Agriculture is the backbone of the African economy and is a critical factor to accomplish sustainable development goals (SDGs) in Africa, most particularly poverty and hunger. At present, farming accounts for about 60 percent of total employment in sub-Saharan Africa and is also a driver of inclusive and sustainable growth. Food and Agriculture Organization of the United Nations estimates that the world population will reach 9.1 billion by 2050, and to feed that number of people, global food production will need to grow by 70 percent. In Africa, to have a population of about two billion people by then, farm productivity needs to be accelerated at a faster rate than the global average to avoid continued mass hunger. Technology has a vast untapped potential to revolutionize and improve the efficiency of agricultural production in the continent.

The drone makes a conspicuous racket as it lifts off on a mission to capture images of the reservoir below. The sight and sound of this strange device stirs interest among locals as they make their way to and from the town of Kasungu in central Malawi. It takes a matter of minutes for a small crowd to form. A few yards away, Patrick Kalonde is wading through grass and mud. Patrick, an intern at Unicef working on humanitarian uses of drones, is carrying a plastic container and a ladle and is looking for mosquito larvae. The contrast between high-tech drones and low-tech "bucket-and-spade" science, metres apart, could not be starker – yet both are equally important to the success of our new project to map where mosquitoes breed. Kasungu, a small town at the base of the picturesque Kasungu Mountain, is the centre of Africa's first humanitarian drone testing corridor. Set up by Unicef in 2017 with support from the Malawi government, the corridor is an 80km-wide area for flying and testing drones to help the local people. Keen to dispel the reputation that drones are only useful for destruction, the Unicef corridor promotes "drones for good".

Humans use signs, e.g., sentences in a spoken language, for communication and thought. Hence, symbol systems like language are crucial for our communication with other agents and adaptation to our real-world environment. The symbol systems we use in our human society adaptively and dynamically change over time. In the context of artificial intelligence (AI) and cognitive systems, the symbol grounding problem has been regarded as one of the central problems related to {\it symbols}. However, the symbol grounding problem was originally posed to connect symbolic AI and sensorimotor information and did not consider many interdisciplinary phenomena in human communication and dynamic symbol systems in our society, which semiotics considered. In this paper, we focus on the symbol emergence problem, addressing not only cognitive dynamics but also the dynamics of symbol systems in society, rather than the symbol grounding problem. We first introduce the notion of a symbol in semiotics from the humanities, to leave the very narrow idea of symbols in symbolic AI. Furthermore, over the years, it became more and more clear that symbol emergence has to be regarded as a multifaceted problem. Therefore, secondly, we review the history of the symbol emergence problem in different fields, including both biological and artificial systems, showing their mutual relations. We summarize the discussion and provide an integrative viewpoint and comprehensive overview of symbol emergence in cognitive systems. Additionally, we describe the challenges facing the creation of cognitive systems that can be part of symbol emergence systems.

Multimodal machine learning is a core research area spanning the language, visual and acoustic modalities. The central challenge in multimodal learning involves learning representations that can process and relate information from multiple modalities. In this paper, we propose two methods for unsupervised learning of joint multimodal representations using sequence to sequence (Seq2Seq) methods: a \textit{Seq2Seq Modality Translation Model} and a \textit{Hierarchical Seq2Seq Modality Translation Model}. We also explore multiple different variations on the multimodal inputs and outputs of these seq2seq models. Our experiments on multimodal sentiment analysis using the CMU-MOSI dataset indicate that our methods learn informative multimodal representations that outperform the baselines and achieve improved performance on multimodal sentiment analysis, specifically in the Bimodal case where our model is able to improve F1 Score by twelve points. We also discuss future directions for multimodal Seq2Seq methods.

Football (popularly know as soccer in USA) as a sport has always been the center of attraction and excitement among the sports lovers as well as among common mass all over the world. Although there are few other sports that has gained popularity in different subcontinents here and there in last few decades but none of them have ever dared to challenge the popularity of football anytime in the past or at present. In fact the popularity and attraction for both football and footballers has increased exponentially over the past few decades with the introduction of humongous platforms like "World Cup Football" organized by prestigious association like FIFA and support from various other independent affluent football clubs. Today, it has become the sign of dignity and status symbol for a country to host a mega-event like World Cup Football and take advantage of the tourism and business opportunities associated with it. Behind the scene a country can showcase the strength of it's infrastructure and attract foreign tourists & investors and can create huge business opportunities by hosting such an event.

ZHENGZHOU: In the Chinese city of Zhengzhou, a police officer wearing facial recognition glasses spotted a heroin smuggler at a train station. In Qingdao, a city famous for its German colonial heritage, cameras powered by artificial intelligence helped police snatch two dozen criminal suspects in the midst of a big annual beer festival. In Wuhu, a fugitive murder suspect was identified by a camera as he bought food from a street vendor. With millions of cameras and billions of lines of code, China is building a high-tech authoritarian future. Beijing is embracing technologies like facial recognition and artificial intelligence to identify and track 1.4 billion people. It wants to assemble a vast and unprecedented national surveillance system, with crucial help from its thriving technology industry.

After working in the Middle East insurance market for a significant time, Rachid Abi Nader and Hadi Radwan noticed a common problem across the region - most people did not really understand the technical insurance jargon and the cover they had purchased. With a vision to make insurance simple, transparent and accessible, they decided to establish Aqeed.com. "We designed our technology solutions to help consumers understand, manage and buy insurance on a friendly and efficient platform," says Abi Nader, CEO and co-founder of Aqeed. Founded with the shareholders of Barents, an A-rated international reinsurance group, and Equitrust, a company member of Choueiri Group (a media and advertising group), Aqeed aims to change the way customers perceive insurance. When we decided on the name, we wanted something that resonates with our target market.

Ibrahim Diallo was allegedly fired by a machine. Recent news reports relayed the escalating frustration he felt as his security pass stopped working, his computer system login was disabled, and finally he was frogmarched from the building by security personnel. His managers were unable to offer an explanation and powerless to overrule the system. Some might think this was a taste of things to come as artificial intelligence is given more power over our lives. Personally, I drew the opposite conclusion.

Learning and logic are distinct and remarkable approaches to prediction. Machine learning has experienced a surge in popularity because it is robust to noise and achieves high performance; however, ML experiences many issues with knowledge transfer and extrapolation. In contrast, logic is easily intepreted, and logical rules are easy to chain and transfer between systems; however, inductive logic is brittle to noise. We then explore the premise of combining learning with inductive logic into AI Reasoning Systems. Specifically, we summarize findings from PAC learning (conceptual graphs, robust logics, knowledge infusion) and deep learning (DSRL, $\partial$ILP, DeepLogic) by reproducing proofs of tractability, presenting algorithms in pseudocode, highlighting results, and synthesizing between fields. We conclude with suggestions for integrated models by combining the modules listed above and with a list of unsolved (likely intractable) problems.

Collectively, machine learning (ML) researchers are engaged in the creation and dissemination of knowledge about data-driven algorithms. In a given paper, researchers might aspire to any subset of the following goals, among others: to theoretically characterize what is learnable, to obtain understanding through empirically rigorous experiments, or to build a working system that has high predictive accuracy. While determining which knowledge warrants inquiry may be subjective, once the topic is fixed, papers are most valuable to the community when they act in service of the reader, creating foundational knowledge and communicating as clearly as possible. Recent progress in machine learning comes despite frequent departures from these ideals. In this paper, we focus on the following four patterns that appear to us to be trending in ML scholarship: (i) failure to distinguish between explanation and speculation; (ii) failure to identify the sources of empirical gains, e.g., emphasizing unnecessary modifications to neural architectures when gains actually stem from hyper-parameter tuning; (iii) mathiness: the use of mathematics that obfuscates or impresses rather than clarifies, e.g., by confusing technical and non-technical concepts; and (iv) misuse of language, e.g., by choosing terms of art with colloquial connotations or by overloading established technical terms. While the causes behind these patterns are uncertain, possibilities include the rapid expansion of the community, the consequent thinness of the reviewer pool, and the often-misaligned incentives between scholarship and short-term measures of success (e.g., bibliometrics, attention, and entrepreneurial opportunity). While each pattern offers a corresponding remedy (don't do it), we also discuss some speculative suggestions for how the community might combat these trends.