Farmers have long struggled with operational optimization and labor concerns. Finding enough labor to get the job done, as well as keeping workers safe is a constant struggle. "There is an immediate need to improve efficiency and reduce costs, especially now that the pandemic has exposed just how fragile the supply chain is," said Suma Reddy, CEO of Future Acres, an agricultural robotics and artificial intelligence company. "We saw shortages in both production and more workers being put at risk when picking specialty crops on a daily basis that have really caused the industry to take a step back and re-examine how we can create greater resiliency in the food chain." One idea is to equip farms with a combination of AI and robotics that can "think through" as well as do some of the physical work of farming.

Text-based games simulate worlds and interact with players using natural language. Recent work has used them as a testbed for autonomous language-understanding agents, with the motivation being that understanding the meanings of words or semantics is a key component of how humans understand, reason, and act in these worlds. However, it remains unclear to what extent artificial agents utilize semantic understanding of the text. To this end, we perform experiments to systematically reduce the amount of semantic information available to a learning agent. Surprisingly, we find that an agent is capable of achieving high scores even in the complete absence of language semantics, indicating that the currently popular experimental setup and models may be poorly designed to understand and leverage game texts. To remedy this deficiency, we propose an inverse dynamics decoder to regularize the representation space and encourage exploration, which shows improved performance on several games including Zork I. We discuss the implications of our findings for designing future agents with stronger semantic understanding.

Learning continuously during all model lifetime is fundamental to deploy machine learning solutions robust to drifts in the data distribution. Advances in Continual Learning (CL) with recurrent neural networks could pave the way to a large number of applications where incoming data is non stationary, like natural language processing and robotics. However, the existing body of work on the topic is still fragmented, with approaches which are application-specific and whose assessment is based on heterogeneous learning protocols and datasets. In this paper, we organize the literature on CL for sequential data processing by providing a categorization of the contributions and a review of the benchmarks. We propose two new benchmarks for CL with sequential data based on existing datasets, whose characteristics resemble real-world applications. We also provide a broad empirical evaluation of CL and Recurrent Neural Networks in class-incremental scenario, by testing their ability to mitigate forgetting with a number of different strategies which are not specific to sequential data processing. Our results highlight the key role played by the sequence length and the importance of a clear specification of the CL scenario.

A desirable goal for autonomous agents is to be able to coordinate on the fly with previously unknown teammates. Known as "ad hoc teamwork", enabling such a capability has been receiving increasing attention in the research community. One of the central challenges in ad hoc teamwork is quickly recognizing the current plans of other agents and planning accordingly. In this paper, we focus on the scenario in which teammates can communicate with one another, but only at a cost. Thus, they must carefully balance plan recognition based on observations vs. that based on communication. This paper proposes a new metric for evaluating how similar are two policies that a teammate may be following - the Expected Divergence Point (EDP). We then present a novel planning algorithm for ad hoc teamwork, determining which query to ask and planning accordingly. We demonstrate the effectiveness of this algorithm in a range of increasingly general communication in ad hoc teamwork problems.

Health challenges represent one of the long-standing issues in the Arab region that hinder its ability to develop. Prevalence of diseases such as cardiovascular diseases, liver cirrhosis and cancer among many others has contributed to the deteriorated health status across the region leading to lower life expectancy compared to other regions. For instance, the average life expectancy in the Arab world is approximately 70 years, which is at least 10 years lower than most high-income countries.2 Among many directions of healthcare development across the region, biomedical computing research represents one main arm of tackling health challenges. Advances in computational technologies have enabled the emergence of biomedical computing as one of the most influential research areas worldwide.

Arabic poses a set of unique speech recognition challenges due to its rich dialectal variety.

The Arab region consists of many teaching-intensive universities that are intrinsically committed to holistic educational excellence. According to a recent UNESCO report,5 the higher education sector in the Arab region is undergoing a need for massive expansion given exponentially growing populations, record-breaking youth cohorts, coupled with a strong recognition of the economic and social value of higher education. Such an enormous need for growth poses a significant challenge for publicly funded universities yet offers many opportunities for private universities to meet the ever-increasing demands of advanced education.2 As is the case with many similar universities worldwide, not being dedicated research institutions often results in limited availability of research funds, resources, and hence innovation throughput. The examples given in this paper are those of universities in the region that were initially focused on consolidating their teaching, except for one which started first as research-intensive. However, it was not long before a shift in policy included research excellence in undergraduate education by harnessing the most valuable resource of any university: the aspiring students themselves.

David Keyes is a professor of applied mathematics and computational science and director of the Extreme Computing Research Center at the King Abdullah University of Science and Technology, Saudi Arabia.

From Hammurabi's stone tablets to papyrus rolls and leather-bound books, the Arab region has a rich history of recordkeeping and transactional systems that closely matches the evolution of data storage mediums. Even modern-day data management concepts like data provenance and lineage have historic roots in the Arab world; generations of scribes meticulously tracked Islamic prophetic narrations from one narrator to the next, forming lineage chains that originated from central Arabia. Database systems research has been part of the academic culture in the Arab world since the 1970s. High-quality computer science and database education was always available at several universities within the Arab region, such as Alexandria University in Egypt. Many students who went through these programs were drawn to database systems research and became globally prominent, such as Ramez Elmasri (professor at University of Texas, Arlington), Amr El Abbadi (professor at University of California, Santa Barbara), and Walid Aref (professor at Purdue University).

The world is facing enormous challenges, ranging from climate change to extreme poverty. The 2030 Agenda for Sustainable Development and its 17 Sustainable Development Goals (SDGs)a were adopted by United Nations Member States in 2015 as an operational framework to address these challenges. The SDGs include No Poverty, Quality Education, Gender Equality, Peace, Justice and Strong Institutions, among others, as well as a meta goal on Partnerships for the Goals. Despite limitations,7 the SDGs form a rare global consensus of all 193 UN member states on where we should collectively be heading. Goals are meaningless without a way to track their progress. Data on the SDGs and the associated indicatorsb are often outdated or unavailable, hindering progress during the Decade of Action leading up to 2030.c