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Environmental sustainability is a critical global imperative and existential challenge for humanity. While computing professionals tend to think of computing as a positive technology, there's no doubt it also has significant negative impacts, such as growing environmental damage. Firstly, computing is a rapidly growing consumer of environmental resources (for example, minerals, water), a producer of greenhouse-gas emissions (for example, operational, embodied), a creator of environmental pollution (for example, e-waste), and an enabler of environmentally harmful activities. This damage has grown steadily over decades with little prospect of slowing (see the recent Communications article by Eeckhout2). But secondly, computing has an important role in understanding climate change and reducing greenhouse gas emissions and other environmental damage in a broad array of societal activities (for example, agriculture, transportation, manufacturing, facility management, power generation, and more) and other applications that hope to promote environmental sustainability.

Dual Horn clauses mirror key properties of Horn clauses. This paper explores the "other side of the looking glass" to reveal some expected and unexpected symmetries and their practical uses. We revisit Dual Horn clauses as enablers of a form of constructive negation that supports goal-driven forward reasoning and is valid both intuitionistically and classically. In particular, we explore the ability to falsify a counterfactual hypothesis in the context of a background theory expressed as a Dual Horn clause program. With Dual Horn clause programs, by contrast to negation as failure, the variable bindings in their computed answers provide explanations for the reasons why a statement is successfully falsified. Moreover, in the propositional case, by contrast to negation as failure as implemented with stable models semantics in ASP systems, and similarly to Horn clause programs, Dual Horn clause programs have polynomial complexity. After specifying their execution model with a metainterpreter, we devise a compilation scheme from Dual Horn clause programs to Horn clause programs, ensuring their execution with no performance penalty and we design the embedded SymLP language to support combined Horn clause and Dual Horn clause programs. As a (motivating) application, we cast LLM reasoning chains into propositional Horn and Dual Horn clauses that work together to constructively prove and disprove goals and enhance Generative AI with explainability of reasoning chains. Keywords: Dual Horn clauses; constructive negation; counterfactual reasoning; theory falsification; LLM generated logic programs; metainterpretation and compilation to Prolog.

Social-ecological systems research aims to understand the nature of social-ecological phenomena, to find ways to foster or manage conditions under which desired phenomena occur or to reduce the negative consequences of undesirable phenomena. Such challenges are often addressed using dynamical systems models (DSM) or agent-based models (ABM). Here we develop an iterative procedure for combining DSM and ABM to leverage their strengths and gain insights that surpass insights obtained by each approach separately. The procedure uses results of an ABM as inputs for a DSM development. In the following steps, results of the DSM analyses guide future analysis of the ABM and vice versa. This dialogue, more than having a tight connection between the models, enables pushing the research frontier, expanding the set of research questions and insights. We illustrate our method with the example of poverty traps and innovation in agricultural systems, but our conclusions are general and can be applied to other DSM-ABM combinations.

But when the heartland needs tech, it still comes to Silicon Valley. On Thursday, John Deere announced that it would acquire Bear Flag Robotics, a Silicon Valley startup that makes fully autonomous tractors for farms, for $250 million. Bear Flag retrofits regular tractors with sensors, control systems, computers, and communications systems needed to operate autonomously. The company's tech lets a lone farmer remotely oversee a fleet of robot tractors autonomously tilling a field. "John Deere putting their stamp on this kind of fully autonomous technology means it's really coming," says George Kantor, a roboticist at Carnegie Mellon University who specializes in the use of robots in agriculture.

How does artificial intelligence-powered precision farming affect food sustainability? This is the question we asked our panel of experts. "Precision farming" is a bit of a buzz phrase; it is often used, but rarely defined. Generally, it means the widespread adoption of new technologies to accurately monitor and control agricultural activity. But which technologies are adopted and which consequences result?

Investment in artificial intelligence is growing in Canada. In 2017, venture capital investment in AI nearly doubled - to $12 billion. And looking at the agriculture sector, AI is helping farmers to increase crop yields, save costs and reduce environmental damages. For generations, farmers have relied on their own knowledge of the land and past experience to get the most profit from their farms, regardless of if they had a dairy or raised food crops. With the new technologies available today, farmers can now target their use of fertilizers or herbicides, saving money and minimizing environmental damage.