WASHINGTON–(BUSINESS WIRE)–The Fasano Longevity Conference, which will be held on November 4th in Washington, DC, will include a wide range of topics bearing on longevity markets. Chris Stroup, CEO of Wilton Re and keynote speaker, will share his Observations on Growth Amidst Evolving Opportunity. He will be followed by Brian Dear, Executive Director of NASP (National Association of Settlement Purchasers), who will present his views on the State of the Structured Settlement Market. The morning sessions will also feature presentations by longevity pioneer, Dr. David Blake, who will discuss the Investment Opportunities in the Risk Transfer Market, and by Northwestern University's Dr. Rick Morimoto, who will present his research on the Molecular Determinants of Healthy Aging. The afternoon sessions will start with an analysis of the Current Dynamics in the Life Settlement Market by Jay Jackson, CEO of Abacus Life Settlements, who will be followed by Washington insiders, Jack Kelly and Michael Steele, who will give their take on 2020 Presidential race.

Third Space Automation, a small technology startup with offices in London, Helsinki, and Bangalore, has partnered with a few retailers to conduct pilot tests delivering take out and groceries via AI enabled drones.

Bengaluru New Delhi: A month ago, Tanmay Saksena, chief operating officer of online pharmacy 1mg, became a student again. He signed up on ed-tech platform Coursera to take the'AI For Everyone' course. With a lot of information thrown around about artificial intelligence and its increasing importance in business, he felt he needed to educate himself on its applications and limitations. As AI and other emerging technologies like machine learning (ML), blockchain, and data analytics are increasingly being seen as game-changers to drive new business models and transform workplaces, the focus has subtly shifted from early and mid-career professionals to senior leaders – those with 12-15 years of experience and more –- who are looking to upskill. The main question on CXOs' minds is how to align their longterm business strategy with today's AI capabilities, say experts.

Around 2017, demand for microtasking crowdwork changed quickly and significantly, both in quantity and quality. Florian Alexander Schmidt tried to figure out, among other things, whether this was "a short-lived phenomenon or [something offering] long-term economic prospects for crowdworkers". This post is my own summary of the resulting report, titled "Crowdsourced Production of AI Training Data" and published in February 2019. What caused the sudden change in the demand for microtasking crowdwork was the equally sudden need of lots of high quality training data for autonomous vehicles. Those data are fed to the so-called self-learning algorithms that "drive" self-driving cars.

You are free to share this article under the Attribution 4.0 International license. A new framework would allow users to understand the rationale behind artificial intelligence decisions. The work is significant, given the push to move away from "black box" AI systems--particularly in sectors like the military and law enforcement, where there is a need to justify decisions. "One thing that sets our framework apart is that we make these interpretability elements part of the AI training process," says Tianfu Wu, first author of the paper and an assistant professor of computer engineering at North Carolina State University. "For example, under our framework, when an AI program is learning how to identify objects in images, it is also learning to localize the target object within an image, and to parse what it is about that locality that meets the target object criteria. This information is then presented alongside the result."

In a novel approach that could help reduce carbon emissions, a team of scientists led by Stony Brook's Anatoly Frenkel have described a way to use artificial intelligence (AI) to facilitate the conversion of carbon dioxide (CO2) into methane. By using this method to track the size, structure, and chemistry of catalytic particles under real reaction conditions, the scientists can identify which properties correspond to the best catalytic performance, and then use that information to guide the design of more efficient catalysts. "Improving our ability to convert CO2 to methane would'kill two birds with one stone' by making a sustainable non-fossil-fuel energy source that can be easily stored and transported while reducing carbon emissions," said Anatoly Frenkel, a chemist with a joint appointment at the U.S. Department of Energy's Brookhaven National Laboratory (BNL) and Stony Brook University. Frenkel is a professor of Materials Science in the College of Engineering and Applied Sciences. Frenkel's group has been developing a machine-learning approach to extract catalytic properties from x-ray signatures of catalysts collected as chemicals are transformed in reactions.

After watching dotNetConf videos over the last couple of weeks, I've been really excited to try out some of the new image classification techniques in Visual Studio. The dotNetConf keynote included a section from Bri Actman, who is a Program Manager on the .NET Team (the relevant section is on YouTube from 58m16 to 1hr06m35s). This section showed how developers can integrate various ML techniques and code into their projects using the ModelBuilder tool in Visual Studio – in her example, photographs of the outdoors were classified according to what kind of weather they showed. As well as the keynote, there's another relevant dotNetConf talk by Cesar de la Torre which is also available here on what's new in ML.NET And the way to integrate this into my project looks very straightforward – right click on the project - Add Machine Learning - and choose what type of scenario you want to use, as shown in the screenshot below. I've highlighted the feature that I'm really interested in – image classification.

Email hacking is a commonly used malicious tactic in our increasingly connected world. Cybercriminals compromise email accounts to enter the IT premises of an organization and carry out attacks ranging from fraud and spying to information and identity theft. Without effective security measures to stop email hacks, potential victims can suffer serious consequences. The cyberespionage group Fancy Bear, which specializes in politically motivated attacks, has reportedly targeted the reelection campaign of a U.S. senator earlier this year via credential phishing tactics. Fancy Bear has been garnering headlines since 2015 for targeting political organizations in the U.S., Ukraine, France, Germany, Montenegro, and Turkey.

The TAIAO project (Time-Evolving Data Science / Artificial Intelligence for Advanced Open Environmental Science) will advance the state-of-the-art in environmental data science by developing new machine learning methods for time series and data streams that are able to deal with large quantities of big data in real time, which are tailored to deal with data collected on the New Zealand environment. We will build a new open source framework to implement machine learning on time series data, provide an open available repository with datasets to improve reproducibility in environmental data science, and build capability in fundamental and applied data science, accessible to all New Zealanders. This programme is a new collaboration between the Universities of Waikato, Auckland and Canterbury, Beca and MetService and includes world-leading data scientists, data engineers, and environmental scientists. We will work with regional councils, iwi and co-governance entities to implement the methods we develop to support governance and management decisions with analyses based on large volumes of data that they cannot currently process. We will also make use of our existing strong international collaborations to grow our own data science capabilities and attract top international researchers to work with us on challenging data science problems.

The Center for Human Nature, Artificial Intelligence, and Neuroscience (CHAIN) is pleased to announce their 7th academic seminar as follows. Artificial Intelligence (AI) is nowadays capable of coming up with creative and inventive outputs that until not long ago just human beings were capable to produce. Music, literature and art are already being created by computers and machines. The talk will delve into these burning legal questions and issues, expanding on the challenges AI pose to the authorship and inventorship requirements as well as the legal provisions on originality and inventive step/non obviousness in several jurisdictions, including United States, United Kingdom and the European Union (EU).