Poodle crossbreeds have grown overwhelmingly popular, sparking controversy in dog parks and kennel clubs alike. The features of doodles such as Peaches (above), a goldendoodle, have become the canine equivalent of Instagram face. Meet the Breeds, the American Kennel Club's annual showcase of purebred dogs, took place over two eye-wateringly cold days in early February at the Javits Center, in Manhattan. About a hundred and fifty of the two hundred and five varieties recognized as official breeds by the A.K.C., the long-standing authority in the U.S. dog world, were in attendance for the public to ogle, fondle, and coo "So cute!" to, including the basset fauve de Bretagne, a hunting hound from France that's one of three newly recognized breeds recently allowed into the purebred pantheon. Some of the dogs had competed in the Westminster Kennel Club Dog Show earlier in the week, and past champions had their ribbons on display. In spite of the frigid weather, pavilions hosting the more popular breeds--the pug, the Doberman pinscher, the Great Dane, the St. Bernard--were packed. Lesser-known varieties, such as the saluki, the Löwchen, and the Lapponian herder, drew sparser crowds. There were exhibition spaces for each breed, and on the back walls were three adjectives supposedly describing that particular type of dog's temperament. There is, in fact, no evidence that temperament is consistent within a breed, but the idea is deeply rooted in dogdom. I stopped to caress the velvety ear leather of a pharaoh hound ("Friendly, Smart, Noble"), a sprinting breed once used to hunt rabbits in Malta; accept kisses from a Portuguese water dog, bred to assist with retrieving tackle ("Affectionate, Adventurous, Athletic"); and have my photograph taken with a Leonberger, a German breed from the town of Leonberg, in southwest Germany ("Friendly, Gentle, Playful"). No one was supposed to be openly selling dogs, but, if you asked, the breeders would share their information. Excluding what are known as companion dogs, like the Leonberger, most of the animals at the show were designed for a purpose that is no longer required of them. In Great Britain, foxhounds are legally barred from chasing foxes. Consider the fate of the otterhound, an ancient variety with a noble heritage which was once used in the U.K. to hunt river otters, which were prized for their thick fur and disliked by wealthy landowners because they ate fish in their stocked ponds.

Hybrid rice breeding crossbreeds different rice lines and cultivates the resulting hybrids in fields to select those with desirable agronomic traits, such as higher yields. Recently, genomic selection has emerged as an efficient way for hybrid rice breeding. It predicts the traits of hybrids based on their genes, which helps exclude many undesired hybrids, largely reducing the workload of field cultivation. However, due to the limited accuracy of genomic prediction models, breeders still need to combine their experience with the models to identify regulatory genes that control traits and select hybrids, which remains a time-consuming process. To ease this process, in this paper, we proposed a visual analysis method to facilitate interactive hybrid rice breeding. Regulatory gene identification and hybrid selection naturally ensemble a dual-analysis task. Therefore, we developed a parametric dual projection method with theoretical guarantees to facilitate interactive dual analysis. Based on this dual projection method, we further developed a gene visualization and a hybrid visualization to verify the identified regulatory genes and hybrids. The effectiveness of our method is demonstrated through the quantitative evaluation of the parametric dual projection method, identified regulatory genes and desired hybrids in the case study, and positive feedback from breeders.

Fox News Correspondent, William La Jeunesse, joins'Fox News Sunday' to discuss the evolution of A.I. and the push lawmakers are making to regulate it. OpenAI has announced a set of new measures to combat bias within its suite of products, including ChatGPT. The artificial intelligence (AI) company recently unveiled an updated Model Spec, a document that defines how OpenAI wants its models to behave in ChatGPT and the OpenAI API. The company says this iteration of the Model Spec builds on the foundational version released last May. "I think with a tool as powerful as this, one where people can access all sorts of different information, if you really believe we're moving to artificial general intelligence (AGI) one day, you have to be willing to share how you're steering the model," Laurentia Romaniuk, who works on model behavior at OpenAI, told Fox News Digital.

In the face of a growing global population, plant breeding is being used as a sustainable tool for increasing food security. A wide range of high-throughput omics technologies have been developed and used in plant breeding to accelerate crop improvement and develop new varieties with higher yield performance and greater resilience to climate changes, pests, and diseases. With the use of these new advanced technologies, large amounts of data have been generated on the genetic architecture of plants, which can be exploited for manipulating the key characteristics of plants that are important for crop improvement. Therefore, plant breeders have relied on high-performance computing, bioinformatics tools, and artificial intelligence (AI), such as machine-learning (ML) methods, to efficiently analyze this vast amount of complex data. The use of bigdata coupled with ML in plant breeding has the potential to revolutionize the field and increase food security. In this review, some of the challenges of this method along with some of the opportunities it can create will be discussed. In particular, we provide information about the basis of bigdata, AI, ML, and their related sub-groups. In addition, the bases and functions of some learning algorithms that are commonly used in plant breeding, three common data integration strategies for the better integration of different breeding datasets using appropriate learning algorithms, and future prospects for the application of novel algorithms in plant breeding will be discussed. The use of ML algorithms in plant breeding will equip breeders with efficient and effective tools to accelerate the development of new plant varieties and improve the efficiency of the breeding process, which are important for tackling some of the challenges facing agriculture in the era of climate change.

There's a reason so much of the produce sold in the grocery store often tastes like cardboard. Actually, there are several reasons. Most of them stem from the fact that tastiness is far down on the list of what the food industry encourages plant breeders to prioritize when developing new varieties -- called "cultivars" -- of produce. When they do want to focus on taste, breeders don't have good tools for quickly sampling the fruit from thousands of cultivars. In a surprising new paper, researchers at the University of Florida describe a new method for "tasting" produce based on its chemical profile.

The Research Brief is a short take about interesting academic work. Which flavors and chemical compounds make a particular variety of fruit more appealing to consumers can be identified and predicted using artificial intelligence, according to our recently published study. Flavor, defined by scientists as the interaction between aroma and taste, is chemically complex. The sugars, acids and bitter compounds in food interact with the taste receptors on our tongues to invoke taste, while volatile compounds that interact with olfactory receptors in our noses are responsible for aroma. Breeding for flavor is a difficult task for many different reasons.

You are free to share this article under the Attribution 4.0 International license. Using artificial intelligence, researchers have developed a genetic model for the "ultimate" chickpea, with the potential to lift crop yields by up to 12%. Researchers genetically mapped thousands of chickpea varieties, and then used this information to identify the most valuable gene combinations using artificial intelligence (AI). Researchers wanted to to develop a "haplotype" genomic prediction crop breeding strategy, for enhanced performance for seed weight. "Most crop species only have a few varieties sequenced, so it was a massive undertaking by the international team to analyze more than 3,000 cultivated and wild varieties," says Ben Hayes, professor at the University of Queensland.

The climate is changing, and our crops have to keep up with it. A team of scientists and companies are joining forces to learn how to make crops more resilient to heat, drought, pests and diseases; also because we want to use less pesticides in the future. In their ten year plan called Plant-XR, the team aims to enable the development of new climate resilient crops with the help of artificial intelligence and computer models. The consortium behind Plant-XR consists of researchers from Utrecht University, the University of Amsterdam, Wageningen University & Research, Delft University of Technology and worldwide leading breeding companies in the Netherlands. With the provisional grant from NWO in its pocket, the team can further develop its plans in the coming months.

By the year 2050, Earth's population is expected to reach nearly ten billion people. With this growth comes a staggering demand for food resources, particularly drought, heat, pest and disease resistant crop varieties that give high yields in the face of climate change. Enter X, Alphabet Inc.'s so-called "moonshot factory," where innovators face the world's biggest challenges head-on and develop ground-breaking technology at a startup pace. Project Mineral, one of X's current efforts, is focused on finding an effective way to address the global food security crisis through "computational agriculture," a term coined by X to describe new technologies that will further increase understanding about the plant world. "The agriculture industry has digitized," says Project Mineral lead Elliott Grant.

Until recently, the field of plant breeding looked a lot like it did in centuries past. A breeder might examine, for example, which tomato plants were most resistant to drought and then cross the most promising plants to produce the most drought-resistant offspring. This process would be repeated, plant generation after generation, until, over the course of roughly seven years, the breeder arrived at what seemed the optimal variety. Researchers at ETH Zürich use standard color images and thermal images collected by drone to determine how plots of wheat with different genotypes vary in grain ripeness. Image credit: Norbert Kirchgessner (ETH Zürich, Zürich, Switzerland). Now, with the global population expected to swell to nearly 10 billion by 2050 (1) and climate change shifting growing conditions (2), crop breeder and geneticist Steven Tanksley doesn’t think plant breeders have that kind of time. “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,” says Tanksley, a professor emeritus at Cornell University in Ithaca, NY. To speed up the process, Tanksley and others are turning to artificial intelligence (AI). Using computer science techniques, breeders can rapidly assess which plants grow the fastest in a particular climate, which genes help plants thrive there, and which plants, when crossed, produce an optimum combination of genes for a given location, opting for traits that boost yield and stave off the effects of a changing climate. Large seed companies in particular have been using components of AI for more than a decade. With computing power rapidly advancing, the techniques are now poised to accelerate breeding on a broader scale. AI is not, however, a panacea. Crop breeders still grapple with tradeoffs such as higher yield versus marketable appearance. And even the most sophisticated AI …