September 05, 2019: The Central Board of Secondary Education (CBSE) has announced that it will conduct Capacity Building Programs for high school teachers in association with Microsoft India with an aim to integrate cloud-powered technology in K12 teaching. Meant for teachers of grades 8-10, the program will be conducted in 10 cities across the country, starting September 11, 2019. AI and intelligent technologies are becoming all-pervasive today, transforming organizations across sectors and redefining the way we work. To equip the workforce of tomorrow, it is critical to the ramp up the institutional set-up and build capability among educators as well as integrate advanced technologies into the teaching process. This program will provide teachers better access to the latest Information and Communication Technology (ICT) tools and help them to integrate technology into teaching in a safe and secure manner, thereby enhancing the learning experience and 21st century skills of all students.

The Joint Artificial Intelligence Center, stood up simply a year ago, has plans to contract an ethicist to help manage the Defense Department's advancement and use of artificial intelligence advances. "One of the positions we are going to fill will be someone who isn't simply taking a gander at specialized principles, yet who is an ethicist," said Air Force Lt. Gen. Jack Shanahan, the JAIC's chief. "We will get somebody who will have a profound foundation in morals, and afterward the attorneys inside the office will take a gander at how we really heat this into the Department of Defense." Speaking Aug. 30 at the Pentagon, Shanahan gave a report on the JAIC, where he's been since January. He'd recently driven Project Maven, an artificial intelligence AI pathfinder venture under the undersecretary of protection for intelligence.

According to statistics, over 1.9 billion users log into YouTube every single month watching more than a billion hours of video daily, which is half the internet. Organizations are integrating video creation and video sharing with their marketing strategies. As on date, YouTube supports 80 different languages, which also adds to its popularity. Cisco predicts that by 2022, video will consume 82 percent of all internet traffic. Considering the massive number of users, high volume of activities and richness of content, it makes sense for YouTube to take advantage of artificial intelligence (AI) and machine learning (ML) to add efficiency to its operations.

Human beings are naturally predisposed to cultural bias. Found in all human sciences -- including economics, psychology, and anthropology -- cultural bias is defined as "the process of judging and interpreting phenomena by standards inherent to one's own cultural preferences or by norms of a particular culture." Cultural bias is why in some cultures averting eye contact can be interpreted as being evasive or shy, and in other cultures, a sign of respect. It's why people born in Argentina likely wear jerseys that honor Lionel Messi unlike their football peers across the Atlantic who revere Portugal's Cristiano Ronaldo. Why soup slurping in Korea is the norm, when it can be considered bad table manners elsewhere.

Andrew Yan-Tak Ng (Chinese: 吳恩達; born 1976) is a Chinese-American computer scientist and statistician, focusing on machine learning and AI. Also a business executive and investor in the Silicon Valley, Ng co-founded and led Google Brain and was a former Vice President and Chief Scientist at Baidu, building the company's Artificial Intelligence Group into a team of several thousand people. Ng is an adjunct professor at Stanford University (formerly associate professor and Director of its AI Lab). Also a pioneer in online education, Ng co-founded Coursera and deeplearning.ai. With his online courses, he has successfully spearheaded many efforts to "democratize deep learning."

Decades of science fiction have primed people to conjure a very specific image when the subject of artificial intelligence arises: The malevolent robotic overlord that has evolved beyond the need for humans, or concern for their safety. And with that image in mind, it's easy to imagine that it's still a long way off. But in actuality, AI is already here, and its most common form is a lot closer to home than many people realize. "It's already hard to interact with a modern technology that doesn't in some way benefit from AI, whether it's obvious or not," said Mason McDaniel, chief technology officer at the Bureau of Alcohol, Tobacco, Firearms and Explosives, said during a Sept. 4 Meritalk webinar. "And I'd say that's going to continue as we move forward. And AI is going to increasingly be embedded and hidden behind the scenes simply as part of the applications; you're not going to go specifically to an AI app." "And I see that as those mature, as those get better, those are going to become more of a primary interface, whether it's spoken or using natural language text typing, to ask the devices for what you want," McDaniel said.

Elon Musk's ambitious brain-computer start-up Neuralink is looking to start trials on humans next year. Musk talked about the project at an event in San Francisco that was streamed live -- with an eye toward recruiting more talent -- late on Tuesday. Neuralink aligns with a broader trend of technology minds seeking to merge their approaches with the world of healthcare. Facebook has previously devoted resources to exploring computer systems that people could communicate with simply by thinking. The start-up envisions drilling holes into the brain with a custom machine to embed thin threads that connect to a tiny processor, which can then be connected to a smartphone over Bluetooth.

Change detection in heterogeneous multitemporal satellite images is an emerging and challenging topic in remote sensing. In particular, one of the main challenges is to tackle the problem in an unsupervised manner. In this paper we propose an unsupervised framework for bitemporal heterogeneous change detection based on the comparison of affinity matrices and image regression. First, our method quantifies the similarity of affinity matrices computed from co-located image patches in the two images. This is done to automatically identify pixels that are likely to be unchanged. With the identified pixels as pseudo-training data, we learn a transformation to map the first image to the domain of the other image, and vice versa. Four regression methods are selected to carry out the transformation: Gaussian process regression, support vector regression, random forest regression, and a recently proposed kernel regression method called homogeneous pixel transformation. To evaluate the potentials and limitations of our framework, and also the benefits and disadvantages of each regression method, we perform experiments on two real data sets. The results indicate that the comparison of the affinity matrices can already be considered a change detection method by itself. However, image regression is shown to improve the results obtained by the previous step alone and produces accurate change detection maps despite of the heterogeneity of the multitemporal input data. Notably, the random forest regression approach excels by achieving similar accuracy as the other methods, but with a significantly lower computational cost and with fast and robust tuning of hyperparameters.

The use of automatic methods, often referred to as Neural Architecture Search (NAS), in designing neural network architectures has recently drawn considerable attention. In this work, we present an efficient NAS approach, named HM- NAS, that generalizes existing weight sharing based NAS approaches. Existing weight sharing based NAS approaches still adopt hand-designed heuristics to generate architecture candidates. As a consequence, the space of architecture candidates is constrained in a subset of all possible architectures, making the architecture search results sub-optimal. HM-NAS addresses this limitation via two innovations. First, HM-NAS incorporates a multi-level architecture encoding scheme to enable searching for more flexible network architectures. Second, it discards the hand-designed heuristics and incorporates a hierarchical masking scheme that automatically learns and determines the optimal architecture. Compared to state-of-the-art weight sharing based approaches, HM-NAS is able to achieve better architecture search performance and competitive model evaluation accuracy. Without the constraint imposed by the hand-designed heuristics, our searched networks contain more flexible and meaningful architectures that existing weight sharing based NAS approaches are not able to discover.

Relational reasoning is a central component of intelligent behavior, but has proven difficult for neural networks to learn. The Relation Network (RN) module was recently proposed by DeepMind to solve such problems, and demo nstrated state-of- the-art results on a number of datasets. However, the RN modu le scales quadrati-cally in the size of the input, since it calculates relations hip factors between every patch in the visual field, including those that do not corresp ond to entities. In this paper, we describe an architecture that enables relati onships to be determined from a stream of entities obtained by an attention mechanism over the input field. The model is trained end-to-end, and demonstrates equivale nt performance with greater interpretability while requiring only a fraction o f the model parameters of the original RN module.