An artificial intelligence system is capable of being an "inventor" under Australian patent law, the federal court has ruled, in a decision that could have wider intellectual property implications. University of Surrey professor Ryan Abbott has launched more than a dozen patent applications across the globe, including in the UK, US, New Zealand and Australia, on behalf of US-based Dr Stephen Thaler. They seek to have Thaler's artificial intelligence device known as Dabus (a device for the autonomous bootstrapping of unified sentience) listed as the inventor. The applications claimed Dabus, which is made up of artificial neural networks, invented an emergency warning light and a type of food container, among other inventions. Several countries, including Australia, had rejected the applications, stating a human must be named the inventor.

Blockchain and AI make a formidable alliance, especially when it comes to security and data management. So in order to protect people's data with blockchain and AI tech for good, we have to build and deploy an ecosystem with an open source set of APIs that limits how much technology makers can learn about individual customers. Artificial Intelligence solutions are already being deployed within all our interactions on social media and the internet, from the sensors on our IoT driven smartphones to our financial, wellness and healthcare data. Adapting future AI, it is now being integrated on the top of blockchains solutions too, especially in the financial industry and supply chain transactions. This will be increasingly intertwined with Machine Learning solutions, capability and even opening up new ways of creating new social media, wellness, and financial products.

Modern datasets, from areas such as neuroimaging and geostatistics, often come in the form of a random sample of tensor-valued data which can be understood as noisy observations of an underlying smooth multidimensional random function. Many of the traditional techniques from functional data analysis are plagued by the curse of dimensionality and quickly become intractable as the dimension of the domain increases. In this paper, we propose a framework for learning multidimensional continuous representations from a random sample of tensors that is immune to several manifestations of the curse. These representations are defined to be multiplicatively separable and adapted to the data according to an $L^{2}$ optimality criteria, analogous to a multidimensional functional principal components analysis. We show that the resulting estimation problem can be solved efficiently by the tensor decomposition of a carefully defined reduction transformation of the observed data. The incorporation of both regularization and dimensionality reduction is discussed. The advantages of the proposed method over competing methods are demonstrated in a simulation study. We conclude with a real data application in neuroimaging.

Human-wildlife conflict—defined here as direct interactions between humans and wildlife with adverse outcomes—costs the global economy billions of dollars annually, threatens human lives and livelihoods, and is a leading cause of biodiversity loss ([ 1 ][1]). These clashes largely stem from the co-occurrence of humans and wildlife seeking limited resources in shared landscapes and often has unforeseen consequences. For example, large carnivore species like leopards may prey upon livestock and disrupt human livelihoods, leading to retaliatory killings that can drive wildlife decline, zoonotic disease outbreaks, and child labor practices ([ 2 ][2]). As dire as these conflicts have been, climate change is intensifying human-wildlife conflict by exacerbating resource scarcity and forcing people and wildlife to share increasingly crowded spaces. Consequently, human-wildlife conflict is rising in frequency and severity, but the complex connections among climate dynamics, ecological dynamics, and social dynamics contributing to the heightened conflict have yet to be fully appreciated. ![Figure][3] Warming temperatures have driven animals to human-dominated areas in search of food. Increased attacks on livestock can spur retaliatory killing of predators. A sheep corral in the Himalayas is covered with wire to protect against attacks from snow leopards. PHOTO: NICK GARBUTT/MINDEN PICTURES Both extreme climate events and directional climate change have the potential to alter the dynamics of human-wildlife conflict. Acute climate events can cause rapid changes in resource availability that drive strong behavioral and spatial responses in animals and people, leading to increased co-occurrence and competition. In terrestrial systems, droughts in particular have intensified some of the most visible conflicts. For example, from 1986 to 1988, a severe drought in India brought about by an extreme El Niño led to a sharp decline in vegetation productivity; loss of food drove elephants to new human-dominated areas, which led to rapid increases in crop damage and fatal attacks on people ([ 3 ][4]). The same drought event in India saw a marked increase in livestock losses to lions, and human fatalities from lion attacks rose by more than 600% in one region to 6.7 deaths per year following the drought ([ 3 ][4]). More recently in 2018, a prolonged drought in Botswana saw some of the highest incidences of livestock depredations by large carnivores on record, compounding drought-induced food and economic insecurity in agricultural and pastoral communities ([ 4 ][5]). Similar connections between climate events and conflicts are occurring in marine systems. For instance, anomalously warm water temperatures off the South African coast drove changes in prey availability that displaced great white sharks into areas of high human use; the increase in spatial overlap between people and sharks led to a nearly fourfold increase in shark attacks within a single year ([ 5 ][6]). A similar increase in spatial overlap that resulted in heightened conflict occurred in 2014 to 2016 off the US West Coast, when an intense marine heat wave drove changes in both large-whale distributions and fisheries management, leading to an unprecedented number of whale entanglements in fishing gear ([ 6 ][7]). Not only did these entanglements cause high rates of whale mortality, but subsequent management restrictions have threatened millions of dollars in lost fishery revenue. Although extreme climate events often create dramatic conflicts, long-term warming is also producing conflicts with interconnected consequences for people and wildlife. In a notable example, over a 30-year period in Canada's Hudson Bay, human–polar bear conflicts involving property damage, life-threatening encounters, or bear killings have more than tripled as sea ice has declined and polar bears have spent more time on land ([ 7 ][8]). In the Himalayas, warming-induced vegetation changes at high elevations have driven the bharal or blue sheep to lower elevations, where they forage on crops, which affects the livelihoods of local subsistence agricultural producers. Simultaneously, the redistribution of bharal has also drawn their primary predator, snow leopards, to lower elevations, leading to increased livestock depredation and retaliatory killing of leopards ([ 8 ][9]). In other examples, crop foraging ([ 9 ][10]), livestock depredation ([ 10 ][11]) or competition ([ 11 ][12]), and human-wildlife encounters ([ 12 ][13]) are inversely correlated with interannual rainfall as a result of reduced food and water availability, and declining rainfall trends in parts of the globe continue to create more frequent and intense conflicts ([ 13 ][14]). Even as climate change restricts resource availability in many contexts, climate-driven expansion of the human footprint further forces people and animals to share spaces and can create new conflicts—for example, agricultural expansion into previously unproductive or inaccessible areas is significantly associated with rises in human-wildlife conflict ([ 9 ][10]). By investigating the interrelated consequences of climate change on wildlife and human populations, we can better anticipate undesired outcomes and identify how human interventions can mitigate cascading ecological and social dynamics. Climate impacts on human-wildlife conflict do not act in isolation—among other factors, socioeconomic drivers such as land-use change and demographic processes such as rising human populations or changes in predator and prey populations play major roles in determining the frequency, scale, and distribution of conflicts ([ 1 ][1]). Thus, illuminating and ultimately addressing the interconnections between climate change and human-wildlife conflict requires a coupled socioecological systems approach, drawing from fields as diverse as ecology, global change biology, human demography, political science, public policy, history, and economics. Although the impact of climate change on human-wildlife conflict has arguably received relatively little research attention, governmental bodies are increasingly recognizing this phenomenon and developing forward-looking policies to explicitly incorporate climate into the management of certain conflicts ([ 3 ][4], [ 4 ][5]). For example, the state of California in the US recently implemented a Risk Assessment and Mitigation Program that assimilates climatic, oceanographic, biological, and economic indices to inform dynamic fisheries management to reduce the risk of whale entanglements ([ 6 ][7]). Knowledge of climate impacts on human-wildlife conflict can also aid long-term planning efforts and public outreach. For instance, livestock compensation programs, one of the most widely implemented tools to mitigate human-carnivore conflict, could plan funding allocations to anticipate higher spending in years with anomalous climate conditions. Furthermore, given early warning from climate predictions or emerging efforts to predict human-wildlife conflicts using artificial intelligence ([ 14 ][15]), governments or nongovernmental organizations can educate and warn the public about possible increased interactions with wildlife ([ 12 ][13]). As climate change continues to drive both increased climate variability and directional change ([ 15 ][16]), climate-driven human-wildlife conflict can be expected to be a recurring challenge. To protect wildlife and humans alike, it is vital that a diverse body of research and institutions considers the role of a changing climate in shaping the complex socioecological dynamics of conflict. 1. [↵][17]1. P. J. Nyhus , Annu. Rev. Environ. Resour. 41, 143 (2016). [OpenUrl][18] 2. [↵][19]1. J. Terborgh, 2. J. A. Estes 1. J. S. Brashares, 2. L. R. Prugh, 3. C. J. Stoner, 4. C. W. Epps , in Trophic Cascades, J. Terborgh, J. A. Estes, Eds. (Island Press, 2010), pp. 221–240. 3. [↵][20]1. J. R. Bhatt, 2. A. Das, 3. K. Shanker , Eds., Biodiversity and Climate Change: An Indian Perspective (Ministry of Environment, Forest and Climate Change, Government of India, New Delhi, 2018), pp. 1–138. 4. [↵][21]Botswana Vulnerability Assessment Committee, (Botswana Ministry of Local Government and Rural Development, 2019); . 5. [↵][22]1. B. K. Chapman, 2. D. McPhee , Ocean Coast. Manage. 133, 72 (2016). [OpenUrl][23] 6. [↵][24]1. J. A. Santora et al ., Nat. Commun. 11, 536 (2020). [OpenUrl][25] 7. [↵][26]1. L. Towns et al ., Polar Biol. 32, 1529 (2009). [OpenUrl][27][CrossRef][28] 8. [↵][29]1. A. Aryal et al ., Theor. Appl. Climatol. 115, 517 (2013). [OpenUrl][30] 9. [↵][31]1. J. M. Mukeka, 2. J. O. Ogutu, 3. E. Kanga, 4. E. Røskaft , Glob. Ecol. Conserv. 18, e00620 (2019). [OpenUrl][32] 10. [↵][33]1. M. Schiess-Meier, 2. S. Ramsauer, 3. T. Gabanapelo, 4. B. Konig , J. Wildl. Manage. 71, 1267 (2007). [OpenUrl][34] 11. [↵][35]1. S. P. Vargas et al ., Oryx 55, 275 (2021). [OpenUrl][36] 12. [↵][37]1. C. S. Zack et al ., Wildl. Soc. Bull. 31, 517 (2003). [OpenUrl][38] 13. [↵][39]1. J. M. Mukeka et al ., Hum. Wildl. Interact. 14, 255 (2020). [OpenUrl][40] 14. [↵][41]1. P. Variyar , Can Artificial Intelligence Predict Human-Wildlife Conflict? (Wildlife Conservation Trust, 2021); [www.wildlifeconservationtrust.org/can-artificial-intelligence-predict-human-wildlife-conflict/][42]. 15. [↵][43]1. D. Coumou, 2. S. Rahmstorf , Nat. Clim. Chang. 2, 491 (2012). [OpenUrl][44] Acknowledgments: I thank K. Gaynor, A. McInturff, E. Pikitch, and J. Samhouri for valuable discussions and comments. 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Dr. Rana el Kaliouby is the author of Girl Decoded and a leading expert on technology and empathy and the ethics of AI. This past June, Affectiva, the company she co-founded, was acquired by Smart Eye. In this virtual sit-down, we set out to learn more about what inspires Dr. el Kaliouby and how new innovations will change how we interface with technology and connect and communicate as humans. Q: Dr. el Kaliouby, tell us how you got started on your journey to exploring the role of emotion in today's technology-driven landscape? My education and career pursuits led me to Cambridge and later MIT, which meant I spent a lot of time in front of devices communicating with family back home.

South Africa has become the first country to award a patent that names an artificial intelligence as its inventor and the AI's owner as the patent's owner. The patent was secured by University of Surrey professor Ryan Abbott and his team, who have been at odds with patent offices around the world for years over the need to recognise artificial intelligences as inventors. Abbott was representing Dr Stephen Thaler, creator of an artificial neural system named Dabus ('device for the autonomous bootstrapping of unified sentience'), which Thaler claims is the sole inventor of a food container that improves grip and heat transfer. Abbott and his team have filed patents listing Dabus as the inventor in more than ten jurisdictions since 2018, including in the UK, Europe and the US. The High Court in England and Wales last year sided with the UK Intellectual Property Office in refusing the applications, accepting that while Dabus created the inventions, it cannot be granted a patent on the grounds that it isn't a'natural person'.

It might not be obvious from the TV coverage, but the Tokyo 2020 Olympics (which of course are being held in 2021) are infused with big data and AI to an extent never before experienced in an Olympic games. It's been 53 years since the Olympics officially adopted electronic time-keeping equipment to track racers in Olympic events. Omega's Magic Eye camera, which debuted in 1948, gave us the first of many "photo-finish" for track events, and was soon adopted in other events too. Now the technology is going up a notch in the Tokyo 2020 Olympics (which perhaps should have been called the 2021 games), and Omega is behind much of it. For example, Omega, which is the official timekeeper for 35 Olympic sports, is using cameras equipped with computer vision capabilities to track the movement of beach volleyball players, as well as the ball.

For over hundreds of millions of years, sea turtles and their ancestors have swum in the vast expanses of the ocean. They have undergone a number of evolutionary changes, leading to speciation and sub-speciation. However, in the past few decades, some of the most notable forces driving the genetic variance and population decline have been global warming and anthropogenic impact ranging from large-scale poaching, collecting turtle eggs for food, besides dumping trash including plastic waste into the ocean. This leads to severe detrimental effects in the sea turtle population, driving them to extinction. This research focusses on the forces causing the decline in sea turtle population, the necessity for the global conservation efforts along with its successes and failures, followed by an in-depth analysis of the modern advances in detection and recognition of sea turtles, involving Machine Learning and Computer Vision systems, aiding the conservation efforts.

The high-quality translation results produced by machine translation (MT) systems still pose a huge challenge for automatic evaluation. Current MT evaluation pays the same attention to each sentence component, while the questions of real-world examinations (e.g., university examinations) have different difficulties and weightings. In this paper, we propose a novel difficulty-aware MT evaluation metric, expanding the evaluation dimension by taking translation difficulty into consideration. A translation that fails to be predicted by most MT systems will be treated as a difficult one and assigned a large weight in the final score function, and conversely. Experimental results on the WMT19 English-German Metrics shared tasks show that our proposed method outperforms commonly used MT metrics in terms of human correlation. In particular, our proposed method performs well even when all the MT systems are very competitive, which is when most existing metrics fail to distinguish between them. The source code is freely available at https://github.com/NLP2CT/Difficulty-Aware-MT-Evaluation.

We propose to use deep learning to estimate parameters in statistical models when standard likelihood estimation methods are computationally infeasible. We show how to estimate parameters from max-stable processes, where inference is exceptionally challenging even with small datasets but simulation is straightforward. We use data from model simulations as input and train deep neural networks to learn statistical parameters. Our neural-network-based method provides a competitive alternative to current approaches, as demonstrated by considerable accuracy and computational time improvements. It serves as a proof of concept for deep learning in statistical parameter estimation and can be extended to other estimation problems.