Gujarat Techological University (GTU) in association with IIT Kanpur organised a two day workshop on artificial intelligence, deep learning, block chain and machine learning. The workshop was attended by students and professors from both Gujarat and Rajasthan. This course will be available both online and offline said GTU, VC, Navin Sheth during the workshop. "To keep the pace with rapidly changing technologies, Gujarat Technological University (GTU) is planning to launch Diploma and Certificate courses on Emerging Technologies like Artificial Intelligence, Machine Learning, and Block Chain etc. both offline & online. More Skill Development initiatives will be launched for making them more employable. More Faculty Development Programs are being planned for providing training about the latest aspects."

I'll discuss Magenta, a Google Brain project investigating music and art generation using deep learning and reinforcement learning. I'll describe the goals of Magenta and how it fits into the general trend of AI moving into our daily lives. One crucial question is: Where does AI and Machine Learning fit in the creative process? I'll argue that it's about augmenting and extending the artist rather than just creating artifacts (songs, paintings, etc.) with machines. In the first, we explore the use of recurrent neural networks to extend musical phrases in different ways.

Amsterdam-based Open Location Platform company, HERE Technologies, specialists in mapping and location platform services, today announced the establishment of the new international Institute for Advanced Research in Artificial Intelligence (IARAI), with the facility to be located in Vienna. The IARAI will be the world's first global machine-learning research institute to use industrial-scale geolocation data. As an independent institution, the IARAI will conduct research in machine learning that will deepen the location intelligence needed to build an autonomous world - heightened location awareness for autonomous vehicles, more accurate, self-healing maps, better traffic prediction models and optimised fleet management and city infrastructure. HERE will cooperate closely with the institute by providing its unique datasets and expertise. This groundbreaking initiative will give HERE greater access to emerging technological capabilities in the company's core field of interest, developed on its data.

Artificial intelligence (AI) is one of the most hyped terms in the 21st century, and yet one of the most misunderstood. Very often, when talking about AI, we like to automatically couple it with other terms such as Machine Learning, Deep Learning, and Neural Networks. This makes it sound like over 90% of AI is this kind of statistical algorithm that only PhDs can understand. While automated learning and classification algorithms are vital to the development of an artificially intelligent system, they only serve as enablers of true intelligence. These algorithms are necessary to develop intelligence, but not sufficient.

The year 2018 has been an inflection point for machine learning models handling text (or more accurately, Natural Language Processing or NLP for short). Our conceptual understanding of how best to represent words and sentences in a way that best captures underlying meanings and relationships is rapidly evolving. Moreover, the NLP community has been putting forward incredibly powerful components that you can freely download and use in your own models and pipelines (It's been referred to as NLP's ImageNet moment, referencing how years ago similar developments accelerated the development of machine learning in Computer Vision tasks). But I couldn't think of anything else..) One of the latest milestones in this development is the release of BERT, an event described as marking the beginning of a new era in NLP. BERT is a model that broke several records for how well models can handle language-based tasks.

This is a quick guide to run PyTorch with ROCm support inside a provided docker image. See ROCm install for supported operating systems and general information on the ROCm software stack. A ROCm install version 2.1 is required currently. A Vega10 / gfx900 generation discrete graphics card is required (Vega56, Vega64, or MI25). The image contains hipified PyTorch source, a clone of the PyTorch examples, and has PyTorch for gfx900 installed.

Learning to combine foveal glimpses with a third-order Boltzmann machine. Multiple object recognition with visual attention. Show, attend and tell: neural image caption generation with visual attention. Neural machine translation by jointly learning to align and translate. Learning what and where to draw.

AI is not only for engineers. If you want your organization to become better at using AI, this is the course to tell everyone--especially your non-technical colleagues--to take. In this course, you will learn: - The meaning behind common AI terminology, including neural networks, machine learning, deep learning, and data science - What AI realistically can--and cannot--do - How to spot opportunities to apply AI to problems in your own organization - What it feels like to build machine learning and data science projects - How to work with an AI team and build an AI strategy in your company - How to navigate ethical and societal discussions surrounding AI Though this course is largely non-technical, engineers can also take this course to learn the business aspects of AI.

This study develops an online predictive optimization framework for operating a fleet of autonomous vehicles to enhance mobility in an area, where there exists a latent spatio-temporal distribution of demand for commuting between locations. The proposed framework integrates demand prediction and supply optimization in the network design problem. For demand prediction, our framework estimates a marginal demand distribution for each Origin-Destination pair of locations through Quantile Regression, using counts of crowd movements as a proxy for demand. The framework then combines these marginals into a joint demand distribution by constructing a Gaussian copula, which captures the structure of correlation between different Origin-Destination pairs. For supply optimization, we devise a demand-responsive service, based on linear programming, in which route structure and frequency vary according to the predicted demand. We evaluate our framework using a dataset of movement counts, aggregated from WiFi records of a university campus in Denmark, and the results show that our framework outperforms conventional methods for route optimization, which do not utilize the full predictive distribution.

In spite of achieving revolutionary successes in machine learning, deep convolutional neural networks have been recently found to be vulnerable to adversarial attacks and difficult to generalize to novel test images with reasonably large geometric transformations. Inspired by a recent neuroscience discovery revealing that primate brain employs disentangled shape and appearance representations for object recognition, we propose a general disentangled deep autoencoding regularization framework that can be easily applied to any deep embedding based classification model for improving the robustness of deep neural networks. Our framework effectively learns disentangled appearance code and geometric code for robust image classification, which is the first disentangling based method defending against adversarial attacks and complementary to standard defense methods. Extensive experiments on several benchmark datasets show that, our proposed regularization framework leveraging disentangled embedding significantly outperforms traditional unregularized convolutional neural networks for image classification on robustness against adversarial attacks and generalization to novel test data.