We consider filtering in high-dimensional non-Gaussian state-space models with intractable transition kernels, nonlinear and possibly chaotic dynamics, and sparse observations in space and time. We propose a novel filtering methodology that harnesses transportation of measures, convex optimization, and ideas from probabilistic graphical models to yield robust ensemble approximations of the filtering distribution in high dimensions. Our approach can be understood as the natural generalization of the ensemble Kalman filter (EnKF) to nonlinear updates, using stochastic or deterministic couplings. The use of nonlinear updates can reduce the intrinsic bias of the EnKF at a marginal increase in computational cost. We avoid any form of importance sampling and introduce non-Gaussian localization approaches for dimension scalability. Our framework achieves state-of-the-art tracking performance on challenging configurations of the Lorenz-96 model in the chaotic regime.

We introduce a new online learning framework where, at each trial, the learner is required to select a subset of actions from a given known action set. Each action is associated with an energy value, a reward and a cost. The sum of the energies of the actions selected cannot exceed a given energy budget. The goal is to maximise the cumulative profit, where the profit obtained on a single trial is defined as the difference between the maximum reward among the selected actions and the sum of their costs. Action energy values and the budget are known and fixed. All rewards and costs associated with each action change over time and are revealed at each trial only after the learner's selection of actions. Our framework encompasses several online learning problems where the environment changes over time; and the solution trades-off between minimising the costs and maximising the maximum reward of the selected subset of actions, while being constrained to an action energy budget. The algorithm that we propose is efficient and general in that it may be specialised to multiple natural online combinatorial problems.

How and in what areas is AI being adopted in the national security space? What is an AI ecosystem? How can government and the military enhance their AI ecosystems?

The possibility of ending partisan gerrymandering-- the practice of redrawing voting districts in favor one party over another -- was all but obliterated on Thursday, when the Supreme Court ruled in a 5-4 decision that challenges to the controversial practice cannot be heard in federal court. In an impassioned dissent, Justice Elena Kagan warned that in the era of artificial intelligence, such a move could put American democracy at risk. "Gerrymanders will only get worse (or depending on your perspective, better) as time goes on -- as data becomes ever more fine-grained and data analysis techniques continue to improve," she wrote. "What was possible with paper and pen -- or even with Windows 95 -- doesn't hold a candle (or an LED bulb?) to what will become possible with developments like machine learning. And someplace along this road, 'we the people' become sovereign no longer."

We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence. In this report, we set out to capture a snapshot of the exponential progress in AI with a focus on developments in the past 12 months. Consider this report as a compilation of the most interesting things we've seen with a goal of triggering an informed conversation about the state of AI and its implication for the future. This edition builds on the inaugural State of AI Report 2018, which can be found here: www.stateof.ai/2018 We consider the following key dimensions in our report: - Research: Technology breakthroughs and their capabilities.

We will work together to foster global economic growth, while harnessing the power of technological innovation, in particular digitalization, and its application for the benefit of all. We are resolved to build a society capable of seizing opportunities, and tackling economic, social and environmental challenges, presented today and in the future, including those of demographic change. This recovery is supported by the continuation of accommodative financial conditions and stimulus measures taking effect in some countries. However, growth remains low and risks remain tilted to the downside. Most importantly, trade and geopolitical tensions have intensified. We will continue to address these risks, and stand ready to take further action. Fiscal policy should be flexible and growth-friendly while rebuilding buffers where needed and ensuring debt as a share of GDP is on a sustainable path. Monetary policy will continue to support economic activity and ensure price stability, consistent with central banks' mandates. Central bank decisions need to remain well communicated.

An organization that won the Nobel Prize in 2017 for its work to eliminate nuclear weapons is sounding the alarm about the possibility of artificial intelligence leading to unintended wars. Beatrice Fihn, executive director of the International Campaign to Abolish Nuclear Weapons, is worried that hackers could breach A.I. technologies that are used in nuclear programs or that they could use A.I. to dupe countries into launching attacks. For example, deepfakes, or realistic-looking computer-altered videos, may be used to "create a perceived threat that might not be there," she warns, prompting governments to overreact. Fihn told Fortune that she wants to convene a meeting in the fall with nuclear weapons experts and some of the leading companies in A.I. and cybersecurity. Participants in the off-the-record event, she said, would produce a document that her group would use to inform governments and others about the danger.

Any sufficiently advanced technology is indistinguishable from magic. In the world of artificial intelligence & machine learning (AI & ML), black- and white-box categorization of models and algorithms refers to their interpretability. That is, given a model trained to map data inputs to outputs (e.g. And just as the software testing dichotomy is high-level behavior vs low-level logic, only white-box AI methods can be readily interpreted to see the logic behind models' predictions. In recent years with machine learning taking over new industries and applications, where the number of users far outnumber experts that grok the models and algorithms, the conversation around interpretability has become an important one.

If you're a fan of Ubisoft's popular Watch Dogs video game series – a 5-year-old action-adventure franchise played out in real-world cities like Chicago and San Francisco – you'll no doubt want to get your hands on the next installment, slated for March 5, 2020, for PC, Xbox One, PS4, and Google Stadia. "Watch Dogs: Legion," which earned several "Best of Show" awards at the recent Electronic Entertainment Expo, the video game confab known as E3, looks to be the most ambitious title in the series to date. Is Facebook listening to me?: Why those ads appear after you talk about things One of the most ambitious games of 2020, Ubisoft's'Watch Dogs: Legion' takes place in a post-Brexit London, which has become an all-seeing surveillance state. The following is what you need to know about the game – based on what I saw (and played) at E3, along with some details provided by Joel Burgess, world director at Ubisoft Toronto, which is taking the reins on this title with portions of the game being developed simultaneously at Ubisoft studios in Montreal, Paris, Newcastle, England; Bucharest, Romania; and Kiev, Ukraine. One of the most ambitious games of 2020, Ubisoft's'Watch Dogs: Legion' takes place in a post-Brexit London, which has become an all-seeing surveillance state. The game takes place in a near-future London, at a time when people are being oppressed by Big Brother-esque surveillance and a corrupt private military corporation, Albion, patrolling the streets.

SEMI announced a research and development (R&D) project to speed technology progress and problem-solving in microelectronics manufacturing and across the supply chain by driving new efficiencies using machine learning (ML) and artificial intelligence (AI). Under an agreement with SEMI, Cornell University will optimize and accelerate two critical process steps – lithography and plasma etch. Supported through SEMI's R&D program with the U.S. Army Research Laboratory (ARL), the project aims to help accelerate the adoption of data-driven AI methodologies to streamline microelectronics operations. The project will lay the foundation for SEMI to establish data transfer and management standards crucial to the trusted exchange of trade secrets, IP and other sensitive information. New standards and protocols are vital as the industry moves into the era of big data.