According to the World Health Organization (WHO), the world is going hungry. WHO data shows that in 2018, the most recent year for which data is available, 820 million people lacked enough food to eat, an increase of nine million people over the year before. Hunger kills plenty of people worldwide. It also impacts those who survive, causing serious childhood development issues like stunting, where children are too short for their age, and wasting, where they're too thin for their age. The explosion in our planet's population is a major factor in there not being enough food to go around.

The world's small-scale farmers now can see a path to solving global hunger over the next decade, with solutions--such as adopting climate-resilient crops through improving extension services--all culled rapidly via artificial intelligence from more than 500,000 scientific research articles. The results are synthesized in 10 new research papers--authored by 77 scientists, researchers and librarians in 23 countries--as part of Ceres2030: Sustainable Solutions to End Hunger. The project is headquartered at Cornell University, with partners from the International Food Policy Research Institute (IFPRI) and the International Institute for Sustainable Development (IISD). The papers were published concurrently on Oct. 12 in four journals--Nature Plants, Nature Sustainability, Nature Machine Intelligence and Nature Food--and assembled in a comprehensive package online: Sustainable Solutions to End Hunger. Ceres2030 employed machine learning, librarian savvy and research synthesis methods to quickly scan a trove of thousands of scientific journals for ideas and websites from more than 60 agencies that can help eradicate world hunger.

The world's small-scale farmers now can see a path to solving global hunger over the next decade, with solutions – such as adopting climate-resilient crops through improving extension services – all culled rapidly via artificial intelligence from more than 500,000 scientific research articles. The results are synthesized in 10 new research papers – authored by 77 scientists, researchers and librarians in 23 countries – as part of Ceres2030: Sustainable Solutions to End Hunger. The project is headquartered at Cornell, with partners from the International Food Policy Research Institute (IFPRI) and the International Institute for Sustainable Development (IISD). The papers were published concurrently on Oct. 12 in four journals – Nature Plants, Nature Sustainability, Nature Machine Intelligence and Nature Food – and assembled in a comprehensive package online: Sustainable Solutions to End Hunger. Ceres2030 employed machine learning, librarian savvy and research synthesis methods to quickly scan a trove of thousands of scientific journals for ideas and websites from more than 60 agencies that can help eradicate world hunger.

Earlier in 2017, Facebook was working on a new highly-intelligent AI chat-bot that could talk to and negotiate with humans in a realistic manner. When one Facebook engineer had the bright idea to take two of these AI bots and let them talk to each other, that's when something unexpected and terrifying happened. The two AIs invented their own language that us humans couldn't understand and began using it to talk to each other. The Facebook engineers had no idea what they where talking about, but it was very clear that the AIs did communicate. They had invented their own secret code to converse using the power of artificial neural networks.

Researchers have developed an artificial intelligence (AI) capable of identifying diseases in plants; specifically, the cassava plant, which is the most widely grown root on the planet and a decent source of carbohydrates. In May, the startup Abundant Robotics developed an apple-picking robot. Earlier this month, John Deere bought Blue River Technology, a startup that uses AI and machine learning to care for plants and eliminate weeds. Additionally, combining AI with the CRISPR gene editing used to make more crops could make the idea of eliminating world hunger more realistic.