SAN FRANCISCO--Toby Ghassemieh is an inquisitive 12-year-old boy with a pet gecko named Coco and the makings of an ant colony in a bedroom cupboard. He built a forge in his backyard with plaster of Paris to melt aluminum into ingots. He wants to be a physicist when he grows up. All that is on hold, though. What he cares about most is the videogame Fortnite. Same for his buddies Matthew Seiden, Max Howe, Jaren Erville and Reed Leidlein, who all live in or near the city's Richmond neighborhood. These seventh-grade pals used to spend their after-school hours together, either at somebody's house or nearby Rochambeau Park. Now, they spend most of their free time apart, sequestered in their respective homes playing Fortnite and chatting through headsets instead of in person.

Recent successes in Reinforcement Learning have encouraged a fast-growing network of RL researchers and a number of breakthroughs in RL research. As the RL community and the body of RL work grows, so does the need for widely applicable benchmarks that can fairly and effectively evaluate a variety of RL algorithms. This need is particularly apparent in the realm of Hierarchical Reinforcement Learning (HRL). While many existing test domains may exhibit hierarchical action or state structures, modern RL algorithms still exhibit great difficulty in solving domains that necessitate hierarchical modeling and action planning, even when such domains are seemingly trivial. These difficulties highlight both the need for more focus on HRL algorithms themselves, and the need for new testbeds that will encourage and validate HRL research. Existing HRL testbeds exhibit a Goldilocks problem; they are often either too simple (e.g. Taxi) or too complex (e.g. Montezuma's Revenge from the Arcade Learning Environment). In this paper we present the Escape Room Domain (ERD), a new flexible, scalable, and fully implemented testing domain for HRL that bridges the "moderate complexity" gap left behind by existing alternatives. ERD is open-source and freely available through GitHub, and conforms to widely-used public testing interfaces for simple integration and testing with a variety of public RL agent implementations. We show that the ERD presents a suite of challenges with scalable difficulty to provide a smooth learning gradient from Taxi to the Arcade Learning Environment.

Universal supervised learning is considered from an information theoretic point of view following the universal prediction approach, see Merhav and Feder (1998). We consider the standard supervised "batch" learning where prediction is done on a test sample once the entire training data is observed, and the individual setting where the features and labels, both in the training and test, are specific individual quantities. The information theoretic approach naturally uses the self-information loss or log-loss. Our results provide universal learning schemes that compete with a "genie" (or reference) that knows the true test label. In particular, it is demonstrated that the main proposed scheme, termed Predictive Normalized Maximum Likelihood (pNML), is a robust learning solution that outperforms the current leading approach based on Empirical Risk Minimization (ERM). Furthermore, the pNML construction provides a pointwise indication for the learnability of the specific test challenge with the given training examples

This work investigates the ways in which deep learning methods can benefit from random projection (RP), a classic linear dimensionality reduction method. We focus on two areas where, as we have found, employing RP techniques can improve deep models: training neural networks on high-dimensional data and initialization of network parameters. Training deep neural networks (DNNs) on sparse, high-dimensional data with no exploitable structure implies a network architecture with an input layer that has a huge number of weights, which often makes training infeasible. We show that this problem can be solved by prepending the network with an input layer whose weights are initialized with an RP matrix. We propose several modifications to the network architecture and training regime that makes it possible to efficiently train DNNs with learnable RP layer on data with as many as tens of millions of input features and training examples. In comparison to the state-of-the-art methods, neural networks with RP layer achieve competitive performance or improve the results on several extremely high-dimensional real-world datasets. The second area where the application of RP techniques can be beneficial for training deep models is weight initialization. Setting the initial weights in DNNs to elements of various RP matrices enabled us to train residual deep networks to higher levels of performance.

Artificial Intelligence (AI) technologies could be broadly categorised into Analytics and Autonomy. Analytics focuses on algorithms offering perception, comprehension, and projection of knowledge gleaned from sensorial data. Autonomy revolves around decision making, and influencing and shaping the environment through action production. A smart autonomous system (SAS) combines analytics and autonomy to understand, learn, decide and act autonomously. To be useful, SAS must be trusted and that requires testing. Lifelong learning of a SAS compounds the testing process. In the remote chance that it is possible to fully test and certify the system pre-release, which is theoretically an undecidable problem, it is near impossible to predict the future behaviours that these systems, alone or collectively, will exhibit. While it may be feasible to severely restrict such systems\textquoteright \ learning abilities to limit the potential unpredictability of their behaviours, an undesirable consequence may be severely limiting their utility. In this paper, we propose the architecture for a watchdog AI (WAI) agent dedicated to lifelong functional testing of SAS. We further propose system specifications including a level of abstraction whereby humans shepherd a swarm of WAI agents to oversee an ecosystem made of humans and SAS. The discussion extends to the challenges, pros, and cons of the proposed concept.

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Demand for specialized business programs in new technologies has been increasing all over the world, especially in cryptocurrencies and the blockchain technology behind them. Rebecca Guy, an associate at Scotiabank Global Capital Markets, went back to school to learn a second language. But she's not trying to become fluent in French, Italian or Mandarin. Last September, the 24-year-old started the master of management in artificial intelligence (MMAI) program at Smith School of Business in Kingston to help her become as savvy in new technologies as in business. "I found the program because one day I found myself becoming so frustrated working on a project with a team because I felt like I only understood half of the puzzle," recalls Ms. Guy.

At Amazon Web Services (AWS), we are continually innovating with new services and solutions. That's why we're excited to announce several new offerings from AWS Training and Certification to help customers and AWS Partner Network (APN) Partners build new cloud skills and learn about the latest AWS services. Dive deep into the same ML curriculum we use to train Amazon's developers and data scientists. Choose from four role-based learning paths, with more than 30 digital ML courses and hands-on labs totaling 45 hours of training. Take our new AWS Certified Machine Learning – Specialty beta exam.

As far as B2B FinTech goes, much attention has been paid to empowering a range of processes, including accounts payable, expense management and financial analysis -- many of which had previously been a part of the Enterprise Resource Planning (ERP) solution. While innovation and digital disruption have taken these financial processes out of the ERP and made them independent platforms for corporate users, the ERP remains a critical component of financial management in the enterprise, particularly for larger firms. In the age of digitization, the ERP has also emerged as an important collection spot for essential company data. New research from Evans Data found the ERP isn't immune to FinTech innovation, either. According to its report, the "Artificial Intelligence and Machine Learning Survey," published last week, the ERP software industry remains the number-one target of artificial intelligence (AI) and machine learning (ML) developers.

We study object recognition under the constraint that each object class is only represented by very few observations. In such cases, naive supervised learning would lead to severe over-fitting in deep neural networks due to limited training data. We tackle this problem by creating much more training data through label propagation from the few labeled examples to a vast collection of unannotated images. Our main insight is that such a label propagation scheme can be highly effective when the similarity metric used for propagation is learned and transferred from other related domains with lots of data. We test our approach on semi-supervised learning, transfer learning and few-shot recognition, where we learn our similarity metric using various supervised/unsupervised pretraining methods, and transfer it to unlabeled data across different data distributions. By taking advantage of unlabeled data in this way, we achieve significant improvements on all three tasks. Notably, our approach outperforms current state-of-the-art techniques by an absolute $20\%$ for semi-supervised learning on CIFAR10, $10\%$ for transfer learning from ImageNet to CIFAR10, and $6\%$ for few-shot recognition on mini-ImageNet, when labeled examples are limited.