Fashion e-tailer Myntra plans to turn its artificial intelligence (AI) and machine learning platfrom called Rapid into a separate business vertical, just like parent Flipkart.

By all accords, 2017 has been a busy, bittersweet year for the tech industry. Cutting-edge product designs have been balanced out by much-hyped products, and sometimes entire companies, going bust. This has not really been the year of consistent breakneck innovation, but there is still quite a lot to take a look at.

SingularityNET, is an ambitions project to create a decentralized marketplace for AI, has raised a lot of money in its token sale. In around 60 seconds after opening the sale to the public, it sold out of the whole amount of available tokens (the AGI token), bringing the total to $36 million.

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Human resources as a function has experienced significant changes in the last decade due to the evolution of technologies. Today, artificial intelligence (AI) is reshaping the way companies hire, manage and engage with their workforce. Advanced data-driven technology is rapidly making its way into the HR industry as businesses are focusing more on creating an employee-oriented corporate culture. Recruitment is no more a tedious process for HR practitioners as it no longer entails time-consuming activities such as manually screening the resumes of the prospective candidates, making phone calls or replying to candidates via emails. These mundane errands are now managed by smart technologies designed to replicate human conversation, thus enabling HR experts to contemplate the bigger picture. According to the India Report of Deloitte's 5th Annual Global Human Capital Trends, 53% of companies are revamping their HR programmes to deploy digital tools, while 22% have already leveraged AI to deliver HR solutions.

Recent news on Sophia the robot getting citizenship in the Saudi Arabia has widely attracted daily news and social media. Despite the debates and agitations on a robot getting recognition as humans, experts view this event as a phenomenal milestone in the research of AI.

You can't understand AI until you know the categories: robotic process automation, cognitive augmentation and cognitive automation.

COMMANDING the plot lines of Hollywood films, covers of magazines and reams of newsprint, the contest between artificial intelligence (AI) and mankind draws much attention. Doomsayers warn that AI could eradicate jobs, break laws and start wars. The competition today is not between humans and machines but among the world's technology giants, which are investing feverishly to get a lead over each other in AI.

With the widespread use of information technologies, information networks have increasingly become popular to capture complex relationships across various disciplines, such as social networks, citation networks, telecommunication networks, and biological networks. Analyzing these networks sheds light on different aspects of social life such as the structure of society, information diffusion, and different patterns of communication. However, the large scale of information networks often makes network analytic tasks computationally expensive and intractable. Recently, network representation learning has been proposed as a new learning paradigm that embeds network vertices into a low-dimensional vector space, by preserving network topology structure, vertex content, and other side information. This facilitates the original network to be easily handled in the new vector space for further analysis. In this survey, we perform a thorough review of the current literature on network representation learning in the field of data mining and machine learning. We propose a new categorization to analyze and summarize state-of-the-art network representation learning techniques according to the methodology they employ and the network information they preserve. Finally, to facilitate research on this topic, we summarize benchmark datasets and evaluation methodologies, and discuss open issues and future research directions in this field.

Analyzing the temporal behavior of nodes in time-varying graphs is useful for many applications such as targeted advertising, community evolution and outlier detection. In this paper, we present a novel approach, STWalk, for learning trajectory representations of nodes in temporal graphs. The proposed framework makes use of structural properties of graphs at current and previous time-steps to learn effective node trajectory representations. STWalk performs random walks on a graph at a given time step (called space-walk) as well as on graphs from past time-steps (called time-walk) to capture the spatio-temporal behavior of nodes. We propose two variants of STWalk to learn trajectory representations. In one algorithm, we perform space-walk and time-walk as part of a single step. In the other variant, we perform space-walk and time-walk separately and combine the learned representations to get the final trajectory embedding. Extensive experiments on three real-world temporal graph datasets validate the effectiveness of the learned representations when compared to three baseline methods. We also show the goodness of the learned trajectory embeddings for change point detection, as well as demonstrate that arithmetic operations on these trajectory representations yield interesting and interpretable results.