Omar bin Sultan Al Olama, the UAE's minister of state for Artificial Intelligence, and Deepak Bagla, the managing director and CEO of Invest India, signed a Memorandum of Understanding (MoU) to create a bilateral Artificial Intelligence Bridge that envisages the generation of $20 billion in economic benefits during the next decade. WAM said that the AI Bridge aims to spur discussion and explore options for the UAE and India to grow their Artificial Intelligence economies, according to an announcement after the signing ceremony by Invest India, the national investment promotion ad facilitation agency of the Indian government. The UAE Minister said that in the coming years, "how a country chooses to embrace Artificial Intelligence will have a tremendous impact on its ability to innovate and prosper. Data and processing will be a catalyst for innovation and business growth and serve as the backbone of more effective and efficient service delivery system." The designated Indian signatory said his country, which is "the world's fastest expanding market opportunity with its talent pool of human capital, well-acknowledged for innovation – and the UAE, a hub of cutting edge technologies – are natural partners in the field of Artificial Intelligence."

Previously I have written about the reality and potential of ongoing efforts to integrate blockchain with the internet of things (IoT). Now I am going to look at how encrypted, distributed ledgers could unlock new frontiers for another cutting-edge technology: artificial intelligence (AI). AI, as the term is most often used today is, simply put, the theory and practice of building machines capable of performing tasks that seem to require intelligence. Currently, cutting-edge technologies striving to make this a reality include machine learning, artificial neural networks and deep learning. Meanwhile, blockchain is essentially a new filing system for digital information, which stores data in an encrypted, distributed ledger format.

Deep learning constitutes a recent, modern technique for image processing and data analysis, with promising results and large potential. As deep learning has been successfully applied in various domains, it has recently entered also the domain of agriculture. In this paper, we perform a survey of 40 research efforts that employ deep learning techniques, applied to various agricultural and food production challenges. We examine the particular agricultural problems under study, the specific models and frameworks employed, the sources, nature and pre-processing of data used, and the overall performance achieved according to the metrics used at each work under study. Moreover, we study comparisons of deep learning with other existing popular techniques, in respect to differences in classification or regression performance. Our findings indicate that deep learning provides high accuracy, outperforming existing commonly used image processing techniques.

Interpretable machine learning tackles the important problem that humans cannot understand the behaviors of complex machine learning models and how these classifiers arrive at a particular decision. Although many approaches have been proposed, a comprehensive understanding of the achievements and challenges is still lacking. This paper provides a survey covering existing techniques and methods to increase the interpretability of machine learning models and also discusses the crucial issues to consider in future work such as interpretation design principles and evaluation metrics in order to push forward the area of interpretable machine learning.

We present a scalable approach for Detecting Objects by transferring Common-sense Knowledge (DOCK) from source to target categories. In our setting, the training data for the source categories have bounding box annotations, while those for the target categories only have image-level annotations. Current state-of-the-art approaches focus on image-level visual or semantic similarity to adapt a detector trained on the source categories to the new target categories. In contrast, our key idea is to (i) use similarity not at the image-level, but rather at the region-level, and (ii) leverage richer common-sense (based on attribute, spatial, etc.) to guide the algorithm towards learning the correct detections. We acquire such common-sense cues automatically from readily-available knowledge bases without any extra human effort. On the challenging MS COCO dataset, we find that common-sense knowledge can substantially improve detection performance over existing transfer-learning baselines.

In machine learning, the notion of multi-armed bandits refers to a class of online learning problems, in which an agent is supposed to simultaneously explore and exploit a given set of choice alternatives in the course of a sequential decision process. In the standard setting, the agent learns from stochastic feedback in the form of real-valued rewards. In many applications, however, numerical reward signals are not readily available -- instead, only weaker information is provided, in particular relative preferences in the form of qualitative comparisons between pairs of alternatives. This observation has motivated the study of variants of the multi-armed bandit problem, in which more general representations are used both for the type of feedback to learn from and the target of prediction. The aim of this paper is to provide a survey of the state of the art in this field, referred to as preference-based multi-armed bandits or dueling bandits. To this end, we provide an overview of problems that have been considered in the literature as well as methods for tackling them. Our taxonomy is mainly based on the assumptions made by these methods about the data-generating process and, related to this, the properties of the preference-based feedback.

Dubai: The UAE and India on Saturday signed an agreement to spur discussion and explore options for both countries to grow their artificial intelligence economies. The UAE Minister for Artificial Intelligence and Invest India signed a Memorandum of Understanding for the India-UAE Artificial Intelligence Bridge. The partnership will generate an estimated $20 billion (Dh73.4 billion) in economic benefits during the next decade. The UAE-India collaboration will seek to evaluate the dynamic nature of innovation and technology by convening a UAE-India AI Working Committee (TWG) between the UAE Ministry for Artificial Intelligence, Invest India and Startup India. The TWG will meet once a year with the mandate to increase investment in AI start-ups and research activities in partnership with the private sector.

Interest in artificial intelligence (AI) research has grown rapidly over the past few years, in part thanks to the numerous successes of modern machine learning techniques such as deep learning, the availability of large datasets and improvements in computing power. AI is proving to be increasingly applicable to healthcare and there is a growing list of tasks where algorithms have matched or surpassed physician performance. Despite the successes there remain significant concerns and challenges surrounding algorithm opacity, trust and patient data security. Notwithstanding these challenges, AI technologies will likely become increasingly integrated into emergency medicine in the coming years. This perspective presents an overview of current AI research relevant to emergency medicine.

When it comes to work, workers, and jobs, much of the angst of the modern era boils down to the fear that we're witnessing the automation endgame, and that there will be nowhere for humans to retreat as machines take over the last few tasks. The most recent wave of commentary on this front stems from the use of artificial intelligence (AI) to capture and automate tacit knowledge and tasks, which were previously thought to be too subtle and complex to be automated. Is there no area of human experience that can't be quantified and mechanized? And if not, what is left for humans to do except the menial tasks involved in taking care of the machines? At the core of this concern is our desire for good jobs--jobs that, without undue intensity or stress, make the most of workers' natural attributes and abilities; where the work provides the worker with motivation, novelty, diversity, autonomy, and work/life balance; and where workers are duly compensated and consider the employment contract fair. Crucially, good jobs support workers in learning by doing--and, in so doing, deliver benefits on three levels: to the worker, who gains in personal development and job satisfaction; to the organization, which innovates as staff find new problems to solve and opportunities to pursue; and to the community as a whole, which reaps the economic benefits of hosting thriving organizations and workers. This is what makes good jobs productive and sustainable for the organization, as well as engaging and fulfilling for the worker. It is also what aligns good jobs with the larger community's values and norms, since a community can hardly argue with having happier citizens and a higher standard of living.1 Does the relentless advance of AI threaten to automate away all the learning, creativity, and meaning that make a job a good job?

The relation between mostly concept-based lexical-semantic networks (wordnets) and lemma-based lexical resources (dictionaries) has been explored so far mainly for wordnet-building purposes, and such projects and related issues are well documented. In spite of not being meant to serve lexicographical purposes (in the case of most wordnets, with some notable exceptions), wordnets have become a de facto standard for the drafting of dictionary content. Experience resulting from using wordnets as a data source for lexicography and issues related to them have just started to be systematically discussed. In the WNLEX Workshop, we define the state of the art in the discussed topics, provide a survey of solved and unsolved issues, and an outlook on future work regarding wordnet as a resource in lexicographical workflows. Target group for this workshop is lexicographers.