use advanced machine learning techniques to harden defenses. These systems will also likely allow humans to interact flexibly with algorithmic decision making.

Employing big data and AI prediction models could accelerate the de-confinement process by assessing each person on a risk scale. The coronavirus pandemic has been dealt in the same way by almost every country, self-isolation measures (usually implemented a few weeks too late), economic shutdown, with an aim to flatten the curve in a few months. Now, we are observing a few different ideas on how to transition back to normality. In the U.S., President Trump and several states want to re-open, stressing the economic impact of maintaining quarantine over several months. Other countries are planning a slow re-open, to avoid an even larger pandemic in the winter.

An educated and informed consumption of media content has become a challenge in modern times. With the shift from traditional news outlets to social media and similar venues, a major concern is that readers are becoming encapsulated in "echo chambers" and may fall prey to fake news and disinformation, lacking easy access to dissenting views. We suggest a novel task aiming to alleviate some of these concerns -- that of detecting articles that most effectively counter the arguments -- and not just the stance -- made in a given text. We study this problem in the context of debate speeches. Given such a speech, we aim to identify, from among a set of speeches on the same topic and with an opposing stance, the ones that directly counter it. We provide a large dataset of 3,685 such speeches (in English), annotated for this relation, which hopefully would be of general interest to the NLP community. We explore several algorithms addressing this task, and while some are successful, all fall short of expert human performance, suggesting room for further research. All data collected during this work is freely available for research.

Foreign affairs journalist Gordon Chang joins Jon Scott to discuss the U.S. probe into whether the virus escaped from Wuhan lab. Get all the latest news on coronavirus and more delivered daily to your inbox. A research dossier compiled by the so-called "Five Eyes" intelligence alliance, that reportedly concludes China intentionally hid or destroyed evidence of the coronavirus pandemic, is consistent with U.S. findings about the origins of the outbreak so far, senior U.S. officials told Fox News on Saturday. The 15-page document from the intelligence agencies of the U.S., Canada, the U.K., Australia and New Zealand, was obtained by Australia's Saturday Telegraph newspaper and finds that China's secrecy amounted to an "assault on international transparency." The dossier, which is likely to further increase pressure on the Chinese government to explain its actions and early statements, points to the initial denial by the government that the virus could be transmitted between humans, the silencing of doctors, destruction of evidence, and a refusal to provide samples to scientists working on a vaccine. While U.S. intelligence is not confirming the existence of the 15-page document, a senior official told Fox that reports of the document aligns with U.S. intelligence that China knew the spread between humans earlier than it said, that it knew it was a novel coronavirus earlier than it said and that it was spread wider than they reported to the international community in the first weeks of the outbreak.

Alfredo joined Element AI as a Research Engineer in the AI for Good lab in London, working on applications that enable NGOs and non-profits. He is one of the primary co-authors of the first technical report made in partnership with Amnesty International, on the large-scale study of online abuse against women on Twitter from crowd-sourced data. He's been a Machine Learning mentor at NASA's Frontier Development Program, helping teams apply AI for scientific space problems. More recently, he led the joint-research with Mila Montreal on Multi-Frame Super-Resolution, which was awarded by the European Space Agency for their top performance on the PROBA-V Super-Resolution challenge. His research interests lie in computer vision for satellite imagery, probabilistic modeling, and AI for Social Good.

The rapid development of artificial intelligence has the potential to remake how the federal government delivers a broad range of core services to citizens in a profound way. When implemented correctly, these technologies can help the government to render decisions faster, using better data, at a far lower cost in vital areas ranging from awarding disability benefits to granting patents to adjudicating immigration applications and healthcare insurance benefits. Pursuing AI transformation will also position the government to better respond to sudden demand surges for relief as a result of pandemics or other unforeseen future emergencies. Right now, AI strategies are at an early adoption stage in most administrative agencies, with just 45 percent of surveyed agencies having an AI use case according to a recent report, Government by Algorithm, issued by Stanford University and NYU to the Administrative Conference of the United States. Out of those agencies that have implemented AI, only 12 percent were considered to be highly sophisticated applications, according to Stanford's computer scientists.

Dascena, a machine learning diagnostic algorithm company that is targeting early disease intervention to improve patient care outcomes, announced it raised $50 million in Series B funding led by Frazier Healthcare Partners with participation from Longitude Capital, existing investor Euclidean Capital, and an undisclosed investor. This round of funding will enable Dascena to advance a suite of machine learning algorithms to inform patient care strategies and improve outcomes. And Dascena algorithms have been validated through eighteen peer-reviewed publications in several studies funded by the National Institutes of Health and the National Science Foundation. According to a randomized controlled trial of hospitalized patients in the intensive care unit (ICU), Dascena's InSight algorithm resulted in a 58% reduction in patient mortality and a 21% reduction in length of hospital stay. And data from this prospective study of InSight were published in the BMJ Open Respiratory Research in 2017.