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.

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.

But while it conveys change to the jobs market, its implications for higher education and society are paramount. If careers are changing, then it stands to reason that higher education needs to change along with it. Higher education finds itself at the very front of one of the most significant workplace shifts this century, and how it interprets and responds to that change to ensure everybody benefits will have a considerable impact not only on the global flow of students but the whole of society. As tomorrowland approaches, international educators should realise how key the classroom will be. Welcome to the machine Self-driving cars are a typical example of the way artificial intelligence is starting to replace humans in the workforce, says UK-based futurist Calum Chace. Replacing professional drivers not only makes economic sense – a driver can account for up to a half of a vehicle's operational costs – but self-driving cars have proven themselves to be significantly safer than humans.

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."

A recent study by the McKinsey Global Institute found that at least one-third of workers in most occupations can be replaced by robots. That translates into 800 million lost jobs, worldwide. In the U.S., according to McKinsey, 70 million Americans could become casualties to robotics, artificial intelligence and machine learning by the year 2030. The equivalent figure for Canada is about eight million jobs -- in both cases, roughly 30 per cent of the workforce. It might be thought the introduction of such revolutionary technologies would generate enough new employment to make up the difference. But that is not the conclusion of the study.

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.

In music and audio production, attenuation of spectral resonances is an important step towards a technically correct result. In this paper we present a two-component system to automate the task of resonance equalization. The first component is a dynamic equalizer that automatically detects resonances and offers to attenuate them by a user-specified factor. The second component is a deep neural network that predicts the optimal attenuation factor based on the windowed audio. The network is trained and validated on empirical data gathered from an experiment in which sound engineers choose their preferred attenuation factors for a set of tracks. We test two distinct network architectures for the predictive model and find that a dilated residual network operating directly on the audio signal is on a par with a network architecture that requires a prior audio feature extraction stage. Both architectures predict human-preferred resonance attenuation factors significantly better than a baseline approach.

Machine Learning models become increasingly proficient in complex tasks. However, even for experts in the field, it can be difficult to understand what the model learned. This hampers trust and acceptance, and it obstructs the possibility to correct the model. There is therefore a need for transparency of machine learning models. The development of transparent classification models has received much attention, but there are few developments for achieving transparent Reinforcement Learning (RL) models. In this study we propose a method that enables a RL agent to explain its behavior in terms of the expected consequences of state transitions and outcomes. First, we define a translation of states and actions to a description that is easier to understand for human users. Second, we developed a procedure that enables the agent to obtain the consequences of a single action, as well as its entire policy. The method calculates contrasts between the consequences of a policy derived from a user query, and of the learned policy of the agent. Third, a format for generating explanations was constructed. A pilot survey study was conducted to explore preferences of users for different explanation properties. Results indicate that human users tend to favor explanations about policy rather than about single actions.

Following the recent successful examples of large technology companies, many modern enterprises seek to build knowledge graphs to provide a unified view of corporate knowledge and to draw deep insights using machine learning and logical reasoning. There is currently a perceived disconnect between the traditional approaches for data science, typically based on machine learning and statistical modelling, and systems for reasoning with domain knowledge. In this paper we present a state-of-the-art Knowledge Graph Management System, Vadalog, which delivers highly expressive and efficient logical reasoning and provides seamless integration with modern data science toolkits, such as the Jupyter platform. We demonstrate how to use Vadalog to perform traditional data wrangling tasks, as well as complex logical and probabilistic reasoning. We argue that this is a significant step forward towards combining machine learning and reasoning in data science.