These systems can fail unexpectedly in a variety of different ways. Notably, applications Open Banking powered machine learning applications require novel that rely on online inference are subject to their inability robustness approaches to deal with challenging stress and failure to keep up with the expected operating procedures while, now scenarios. In this paper we propose an hierarchical fallback architecture additionally, having to make tedious computational tasks for these for improving robustness in high risk machine learning AI/ML applications, typically resulting in timeouts, infrastructure applications with a focus in the financial domain. We define generic outages and, often, failures in external dependencies such as third failure scenarios often found in online inference that depend on party data providers (external API calls) [7]. When the underlying external data providers and we describe in detail how to apply the machine learning applications are presented with strong robustness hierarchical fallback architecture to address them. Finally, we offer requirements, fallback or fall-over strategies are needed to keep a real world example of its applicability in the industry for near-real operations running, even in the event of unexpected failures. In time transactional fraud risk evaluation using Open Banking data finance, specifically applications that require real time risk mitigation and under extreme stress scenarios.

We introduce the Concept Bottleneck Large Language Model (CB-LLM), a pioneering approach to creating inherently interpretable Large Language Models (LLMs). Unlike traditional black-box LLMs that rely on post-hoc interpretation methods with limited neuron function insights, CB-LLM sets a new standard with its built-in interpretability, scalability, and ability to provide clear, accurate explanations. This innovation not only advances transparency in language models but also enhances their effectiveness. Our unique Automatic Concept Correction (ACC) strategy successfully narrows the performance gap with conventional black-box LLMs, positioning CB-LLM as a model that combines the high accuracy of traditional LLMs with the added benefit of clear interpretability -- a feature markedly absent in existing LLMs.

The market for domestic robots made to perform household chores is growing as these robots relieve people of everyday responsibilities. Domestic robots are generally welcomed for their role in easing human labor, in contrast to industrial robots, which are frequently criticized for displacing human workers. But before these robots can carry out domestic chores, they need to become proficient in several minor activities, such as recognizing their surroundings, making decisions, and picking up on human behaviors. Reinforcement learning, or RL, has emerged as a key robotics technology that enables robots to interact with their environment and learn how to optimize their actions to maximize rewards. However, the goal of Deep Reinforcement Learning is to address more complicated, continuous action-state spaces in real-world settings by combining RL with Neural Networks. The efficacy of DeepRL can be further augmented through interactive feedback, in which a trainer offers real-time guidance to expedite the robot's learning process. Nevertheless, the current methods have drawbacks, namely the transient application of guidance that results in repeated learning under identical conditions. Therefore, we present a novel method to preserve and reuse information and advice via Deep Interactive Reinforcement Learning, which utilizes a persistent rule-based system. This method not only expedites the training process but also lessens the number of repetitions that instructors will have to carry out. This study has the potential to advance the development of household robots and improve their effectiveness and efficiency as learners.

A General Motors pickup has never hauled something like this. GM Defense is collaborating with military contractor Black Sage Technologies to integrate a drone defense system into the Infantry Squad Vehicle (ISV) that GM Defense recently began supplying to the US Army. The ISV is based on the last-generation Chevrolet Colorado ZR2 midsize pickup and manufactured in Concord, N.C., using frames supplied by NASCAR's Hendrick Motorsports. The midsize truck was engineered for high-speed off-road driving and designed to fit inside a CH-47 Chinook helicopter, slung from a UH-60 Blackhawk helicopter, or air-dropped from a cargo plane by parachute for quick deployment into the field. The vehicle can be outfitted to fit nine troops, but there are several configurations that mix passenger, cargo and arms carrying capabilities.

Neural network forms the foundation of deep learning and numerous AI applications. Classical neural networks are fully connected, expensive to train and prone to overfitting. Sparse networks tend to have convoluted structure search, suboptimal performance and limited usage. We proposed the novel uniform sparse network (USN) with even and sparse connectivity within each layer. USN has one striking property that its performance is independent of the substantial topology variation and enormous model space, thus offers a search-free solution to all above mentioned issues of neural networks. USN consistently and substantially outperforms the state-of-the-art sparse network models in prediction accuracy, speed and robustness. It even achieves higher prediction accuracy than the fully connected network with only 0.55% parameters and 1/4 computing time and resources. Importantly, USN is conceptually simple as a natural generalization of fully connected network with multiple improvements in accuracy, robustness and scalability. USN can replace the latter in a range of applications, data types and deep learning architectures. We have made USN open source at https://github.com/datapplab/sparsenet.

While drone delivery services are yet to become a practical reality in the consumer world, they're already proving their mettle in terms of crisis response. After deploying its UAVs (unmanned aerial vehicles) in parts of Africa to facilitate medical care, Californian robotics company Zipline is now using its technology closer to home, to help tackle the coronavirus pandemic in the US. In partnership with Novant Health, Zipline's drones will undertake 32-mile flights on two routes between Novant's emergency drone fulfilment centre in Kannapolis, North Carolina, and its medical center in Huntersville. Each delivery will ferry personal protective gear and medical equipment to frontline healthcare workers treating COVID-19 patients. The two companies were already in talks about a potential partnership prior to the coronavirus outbreak, but the escalating situation helped to catalyze the deal.

A few weeks ago we suggested you follow 5 topics that cover the more basic aspects of the business of blogging. Today let's focus on something equally important: the life of a blogger. This week I attended MMA SM2 Innovation Summit in New York, which took place during Advertising Week. The lineup of speakers did not disappoint and I gained many new insights. Here are the four that most stuck with me.