Colourisation or adding colours to the black and white or monochrome images and videos has witnessed a widespread adoption for a few decades now. Traditional colourisation techniques need a lot of human efforts as well as are costlier. However, with the advent of emerging technologies like artificial intelligence, these two major issues are disappearing slowly. Not only this but also we have witnessed how researchers are using deepfake techniques to swap faces of celebrities and other popular faces around the globe. Let's take a look at the few movies that have been coloured using artificial intelligence.

"The Ultimate Computer" is the twenty-fourth episode of the second season of the American science fiction television series Star Trek. Written by D.C. Fontana (based on a story by Laurence N. Wolfe) and directed by John Meredyth Lucas, it was first broadcast on March 8, 1968.

It is a challenging task to extract the best of both worlds by combining the spatial characteristics of a visible image and the spectral content of an infrared image. In this work, we propose a spatially constrained adversarial autoencoder that extracts deep features from the infrared and visible images to obtain a more exhaustive and global representation. In this paper, we propose a residual autoencoder architecture, regularised by a residual adversarial network, to generate a more realistic fused image. The residual module serves as primary building for the encoder, decoder and adversarial network, as an add on the symmetric skip connections perform the functionality of embedding the spatial characteristics directly from the initial layers of encoder structure to the decoder part of the network. The spectral information in the infrared image is incorporated by adding the feature maps over several layers in the encoder part of the fusion structure, which makes inference on both the visual and infrared images separately. In order to efficiently optimize the parameters of the network, we propose an adversarial regulariser network which would perform supervised learning on the fused image and the original visual image.

With the advancements in the field of artificial intelligence, many sectors have been in fear of losing human employees over this advanced technology. And with the rise of machines amid this crisis for business continuity, the fear has started looming in the journalism industry where media houses are publishing automated news for their publications. In fact, Bloomberg News, one of the leading media publishing houses, has claimed that the company has been using automated technology for publishing one-third of their news content on their platform. According to news reports: Editor-in-chief John Micklethwait of Bloomberg News stated in their company memo a few years back, "I think automation is crucial to the future of journalism in a much broader way than many of us realise. Bloomberg already uses automation for customised news and trending stories …" Also, in the recent news, Microsoft has announced laying off a considerable number of journalists from their MSN in order to replace them with artificial intelligence.

Machine learning, the subset of artificial intelligence that teaches computers to perform tasks through examples and experience, is a hot area of research and development. Many of the applications we use daily use machine learning algorithms, including AI assistants, web search and machine translation. Your social media news feed is powered by a machine learning algorithm. The recommended videos you see on YouTube and Netflix are the result of a machine learning model. And Spotify's Discover Weekly draws on the power of machine learning algorithms to create a list of songs that conform to your preferences. But machine learning comes in many different flavors.

After two virus cases were reported in February, Mayor Sergei Sobyanin authorized facial recognition software to track Chinese citizens in the capital, drawing complaints from rights groups. When the city introduced digital passes for commuters in April, tightly packed crowds formed at Metro stations as police checked smartphones individually.

Knowledge Graphs (KGs) have gained considerable attention recently from both academia and industry. In fact, incorporating graph technology and the copious of various graph datasets have led the research community to build sophisticated graph analytics tools. Therefore, the application of KGs has extended to tackle a plethora of real-life problems in dissimilar domains. Despite the abundance of the currently proliferated generic KGs, there is a vital need to construct domain-specific KGs. Further, quality and credibility should be assimilated in the process of constructing and augmenting KGs, particularly those propagated from mixed-quality resources such as social media data. This paper presents a novel credibility domain-based KG Embedding framework. This framework involves capturing a fusion of data obtained from heterogeneous resources into a formal KG representation depicted by a domain ontology. The proposed approach makes use of various knowledge-based repositories to enrich the semantics of the textual contents, thereby facilitating the interoperability of information. The proposed framework also embodies a credibility module to ensure data quality and trustworthiness. The constructed KG is then embedded in a low-dimension semantically-continuous space using several embedding techniques. The utility of the constructed KG and its embeddings is demonstrated and substantiated on link prediction, clustering, and visualisation tasks.

As voice-based assistants such as Alexa, Siri, and Google Assistant become ubiquitous, users increasingly expect to maintain natural and informative conversations with such systems. However, for an open-domain conversational system to be coherent and engaging, it must be able to maintain the user's interest for extended periods, without sounding boring or annoying. In this paper, we investigate one natural approach to this problem, of modulating response prosody, i.e., changing the pitch and cadence of the response to indicate delight, sadness or other common emotions, as well as using pre-recorded interjections. Intuitively, this approach should improve the naturalness of the conversation, but attempts to quantify the effects of prosodic modulation on user satisfaction and engagement remain challenging. To accomplish this, we report results obtained from a large-scale empirical study that measures the effects of prosodic modulation on user behavior and engagement across multiple conversation domains, both immediately after each turn, and at the overall conversation level. Our results indicate that the prosody modulation significantly increases both immediate and overall user satisfaction. However, since the effects vary across different domains, we verify that prosody modulations do not substitute for coherent, informative content of the responses. Together, our results provide useful tools and insights for improving the naturalness of responses in conversational systems.

Since data breaches are becoming more and more frequent.