An Israeli agtech company called Seedo might have the solution for the challenges of urban agriculture in vulnerable areas such as the Caribbean, that struggle with environmental and climate factors that lead to crop loss. Latin and America and the Caribbean is the most urbanised region in the world with up to 80% of the region's population residing in cities (UN-Habitat 2012). While urbanization is an important element of economic growth and modernization, the diminishing ratio of food producers to food consumers in urban settings negatively impacts local food systems, causing populations to be more susceptible to non-communicable diseases, obesity and undernourishment. Urban farming practices such as rooftop gardens, community greenhouses and vertical farms have provided an alternative to rural agriculture, but given the high cost of urban land, space and size limitations, non-conducive environmental conditions and limited human resources, these methods have not been without their challenges. Vertical farming's "closed and controlled" approach has been successful in eliminating the risk of insects, pests and diseases that are prevalent in traditional agricultural systems but the infrastructure required has typically been cost-prohibitive and highly reliant on fossil fuels (solar power is typically not enough).

Residential homes constitute roughly one-fourth of the total energy usage worldwide. Providing appliance-level energy breakdown has been shown to induce positive behavioral changes that can reduce energy consumption by 15%. Existing approaches for energy breakdown either require hardware installation in every target home or demand a large set of energy sensor data available for model training. However, very few homes in the world have installed sub-meters (sensors measuring individual appliance energy); and the cost of retrofitting a home with extensive sub-metering eats into the funds available for energy saving retrofits. As a result, strategically deploying sensing hardware to maximize the reconstruction accuracy of sub-metered readings in non-instrumented homes while minimizing deployment costs becomes necessary and promising. In this work, we develop an active learning solution based on low-rank tensor completion for energy breakdown. We propose to actively deploy energy sensors to appliances from selected homes, with a goal to improve the prediction accuracy of the completed tensor with minimum sensor deployment cost. We empirically evaluate our approach on the largest public energy dataset collected in Austin, Texas, USA, from 2013 to 2017. The results show that our approach gives better performance with a fixed number of sensors installed when compared to the state-of-the-art, which is also proven by our theoretical analysis.

Heat pumps are already incredibly efficient. Researchers in Switzerland say they can push efficiencies even further using artificial intelligence. A research team led by Jürg Alexander Schiffmann at the L'Ecole Polytechnique Fédérale de Lausanne (Swiss Federal Institute of Technology Lausanne, or EPFL) is using AI to design compressors that slash heat pumps' electricity consumption by around 25 percent. Unlike conventional furnaces or boilers, which combust fuels to generate heat, heat pumps use electricity to move heat from one place to another. Employing a compressor and refrigerant, heat pumps expel heat from the indoors to the outside during the cooling season, or capture heat outdoors from the ground or air and draw it indoors in winter.

A team of researchers from Bioversity International in Africa has created a smartphone app to help banana farmers protect their crops against diseases and pests. The Tumaini App (meaning'hope' in Swahili) is based on artificial intelligence algorithms that have been trained to recognize five major diseases and one common pest affecting the world's favorite fruit, demonstrating accuracy of more than 90 per cent in most models. The software has been tested in Colombia, the Democratic Republic of the Congo, India, Benin, China, and Uganda. Tumaini can recommend the means of addressing a specific disease and automatically upload identification data into a global database to help coordinate international response. It is hoped that the app can stop disease outbreaks and protect the livelihood of small, independent farmers.

SqwidNet, in partnership with Sigfox, has concluded the second round of its Internet of Things (IoT) SA University Challenge with ten university teams competing in the final pitch presentation day this week. The programme is designed to challenge students to develop and create innovative projects focused on building solutions that support the UN Sustainable Development Goals using SqwidNet / Sigfox technology. "We were astounded by the creative thinking displayed by the ten teams that presented their solutions to the judges this week," says Phathizwe Malinga, managing director of SqwidNet. "The solutions presented ranged from agricultural solutions for early pest detection to avoid crop losses, to generating electricity from plants by collecting electrons from roots in an anode and converting that into electricity. We also saw an IoT water monitoring solution, an early fire detection for rural communities and a two-way learning solution using artificial intelligence."

During The AI Summit London 2019, TechXLR8's own Tech TV team sat down with Rema Algunaibet, AI Developer for Saudi Aramco, to discuss the business AI landscape today, the challenges facing enterprises, and the steps ahead for industry. With flagship shows in San Francisco, London, New York, Munich, Hong Kong, Singapore, and Cape Town, 2019 will see over 30,000 delegates from businesses globally joining the AI revolution through The AI Summit events. The AI Summit series uniquely has the support of tech's elite, with our 2019 Industry Partners featuring Agorai, AWS, IBM Watson, Microsoft, Oracle, Google, HCL, Publicis Sapient, Genpact, Intel alongside 300 sponsors and partners. Exclusive, inspirational insights from acclaimed speakers are frequently reported by the world's foremost press including official media partners CBS, Reuters, BBC, The Times, Quartz, Tech Radar.

Artificial Intelligence is nothing new to anyone reading this blog, or most of the people on the planet. Siri, Alexa, and web chatbots have made AI commonplace. Yet, imagine what AI can do when you give it a pair of eyes and a training to analyze its surroundings. This is just what the combination of computer vision and machine learning offers to users. Machine learning is the application of statistical models and algorithms to perform tasks without the need to introduce explicit instructions.

Artificial intelligence (AI) may help develop safe, clean and virtually limitless fusion energy for generating electricity, scientists say. A team, including researchers from Princeton University and Harvard University, are applying deep learning to forecast sudden disruptions that can halt fusion reactions and damage the doughnut-shaped tokamaks or apparatus that house the reactions. Deep learning is a powerful new version of the machine learning form of AI, according to the findings published in the journal Nature magazine. "This research opens a promising new chapter in the effort to bring unlimited energy to Earth," Steven Cowley, director of US Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL). "Artificial intelligence is exploding across the sciences and now it's beginning to contribute to the worldwide quest for fusion power," Cowley said in a statement.

The promise of big data and artificial intelligence is everywhere. One almost gets the impression that there is no problem that cannot be solved with these new technologies. The answer to everything is'big data and artificial intelligence'. This article was originally published in Smart Energy International 4-2019. Read the full digimag here or subscribe to receive a print copy here.

In April, for the first time in the U.S., renewables generated more electricity than coal, according to the Energy Information Administration.