Given a combinatorial optimization problem taking an input, can we learn a strategy to solve it from the examples of input-solution pairs without knowing its objective function? In this paper, we consider such a setting and study the misinformation prevention problem. Given the examples of attacker-protector pairs, our goal is to learn a strategy to compute protectors against future attackers, without the need of knowing the underlying diffusion model. To this end, we design a structured prediction framework, where the main idea is to parameterize the scoring function using random features constructed through distance functions on randomly sampled subgraphs, which leads to a kernelized scoring function with weights learnable via the large margin method. Evidenced by experiments, our method can produce near-optimal protectors without using any information of the diffusion model, and it outperforms other possible graph-based and learning-based methods by an evident margin.

Meta-World is widely used for evaluating multi-task and meta-reinforcement learning agents, which are challenged to master diverse skills simultaneously. Since its introduction however, there have been numerous undocumented changes which inhibit a fair comparison of algorithms. This work strives to disambiguate these results from the literature, while also leveraging the past versions of Meta-World to provide insights into multi-task and meta-reinforcement learning benchmark design. Through this process we release a new open-source version of Meta-World (https://github.com/Farama-Foundation/Metaworld/) that has full reproducibility of past results, is more technically ergonomic, and gives users more control over the tasks that are included in a task set.

Enables metric-driven B2B/B2C Growth: Ask me how! @edvaldez8888 Highlights: In 2020, AI companies raised $33B despite a slump in total deals. There were also major exits: * Amazon's $1.2B acquisition of autonomous driving startup Zoox in June 2020; * Medical imaging unicorn Butterfly Network, Inc.'s $1.5B public market debut via a merger with Longview Acquisition Corp. in February 2021, and * Risk analytics company QOMPLX's $1.4B merger announced in March 2021. To read more, click the link in the 1st comment.

AutoML addresses the skills gap between the demand for data science talent and the availability of this talent.

Much anticipated and long awaited! The Defense Innovation Board released its final report "AI Principles: Recommendations on the Ethical Use of Artificial Intelligence by the United States Department of Defense What an exciting moment! Incredible leadership and a very thoughtful deliberative process that brought a multitude of stakeholders to the convening table.

Only one in 20 companies has extensively incorporated AI in offerings or processes. Less than 39% of all companies have an AI strategy in place. According to MIT Sloan Review, the largest companies -- those with at least 100,000 employees -- are the most likely to have an AI strategy, but only half have one. Despite claims that AI is already being subsumed into an array of applications, we're not there yet and won't be in 2018. It is still the early days of adoption, and those companies that are implementing AI now will see the biggest competitive value.

It seems like every vendor in the data security industry makes predictions this time of year. Which ones should you pay attention to? All of them, says Dan Lohrmann, who formerly served as the state of Michigan's CISO and CTO. See Also: IoT is Happening Now: Are You Prepared? "I really view it as something that professionals need to widen their perspectives," Lohrmann says in an interview with Information Security Media Group.

Any AI effort will rely on three main building blocks: data, infrastructure, and talent. The following is a guest post by Rita C. Waite, a Growth Strategy & Investments Manager at Juniper Networks. Artificial Intelligence (AI) is fundamentally changing how businesses operate across all sectors, including manufacturing, healthcare, IT, and transportation. Advancements in AI over the last decade are presenting opportunities for companies to automate business processes, transform customer experiences, and differentiate products offerings. AI pioneers like Google and Amazon, who have adopted these new technologies to create a growing competitive advantage, have already witnessed bottom-line benefits from their AI strategies.

Any AI effort will rely on three main building blocks: data, infrastructure, and talent. The following is a guest post by Rita C. Waite, a Growth Strategy & Investments Manager at Juniper Networks. Artificial Intelligence (AI) is fundamentally changing how businesses operate across all sectors, including manufacturing, healthcare, IT, and transportation. Advancements in AI over the last decade are presenting opportunities for companies to automate business processes, transform customer experiences, and differentiate products offerings. AI pioneers like Google and Amazon, who have adopted these new technologies to create a growing competitive advantage, have already witnessed bottom-line benefits from their AI strategies.

This article summarizes 16 agent strategies that were designed for the 2002 Trading Agent Competition. Agent architects use numerous general-purpose AI techniques, including machine learning, planning, partially observable Markov decision processes, Monte Carlo simulations, and multiagent systems. Ultimately, the most successful agents were primarily heuristic based and domain specific. It would be quite a daunting task to manually monitor prices and make bidding decisions at all web sites currently offering the camera--especially if accessories such as a flash and a tripod are sometimes bundled with the camera and sometimes auctioned separately. However, for the next generation of trading agents, autonomous bidding in simultaneous auctions will be a routine task.