We introduce a voting model with multi-agent ranked delegations. This model generalises liquid democracy in two aspects: first, an agent's delegation can use the votes of multiple other agents to determine their own -- for instance, an agent's vote may correspond to the majority outcome of the votes of a trusted group of agents; second, agents can submit a ranking over multiple delegations, so that a backup delegation can be used when their preferred delegations are involved in cycles. The main focus of this paper is the study of unravelling procedures that transform the delegation ballots received from the agents into a profile of direct votes, from which a winning alternative can then be determined by using a standard voting rule. We propose and study six such unravelling procedures, two based on optimisation and four using a greedy approach. We study both algorithmic and axiomatic properties, as well as related computational complexity problems of our unravelling procedures for different restrictions on the types of ballots that the agents can submit.

As part of our AI for children project, UNICEF has developed this policy guidance to promote children's rights in government and private sector AI policies and practices, and to raise awareness of how AI systems can uphold or undermine these rights. The policy guidance explores AI systems, and considers the ways in which they impact children. To see how the guidance has been applied in practice, read about the eight case studies. Artificial intelligence (AI) is about so much more than self-driving cars and intelligent assistants on your phone. AI systems are increasingly being used by governments and the private sector to, for example, improve the provision of education, healthcare and welfare services.

Artificial intelligence, or AI, has created a lot of buzz, and rightfully so. Military AI is no different. From self-driving vehicles to drone swarms, military AI will be used to increase the speed of operations and combat effectiveness. Let's look at the future of military AI -- including some ethical implications. Military AI is a topic that's been around for a while.

An additional 301 exoplanets have been confirmed, thanks to a new deep learning algorithm, NASA said. The significant addition to the ledger was made possible by the ExoMiner deep neural network, which was created using data from NASA's Kepler spacecraft and its follow-on, K2. It uses the space agency's supercomputer, Pleiades and is capable of deciphering the difference between real exoplanets and'false positives.' The newly confirmed planets, which orbit distant stars in the universe, brings the total of confirmed exoplanets to 4,870. 'Unlike other exoplanet-detecting machine learning programs, ExoMiner isn't a black box – there is no mystery as to why it decides something is a planet or not,' one of the study's authors, Jon Jenkins, exoplanet scientist at NASA's Ames Research Center in a statement.

About Swayable Swayable is a data science platform that doubles the impact of advertising by empirically measuring how it changes people's opinions. Our customers include high-profile political campaigns, advocacy organizations, and brands. We launched in 2018 out of Y Combinator, gained immediate traction, and had a significant impact on the 2018 and 2020 US elections. We're now continuing our rapid growth by enabling the same scientific measurement of ads for major global brands. This position is the perfect opportunity to join a fast-moving, high-impact, mission-oriented startup. Technology Stack Swayable uses MongoDB, Python (Numpy, Scipy, Pandas, Celery, Flask), Javascript ([Node.js](http://node.js/), [Express.js](http://express.js/), [Vue.js](http://vue.js/)), and GraphQL. About the Role Swayable is looking for a Senior Machine Learning Engineer to advance the ML-based analytics engine that powers our core product. In this role, you will write production code that trains hundreds of machine-learning models, automatically, in the cloud, each day. You will work closely with your cross-functional team to build new features and solve novel problems across the spectrum of software engineering, data visualization, and science. The Senior Machine Learning Engineer will report to our CTO, Valerie Coffman, and work within our Scrum-lite work cycles but must be flexible and self-directed enough to get work done on their own. **Requirements** - You have a Bachelor's degree in Computer Science, Applied Math, Engineering, or Science. - You have 5+ years of professional experience building productized ML models with Python. - You have a deep understanding of machine learning algorithms. - You have experience with ML packages such as Tensorflow, Numpyro, and Scikit-learn. - You can write "vectorized code" using Numpy, Scipy, and Pandas. - You understand the concepts of software architecture and can design scalable, performant solutions. - You value code quality and write maintainable, testable code. **Preferred Qualifications** - You have knowledge of the constantly evolving toolset for MLOps including tools for workflow management, feature storage, and model monitoring. - You have experience with distributed, parallel computing. - You have experience with Celery, Flask, MongoDB, or JavaScript. - You believe in the scientific method and prefer to use data to drive decisions. - You can give and receive informed, actionable feedback on both technical and non-technical skills. - You are a strong communicator, especially in text. You can write documentation and discuss the tradeoffs of different implementations. Our team is centered in NYC and SF, with flexible work arrangements for remote work. We offer a benefits package that is well above-market. Swayable covers 100% of the health, dental, and vision insurance premiums for full-time employees. We also offer generous PTO, parental leave, equity, 401k, FSA, and an ongoing professional development stipend. We are an equal opportunity employer. We strive to promote an organizational environment that values diversity and fosters growth. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other basis prohibited by law. The team especially encourages applicants from underrepresented backgrounds.

A solution like ML is capable of dealing with enormous amounts of data from several sources and knows what the normalized levels of activity are with regard to banking and other financial transactions. Consequently, it can alert the supervisor in case of any deviations from the expected trends. In addition to account owners, fraud can come from merchants and issuers, and their transaction information can be used to train a machine learning model to recognize transactions processing properly.

Current trends in AI are nothing if not remarkable. Day after day, we hear stories about systems and machines taking on tasks that, until very recently, we saw as the exclusive and permanent preserve of humankind: making medical diagnoses, drafting legal documents, designing buildings, and even composing music. Our concern here, though, is with something even more striking: the prospect of high-level machine intelligence systems that outperform human beings at essentially every task. This is not science fiction. In a recent survey the median estimate among leading computer scientists reported a 50% chance that this technology would arrive within 45 years.

China is working to update the rugged old computer systems on nuclear submarines with artificial intelligence to enhance the potential thinking skills of commanding officers, a senior scientist involved with the programme told the South China Morning Post. A submarine with AI-augmented brainpower not only would give China's large navy an upper hand in battle under the world's oceans but would push applications of AI technology to a new level, according to the researcher, who spoke on condition of anonymity because of the project's sensitivity. "Though a submarine has enormous power of destruction, its brain is actually quite small," the researcher said. While a nuclear submarine depends on the skill, experience and efficiency of its crew to operate effectively, the demands of modern warfare could introduce variables that would cause even the smoothest-run operation to come unglued. For instance, if the 100 to 300 people in the sub's crew were forced to remain together in their canister in deep, dark water for months, the rising stress level could affect the commanding officers' decision-making powers, even leading to bad judgment.

In the wake of the woefully insufficient Pentagon report from June in which the U.S. government admitted it could not explain the vast majority of unidentified aerial phenomena, the Department of Defense is increasing its effort, creating an official group to study these events. The announcement, made late Tuesday, will see the establishment of the Airborne Object Identification and Management Synchronization Group (AOIMSG), succeeding the U.S. Navy's Unidentified Aerial Phenomena Task Force; it will be part of the office of Under Secretary of Defense for Intelligence & Security. The AOIMSG will work across the Department of Defense and the entire U.S. government'to detect, identify and attribute objects of interests in Special Use Airspace, and to assess and mitigate any associated threats to safety of flight and national security,' according to a press release issued by the DoD. The move to formally establish the office was made the Under Secretary of Defense for Intelligence & Security Ronald S. Moultrie, who was directed by Deputy Secretary of Defense Kathleen Hicks and Director of National Intelligence Avril Haines. The Pentagon is creating a group to study unidentified aerial phenomena.

"Unlike other exoplanet-detecting machine learning programs, ExoMiner isn't a black box--there is no mystery as to why it decides something is a …