Unilever and Lindt & Sprungli are among the companies in the food industry best positioned to take advantage of developments in artificial intelligence, our analysis shows. The assessment comes from GlobalData's Thematic Research, which ranks companies on a scale of one to five based on their likelihood to tackle challenges like artificial intelligence and emerge as long-term winners of the food sector. According to our analysis, Unilever, Lindt and Japan-based snacks group Calbee feature in the list of companies best positioned to benefit from investments in artificial intelligence, all of them recording scores of five out of five. Unilever indicated good levels of investment in AI, with the company looking for 323 new jobs in the field since October 2020, for example. The table below shows how GlobalData analysts scored the biggest companies among packaged food and food ingredients manufacturers on their AI performance, as well as the number of new artificial intelligence-related jobs, deals, patents and mentions in company reports since October 2020.

Some people call this Artificial Intelligence (AI), but the reality is this technology will enhance us. So instead of AI, I think we'll augment our intelligence, quoted by Ginni Rometty, CEO (Chief executive officer) of IBM. The business of selling food to customers is being disturbed to a level not since the last pandemic, over 100 years ago. So, it's not true that the crisis accelerated the adoption of technology in the manner that is occurring today with Artificial Intelligence (AI) in the food industry. It is increasingly possible that our food system was ill-prepared (antifragile) for this Covid-19 induced crisis.

As we enter the new decade, we can expect a stronger focus on how technology and data advances will generate more actionable use for the food industry. Food Safety Tech has highlighted many perspectives from subject matter experts in the industry, and 2020 will be no different. Our first Q&A of the year features Sasan Amini, CEO of Clear Labs, as he shares his thoughts on tech improvements and the continued rise consumer expectations for transparency. Food Safety Tech: As we look to the year ahead, where do you see artificial intelligence, machine learning and blockchain advancing in the food industry? Sasan Amini: AI, ML, and blockchain are making headway in the food industry through advances in supply chain management, food sorting and anomaly detection, and tracing the origin of foodborne outbreaks.

WASHINGTON – Looking for that perfect recipe, or a new flavor combination that delights the senses? Increasingly, players in the food industry are embracing artificial intelligence to better understand the dynamics of flavor, aroma and other factors that go into making a food product a success. Earlier this year, IBM became a surprise entrant to the food sector, announcing a partnership with seasonings maker McCormick to "explore flavor territories more quickly and efficiently using AI to learn and predict new flavor combinations" by utilizing data collected from millions of data points. The partnership highlights how technology is being used to disrupt the food industry by helping develop new products and respond to consumer preferences and offer improved nutrition and flavor. "More and more, food companies are embracing digitization and becoming data-driven," said Bernard Lahousse, co-founder of Foodpairing, a startup with offices in Belgium and New York that develops digital food "maps" and algorithms to recommend food and drink combinations.

WASHINGTON - Looking for that perfect recipe, or a new flavor combination that delights the senses? Increasingly, players in the food industry are embracing artificial intelligence to better understand the dynamics of flavor, aroma and other factors that go into making a food product a success. Earlier this year, IBM became a surprise entrant to the food sector, announcing a partnership with seasonings maker McCormick to "explore flavor territories more quickly and efficiently using AI to learn and predict new flavor combinations" by utilizing data collected from millions of data points. The partnership highlights how technology is being used to disrupt the food industry by helping develop new products and respond to consumer preferences and offer improved nutrition and flavor. "More and more, food companies are embracing digitization and becoming data-driven," said Bernard Lahousse, co-founder of Foodpairing, a startup with offices in Belgium and New York that develops digital food "maps" and algorithms to recommend food and drink combinations.

Sean McBride is the founder of DSM Strategic Communications. He is the former executive vice president of communications & membership services at the Grocery Manufacturers Association and former director of communications at the American Beverage Association. We are on the precipice of an artificial intelligence revolution. The signs are all around us. Autonomous vehicles traverse our roads, drones crisscross the sky and robots navigate grocery store aisles.

For many years, precision agriculture has largely been compartmentalized with the promise of true integration being just out of reach. Now, through the use of super-computer Watson and artificial intelligence (AI), IBM says it will draw all the disparate bits together into a whole that will usher in a more efficient, easier manner of growing crops and doing business through the agricultural chain. To explain the company's vision and what it expects in the near future, Mark Gildersleeve, vice president, Head of Business Solutions Watson Media and Weather, spoke with Delta Farm Press in late November. "As a personal story, I did a startup in precision agriculture in the late 1990s that ended up getting sold. Coming back to agriculture now, I was surprised at how little progress had been made over the last 25 years. "The problems that farmers have are still here.

Computer vision is infiltrating just about every industry -- to bring analytics to retailers' shelves, identify early signs of Alzheimer's, and even help security cameras identify weapons. Then there is ImpactVision, which is leveraging machine learning and hyperspectral imaging, a technique that combines spectroscopy and computer vision, to automatically assess the quality of food in factories and elsewhere. The San Francisco-based startup previously raised around $1.6 million in funding, and today it's lifting the lid on another $1.3 million in a round led by transport and logistics giant Maersk. Participants in the round include the Yield Lab, Acre Venture Partners, AgFunder, and Xandex Ventures. A spokesperson told VentureBeat that the fresh funds will be used to "accelerate product development" and grow the company's sales and engineering teams.

When you think about artificial intelligence (AI), you probably don't imagine using it for a farm. IBM is bringing data and AI together with the global release of the Watson Decision Platform for Agriculture to help growers makes better decisions. This new platform is an innovation that draws upon IBM's most advanced capabilities in AI, analytics, IoT, Cloud, and weather to create a powerful new resource that spans the full farm-to-fork ecosystem. Farming has always been a complex undertaking that requires growers to manage an interconnected web of pre-season and in-season decisions while at the mercy of mother nature. With the explosion of data from farm equipment, environmental sensors, and remote input, it's impractical to rely on intuition or traditional technology to understand what drives variation in yield or provide guidance to growers.

When you think about artificial intelligence (AI), you probably don't imagine using it for a farm. But you should: this week, IBM is bringing data and AI together with the global release of the Watson Decision Platform for Agriculture to help growers and enterprises make better decisions. This new platform is an innovation that draws upon IBM's most advanced capabilities in AI, analytics, IoT, Cloud, and weather to create a suite of solutions that span the farm-to-fork ecosystem. Farming has always been a complex undertaking that requires growers to manage an interconnected web of pre-season and in-season decisions while at the mercy of mother nature. With the explosion of data from farm equipment, environmental sensors, and remote input, it's impractical to rely on intuition or traditional technology to understand what drives variation in yield or provide guidance to growers.