The International Data Centre Day was launched by 7X24 Exchange International, an NGO that provides an educational forum mainly focused on the challenges faced by industry professionals. The first International Data Centre Day was celebrated four years ago, on October 29, 2019. A dedicated website, internationaldatacenterday.org, was also launched with objectives to create awareness about data centres and their future. This way, the industry can connect with future generations, ensure a steady flow of skilled labour, enhance the public image of data centres, and safeguard the ongoing prosperity of the data centre industry. Robert Cassiliano, Chairman & CEO of 7 24 Exchange International, said, "In 2015, few members challenged our organisation to find qualified talent for the data centre industry. However, we later realised that many parents, teachers and students are unaware of the data centre industry. There was a need to create awareness about what data centres are and how they operate. Hence, we started the STEM initiative, which includes mentoring programmes like – the Women in Mission Critical Operations (WiMCO) community, a Data Centre 101 session, video and then creation of International Data Centre Day".

The domain of Artificial Intelligence (AI) ethics is not new, with discussions going back at least 40 years. Teaching the principles and requirements of ethical AI to students is considered an essential part of this domain, with an increasing number of technical AI courses taught at several higher-education institutions around the globe including content related to ethics. By using Latent Dirichlet Allocation (LDA), a generative probabilistic topic model, this study uncovers topics in teaching ethics in AI courses and their trends related to where the courses are taught, by whom, and at what level of cognitive complexity and specificity according to Bloom’s taxonomy. In this exploratory study based on unsupervised machine learning, we analyzed a total of 166 courses: 116 from North American universities, 11 from Asia, 36 from Europe, and 10 from other regions. Based on this analysis, we were able to synthesize a model of teaching approaches, which we call BAG (Build, Assess, and Govern), that combines specific cognitive levels, course content topics, and disciplines affiliated with the department(s) in charge of the course. We critically assess the implications of this teaching paradigm and provide suggestions about how to move away from these practices. We challenge teaching practitioners and program coordinators to reflect on their usual procedures so that they may expand their methodology beyond the confines of stereotypical thought and traditional biases regarding what disciplines should teach and how. This article appears in the AI & Society track.

Thaler first brought the image created by his "Creativity Machine" algorithm to the USCO in November 2018, Eileen Kinsella reported for Artnet News. A Recent Entrance to Paradise is part of a series Thaler describes as a "simulated near-death experience," where an algorithm repurposes pictures to create images seen by a synthetic dying brain. Thaler noted to the USCO he was "seeking to register this computer-generated work as a work-for-hire to the owner of the Creativity Machine." Providing this protection is required under current legal frameworks." Thaler has previously tested the limits of patent laws in numerous countries.

Artificial intelligence (AI) currently plays a central role in the digitisation and modernisation strategies of public administrations and companies throughout Europe, the United States and China. The potential improvements and advances in efficiency that the incorporation of AI can offer strategic sectors in different countries have made it indispensable in a new era of technological transformation. And while no one wants to be left behind, the main players of this new digital era have from the very beginning approached these technologies in significantly different ways. While the United States and China have already embraced AI as one more component of their geopolitical strategies, the European Union (EU) is positioning itself as a global leader in its ethical use. According to the EU, in order to be considered ethical, any AI technology used in its territory must ensure respect for the fundamental rights of EU citizens.

Artificial intelligence (AI) is now firmly in the sights of African banks. From better understanding consumer needs to reducing risk, there are few areas where AI can't make a key impact on operations. Christine Wu, Managing Executive, Customer Value Management at Absa Retail and Business Bank, views artificial intelligence (AI) as an important enabler of the journey to a new banking model that is truly responsive to customer needs. "All areas of the bank's operations can benefit from AI – from the frontline, where we can make use of smarter profiling and customer interactions that are needs-based and tailored to a customer's profile, to customer servicing, where we can include clearer and more bespoke solutions to customers before they even ask – such as the automation of repetitive tasks," says Wu. AI is often defined as human-like intelligence achieved by machines – any system that "perceives its environment and takes actions that maximise its chance of achieving its goals". Advanced AI, according to experts, is also capable of learning and problem-solving.

Conditional Neural Processes~(CNPs) bridge neural networks with probabilistic inference to approximate functions of Stochastic Processes under meta-learning settings. Given a batch of non-{\it i.i.d} function instantiations, CNPs are jointly optimized for in-instantiation observation prediction and cross-instantiation meta-representation adaptation within a generative reconstruction pipeline. There can be a challenge in tying together such two targets when the distribution of function observations scales to high-dimensional and noisy spaces. Instead, noise contrastive estimation might be able to provide more robust representations by learning distributional matching objectives to combat such inherent limitation of generative models. In light of this, we propose to equip CNPs by 1) aligning prediction with encoded ground-truth observation, and 2) decoupling meta-representation adaptation from generative reconstruction. Specifically, two auxiliary contrastive branches are set up hierarchically, namely in-instantiation temporal contrastive learning~({\tt TCL}) and cross-instantiation function contrastive learning~({\tt FCL}), to facilitate local predictive alignment and global function consistency, respectively. We empirically show that {\tt TCL} captures high-level abstraction of observations, whereas {\tt FCL} helps identify underlying functions, which in turn provides more efficient representations. Our model outperforms other CNPs variants when evaluating function distribution reconstruction and parameter identification across 1D, 2D and high-dimensional time-series.

Christine Wu, Managing Executive, Customer Value Management at Absa Retail and Business Bank, views artificial intelligence (AI) as an important enabler of the journey to a new banking model that is truly responsive to customer needs. "All areas of the bank's operations can benefit from AI – from the frontline, where we can make use of smarter profiling and customer interactions that are needs-based and tailored to a customer's profile, to customer servicing, where we can include clearer and more bespoke solutions to customers before they even ask – such as the automation of repetitive tasks," says Wu. AI is often defined as human-like intelligence achieved by machines – any system that "perceives its environment and takes actions that maximise its chance of achieving its goals". Advanced AI, according to experts, is also capable of learning and problem-solving. AI has been taken up enthusiastically across Africa, although the expert view is that it needs some fine-tuning to adapt to the African social and cultural environment. Still, the potential is as great in the banking landscape as it is in online and mobile transactions.

Artificial intelligence (AI) currently plays a central role in the digitisation and modernisation strategies of public administrations and companies throughout Europe, the United States and China. The potential improvements and advances in efficiency that the incorporation of AI can offer strategic sectors in different countries have made it indispensable in a new era of technological transformation. And while no one wants to be left behind, the main players of this new digital era have from the very beginning approached these technologies in significantly different ways. While the United States and China have already embraced AI as one more component of their geopolitical strategies, the European Union (EU) is positioning itself as a global leader in its ethical use. According to the EU, in order to be considered ethical, any AI technology used in its territory must ensure respect for the fundamental rights of EU citizens.

When trained on diverse labeled data, machine learning models have proven themselves to be a powerful tool in all facets of society. However, due to budget limitations, deliberate or non-deliberate censorship, and other problems during data collection and curation, the labeled training set might exhibit a systematic shortage of data for certain groups. We investigate a scenario in which the absence of certain data is linked to the second level of a two-level hierarchy in the data. Inspired by the idea of protected groups from algorithmic fairness, we refer to the partitions carved by this second level as "subgroups"; we refer to combinations of subgroups and classes, or leaves of the hierarchy, as "sources". To characterize the problem, we introduce the concept of classes with incomplete subgroup support. The representational bias in the training set can give rise to spurious correlations between the classes and the subgroups which render standard classification models ungeneralizable to unseen sources. To overcome this bias, we make use of an additional, diverse but unlabeled dataset, called the "deployment set", to learn a representation that is invariant to subgroup. This is done by adversarially matching the support of the training and deployment sets in representation space. In order to learn the desired invariance, it is paramount that the sets of samples observed by the discriminator are balanced by class; this is easily achieved for the training set, but requires using semi-supervised clustering for the deployment set. We demonstrate the effectiveness of our method with experiments on several datasets and variants of the problem.

Since the weeks leading up to Russia's invasion of Ukraine, warnings have been circulating that Russia might use deepfake videos--convincing fake videos created with artificial intelligence-- in the surrounding information war. Perhaps they would use deepfakes to fabricate a pretext for the invasion, or to have Ukrainian President Zelensky issue an order to surrender. With bated breath we waited, but no sign of deepfakery occurred. Then finally, on March 16--20 days into the invasion and 13 days after Ukraine warned this exact scenario might happen--a deepfake of Zelensky surrendering indeed appeared, and it was … unconvincing and obvious. The video editing was low-quality, and the voice was noticeably off; few people seem to have been fooled by it.