Ultrasound (US) imaging is commonly used to assist in the diagnosis and interventions of spine diseases, while the standardized US acquisitions performed by manually operating the probe require substantial experience and training of sonographers. In this work, we propose a novel dual-agent framework that integrates a reinforcement learning (RL) agent and a deep learning (DL) agent to jointly determine the movement of the US probe based on the real-time US images, in order to mimic the decision-making process of an expert sonographer to achieve autonomous standard view acquisitions in spinal sonography. Moreover, inspired by the nature of US propagation and the characteristics of the spinal anatomy, we introduce a view-specific acoustic shadow reward to utilize the shadow information to implicitly guide the navigation of the probe toward different standard views of the spine. Our method is validated in both quantitative and qualitative experiments in a simulation environment built with US data acquired from 17 volunteers. The average navigation accuracy toward different standard views achieves 5.18mm/5.25deg and 12.87mm/17.49deg in the intra- and inter-subject settings, respectively. The results demonstrate that our method can effectively interpret the US images and navigate the probe to acquire multiple standard views of the spine.

Exploration is one of the most important tasks in Reinforcement Learning, but it is not well-defined beyond finite problems in the Dynamic Programming paradigm (see Subsection 2.4). We provide a reinterpretation of exploration which can be applied to any online learning method. We come to this definition by approaching exploration from a new direction. After finding that concepts of exploration created to solve simple Markov decision processes with Dynamic Programming are no longer broadly applicable, we reexamine exploration. Instead of extending the ends of dynamic exploration procedures, we extend their means. That is, rather than repeatedly sampling every state-action pair possible in a process, we define the act of modifying an agent to itself be explorative. The resulting definition of exploration can be applied in infinite problems and non-dynamic learning methods, which the dynamic notion of exploration cannot tolerate. To understand the way that modifications of an agent affect learning, we describe a novel structure on the set of agents: a collection of distances (see footnote 7) $d_{a} \in A$, which represent the perspectives of each agent possible in the process. Using these distances, we define a topology and show that many important structures in Reinforcement Learning are well behaved under the topology induced by convergence in the agent space.

Sustainable consumption aims to minimize the environmental and societal impact of the use of services and products. Over-consumption of services and products leads to potential natural resource exhaustion and societal inequalities, as access to goods and services becomes more challenging. In everyday life, a person can simply achieve more sustainable purchases by drastically changing their lifestyle choices and potentially going against their personal values or wishes. Conversely, achieving sustainable consumption while accounting for personal values is a more complex task, as potential trade-offs arise when trying to satisfy environmental and personal goals. This article focuses on value-sensitive design of recommender systems, which enable consumers to improve the sustainability of their purchases while respecting their personal values. Value-sensitive recommendations for sustainable consumption are formalized as a multi-objective optimization problem, where each objective represents different sustainability goals and personal values. Novel and existing multi-objective algorithms calculate solutions to this problem. The solutions are proposed as personalized sustainable basket recommendations to consumers. These recommendations are evaluated on a synthetic dataset, which comprises three established real-world datasets from relevant scientific and organizational reports. The synthetic dataset contains quantitative data on product prices, nutritional values and environmental impact metrics, such as greenhouse gas emissions and water footprint. The recommended baskets are highly similar to consumer purchased baskets and aligned with both sustainability goals and personal values relevant to health, expenditure and taste. Even when consumers would accept only a fraction of recommendations, a considerable reduction of environmental impact is observed.

Transformer-based language models are applied to a wide range of applications in natural language processing. However, they are inefficient and difficult to deploy. In recent years, many compression algorithms have been proposed to increase the implementation efficiency of large Transformer-based models on target hardware. In this work we present a new method for training sparse pre-trained Transformer language models by integrating weight pruning and model distillation. These sparse pre-trained models can be used to transfer learning for a wide range of tasks while maintaining their sparsity pattern. We demonstrate our method with three known architectures to create sparse pre-trained BERT-Base, BERT-Large and DistilBERT. We show how the compressed sparse pre-trained models we trained transfer their knowledge to five different downstream natural language tasks with minimal accuracy loss. Moreover, we show how to further compress the sparse models' weights to 8bit precision using quantization-aware training. For example, with our sparse pre-trained BERT-Large fine-tuned on SQuADv1.1 and quantized to 8bit we achieve a compression ratio of $40$X for the encoder with less than $1\%$ accuracy loss. To the best of our knowledge, our results show the best compression-to-accuracy ratio for BERT-Base, BERT-Large, and DistilBERT.

Series expansions have been a cornerstone of applied mathematics and engineering for centuries. In this paper, we revisit the Taylor series expansion from a modern Machine Learning perspective. Specifically, we introduce the Fast Continuous Convolutional Taylor Transform (FC2T2), a variant of the Fast Multipole Method (FMM), that allows for the efficient approximation of low dimensional convolutional operators in continuous space. We build upon the FMM which is an approximate algorithm that reduces the computational complexity of N-body problems from O(NM) to O(N+M) and finds application in e.g. particle simulations. As an intermediary step, the FMM produces a series expansion for every cell on a grid and we introduce algorithms that act directly upon this representation. These algorithms analytically but approximately compute the quantities required for the forward and backward pass of the backpropagation algorithm and can therefore be employed as (implicit) layers in Neural Networks. Specifically, we introduce a root-implicit layer that outputs surface normals and object distances as well as an integral-implicit layer that outputs a rendering of a radiance field given a 3D pose. In the context of Machine Learning, $N$ and $M$ can be understood as the number of model parameters and model evaluations respectively which entails that, for applications that require repeated function evaluations which are prevalent in Computer Vision and Graphics, unlike regular Neural Networks, the techniques introduce in this paper scale gracefully with parameters. For some applications, this results in a 200x reduction in FLOPs compared to state-of-the-art approaches at a reasonable or non-existent loss in accuracy.

The AI industry has expanded massively over the last years and is expected to grow even further. Many of the major corporations employ AI-assisted technicians to solve problems. The median salary of an AI engineer is $150,000 which is up to $171,765 per year. Companies need AI experts who can build and deploy scalable models to meet growing industry demands. There are various AI projects you can do to learn about the library.

As of next year, IIT Delhi will offer a new postgraduate studies in artificial intelligence. Students getting a bachelor's degree in science or engineering will be able to participate in the programme. According to authorities, the Indian Institute of Technology (IIT) Delhi would begin a new postgraduate studies focusing on machine learning after this year. "IIT Delhi's School of Artificial Intelligence (ScAI) will launch a new postgraduate advanced machine learning programme. The proposed course titled "M Tech in Machine Intelligence and Data Science (MINDS)" has been accepted by the IIT Delhi Council, the academic body responsible for all important educational matters, according to a senior official at the institution. MTech in MINDS will be the school's main program for students, with classes starting in July 2022. This will be the dept's second degree programme. The institution had previously begun a PhD programme in Artificial Intelligence. "In its initial year, our PhD programme drew a lot of attention.

Artificial Intelligence has already become an indispensable part of our everyday life, whether when we browse the Internet, shop online, watch videos and images on social networks, and even when we drive a car or use our smartphones. AI is widely used in medicine, sales forecasting, space industry and construction. Since we are surrounded by AI technologies everywhere, we need to understand how these technologies work. And for such understanding at a basic level, it is not necessary to have a technical or IT education. In this course, you will learn about the fundamental concepts of Artificial Intelligence and Machine learning.

Artificial Intelligence helps find out what a student does and does not know, building a personalized study schedule for each learner considering the knowledge gaps. Artificial Intelligence is finally here and most of us are already actively using it in our day-to-day life (even without knowing it). To prepare our future generation in order to harness these technologies, people need to understand how they can use AI first of all! Only then can they use it to facilitate learning and solve real-world problems. The course is aimed at all those people, irrespective of their profession, who would like to learn how to make active use of AI.

Learn how to use Python in real world case scenarios and projects Learn the basics of Python programming starting with installing Python on your computer Build the confidence to go out on your own and search for coding ideas online and write your own simple programs. Learn how to make best GUI games with Python Learn how object oriented programming works in practice. Build the confidence to go out on your own and search for coding ideas online and write your own simple programs. Learn how object oriented programming works in practice. Most beginners study core part of the programming easily but actual programming starts after core programming is finished, since most of real world problems are solved by using programming paradigms such as comprehension, object oriented programming etc.