It is observed that since 1970s computer generated art works have attracted a lot of attention. Most of these computer-generated artworks are relied heavily on the programmer who provides the input for creation of the work. However, with technological advancement, artificial intelligence has developed to the extent that it is capable of understanding and creating results/ outputs without any interference by the human.8 Major question raised in this regard, is with respect to the protection over the work created by the Artificial Intelligence. The works created by AI can be categorized as "works created by AI with human interference" and "works created by AI without any human interference".

With respect to climate change, poaching, and encroachment on natural habitats, some animal populations have fared far worse than others. It's estimated that the populations of more than 4,000 species shrunk by 60% between 1970 and 2014, and a recent United Nations global assessment found that as many as 1 million species are at risk of extinction within the next decade. That's why Google has partnered with Conservation International and other organizations -- the Smithsonian's National Zoo and Conservation Biology Institute, North Carolina Museum of Natural Sciences, Map of Life, World Wide Fund for Nature, Wildlife Conservation Society, and Zoological Society of London, with support from Google's Earth Outreach program and the Gordon and Betty Moore Foundation and Lyda Hill Philanthropies. The goal is to help process one of the world's largest and most diverse databases of photographs taken from motion-activated cameras. As of today, the fruits of their labor is available through Google Cloud as a part of Wildlife Insights, an AI-enabled platform that streamlines conservation monitoring by expediting trap-camera photo analysis.

AI has a "black box" problem. We cram data in one side of a machine learning system and we get results out the other, but we're often unsure what happens in the middle. Researchers and developers nearly had the issue licked, with "explainable algorithms" and "transparent AI" trending over the past few years. Black box AI isn't as complex as some experts make it out to be. Imagine you have 1,000,000 different spices and 1,000,000 different herbs and you only have a couple of hours to crack Kentucky Fried Chicken's secret recipe.

Barely a day passes without a new scandal emerging, from questionable surveillance to racist AI algorithms. But it hasn't all been bad: while negatives get a lot of attention (and so they should – the consequences of tech can be lethal, both societally and literally), there was still plenty to get excited about. And for those working in the data profession – as analysts, scientists, and engineers, there were several important trends that really helped to define where we are now from a purely practical perspective – as well as hinting at where we might go in the future. With just a few weeks left to go of the year (and the decade!), let's look at some of the key things that defined this year in the field of data science and data engineering. TensorFlow is undoubtedly the most popular deep learning framework.

SPRINGFIELD, MASSACHUSETTS – Police departments around the U.S. are asking citizens to trust them to use facial recognition software as another handy tool in their crime-fighting toolbox. But some lawmakers -- and even some technology giants -- are hitting the brakes. Are fears of an all-seeing, artificially intelligent security apparatus overblown? Not if you look at China, where advancements in computer vision applied to vast networks of street cameras have enabled authorities to track members of ethnic minority groups for signs of subversive behavior. American police officials and their video surveillance industry partners contend that won't happen here.

Box 1053 Blindern 0316 Oslo, Norway Abstract The Brier score is commonly used for evaluating probability predictions. In survival analysis, with right-censored observations of the event times, this score can be weighted by the inverse probability of censoring (IPCW) to retain its original interpretation. It is common practice to estimate the censoring distribution with the Kaplan-Meier estimator, even though it assumes that the censoring distribution is independent of the covariates. This paper discusses the general impact of the censoring estimates on the Brier score and shows that the estimation of the censoring distribution can be problematic. In particular, when the censoring times can be identified from the covariates, the IPCW score is no longer valid. For administratively censored data, where the potential censoring times are known for all individuals, we propose an alternative version of the Brier score. This administrative Brier score does not require estimation of the censoring distribution and is valid even if the censoring times can be identified from the covariates. Keywords: survival analysis, time-to-event-prediction, customer churn, inverse probability weighting, progressive type I censoring 1. Introduction Recently, there has been an increasing interest in combining machine learning methodology with survival analysis for improved time-to-event prediction. Also worth mentioning is the Random Survival Forest (Ishwaran et al., 2008) which makes decision trees based on the log-rank test and estimates the cumulative hazards with the Nelson-Aalen estimator. Although these methods are available for right-censored event times, a substantial part of the machine learning community is not familiar with survival analysis and might find it reasonable to instead apply binary classifiers for time-to-event prediction. In short, a binary classifier estimates the probability that an individual experience the event by time t, and can be fitted by disregarding individuals censored before that time. Arguably, the two most common evaluation criteria for survival predictions are the inverse probability of censoring weighted (IPCW) Brier score (Graf et al., 1999; Gerds and Schumacher, 2006) and different versions of the concordance index (Harrell Jr et al., 1982; Antolini et al., 2005; Uno et al., 2011; Gerds et al., 2013).

We present an innovative methodology for studying and teaching the impacts of AI through a role - play game. The game serves two primary purposes: 1) training AI developers and AI policy professionals to reflect on and prepare for future social and ethical challenges related to AI and 2) exploring possible futures involving AI technology developm ent, deployment, social impacts, and governance. While the game currently focuses on the inter - relations between short -, mid - and long - term impacts of AI, it has potential to be adapted for a broad range of scenarios, exploring in greater depths issues of AI policy research and affording training within organizations. The game presented here has undergone two years of development and has been tested through over 30 events involving between 3 and 70 participants. The game is under active development, but pre liminary findings suggest that role - play is a promising methodology for both exploring AI futures and training individuals and organizations in thinking about, and reflecting on, the impacts of AI and strategic mistakes that can be avoided today.

Artificial intelligence is considered as a key technology. It has a huge impact on our society. Besides many positive effects, there are also some negative effects or threats. Some of these threats to society are well-known, e.g., weapons or killer robots. But there are also threats that are ignored. These unknown-knowns or blind spots affect privacy, and facilitate manipulation and mistaken identities. We cannot trust data, audio, video, and identities any more. Democracies are able to cope with known threats, the known-knowns. Transforming unknown-knowns to known-knowns is one important cornerstone of resilient societies. An AI-resilient society is able to transform threats caused by new AI tecchnologies such as generative adversarial networks. Resilience can be seen as a positive adaptation of these threats. We propose three strategies how this adaptation can be achieved: awareness, agreements, and red flags. This article accompanies the TEDx talk "Why we urgently need an AI-resilient society", see https://youtu.be/f6c2ngp7rqY.

Using machines to augment human activity is nothing new. Egyptian hieroglyphs show the use of horse-drawn carriages even before 300 B.C. Ancient Indian literature such as "Silapadikaram" has described animals being used for farming. And one glance outside shows that today people use motorized vehicles to get around. Where in the past human beings have augmented ourselves in physical ways, now the nature of augmentation also is more intelligent. Again, all one needs to do is look to cars – engineers are seemingly on the cusp of self-driving cars guided by artificial intelligence.

Artificial intelligence (AI) is a key area of focus for the Information Commissioner's Office (ICO). The ICO is already working on a related AI project that focuses on building the ICO's Auditing Framework. One of the goals of the ICO is to increase the public's trust and confidence in how data is used and made available. In line with this, on 2 December 2019, the ICO published a blog on explaining decisions made by AI (here). The'Explaining decisions made with AI' guidance (Guidance) has been prepared in collaboration with the UK's national institute for data science and artificial intelligence, the Alan Turing Institute.