When we talk about artificial intelligence (AI), we often speak in giant, world-shifting terms about revolutionizing a certain industry. But AI can also benefit a single person at a time. In the case of Intello Labs, its AI can be used to help prevent a poor farmer from getting screwed. Food inspection is often still done manually. One person's perfect tomato may be another's piece of trash, and these basic biases can lead to an imbalance of power.
There are two global hotspots in the development of artificial intelligence: Silicon Valley and China. Today, the European Commission will lay out its plan to catch up, publishing a document outlining its long-term AI strategy. It casts Europe as a middle ground, with a stance somewhere between the state-backed firms of China and America's data-rich tech outfits, such as Google. The plan emphasises digital rights and ethics--the commission promises an ethics charter next year, and a global push for co-operation on standards co-ordinated through bodies such as the UN and the OECD. It also commits to spending €110m ($134m) to set up repositories of data and algorithms available to smallish firms getting started in AI.
Anyone who regularly calls for takeout has likely had an order screwed up due to human error. You want two large pizzas, extra pepperoni, light cheese, and instead, your pies arrive light on the pepperoni and heavy on the mozzarella. These situations can be frustrating, not least because of the time involved. If you decide to reject the order, you have to call the company back, then wait for the correct (hopefully) meal to show up. That's the sort of hassle that sometimes leads to people picking the unwanted pickles off their burgers, or deciding that they can live with the General Tso when we wanted the Kung Pao.
Machine learning is a great way to extract maximum predictive or categorization value from a large volume of structured data. The idea (at least for "supervised learning," by far the most common type in business) is to train a model on a one set of labeled data and then use the resulting models to make predictions or classifications on data where we don't know the outcome. The approach works well in concept, but it can be labor-intensive to develop and deploy the models. One company, however, is rapidly developing a "machine learning machine" that can build and deploy very large numbers of models with relatively little human intervention. You may have heard of dunnhumby, a UK-based analytics company that's owned by the big retailer Tesco.
Agriculture companies are always striving to produce better tasting, longer lasting fruits and vegetables. Whether it's corn or berries, produce diminishes in value the minute it goes from the stalk or vine to the market. Driscoll's, a $3.5 billion provider of berry plants, is turning to emerging technologies, such as artificial intelligence (AI), machine learning (ML), the internet of things (IoT) and blockchain, to produce hardier plants and fortify its supply chain. "We're just scratching the surface on building an integrated data platform strategy that will take advantage of artificial intelligence and machine learning, both for R&D genetics and on the value chain of fruits as well as business operations," Driscoll's CIO Tom Cullen tells CIO.com. Driscoll's develops and leases strains of berry nursery plants -- strawberries, blueberries, blackberries and raspberries -- to growers around the world, from the Americas to New Zealand, China and Australia.
There has been much speculation since Amazon's acquisition of Whole Foods was announced in June. Commentators have been debating what Amazon will do with that footprint or customer base. Is this hopeful or the end of civilization? While Whole Foods has already dropped prices and begun stocking Amazon Echoes next to the produce, there's likely to be a far greater transformation to come. I'd like to pose a thought to my fellow marketers: Conditions are forming for a massive tsunami to hit the food marketing industry.
The episode highlights the risks large corporations run when they tie their brands so closely to social messaging. In 2015, then-CEO Howard Schultz shrugged of the "Race Together" fiasco as well-intention mistake and pressed on with his public efforts to engage in the debate over race in America. His successor, Kevin Johnson, is now scrambling to keep the Philadelphia incident from shattering the message Schultz was going for: Starbucks is a corporation that stands for something beyond profit.
Innovative food-related gadgets and practices don't always have to rely on things like sensors, apps, and machine learning to have a positive impact. In fact, in some parts of the world, these "low-tech" (that is, technologically simple) solutions are often all that's needed to prevent waste, improve farming practices, and even boost the local economy. That is to say that low tech, while maybe not as alluring as, say, using sensors to save the bees, plays a bigger role in advancing food than one might initially think. Their simplicity is effective, and often just as interesting, or at least thought provoking, as a high-tech alternative. Consider fermentation, specifically as a way to curb food waste.
The area that intrigued me the most was in enhancing customer experiences. Mathers elaborates, "We've seen consumer companies like L'Oreal, Whole Foods, and a membership club in the wine and spirits space, innovating how they can simplify, improve and remove barriers to purchase. L'Oreal has built tools into their mobile app so you can apply cosmetics while standing in a drug store and see whether it's the right shade for you. Whole Foods is using some of their tools with their recipes site so you can pop in a couple of ingredients that you have and it starts pushing you a couple more things that you might want to buy in the store. So, growing basket size or helping people expand their grocery list if they're meal planning.
As computers integrated into everyday life, a romanticism emerged: the idea that they might be able to do everything perfectly--from handling your finances to even finding you a mate. And as the field of artificial intelligence continues to grow, a brewery in Virginia has even used this technology to create what it hopes could be the perfect IPA--and the methodology they used is certainly intriguing. Charlottesville's Champion Brewing company recently teamed up with the nearby machine learning company Metis Machine to brew their new ML IPA--a computer's vision of what should essentially be the ideal IPA. And since the project is based in science, Champion was very specific about what data it chose to feed into the computer. "We provided the parameters on which IPAs are judged at the Great American Beer Festival (SRM, ABV, IBU) and matched that range with the 10-best-selling IPAs nationally, as well as the 10 worst selling IPAs at a local retailer and Metis came up with the results," Hunter Smith, owner of Champion Brewing Company said announcing the beer.