Food shortage is looming in a time when researchers harness artificial intelligence to breed new crop varieties for the changing climate. In an article published by PNAS, Tanksley, a professor emeritus at Cornell University in Ithaca, NY said that "We have to double the productivity per acre of our major crops if we're going to stay on par with the world's needs." Even if we prepare the land, lay out a water irrigation system and make a fence around the land, we still need to have the right genetic material (varieties) to be able to have a harvest with yields to meet both household and market demands as well as needs in terms of quantity and quality including nutrition. To speed up the process of developing these varieties within a short period of time, researchers are turning to machine learning and artificial intelligence (AI). Researchers are using ML/AI based techniques to help assess rapidly genetic resources for plants with the fastest growth in a particular climate and which genes helped these plants to thrive under such climate condition.

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.

In this post, I will continue sharing IndexBox's insights on how to use big data to make informed business development decisions. Previously, we talked about sourcing new market opportunities to boost a company's exports, and the AI platform which makes this happen. The next question the platform can answer is how to find the best product or industry to diversify your business. Our AI platform can answer this question, like many other, using its own algorithm. It was primarily developed by our in-house analysts and was powered by machine learning principles.

This paper is concerned with the inference of the conditional independence graph G of a multivariate random vector Y of dimension n, a problem sometimes referred to as structure learning. We focus here on undirected decomposable graphs, whose popularity is mainly due to the tractable factorization they allow for the likelihood ([9, 20]); related work for directed graphical models can be found in [18]. Learning the conditional 1 independence graph G is an onerous task due to the large number of graphs on a set of n nodes, or variables. It is possible using optimization methods to find the graph which best fits the data according to some metric [23, 30, 13]; alternatively Bayesian model averaging may be used to accommodate for uncertainty in the estimated graph, or maximum a posteriori estimation may be used to select a given model from the posterior over graphs. Such an approach relies on a prior distribution π(G) over the set of decomposable graphs of a given size; through Bayes theorem, this prior is updated based on the data to give an a posteriori estimate of the distribution over graphs.