An artificial intelligence that can predict how a beer will taste from its chemical make-up could help create alcohol-free versions that taste just like regular ones. Predicting flavour from chemical compounds is difficult, as complex interactions between ingredients and the psychology of taste can make for surprisingly different perceptions, even between people sampling the same thing. To address this, Kevin Verstrepen at KU Leuven in Belgium and his colleagues have developed an AI model that can predict flavour profiles based on a beer's chemical components and make suggestions for how to improve the flavour. The model was trained on beer reviews from a panel of 16 expert tasters, who scored each brew for 50 attributes, as well as 180,000 public ratings from an online beer reviewing website. It compared these subjective descriptions with measurements of 226 chemical compounds in 250 Belgian beers.

Artificial intelligence (AI) is emerging as a valuable tool for food and beverage makers looking to bolster front-end innovation. Manufacturers, restaurants, ingredient suppliers, flavor houses and more are leveraging insights from machine learning to get closer to consumer trends and market more nuanced propositions. New food and flavor concepts traditionally have been ascribed to culinary experts, chefs and product developers, said Ron Harnik, vice president of marketing at Tastewise, an Israel-based AI food and beverage platform. Translating an idea into a finished product can take months or even years. "The processes that are set up to take products to market simply aren't built to be quick and accurate enough to reflect how fast consumers are changing," Mr. Harnik said.

Artificial Intelligence, Machine Learning, Deep Learning, Smart Devices, terms that we are constantly bombarded with in the media, making us believe that these technologies are capable of doing anything and solving any problem we face. Nothing is further from reality!! According to the European Commission, "Artificial intelligence (AI) systems are software (and possibly also hardware) systems designed by humans that, given a complex goal, act in the physical or digital dimension by perceiving their environment through data acquisition, interpreting the collected structured or unstructured data, reasoning on the knowledge, or processing the information, derived from this data and deciding the best action(s) to take to achieve the given goal."1. AI encompasses multiple approaches and techniques, among others machine learning, machine reasoning and robotics. Within them we will focus our reflection on machine learning from data, and more specifically on Intelligent Data Analysis aimed at extracting information and knowledge to make decisions. Those data (historical or streaming) that are stored by companies over time and that are often not put into value.

A tea bag is an extraordinary thing. Each small sachet contains a mix of leaves from different producers and different places. Hundreds of factors can affect the flavour of each leaf, from the amount of sunlight and rainfall to the type of soil it was grown in, how it was plucked and how it was dried. Yet when you drink a cup of your favourite brew, you expect it to taste exactly like the last one. Tetley, a British teamaker, boasts that its basic blend has had the same distinctive taste since the company was set up in 1822.

The capabilities of artificial intelligence (AI) are growing exponentially, especially in the area of creating synthetic images that are photorealistic. In 2014, generative adversarial networks (GANs) were introduced. A few years later, bidirectional GANs (BiGANs) were created. Then came along BigGANs that outperformed state-of-the-art GANs in image synthesis. But wait, there's more: Last week researchers from Alphabet Inc.'s DeepMind debuted BigBiGANs.

The rapid rise of meal delivery services is happening in plain sight, as companies like Deliveroo and Uber Eats send riders and drivers zipping around town with takeout of every variety. But as so often happens with new platforms, a secondary and less visible revolution is rippling across the restaurant industry thanks to the rise of virtual kitchens. London-based Taster is an example of how the intersection of meal delivery services, artificial intelligence, and data is creating opportunities that threaten the restaurant industry with even greater disruption. While meal delivery services initially seemed like a boom for local restaurants, it is virtual kitchens -- with their ability to optimize and automate -- that may ultimately win the food wars. Taster was founded two years ago by Anton Soulier, an early employee of Deliveroo who wanted to take this food transformation further. "I thought there was a big opportunity to build a food company on top of these platforms," he said.

"My take is that pretty much all the food and beverage products on the market today are awful," Jason Cohen tells me, with fierce conviction. "There are literally no products engineered for me." Cohen is the founder and CEO of Analytic Flavor Systems, an NYC-dobased start-up that aims to usher in a new era of hyper-personalized food. We are meeting at a swank Australian coffee shop near the company's office in the financial district--the kind of place that offers multiple single-origin pour-over options--so he can tell me about his artificial intelligence (A.I.) platform, Gastrograph, which he says can be used to map taste preferences with unprecedented ease and precision. Cohen is lanky and self-possessed, with hair the color of damp straw. He drinks his coffee with the studied concentration of someone who takes flavor extremely seriously. Like many start-up CEOs, Cohen interprets his own dissatisfaction as a sign of a more general problem. They're also aimed at the lowest common denominator: There's nothing out there truly designed for you. The world of food and beverage manufacturing, Cohen says, is still oriented around "the predominant demographic," the flavors of things tailored to please a coarse approximation of majority appetites. Endless shelves of products that most people like, but few people really love.

A robotic wine taster, capable of distinguishing between 30 different varieties or blends of grape, has been developed by engineers in Japan. The idea is to automate wine analysis so that retailers and customs officials can easily check that a wine is indeed what its label declares. The wine-bot was developed by scientists from NEC's System Technologies laboratory and Mie University, both in Japan. It is about twice the size of a 3-litre wine box and consists of a microcomputer and an optical sensing instrument. For analysis, a 5 millilitre sample of wine is poured into a tray in front of the machine.