In recent times, organizations have been competing with one another to implement chatbots for various reasons, including enhancing customer experience, streamlining processes, and fueling the demand for digital and innovative technologies. Cognitive technologies such as chatbots have become an apt candidate for end-use application as they have high automation feasibility, high potential of accuracy, low complexity and low execution time. Raising the bar through intelligence, virtual assistants have been propelled by advancements of mobile technology. Technology giants are putting their weight on a platform designed to answer ad-hoc queries in real-time and fuel sales as chatbots can remember customer preference and use order history to learn from customer responses to the product advertisements, suggest products, and cross-sell aptly. For instance, if a customer asks for a pizza recommendation with a chatbot, it can remember which pizza the customer ordered and follow up with it when offering a recommendation for another pizza or a restaurant.

The Indian government on Friday announced to postpone the nation's first summit on Artificial Intelligence (AI) that was scheduled to be held in the Capital in October amid the growing new coronavirus (COVID-19) pandemic. Titled'RAISE 2020 (Responsible AI for Social Empowerment 2020),' the summit on October 5-6 was announced as a global meeting of minds to exchange ideas and charter a course to use AI for social transformation, inclusion and empowerment in key areas like healthcare, agriculture, education and smart mobility, among other sectors. "The registrations are open for all participants and the process will remain the same as before. All AI startups are welcome to participate through the same procedure for the Startup Pitchfest," said Abhishek Singh, President and CEO, National e-Governance Division (NeGD), the Ministry of Electronics and Information Technology (MeitY). "The step to postpone the summit is a public health measure for containment and mitigation of the current COVID-19 outbreak," he added.

Consider the grocery clerks at two Safeway stores in the San Francisco Bay Area. A few weeks ago, over 200 workers who are members of the United Food and Commercial Workers Local 5 (UFCW5) union picketed a Safeway store in San Jose, Calif. to voice concerns about a push by parent company Albertsons to add more A.I to its operations. Albertsons recently partnered with the startup Takeoff Technologies to create mini warehouses where computer vision technology automatically sorts items that shoppers order online. Using A.I. reduces the need for Safeway staff to manually locate and grab items for delivery--workers now just retrieve the finalized orders from a conveyor belt and sign off on them for eventual delivery. Several grocery store chains are investing heavily in micro-fulfillment centers after Amazon helped to popularize as-fast-as-you-can deliveries, said Andrew Lipsman, a principal analyst at research firm eMarketer.

It includes detailed explanations of the fundamental concepts in machine learning, data processing, model evaluation and the typical machine learning workflow. It provides many coded examples using scikit-learn.

Deep learning techniques are proving to be extremely useful for analyzing all kinds of data, ranging from images to text, online posts and audio recordings. These techniques are designed to identify patterns in large datasets, separate items in different categories and make predictions far quicker than humans. In a recent study, researchers at Simon Fraser University, Academia Sinica and Dartmouth College have applied deep learning techniques to identify similarities and differences between Chinese and Western classical music. Their paper, pre-published on arXiv, presents a comparative analysis of music recordings using sound event detection (SED) and soundscape emotion recognition (SER) models. "We have listened to both Chinese and Western classical music," Jianyu Fan, one of the researchers who carried out the study, told TechXplore.

An Australian team is using machine learning to tackle the threat of space junk wrecking new satellites. Research to tackle the growing need to find, capture and remove junk from space is advancing at the Australian Institute for Machine Learning in Adelaide, South Australia. Machine Learning for Space director Tat-Jun Chin and his Adelaide-based team have won a $600,000 grant from Australia's SmartSat CRC to continue their work in detecting, tracking and cataloging space junk. SmartSat CRC was established last year to work with the Australian Space Agency based in Adelaide, contributing to the Australian government's goal of tripling the size of the space sector to $12 billion and creating as many as 20,000 jobs by 2030. The space junk project is based on developing a space-based surveillance network and tackling the growing challenge of crowding in space.

These draft notebooks cover an introduction to deep learning, fastai, and PyTorch. These notebooks will be used for a course we're teaching in San Francisco from March 2020, and will be available as a MOOC from around July 2020. In addition, our plan is that these notebooks will form the basis of this book, which you can pre-order. It will not have the same GPL restrictions that are on this draft. The remainder (including all markdown cells in the notebooks and other prose) is not licensed for any redistribution or change of format or medium, other than making copies of the notebooks or forking this repo for your own private use.

Explainable Artificial Intelligence (XAI) seems to be a hot topic nowadays. It is a topic I came across recently in a number of instances: workshops organized by the European Defense Agency (EDA), posts from technology partners such as Expert System (here) or internal discussion with SDL's Research team. The straightforward definition of XAI comes from Wikipedia: "Explainable AI (XAI) refers to methods and techniques in the application of artificial intelligence technology (AI) such that the results of the solution can be understood by human experts. It contrasts with the concept of the "black box" in machine learning where even their designers cannot explain why the AI arrived at a specific decision. XAI is an implementation of the social right to explanation."

Bashing Silicon Valley has become one of the few things both political parties agree on this election cycle. And they have good reason--the artificial intelligence (AI) and automation technology developed by the Valley's best and brightest minds is projected to displace the jobs of between one-quarter and one-third of American workers by 2030. The Brookings Institute estimates that 36 million Americans could have 70% of their work tasks replaced by automation. These alarming figures are attracting the attention of policymakers and politicians alike. Some are calling for a tax on robots. Others believe massive open online courses (MOOCs) are the answer to retraining millions of displaced workers.