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Deep learning among top in demand skills of 2020 in India


According to a report by silicon-valley-based Udacity, Karnataka holds the lion's share for maximum nanodegree programmes in 2020. As much as 24 per cent demand for deep learning and 34 per cent of the total demand for data engineering nanodegree programmes comes from Karnataka, the company said in a statement. The demand for AI product manager (38 per cent) and product manager (60 per cent) is also the highest in the state. Data science and deep learning are the most popular nanodegree programmes in Maharashtra. More than 40 per cent of the enrollments come from this state. New Delhi is a frontrunner in the mainstream programming languages.

What is The Potential for Machine Learning in The Future


Machine Learning works on the principles of computer algorithms that learn in a reflex manner through trials and experiences. It is an application of Artificial Intelligence that permits program applications to anticipate results with utmost precision. It makes a distinction to create computer programs and to assist computers to memorize without human intercession. The future of machine learning is exceptionally exciting. At present, almost every common domain is powered by machine learning applications.

Report: State of Artificial Intelligence in India - 2020


Artificial Intelligence or AI is a field of Data Science that trains computers to learn from experience, adjust to inputs, and perform tasks of certain cognitive levels. Over the last few years, AI has emerged as a significant data science function and, by utilizing advanced algorithms and computing power, AI is transforming the functional, operational, and strategic landscape of various business domains. AI algorithms are designed to make decisions, often using real-time data. Using sensors, digital data, and even remote inputs, AI algorithms combine information from a variety of different sources, analyze the data instantly, and act on the insights derived from the data. Most AI technologies – from advanced recommendation engines to self-driving cars – rely on diverse deep learning models. By utilizing these complex models, AI professionals are able to train computers to accomplish specific tasks by recognizing patterns in the data. Analytics India Magazine (AIM), in association with Jigsaw Academy, has developed this study on the Artificial Intelligence market to understand the developments of the AI market in India, covering the market in terms of Industry and Company Type. Moreover, the study delves into the market size of the different categories of AI and Analytics startups / boutique firms. As a part of the broad Data Science domain, the Artificial Intelligence technology function has so far been classified as an emerging technology segment. Moreover, the AI market in India has, till now, been dominated by the MNC Technology and the GIC or Captive firms. Domestic firms, Indian startups, and even International Technology startups across various sectors have, so far, not made a significant investment, in terms of operations and scale, in the Indian AI market. Additionally, IT services and Boutique AI & Analytics firms had not, till a couple of years ago, developed full-fledged AI offerings in India for their clients.

DeepPeep: Exploiting Design Ramifications to Decipher the Architecture of Compact DNNs Machine Learning

The remarkable predictive performance of deep neural networks (DNNs) has led to their adoption in service domains of unprecedented scale and scope. However, the widespread adoption and growing commercialization of DNNs have underscored the importance of intellectual property (IP) protection. Devising techniques to ensure IP protection has become necessary due to the increasing trend of outsourcing the DNN computations on the untrusted accelerators in cloud-based services. The design methodologies and hyper-parameters of DNNs are crucial information, and leaking them may cause massive economic loss to the organization. Furthermore, the knowledge of DNN's architecture can increase the success probability of an adversarial attack where an adversary perturbs the inputs and alter the prediction. In this work, we devise a two-stage attack methodology "DeepPeep" which exploits the distinctive characteristics of design methodologies to reverse-engineer the architecture of building blocks in compact DNNs. We show the efficacy of "DeepPeep" on P100 and P4000 GPUs. Additionally, we propose intelligent design maneuvering strategies for thwarting IP theft through the DeepPeep attack and proposed "Secure MobileNet-V1". Interestingly, compared to vanilla MobileNet-V1, secure MobileNet-V1 provides a significant reduction in inference latency ($\approx$60%) and improvement in predictive performance ($\approx$2%) with very-low memory and computation overheads.

A Little Fog for a Large Turn Machine Learning

A Little Fog for a Large T urn Harshitha Machiraju, Vineeth N Balasubramanian Indian Institute of Technology, Hyderabad, India {ee14btech11011, vineethnb } Abstract Small, carefully crafted perturbations called adversarial perturbations can easily fool neural networks. However, these perturbations are largely additive and not naturally found. W e turn our attention to the field of Autonomous navigation wherein adverse weather conditions such as fog have a drastic effect on the predictions of these systems. These weather conditions are capable of acting like natural adversaries that can help in testing models. T o this end, we introduce a general notion of adversarial perturbations, which can be created using generative models and provide a methodology inspired by Cycle-Consistent Generative Adversarial Networks to generate adversarial weather conditions for a given image. Our formulation and results show that these images provide a suitable testbed for steering models used in Autonomous navigation models. Our work also presents a more natural and general definition of Adversarial perturbations based on Perceptual Similarity. 1 1. Introduction Autonomous navigation has occupied a central position in the efforts of computer vision researchers in recent years. Autonomous vehicles can not only aid navigation in urban areas but also provide critical support in disaster-affected areas, places with unknown topography (such as Mars), and many more. The vast potential of the applications thereof and the feasibility of the solutions in contemporary times has led to the growth of several organizations across industry, academia, and government institutions that are investing significant efforts on self-driving vehicles.

5G will enable a new era of opportunity, says David Bader


Recently, David Bader visited India to give a keynote talk at IEEE International Conference on Machine Learning and Data Science at Bennett University, Greater Noida. David A. Bader is Professor and Chair of the School of Computational Science and Engineering, College of Computing, at Georgia Institute of Technology. He is a fellow of the IEEE and AAAS and served on the White House's National Strategic Computing Initiative (NSCI) panel. He was in conversation with Prof. Deepak Garg, Chair, of Computer Science Engineering at Bennett University.

Inteligence artificielle, Machine Learning, IoT, VR, Robot... on Flipboard


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