Artificial intelligence is taking away jobs in the field of healthcare and information technology and it is a cause of concern, Congress MP Shashi Tharoor said. "Artificial intelligence is also making inroads into jobs like medical transcription. A World Bank report points out that 69% of Indian jobs would be taken away by robots," he said, speaking at the third day of Jain International Trade Organisation (JITO) conclave in Chennai. Mr. Tharoor spoke on the theme of youth empowerment and also inaugurated JITO's first Youth Conclave. "In the U.S., they are talking about driverless cars. What will happen to 25 million drivers in India?" he asked.

JD.com, one of China's largest e-commerce companies, is launching a new Beijing-based accelerator program for artificial intelligence and blockchain startups. Called AI Catapult, its first batch includes six companies: Bankorus, CanYa, Bluezelle, Nuggets, Republic Protocol and Devery. In an announcement, JD.com said startups will work with its operational teams to "test real-world applications of their technologies at scale." This includes its logistics unit, which recently raised $2.5 billion and claims to run the largest last-mile logistics network in China. Though Alibaba Group is probably better known outside of China, JD.com is a formidable rival.

Due to advances in the technology most of the mundane Americans have lost their jobs, they felt resentment against the ruling elite of the America. This was the one of the reason American people voted in the great number for the Donald Trump. This was the most consequential surprise for the most of the world. Despite the very controversial stand of Donald trump he became the president of the America. Hilary Clinton was the symbol of the elite and establishment in America so people voted against her.

Can a repressive state, led by a central government specializing in five-year plans and surveillance of its own people, make such a leap? Economic history includes few examples of authoritarian states becoming innovative business leaders. But China aims to make that jump in artificial intelligence--or high-level machine learning--with an unusual approach that can't be dismissed. Beijing is bankrolling a big effort in AI, in part, to keep better track of homegrown individuals it considers criminals and dissidents, and to intimidate would-be opponents. That work involves fundamental research in image recognition, data collection and sorting that could have commercial spinoffs in the software used to run complex systems.

We're excited to announce that the following speakers will be joining us at the Applied AI Conference 2018, on April 12th in San Francisco: See Full Speaker Line-up here. The conference is limited to 800 attendees and we usually sell out weeks ahead of our event. Backstory: Most algorithms can be trained in only one domain, and can't use what's been learned for one task to perform another, new one. A big hope for AI is to have systems take insights from one setting and apply them elsewhere--what's called transfer learning. In July, China unveiled a plan to become the world leader in artificial intelligence and create an industry worth $150 billion to its economy by 2030.

One of the most important jobs of any national leader is to quit. National liberation heroes, from George Washington to Nelson Mandela, who stepped down without being forced to, ought to be venerated for that as much as for any good they accomplished while in office. Generally, rulers do not give up power unless they have to. In Africa, peaceful transfers of power are rare enough that a billionaire has set up a generous annual prize to reward leaders who step down voluntarily; many years it goes unclaimed. Around the world, cases like Bashar al-Assad, willing to watch his country crumble rather than give up power over it, or Robert Mugabe, forced out by his own military after 47 years, are more common.

Artificial intelligence (A.I.) is making the "rise of machines" -- once the stuff of science fiction -- a reality. As 60 Minutes reported on October 9, "It might not be long before machines begin thinking for themselves -- creatively, independently, and sometimes with better judgment than a human." On February 28, 2018, you're invited to "Meet the Future" at DePauw University as the Ubben Lecture Series presents the world's first artificial intelligence-fueled android, Sophia, and her creator, David Hanson. In a 7:30 p.m. program in Kresge Auditorium, Dr. Hanson -- founder, CEO and chief designer of Hong Kong-based Hanson Robotics -- will be joined by his one-of-a-kind robot character. At the free event, which is open to all, the two will deliver a speech, take questions from the audience, and offer insights into the world of tomorrow that we're already entering today.

As artificial intelligence technology improves and matures, it will move on from an Apple Siri or a Google Assistant merely answering your questions to becoming your best friend and adviser. As per research firm Gartner Inc, personal devices such as mobile phones will know more about an individual's emotional state than his or her own family by 2022. According to statistics website Statista, the number of mobile phone users in India is expected to rise to 775.5 million in 2018. The number of smartphone users is expected to reach almost 443 million by 2022. In a report last year, networking company Cisco said India will have 1,380 million mobile-connected devices in India by 2021, with 60 percent of these being "smart" mobile connections. This means that Indians are looking at a huge number of "friendly" devices that will be able to detect human emotions and provide an option according to their moods.

Stochastic gradient descent (SGD) has achieved great success in training deep neural network, where the gradient is computed through back-propagation. However, the back-propagated values of different layers vary dramatically. This inconsistence of gradient magnitude across different layers renders optimization of deep neural network with a single learning rate problematic. We introduce the back-matching propagation which computes the backward values on the layer's parameter and the input by matching backward values on the layer's output. This leads to solving a bunch of least-squares problems, which requires high computational cost. We then reduce the back-matching propagation with approximations and propose an algorithm that turns to be the regular SGD with a layer-wise adaptive learning rate strategy. This allows an easy implementation of our algorithm in current machine learning frameworks equipped with auto-differentiation. We apply our algorithm in training modern deep neural networks and achieve favorable results over SGD.

Flood of information is produced in a daily basis through the global Internet usage arising from the on-line interactive communications among users. While this situation contributes significantly to the quality of human life, unfortunately it involves enormous dangers, since on-line texts with high toxicity can cause personal attacks, on-line harassment and bullying behaviors. This has triggered both industrial and research community in the last few years while there are several tries to identify an efficient model for on-line toxic comment prediction. However, these steps are still in their infancy and new approaches and frameworks are required. On parallel, the data explosion that appears constantly, makes the construction of new machine learning computational tools for managing this information, an imperative need. Thankfully advances in hardware, cloud computing and big data management allow the development of Deep Learning approaches appearing very promising performance so far. For text classification in particular the use of Convolutional Neural Networks (CNN) have recently been proposed approaching text analytics in a modern manner emphasizing in the structure of words in a document. In this work, we employ this approach to discover toxic comments in a large pool of documents provided by a current Kaggle's competition regarding Wikipedia's talk page edits. To justify this decision we choose to compare CNNs against the traditional bag-of-words approach for text analysis combined with a selection of algorithms proven to be very effective in text classification. The reported results provide enough evidence that CNN enhance toxic comment classification reinforcing research interest towards this direction.