Graphcore has released significant new features for the PopVisionTM family of analysis tools as part of a major Poplar software stack update, Poplar SDK 2.0. We created the PopVision Graph Analyser and System Analyser to help developers maximise the performance of their applications on IPU systems. To mark this update, we are looking at how PopVision tools can be used most effectively to inform and optimise machine learning programs. With its massively parallel architecture, the IPU has been specifically built from the ground up for machine intelligence workloads and is therefore able to deliver state of the art performance across even the most complex AI models. For this reason, many of our users are usually not just looking to run standard machine intelligence models, but to exploit the highest possible performance from IPU systems, beyond what they have been able to achieve with other systems.

At CAIA – Center for Artificial Intelligence and Advanced Analytics, our mission is to give your career an Integral Boost. We welcome you with an opportunity to enter the world of technology – Artificial Intelligence, Data Analytics, Data Science, Data engineering & Data Visualization . We are witnessing a profound change and disruption in the social and technological sphere at a global level. These changes are posing challenges and providing exciting opportunities at the same time. The exponential growth of digital technologies offer unprecedented support to job seekers.

Master Robotic Process Automation (RPA) and UiPath – go from beginner to advanced. Welcome to the Complete UiPath RPA Developer Course: Build 7 Robots where you will learn everything you need to know to get started as a Robotic Process Automation (RPA) developer. Learn and master UiPath Studio and then build state-of-the-art software robots from scratch. The best part about this course is that this course is entirely project-based, which means you will be getting hands-on experience and learn the skills you need on how to build real-world software robots in an enterprise setting. So if you're the type of person who'learns by doing', then this course is for you.

In early December, Harsh Shrivastava became the first person to be awarded a doctorate in machine learning from Georgia Tech. "It's a special, happy feeling to be the first recipient of a ML Ph.D. degree. I am much obliged to my advisor, thesis committee members, friends and academic staff of Georgia Tech for their help and support throughout my Ph.D. years," said Shrivastava. Though machine learning has long been a research interest of the Institute, it wasn't until 2016 that the Machine Learning Center at Georgia Tech (ML@GT) was created. The center began offering a doctorate program in 2017.

Up-directed rough sets are introduced and studied by the present author in earlier papers. This is extended by her in two different granular directions in this research, with a surprising algebraic semantics. The granules are based on ideas of generalized closure under up-directedness that may be read as a form of weak consequence. This yields approximation operators that satisfy cautious monotony, while pi-groupoidal approximations (that additionally involve strategic choice and algebraic operators) have nicer properties. The study is primarily motivated by possible structure of concepts in distributed cognition perspectives, real or virtual classroom learning contexts, and student-centric teaching. Rough clustering techniques for datasets that involve up-directed relations (as in the study of Sentinel project image data) are additionally proposed. This research is expected to see significant theoretical and practical applications in related domains.

Robotics simulation has been an integral part of research and development in the robotics area. The simulation eliminates the possibility of harm to sensors, motors, and the physical structure of a real robot by enabling robotics application testing to be carried out quickly and affordably without being subjected to mechanical or electronic errors. Simulation through virtual reality (VR) offers a more immersive experience by providing better visual cues of environments, making it an appealing alternative for interacting with simulated robots. This immersion is crucial, particularly when discussing sociable robots, a subarea of the human-robot interaction (HRI) field. The widespread use of robots in daily life depends on HRI. In the future, robots will be able to interact effectively with people to perform a variety of tasks in human civilization. It is crucial to develop simple and understandable interfaces for robots as they begin to proliferate in the personal workspace. Due to this, in this study, we implement a VR robotic framework with ready-to-use tools and packages to enhance research and application development in social HRI. Since the entire VR interface is an open-source project, the tests can be conducted in an immersive environment without needing a physical robot.

Question answering (QA) is a natural language understanding task within the fields of information retrieval and information extraction that has attracted much attention from the computational linguistics and artificial intelligence research community in recent years because of the strong development of machine reading comprehension-based models. A reader-based QA system is a high-level search engine that can find correct answers to queries or questions in open-domain or domain-specific texts using machine reading comprehension (MRC) techniques. The majority of advancements in data resources and machine-learning approaches in the MRC and QA systems especially are developed significantly in two resource-rich languages such as English and Chinese. A low-resource language like Vietnamese has witnessed a scarcity of research on QA systems. This paper presents XLMRQA, the first Vietnamese QA system using a supervised transformer-based reader on the Wikipedia-based textual knowledge source (using the UIT-ViQuAD corpus), outperforming the two robust QA systems using deep neural network models: DrQA and BERTserini with 24.46% and 6.28%, respectively. From the results obtained on the three systems, we analyze the influence of question types on the performance of the QA systems.

We present MetricBERT, a BERT-based model that learns to embed text under a well-defined similarity metric while simultaneously adhering to the ``traditional'' masked-language task. We focus on downstream tasks of learning similarities for recommendations where we show that MetricBERT outperforms state-of-the-art alternatives, sometimes by a substantial margin. We conduct extensive evaluations of our method and its different variants, showing that our training objective is highly beneficial over a traditional contrastive loss, a standard cosine similarity objective, and six other baselines. As an additional contribution, we publish a dataset of video games descriptions along with a test set of similarity annotations crafted by a domain expert.

This paper describes a method of online refinement of a scene recognition model for robot navigation considering traversable plants, flexible plant parts which a robot can push aside while moving. In scene recognition systems that consider traversable plants growing out to the paths, misclassification may lead the robot to getting stuck due to the traversable plants recognized as obstacles. Yet, misclassification is inevitable in any estimation methods. In this work, we propose a framework that allows for refining a semantic segmentation model on the fly during the robot's operation. We introduce a few-shot segmentation based on weight imprinting for online model refinement without fine-tuning. Training data are collected via observation of a human's interaction with the plant parts. We propose novel robust weight imprinting to mitigate the effect of noise included in the masks generated by the interaction. The proposed method was evaluated through experiments using real-world data and shown to outperform an ordinary weight imprinting and provide competitive results to fine-tuning with model distillation while requiring less computational cost.

A recent trend in binary code analysis promotes the use of neural solutions based on instruction embedding models. An instruction embedding model is a neural network that transforms sequences of assembly instructions into embedding vectors. If the embedding network is trained such that the translation from code to vectors partially preserves the semantic, the network effectively represents an assembly code model. In this paper we present BinBert, a novel assembly code model. BinBert is built on a transformer pre-trained on a huge dataset of both assembly instruction sequences and symbolic execution information. BinBert can be applied to assembly instructions sequences and it is fine-tunable, i.e. it can be re-trained as part of a neural architecture on task-specific data. Through fine-tuning, BinBert learns how to apply the general knowledge acquired with pre-training to the specific task. We evaluated BinBert on a multi-task benchmark that we specifically designed to test the understanding of assembly code. The benchmark is composed of several tasks, some taken from the literature, and a few novel tasks that we designed, with a mix of intrinsic and downstream tasks. Our results show that BinBert outperforms state-of-the-art models for binary instruction embedding, raising the bar for binary code understanding.