Seeking a firm foothold in the United States, Turkish startups are revving up their investments in the world's most prominent market. Following in the footsteps of gaming ventures that have placed themselves on top among most downloaded games, Turkey's ultrafast grocery delivery company Getir just recently launched operations in the U.S. only a few months after expanding into Europe. On the other hand, Vispera, which offers tech solutions in the field of machine vision and machine learning for the fast consumption and retail sectors across 25 countries, has decided to develop and strengthen its subsidiary in the U.S., Vispera Corp. The company also plans to further develop the strong business partnerships and product developments it has established in 2021. Offering a range of image recognition tools that can solve common issues in retail spaces like stocking inventory levels and more effective displays of current promotions, Vispera has announced it would complete a new investment round in the first months of 2022. The Istanbul-based company seeks to boost its activities in the U.S. through its subsidiary Vispera Corp.

Bengaluru features in the top five cities on Harvard Business review where diversity is high in the AI sector. Diversity and inclusive pool of talent developing AI matters to the reviewers as AI developers are influenced by their own world views, which, in turn, guide them in their selection of applications, datasets, and training of algorithms. The data from the Fletcher school, Tufts university is derived and pitted against indicators such as talent pool, investments, diversity of talent, evolution of the country's digital foundations or TIDE. The reviewers believe that the factors collectively give companies a way to prioritise their AI talent sourcing choices by scoring the different locations on the concentration, quality and diversity of the AI talent pool. Top four cities are San Francisco, New York, Boston and Seattle respectively.

For many years, research in Artificial Intelligence (AI) has directed efforts towards automating processes to perform specific academic, industrial or economic tasks for society. However, the investigation and development of procedures for the automation of human artistic and creative processes has not had as much attention due to the complexities involved in these activities. Procedures developed for these purposes involve mathematical-computational methods designed to process and learn from a large quantity of digital data, so as to detect patterns in order to simulate the creative process (CP), as explained by Boden in [3]. In this paper, we introduce a model for the generation of rhymes with literary components. Our proposal is based on findings detailed in [11], where Automatic Text Generation (ATG) techniques are combined with neural network (NN) based models, such as the Word2vec algorithm [9], for the generation of literary texts.

Deep neural networks (DNNs) have demonstrated their outperformance in various domains. However, it raises a social concern whether DNNs can produce reliable and fair decisions especially when they are applied to sensitive domains involving valuable resource allocation, such as education, loan, and employment. It is crucial to conduct fairness testing before DNNs are reliably deployed to such sensitive domains, i.e., generating as many instances as possible to uncover fairness violations. However, the existing testing methods are still limited from three aspects: interpretability, performance, and generalizability. To overcome the challenges, we propose NeuronFair, a new DNN fairness testing framework that differs from previous work in several key aspects: (1) interpretable - it quantitatively interprets DNNs' fairness violations for the biased decision; (2) effective - it uses the interpretation results to guide the generation of more diverse instances in less time; (3) generic - it can handle both structured and unstructured data. Extensive evaluations across 7 datasets and the corresponding DNNs demonstrate NeuronFair's superior performance. For instance, on structured datasets, it generates much more instances (~x5.84) and saves more time (with an average speedup of 534.56%) compared with the state-of-the-art methods. Besides, the instances of NeuronFair can also be leveraged to improve the fairness of the biased DNNs, which helps build more fair and trustworthy deep learning systems.

Despite the myriad peer-reviewed papers demonstrating novel Artificial Intelligence (AI)-based solutions to COVID-19 challenges during the pandemic, few have made significant clinical impact. The impact of artificial intelligence during the COVID-19 pandemic was greatly limited by lack of model transparency. This systematic review examines the use of Explainable Artificial Intelligence (XAI) during the pandemic and how its use could overcome barriers to real-world success. We find that successful use of XAI can improve model performance, instill trust in the end-user, and provide the value needed to affect user decision-making. We introduce the reader to common XAI techniques, their utility, and specific examples of their application. Evaluation of XAI results is also discussed as an important step to maximize the value of AI-based clinical decision support systems. We illustrate the classical, modern, and potential future trends of XAI to elucidate the evolution of novel XAI techniques. Finally, we provide a checklist of suggestions during the experimental design process supported by recent publications. Common challenges during the implementation of AI solutions are also addressed with specific examples of potential solutions. We hope this review may serve as a guide to improve the clinical impact of future AI-based solutions.

BENGALURU: Bengaluru has emerged among the top five cities in the world for Artificial Intelligence (AI), ranked at No. 5, with the first four being cities in the USA. The ranking is among top 50 AI cities, measured by the TIDE Framework and listed by Harvard Business Review (HBR). The top four cities are San Francisco, New York, Boston and Seattle. Reviewers have noted that Bengaluru also has the world's second-largest AI talent pool and is ranked fifth for diversity among AI workers, as measured by data from Fletcher school, Tufts University, and derived at based on a framework of indicators such as talent pool, investments, diversity of talent, evolution of the country's digital foundations (TIDE). Another feather in Bengaluru's cap is that it is also among cities on HBR's list of AI hotspots in the developing world -- these cities also score favourably on the cost of living, which could be a powerful draw for diverse talent, the reviewers noted.

The coronavirus pandemic has tested public health systems globally. Few novel and infectious diseases around the world have ever posed such dramatic challenges as the novel coronavirus SARS-CoV-2, which causes COVID-19. With highly efficient human-to-human transmission and high mortality rates, COVID19 led the World Health Organization to declare a public health emergency of international concern and caused countries around the world to reassess their public health capabilities. The United Arab Emirates, like other members of the international community, faced the unprecedented challenge of ensuring public health and safety while minimizing economic fallout. These efforts by the U.A.E.'s leadership allowed the U.A.E. to be globally ranked as one of the top countries, and the highest in the Arab world, in terms of its COVID-19 response. VPS Healthcare is an integrated healthcare service provider with 22 operational hospitals, over 125 healthcare centres, 13000 employees, one of the largest pharmaceutical manufacturing plants in Dubai and medical support services spread across the Middle East, Europe and India. By providing comprehensive patient management at international quality standards across the MENA Region and beyond and to the entire strata of community, VPS Healthcare reflects a brand image of excellence in healthcare delivery system.

Chile's The Not Company uses "Giuseppe"--its patented artificial intelligence technology platform--to create vegan chicken that tastes just like the …

The disruption of supplies during the Covid-19 crisis has led to shortages but has also shown the adaptability of some companies, which have succeeded in adapting their production chains quickly to produce goods experiencing shortages: hydroalcoholic gel, masks, and medical gowns. These productive jumps from product A to product B are feasible because of the know-how proximity between the two classes of products. The proximities were computed from the analysis of co-exports and resulted in the construction of the product space. Based on the product space, as well as the customer-supplier relationships resulting from the input-output matrices, we propose a recommender system for companies. The goal is to promote distributed manufacturing by recommending a list of local suppliers to each company. As there is not always a local supplier for a desired product class, we consider the proximity between products to identify, in the absence of a supplier, a substitute supplier able to adapt its production tools to provide the required product. Our experiments are based on French data, from which we build a graph of synergies illustrating the potential productive links between companies. Finally, we show that our approach offers new perspectives to determine the level of territories' industrial resilience considering potential productive jumps.

This paper proposes an algorithm called Forward Composition Propagation (FCP) to explain the predictions of feed-forward neural networks operating on structured pattern recognition problems. In the proposed FCP algorithm, each neuron is described by a composition vector indicating the role of each problem feature in that neuron. Composition vectors are initialized using a given input instance and subsequently propagated through the whole network until we reach the output layer. It is worth mentioning that the algorithm is executed once the network's training network is done. The sign of each composition value indicates whether the corresponding feature excites or inhibits the neuron, while the absolute value quantifies such an impact. Aiming to validate the FCP algorithm's correctness, we develop a case study concerning bias detection in a state-of-the-art problem in which the ground truth is known. The simulation results show that the composition values closely align with the expected behavior of protected features.