In a previous article of February 6, 2020, we discussed the EPO Receiving Section's refusal, in January 2020, of two European patent applications where an AI system called DABUS was indicated as the inventor1 . We then looked at the grounds of the decisions2 (concerning applications EP 18 275 163 and EP 18 275 174 for "food container" and "devices and methods for attracting enhanced attention"), and predicted that the EPO Board of Appeal (BoA) was bound to shed light on the novel and intriguing legal issue of whether a non-human, such as an artificial intelligence (AI), could be named as inventor in the system of the EPC. The BoA has now issued its decision, which is worth commenting. The applicant, one Mr. Stephen Thaler, had filed his appeals against the refusal (cases J 8/20 and J 9/20), along with an auxiliary request whereby no person was allegedly identified as inventor, but a natural person was indicated to hold "the right to the European Patent by virtue of being the owner and creator of" the DABUS AI system. By decision of December 21, 20213, the BoA dismissed the appeal, confirming that the EPC required the inventor to be a person with legal capacity.

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Detailed feature investigations and comparisons across climates, continents and time series types can progress our understanding and modelling ability of the Earth's hydroclimate and its dynamics. As a step towards these important directions, we here propose and extensively apply a multifaceted and engineering-friendly methodological framework for the thorough characterization of seasonal hydroclimatic dependence, variability and change at the global scale. We apply this framework using over 13 000 quarterly temperature, precipitation and river flow time series. In these time series, the seasonal hydroclimatic behaviour is represented by 3-month means of earth-observed variables. In our analyses, we also adopt the well-established Koppen-Geiger climate classification system and define continental-scale regions with large or medium density of observational stations. In this context, we provide in parallel seasonal hydroclimatic feature summaries and comparisons in terms of autocorrelation, seasonality, temporal variation, entropy, long-range dependence and trends. We find notable differences to characterize the magnitudes of most of these features across the various Koppen-Geiger climate classes, as well as between several continental-scale geographical regions. We, therefore, deem that the consideration of the comparative summaries could be more beneficial in water resources engineering contexts than the also provided global summaries. Lastly, we apply explainable machine learning to compare the investigated features with respect to how informative they are in explaining and predicting either the main Koppen-Geiger climate or the continental-scale region, with the entropy, long-range dependence and trend features being (roughly) found to be less informative than the remaining ones at the seasonal time scale.

Connected and autonomous vehicles (CAVs) improve the throughput of intersections by crossing in a lane-free order as compared to the signalised crossing of human drivers. However, it is challenging to quantify such an improvement because the available frameworks to analyse the capacity (i.e., the maximum throughput) of the conventional intersections does not apply to the lane-free ones. This paper proposes a novel theoretical framework to numerically simulate and compare the capacity of lane-free and conventional intersections. The results show that the maximum number of vehicles passing through a lane-free intersection is up to seven times more than a signalised intersection managed by the state-of-the-art max-pressure and Webster algorithms. A sensitivity analysis shows that, in contrast to the signalised intersections, the capacity of the lane-free intersections improves by an increase in initial speed, the maximum permissible speed and acceleration of vehicles.

Te Herenga Waka-Victoria University of Wellington PhD graduate Dr Qurrat Ul Ain has developed an artificial intelligence programme that could help diagnose skin cancer, using just a photograph. During her PhD, Dr Qurrat Ul Ain developed a computer-aided diagnostic system that can identify certain characteristics of the disease from a photograph of a skin lesion. "Skin cancer has certain unique visual features that help to differentiate it from normal skin," Dr Qurrat Ul Ain says. "These include colour, texture, and the shape of lesions. By showing our artificial intelligence programme images of cancerous skin, we were able to teach it to identify cancer when shown other photographs."

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Organizations are increasingly adopting AI-enabled technologies to address existing and emerging problems within the enterprise ecosystem, meet changing market demands and deliver business outcomes at scale. Shubhangi Vashisth, senior principal research analyst at Gartner, said that AI innovation is happening at a rapid pace. Vashisth further noted that innovations including edge AI, computer vision, decision intelligence and machine learning will have a transformational impact on the market in coming years. However, while AI-powered technologies are helping to build more agile and effective enterprise systems, they usher in new challenges. For example, Gartner notes that AI-based approaches if left unchecked can perpetuate bias, leading to issues, loss of productivity and revenue.

Welcome to Pushing Buttons, the Guardian's gaming newsletter. If you'd like to receive it in your inbox every week, just pop your email in below – and check your inbox (and spam) for the confirmation email. I spent the latter half of last week in London for the Bafta Games Awards – a ceremony whose existence still seems to surprise people, despite the fact that they've been running in some form for 18 years. I suppose it doesn't help that the institution is literally called the British Academy of Film and Television Arts, but video games are a big deal at the UK's prestigious arts organisation, more so now than ever. It's never been a paid thing, though I have eaten a shameful number of cocktail sausages during jury deliberations.)

Artificial intelligence (AI) plays a crucial role in the healthcare industry by helping doctors, patients and hospital administrators. Artificial Intelligence (AI) is defined as computing systems which are capable of performing tasks that humans are very good at, for example recognising objects, recognising and making sense of speech, and decision making in a constrained environment. For the purposes of this article, Machine Learning and Deep Learning (Deep Neural Networks) are defined as sub-branches of AI. See the Appendix for a more detailed explanation of these areas. Healthcare systems were already under a substantial strain before the arrival of the Covid-19 pandemic. This strain has only increased since the pandemic and may cause challenges that persist for many years. It takes many years and costly resources to train healthcare workers. Specialist medical practitioners tend to be in short supply and often work long hours.

DARPA, In-Q-Tel, US National Laboratories (examples: Argonne, Oak Ridge) are famous government funding agencies for deep tech on the forward boundaries, the near impossible, that have globally transformative solutions. The Internet is a prime example where more than 70% of the 7.8 billion population are online in 2022, closing in on 7 hours daily mobile usage, and global wealth of $500 Trillion is powered by the Internet. There is convergence between the early bets led by government funding agencies and the largest corporations and their investments. An example is from 2015, where I was invited to help the top 100 CEOs, representing nearly $100 Trillion in assets under management, to look ten years into the future for their investments. The resulting working groups, and private summits resulted in the member companies investing in all the areas identified: quantum computing, block chain, cybersecurity, big data, privacy and data, AI/ML, future in fintech, financial inclusion, ...

DARPA, In-Q-Tel, US National Laboratories (examples: Argonne, Oak Ridge) are famous government funding agencies for deep tech on the forward boundaries, the near impossible, that have globally transformative solutions. The Internet is a prime example where more than 70% of the 7.8 billion population are online in 2022, closing in on 7 hours daily mobile usage, and global wealth of $500 Trillion is powered by the Internet. There is convergence between the early bets led by government funding agencies and the largest corporations and their investments. An example is from 2015, where I was invited to help the top 100 CEOs, representing nearly $100 Trillion in assets under management, to look ten years into the future for their investments. The resulting working groups, and private summits resulted in the member companies investing in all the areas identified: quantum computing, block chain, cybersecurity, big data, privacy and data, AI/ML, future in fintech, financial inclusion, ...