The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Given that natural language processing (NLP) is a subset of artificial intelligence (AI), models need to train on large volumes of data. Unfortunately, many researchers are unable to access or develop the models and datasets necessary for robust systems -- they are mostly the purview of large technology giants. Hugging Face, the winner of VentureBeat's Innovation in Natural Language Process/Understanding Award for 2021, is looking to level the playing field. The team, launched by Clément Delangue and Julien Chaumond in 2016, was recognized for its work in democratizing NLP, the global market value for which is expected to hit $35.1 billion by 2026.

Access to resources by users may need to be granted only upon certain conditions and contexts, perhaps particularly in cyber-physical settings. Unfortunately, creating and modifying context-sensitive access control solutions in dynamic environments creates ongoing challenges to manage the authorization contexts. This paper proposes RASA, a context-sensitive access authorization approach and mechanism leveraging unsupervised machine learning to automatically infer risk-based authorization decision boundaries. We explore RASA in a healthcare usage environment, wherein cyber and physical conditions create context-specific risks for protecting private health information. The risk levels are associated with access control decisions recommended by a security policy. A coupling method is introduced to track coexistence of the objects within context using frequency and duration of coexistence, and these are clustered to reveal sets of actions with common risk levels; these are used to create authorization decision boundaries. In addition, we propose a method for assessing the risk level and labelling the clusters with respect to their corresponding risk levels. We evaluate the promise of RASA-generated policies against a heuristic rule-based policy. By employing three different coupling features (frequency-based, duration-based, and combined features), the decisions of the unsupervised method and that of the policy are more than 99% consistent.

Entrepreneurs and academics will talk about the application of Machine Learning, a subfield of Artificial Intelligence, which gives computers the ability to …

In order to solve complex, long-horizon tasks, intelligent robots need to carry out high-level, abstract planning and reasoning in conjunction with motion planning. However, abstract models are typically lossy and plans or policies computed using them can be inexecutable. These problems are exacerbated in stochastic situations where the robot needs to reason about and plan for multiple contingencies. We present a new approach for integrated task and motion planning in stochastic settings. In contrast to prior work in this direction, we show that our approach can effectively compute integrated task and motion policies whose branching structures encode agent behaviors that handle multiple execution-time contingencies. We prove that our algorithm is probabilistically complete and can compute feasible solution policies in an anytime fashion so that the probability of encountering an unresolved contingency decreases over time. Empirical results on a set of challenging problems show the utility and scope of our method.

Reinforcement learning (RL) has shown to be a valuable tool in training neural networks for autonomous motion planning. The application of RL to a specific problem is dependent on a reward signal to quantify how good or bad a certain action is. This paper addresses the problem of reward signal design for robotic control in the context of local planning for autonomous racing. We aim to design reward signals that are able to perform well in multiple, competing, continuous metrics. Three different methodologies of position-based, velocity-based, and action-based rewards are considered and evaluated in the context of F1/10th racing. A novel method of rewarding the agent on its state relative to an optimal trajectory is presented. Agents are trained and tested in simulation and the behaviors generated by the reward signals are compared to each other on the basis of average lap time and completion rate. The results indicate that a reward based on the distance and velocity relative to a minimum curvature trajectory produces the fastest lap times.

The problem of autonomous racing is to navigate through a race course as quickly as possible while not colliding with any obstacles. We approach the autonomous racing problem with the added constraint of not maintaining an updated obstacle map of the environment. Several current approaches to this problem use end-to-end learning systems where an agent replaces the entire navigation pipeline. This paper presents a hierarchical planning architecture that combines a high level planner and path following system with a reinforcement learning agent that learns that subsystem of obstacle avoidance. The novel "modification planner" uses the path follower to track the global plan and the deep reinforcement learning agent to modify the references generated by the path follower to avoid obstacles. Importantly, our architecture does not require an updated obstacle map and only 10 laser range finders to avoid obstacles. The modification planner is evaluated in the context of F1/10th autonomous racing and compared to a end-to-end learning baseline, the Follow the Gap Method and an optimisation based planner. The results show that the modification planner can achieve faster average times compared to the baseline end-to-end planner and a 94% success rate which is similar to the baseline.

Several metrics have been proposed for assessing the similarity of (abstract) meaning representations (AMRs), but little is known about how they relate to human similarity ratings. Moreover, the current metrics have complementary strengths and weaknesses: some emphasize speed, while others make the alignment of graph structures explicit, at the price of a costly alignment step. In this work we propose new Weisfeiler-Leman AMR similarity metrics that unify the strengths of previous metrics, while mitigating their weaknesses. Specifically, our new metrics are able to match contextualized substructures and induce n:m alignments between their nodes. Furthermore, we introduce a Benchmark for AMR Metrics based on Overt Objectives (BAMBOO), the first benchmark to support empirical assessment of graph-based MR similarity metrics. BAMBOO maximizes the interpretability of results by defining multiple overt objectives that range from sentence similarity objectives to stress tests that probe a metric's robustness against meaning-altering and meaning-preserving graph transformations. We show the benefits of BAMBOO by profiling previous metrics and our own metrics. Results indicate that our novel metrics may serve as a strong baseline for future work.

Abstract--Adenocarcinoma and squamous cell carcinoma constitute approximately 40% and 30% of all lung cancer subtypes, respectively, and display broad heterogeneity in terms of clinical and molecular responses to therapy. Molecular subtyping has enabled precision medicine to overcome these challenges and provide significant biological insights to predict prognosis and improve clinical decision making. Over the past decade, conventional machine learning algorithms and DL-based CNNs have been espoused for the classification of cancer subtypes from gene expression datasets. However, these methods are potentially biased toward identification of cancer biomarkers. Recently proposed transformer-based architectures that leverage the self-attention mechanism can encode high throughput gene expressions and learn representations that are computationally complex and parametrically expensive. However, compared to the datasets for natural language processing applications, gene expression consists of several hundreds of thousands of genes from a limited number of observations, making it difficult to efficiently train transformers for bioinformatics applications. Hence, we propose an end-to-end deep learning approach, Gene Transformer, which addresses the complexity of high-dimensional gene expression with a multi-head self-attention module by identifying relevant biomarkers across multiple cancer subtypes without requiring feature selection as a prerequisite for the current classification algorithms. The proposed architecture achieved an overall improved performance for all evaluation metrics and had fewer misclassification errors than the commonly used traditional classification algorithms. The classification results show that Gene Transformer can be an efficient approach for classifying cancer subtypes, indicating that any improvement in deep learning models in computational biology can also be reflected well in this domain. I. Introduction enes, a unit of inheritance, are responsible for storing genetic information in all living organisms, and their expression in a Therefore, any type of gene mutation, single or multiple, can lead to a dysregulation in gene expression, which is broadly termed, genetic disorder.

Artificial Intelligence (AI) can be a useful weapon in the fight against corruption. Its capacity to handle huge data is unrivaled, as is its ability to spot abnormalities or trends, such as in financial transaction data. Some of the ways AI is used in society have skeptics, who fear a society that is more monitored, putting privacy and individual freedom in danger. Let's get into the topic in more detail. Artificial intelligence (AI) refers to technologies that allow machines to simulate human intelligence in order to tackle complicated issues.

Developing new packaged foods and consumer goods can take a couple years as companies research, prototype and test products. In a society that runs on social media, however, people expect to see trends land on store shelves much more quickly. Founded in 2018, Ai Palette uses machine learning to help companies spot trends in real time and get them retail-ready, often within a few months. The startup, whose clients include Danone, Kellogg's, Cargill and Dole, announced today it has raised an oversubscribed $4.4 million Series A co-led by pi Ventures and Exfinity Venture Partners. Both will join Ai Palette's board.