Titled "New Digital Horizons: Connect, Create, Innovate", the second edition of the event is being jointly organised by Cellular Operators Association of India (COAI), the Department of Telecommunications (DoT) and other government departments. The aim behind IMC2018 is building ideas, sharing knowledge and best practices, forming lasting industry relationships, fostering commercial opportunities, showcasing game changing mobile technology and product trends, providing sectoral insights, industrial solutions, case studies and workshops. The biggest ICTevent in South Asia, comprising of conclave and exhibition will include ministerial and partner programs in Digital India, Smart City, emerging technologies, Make in India projects, skill harmonisation, business innovation and knowledge sharing etc. There will be a technology showcase offering a glimpse into virtual reality, connected cars, m-health, smart wearables, smart home, artificial intelligence, drones, robotics, smart energy, internet of things, block chain, bitcoin, Machine Vision, Cloud Computing Holography, among others. The three day event will cover a wide array of topics, including but not confined to emerging technologies to new digital ecosystems, m-education, digital marketing to e-health, 5G and retail.

AI will change radiology, but it won't replace radiologists.

Chatbots have become standard tools in digital workplaces globally. Driven by consumer demand for connected customer experiences, Gartner predicted in research last year that 25 percent of customer service operations will use virtual customer assistant or chatbot technology by the year 2020. However, research released his week by MuleSoft, which provides a platform for building application networks, entitled Consumer Connectivity Insights 2018 (registration required), shows that there is still considerable work to be done in the deployment and use of chatbots. The research, which is based off data from a survey of more than 8,000 consumers demonstrated that customer loyalty is at risk for organizations unable to provide seamless experiences across all channels and timely access to information. It also pointed to problems with chatbots.

After an interesting year for Google Cloud's artificial intelligence group, Andrew Moore, dean of computer science at Pittsburgh's Carnegie Mellon University, will become head of the division at the end of the year, with current leader Fei Fei Li returning to Stanford in a move that Google said was all part of the original plan. Moore, a former Google employee, will rejoin the company at the end of the current semester at Carnegie Mellon, Google Cloud CEO Diane Greene announced in a blog post. "We are incredibly fortunate to have Andrew's leadership at this point in our development as we define how we will expand bringing AI and ML technologies and solutions to developers and organizations all over the world," she wrote. Google's artificial intelligence research team is considered among the best in the world, but it endured some high-profile setbacks this year after employees demanded that the cloud group stop working with the Department of Defense on Project Maven, which used image-recognition techniques to target drone strikes. In August, Greene announced that Google would not renew its contract with the Pentagon for those services, a move that also likely took the company out of the running for the $10 billion JEDI cloud computing contract under consideration by the military.

This survey presents an overview of verification techniques for autonomous systems, with a focus on safety-critical autonomous cyber-physical systems (CPS) and subcomponents thereof. Autonomy in CPS is enabling by recent advances in artificial intelligence (AI) and machine learning (ML) through approaches such as deep neural networks (DNNs), embedded in so-called learning enabled components (LECs) that accomplish tasks from classification to control. Recently, the formal methods and formal verification community has developed methods to characterize behaviors in these LECs with eventual goals of formally verifying specifications for LECs, and this article presents a survey of many of these recent approaches.

We study the complexity of Destructive Shift Bribery. In this problem, we are given an election with a set of candidates and a set of voters (each ranking the candidates from the best to the worst), a despised candidate $d$, a budget $B$, and prices for shifting $d$ back in the voters' rankings. The goal is to ensure that $d$ is not a winner of the election. We show that this problem is polynomial-time solvable for scoring protocols (encoded in unary), the Bucklin and Simplified Bucklin rules, and the Maximin rule, but is NP-hard for the Copeland rule. This stands in contrast to the results for the constructive setting (known from the literature), for which the problem is polynomial-time solvable for $k$-Approval family of rules, but is NP-hard for the Borda, Copeland, and Maximin rules. We complement the analysis of the Copeland rule showing W-hardness for the parameterization by the budget value, and by the number of affected voters. We prove that the problem is W-hard when parameterized by the number of voters even for unit prices. From the positive perspective we provide an efficient algorithm for solving the problem parameterized by the combined parameter the number of candidates and the maximum bribery price (alternatively the number of different bribery prices).

Many types of data can be represented as graphs. Edges may correspond to actual links in the data (e.g., users connected by some social network) or to levels of similarity induced from the data (e.g., users having liked a large common set of movies). The resulting graph is typically sparse in the sense that the number of edges is much lower than the total number of node pairs, which makes the data hard to exploit. A standard approach to the analysis of sparse graphs consists in embedding the graph in some vectorial space of low dimension, typically much smaller than the number of nodes [15, 19, 4]. Each node is represented by some vector in the embedding space so that close nodes in the graph (linked either directly or through many short paths in the graph) tend to be represented by close vectors in terms of the Euclidian distance.

Federal officials have largely come around to the idea that research funding is crucial for U.S. leadership in artificial intelligence, but there are ways to accelerate innovation besides pouring in more money, according to tech experts. For one, they said, the government could map a long-term strategy for advancing the technology. "The U.S. has been slow in making this a national imperative," Dean Garfield, president and CEO of the Information Technology Industry Council, said Thursday on a panel hosted by Politico. "The signal that comes from the top … is critically important here and has the opportunity to really catalyze that action in a way that wouldn't happen without it." The Office of Science and Technology Policy on Wednesday requested industry input on updating an AI research and development strategy the White House published in 2016.

"One problem, say roboticists, is that robots often can't'see' behind leaves or reach behind a tree branch without potentially harming themselves or the fruit they're trying to grab," they report. "Roboticists are trying to solve these problems by enhancing the quality of sensors that allow robots to understand and navigate their surroundings." "RootAI, a Somerville, Mass., startup that is testing a prototype tomato picker, has started talking to seed developers who want to design crops that are more amenable to robotic harvesters." California's Driscoll's Inc., the world's largest berry distributor, is looking at raising its growing beds, making it easier for both robots and humans to pick fruit. HSBC's robot is boosting foot traffic in New York.

Kai-Fu Lee, a prominent investor and entrepreneur based in Beijing, has been talking up China's artificial intelligence potential for a while. Now he's got a message for the United States. The real threat to American preeminence in AI isn't China's rise, he says--it's the US government's complacency. Lee is well placed to understand the issue, even if he isn't altogether unbiased. He worked on machine learning at Carnegie Mellon University during the 1980s, led Microsoft's research lab in China in the 1990s, and then spearheaded Google's venture into China in the 2000s.