With the varying degrees of privacy norms of each country, there is a significant impact on the amount of big data collected and utilised by private and public entities with regard to AI innovations. In China, low safeguards on personal data has resulted in an increase in AI innovations. Whereas in Europe, with the enforcement of the GDPR, there are increased privacy and security guidelines resulting in a negative impact on the growth of the AI sector. While acknowledging the need to intensify AI actions, India requires tailored guidelines which is a blend of international principles along with existing local privacy standards to achieve to an effective and responsible AI ecosystem. The curated blend of AI strategies is mandatory as India is currently investing in AI for its defence, legal and financial sectors.

From personal computers (PCs) to servers to IoT devices, Intel has made chips for all of them and continues to chug on. And, this is the reason why the company with a 50-year history globally and a 20-year track record in India is staying relevant despite evolving technologies, said Prakash Mallya MD, Sales and Marketing Group, Intel India in an interview. "We have succeeded because we kept an open mind, taken risks and evolved over a period of years," he said. How has the company evolved over the years? If you look at our portfolio, we started as a memory company.

Mumbai: At the Puravankara Ltd booth at a property exhibition in Bengaluru last month, two humanoid robots recorded visitor data and their queries, partially replacing usual sales staff at the reception. For the real estate firm, which fielded these robots developed by technology firm Invento Markerspaces Pvt. Ltd, the experience was positive. "Instead of 10 young boys/girls manning the reception, this time we just had three," said Anand Narayanan, chief operating officer, Puravankara. For the Bengaluru-based real estate firm, those humanoids are part of a larger plan to completely'dehumanise" its sales process, build a strong data of potential homebuyers and sell homes completely online by 2020, Narayanan said. "What typically took 100 hours to get customer data for a sales guy, it has now dropped to about 7-8 hours," he added. Puravankara is one of the few developers which are adopting new-age technologies like artificial intelligence (AI), machine learning and big data to chase potential homebuyers. In a subdued property market, these technologies are being used to cut marketing spending, fast track sales and widen customer base. Developers like Hiranandani Communities, K Raheja Corp, Oberoi Realty Ltd and Runwal group have also partnered with technology firms and digital marketing agencies to map potential customers online through advanced data analytics and streamline their sale process. According to Vikram Kotnis, managing director, Amura Technologies Ltd, a digital marketing agency, real estate firms are now allocating 60-70% of their total marketing budget on digital campaigns as against 10-15% three-four years ago. The eight-year-old Pune-based firm currently works with around 130 property developers, up from 90 a year ago. "Today, we are doing a lot of push marketing.

What you see and experienced so far is just the beginning of what is forecast to be a billion-dollar industry in less than 10 years. Many top brands, including Uber, Sephora, and CNN, have already adopted chatbots. Still wondering what is a chatbot? Here are 11 amazing facts that might help explain what really it is and how it's changing the world of digital technology. The top four messaging apps are bigger than the top four social networks, according to BI Intelligence. More than 1.4 billion people used messaging apps in 2016, according to eMarketer.

THIRUVANANTHAPURAM: Anyone who uses a mobile phone would know how frustrating a pesky marketing call or SMS can be. But, here is a product that is potential enough to silently redefine the marketing sector of big companies without infringing the personal space or freedom of the customers. Netherlands-headquartered Flytxt firm in Technopark, Thiruvananthapuram, which employs around 400 software professionals, has developed a first-of-its-kind product Robo-X which help even least experienced marketers to provide customized value-added service to every single customer using artificial intelligence and machine learning. Speaking to Express, Vinod Vasudevan, Group CEO of Flytxt, a Gorakhpur IIT graduate who did his PhD in machine learning way back in 90's, said Robo-X is based on more than 20 patented technologies developed over several years of research in association with IIT, Delhi, Mumbai and TNO in the Netherlands. The software which uses the machine-learning algorithms in artificial intelligence platform will study the customer, his pattern of use and discover important relations between consumers, products, offers, channels and competitive environment and how they impact business outcomes.

Tech leaders, including Elon Musk and the three co-founders of Google's AI subsidiary DeepMind, have signed a pledge promising to not develop "lethal autonomous weapons." It's the latest move from an unofficial and global coalition of researchers and executives that's opposed to the propagation of such technology. The pledge warns that weapon systems that use AI to "[select] and [engage] targets without human intervention" pose moral and pragmatic threats. Morally, the signatories argue, the decision to take a human life "should never be delegated to a machine." On the pragmatic front, they say that the spread of such weaponry would be "dangerously destabilizing for every country and individual."

AI can help contractors track progress, spot dangerous behaviors and prevent accidents. AI will one day completely transform the jobsite -- construction equipment will leverage machine learning to become more and more adept at performing complex tasks autonomously. In the meantime, though, here are three ways AI is changing construction now, at least for large contractors with deep pockets. Nothing throws off a schedule like a construction deficiency that's discovered when the work is almost completed. AI systems can help contractors keep a closer eye on all parts of a project throughout the construction process so they can make any necessary corrections right away.

It is debatable whether Ned Ludd ever existed. But the movement he gave his name to certainly did. In the early 19th century, Luddite mobs of English weavers took to smashing the new automated looms they blamed for threatening their livelihoods. Today, Luddite fears are seeing a resurgence. Admittedly, no one is yet torching factories full of industrial robots or taking their baseball bats to banks of computers running "artificial intelligence" deep learning algorithms.

The silver-and-turquoise lounge, in the Miracle Mile Shops mall on the Strip, has 28 counter-style seats, each equipped with a tablet, facing a bar counter topped with two industrial-grade robotic arms. Patrons can order signature and classic cocktails, or fill a virtual cup with up to 14 ingredients of their choosing. Then the robotic arms go to work, gathering ingredients from a kind of futuristic back-bar automat; reaching up to a lattice of 120 liquor bottles; and tipping the resulting cocktail into a plastic cup proffered by a mechanical dispenser in the counter. Drinks take 60 to 90 seconds to make, and cost $12 to $16, said Stephan Mornet, president of Robotic Innovations, Tipsy Robot's parent company. For its automated bar, Tipsy Robot turned to Makr Shakr, an Italian startup that built its first robot bartender for Google I/O, the annual developer conference, in 2013. The company is one of several trying to automate bartending.

Most existing knowledge graphs (KGs) in academic domains suffer from problems of insufficient multi-relational information, name ambiguity and improper data format for large-scale machine pro- cessing. In this paper, we present AceKG, a new large-scale KG in academic domain. AceKG not only provides clean academic information, but also offers a large-scale benchmark dataset for researchers to conduct challenging data mining projects including link prediction, community detection and scholar classification. Specifically, AceKG describes 3.13 billion triples of academic facts based on a consistent ontology, including necessary properties of papers, authors, fields of study, venues and institutes, as well as the relations among them. To enrich the proposed knowledge graph, we also perform entity alignment with existing databases and rule-based inference. Based on AceKG, we conduct experiments of three typical academic data mining tasks and evaluate several state-of- the-art knowledge embedding and network representation learning approaches on the benchmark datasets built from AceKG. Finally, we discuss several promising research directions that benefit from AceKG.