The importance of Artificial Intelligence (AI), Data Analytics and Big Data in helping business drive transformation and customer management has attracted the attention of Pension Fund Administrators(PFAs) and players in the pension industry. According them, these have a key role to play in shaping how industries evolve, and has become a massive force driving the transformation of all businesses in today's world. With this in mind the Pension Fund Operators Association of Nigeria (PenOp) recently put together a seminar for the industry to educate and show the benefits of adopting AI and data analytics. The seminar was tagged: "What has Data Analytics, Artificial Intelligence (AI) and Big Data got to do with Pensions?'' The online session, which was open to pension professionals sought to answer questions such as: What does AI have to do with the pension industry?

Dr. Stephen Thaler developed DABUS (Device for Autonomous Bootstrapping of Unified Science), an artificial intelligence (AI) system that can autonomously create patentable inventions. Thaler has filed patent applications in various jurisdictions for two inventions created by DABUS – a food container with side walls having a fractal profile, and a beacon for attracting enhanced attention for example in a search and rescue scenario[1]. In each application, Thaler listed DABUS as the sole inventor, forcing patent offices in various jurisdictions to address the issue of whether an AI system can be an inventor on a patent application. Thus far, the DABUS patent applications have found very limited success in patent offices and courts around the world. In the latest decision, the United States Court of Appeals for the Federal Circuit (CAFC) held that the US Patent Act requires an inventor to be a natural person, and consequently, an AI system cannot be an inventor on a United States patent application.[2] The DABUS applications were initially rejected by the United States Patent and Trademark Office (USPTO).

The status quo in retail banking is tottering. This has forced banks and credit unions to modify their business models, re-prioritize investments, change products and services offered and ramp up innovation efforts. There has also been a rethinking of distribution options, with digital channels significantly increasing in importance. These shifts are reflected in the sixth iteration of a study of the future of retail banking conducted by The Economist Intelligence Unit, on behalf of Temenos. Until recently, the changes in consumer behavior were believed to be the primary impetus for changes in retail banking strategies.

The wide scale adoption of Artificial Intelligence (AI) software is one of the great transformational technologies of the 21st Century. It is also big business. The global market for AI was estimated at $387.45 billion in 2022 and is expected to reach $1.394 trillion by 2029, according to Fortune Business Insight. One company that is helping to accelerate that transformation, is AI pioneer C3 AI (NYSE: AI). Founded in 2009 long before the wide adoption of either the Cloud or AI by legendary Silicon Valley entrepreneur and billionaire Tom Siebel, C3 provides an open model-AI architecture that simplifies data science and application development to offer an end-to-end platform for developing, deploying, and operating large-scale AI applications; a portfolio of industry-specific SaaS AI applications; a suite of industry-specific CRM applications; and a no-code AI solution to apply data science to everyday business problems.

Environmental problems become more urgent and affect the lives of people, marine life, and various animal species. Only in 2022, forest fires in Spain, France, and many other countries worldwide led to the destruction of animals' natural habitats, a decrease in the number of air producers, and many harmful outcomes for local residents. Although it is hard to say if humanity can still stop global warming and other environmental issues, there are some ways to restrain their development, and the utilization of renewable energy sources is among them. In this article, we list the best and most innovative renewable energy companies to keep on your radar this year. Moreover, if you are seeking more information on how modern technology can help us prevent global environmental catastrophes, read these AITJ articles: 5 Ways AI Can Improve Environmental Sustainability and How AI Helps Clean Oceans from Plastics.

The most common ways to explore latent document dimensions are topic models and clustering methods. However, topic models have several drawbacks: e.g., they require us to choose the number of latent dimensions a priori, and the results are stochastic. Most clustering methods have the same issues and lack flexibility in various ways, such as not accounting for the influence of different topics on single documents, forcing word-descriptors to belong to a single topic (hard-clustering) or necessarily relying on word representations. We propose PROgressive SImilarity Thresholds - ProSiT, a deterministic and interpretable method, agnostic to the input format, that finds the optimal number of latent dimensions and only has two hyper-parameters, which can be set efficiently via grid search. We compare this method with a wide range of topic models and clustering methods on four benchmark data sets. In most setting, ProSiT matches or outperforms the other methods in terms six metrics of topic coherence and distinctiveness, producing replicable, deterministic results.

The predictions of question answering (QA)systems are typically evaluated against manually annotated finite sets of one or more answers. This leads to a coverage limitation that results in underestimating the true performance of systems, and is typically addressed by extending over exact match (EM) with pre-defined rules or with the token-level F1 measure. In this paper, we present the first systematic conceptual and data-driven analysis to examine the shortcomings of token-level equivalence measures. To this end, we define the asymmetric notion of answer equivalence (AE), accepting answers that are equivalent to or improve over the reference, and publish over 23k human judgments for candidates produced by multiple QA systems on SQuAD. Through a careful analysis of this data, we reveal and quantify several concrete limitations of the F1 measure, such as a false impression of graduality, or missing dependence on the question. Since collecting AE annotations for each evaluated model is expensive, we learn a BERT matching (BEM) measure to approximate this task. Being a simpler task than QA, we find BEM to provide significantly better AE approximations than F1, and to more accurately reflect the performance of systems. Finally, we demonstrate the practical utility of AE and BEM on the concrete application of minimal accurate prediction sets, reducing the number of required answers by up to x2.6.

Butterfly Optimization Algorithm (BOA) is a recent metaheuristic that has been used in several optimization problems. In this paper, we propose a new version of the algorithm (xBOA) based on the crossover operator and compare its results to the original BOA and 3 other variants recently introduced in the literature. We also proposed a framework for solving the unknown area exploration problem with energy constraints using metaheuristics in both single- and multi-robot scenarios. This framework allowed us to benchmark the performances of different metaheuristics for the robotics exploration problem. We conducted several experiments to validate this framework and used it to compare the effectiveness of xBOA with wellknown metaheuristics used in the literature through 5 evaluation criteria. Although BOA and xBOA are not optimal in all these criteria, we found that BOA can be a good alternative to many metaheuristics in terms of the exploration time, while xBOA is more robust to local optima; has better fitness convergence; and achieves better exploration rates than the original BOA and its other variants.

In this paper, we examine how generative machine learning systems produce a new politics of visual culture. We focus on DALL-E 2 and related models as an emergent approach to image-making that operates through the cultural techniques of feature extraction and semantic compression. These techniques, we argue, are inhuman, invisual, and opaque, yet are still caught in a paradox that is ironically all too human: the consistent reproduction of whiteness as a latent feature of dominant visual culture. We use Open AI's failed efforts to 'debias' their system as a critical opening to interrogate how systems like DALL-E 2 dissolve and reconstitute politically salient human concepts like race. This example vividly illustrates the stakes of this moment of transformation, when so-called foundation models reconfigure the boundaries of visual culture and when 'doing' anti-racism means deploying quick technical fixes to mitigate personal discomfort, or more importantly, potential commercial loss.

In this paper we propose an adaptive deep neural architecture for the prediction of multiple soil characteristics from the analysis of hyperspectral signatures. The proposed method overcomes the limitations of previous methods in the state of art: (i) it allows to predict multiple soil variables at once; (ii) it permits to backtrace the spectral bands that most contribute to the estimation of a given variable; (iii) it is based on a flexible neural architecture capable of automatically adapting to the spectral library under analysis. The proposed architecture is experimented on LUCAS, a large laboratory dataset and on a dataset achieved by simulating PRISMA hyperspectral sensor. 'Results, compared with other state-of-the-art methods confirm the effectiveness of the proposed solution.