Back in 2016, Juan Pablo Torres-Padilla, who has been the CEO of an artificial intelligence (AI) company in France and has held other key positions in the telecommunications and financial investment world, decided to take the opportunity to buy the historic Napa Valley 26 acre Sullivan Rutherford Estate from the Sullivan family, the custodians of that piece of land for over 40 years. It would prove to be a good partnership in terms of handing over the estate to someone who not only wanted to bring this winery more to the forefront of the Napa fine wine world but that the history and legacy would be appreciated and built upon. The estate lies on land that has a deep and rich history which goes back almost two centuries to 1821 when Mexico took over ownership of Napa Valley from Spain. Mexico divided the Napa Valley into two parts: Rancho Carne Humana in the North and Rancho Caymus in the South. Sullivan Rutherford Estate director of winemaking, Jeff Cole, said that they are "essentially in the middle of the heart of Napa Valley vineyards" since the back of the border of their estate is along the Rancho Caymus line as it is right in the middle of where the property lines of Rancho Caymus and Rancho Carne Humana meet.
Cannes Lions, the glitzy celebration of all things advertising, had been set to take place next week. But when Covid-19 hit in the spring, advertising's biggest event of the year was canceled for 2020. But that hasn't stopped ad-tech firm Cognitiv from celebrating the festival's signature tipple.A tribute to HBO hit series Silicon Valley's Not Hotdog app, Cognitiv created its own Rosé/Not Rosé app to detect whether the drink in a user's hand is indeed rosé. The app, set to be released next week, draws on machine learning to pick out the beverage's light-pink hue--or lack thereof--from a user-submitted selfie.But while the app itself offers a bit of levity, the underlying programming was far from simple, Cognitiv CEO and co-founder Jeremy Fain told Adweek. "It's one thing to train a deep-learning algorithm to identify wine. It's totally another level to develop an algorithm that can accurately discern between rosé, red wine, white wine or water."
Wine growers have a neat, if unusual, trick for making more flavorful wine--don't water the vines. Let the vines go dry right before harvest, and they will yield smaller grapes with more skin and less juice. Smaller grapes produce wine with a deeper color and more complex flavor. Trinchero Family Estates in Napa Valley, California wanted to make sure it was watering its grapes just the right amount, so they worked with Ceres Imaging to map their fields. Ceres used fixed-wing aircrafts to capture color, thermal, and infrared images of the vineyard, and they used artificial intelligence to analyze those images to see if the wine producer was overwatering its grapes.
We propose a novel neural topic model in the Wasserstein autoencoders (W AE) framework. Unlike existing variational autoencoder based models, we directly enforce Dirichlet prior on the latent document-topic vectors. We exploit the structure of the latent space and apply a suitable kernel in minimizing the Maximum Mean Discrepancy (MMD) to perform distribution matching. We discover that MMD performs much better than the Generative Adversarial Network (GAN) in matching high dimensional Dirichlet distribution. We further discover that incorporating randomness in the encoder output during training leads to significantly more coherent topics. To measure the diversity of the produced topics, we propose a simple topic uniqueness metric. Together with the widely used coherence measure NPMI, we offer a more wholistic evaluation of topic quality. Experiments on several real datasets show that our model produces significantly better topics than existing topic models.
Editor's Note: Tech Tracker looks at different technologies that are disrupting the industry and changing the way restaurants operate and interact with customers. Through a partnership with online reservation platform Resy, several critically acclaimed and buzzworthy restaurants across the country are hosting "Off Menu Week" throughout the year starting in late February. Off Menu Week was designed as an alternative to traditional restaurant weeks, which occur in various cities throughout the year. Off Menu Week, by contrast, celebrates experimentation and risk. "As diners, we crave connection to the creative people behind our favorite restaurants. We thought, let's throw out the dated premise of restaurant week and bring to life a program that's fundamentally about that connection and creativity," Resy co-founder and CEO Ben Leventhal said in a statement.
Today, PepsiCo announced that it will be rolling out a fleet of snack-carrying robots on the University of the Pacific's campus in California. The robots -- or "snackbots" -- carry snacks and beverages from the company's Hello Goodness portfolio, which includes choices like Smartfood Delight popcorn, Baked Lays, Pure Leaf Tea, and Starbucks Cold Brew drinks. Students can place their orders on the iOS app and have them delivered to select locations around the 175-acre campus between 9AM and 5PM. The snackbots are nearly identical to the other delivery machines we've seen before. They can travel 20 miles on a single charge, and they have headlights and a camera.
If walking to a regular vending machine seems too inconvenient, what if the vending machine came to you? PepsiCo is doing just that at the University of Pacific campus in Stockton, California with robots called "snackbots." Using a smartphone app, students can order quasi-healthy snacks like Baked Lays, Sunchips or a Starbucks Cold Brew (from PepsiCo's "Hello Goodness" vending platform), and have it delivered between 9 AM and 5 PM to one of 50 locations around the 175 acre campus. The autonomous snackbots, built by Y-Combinator startup Robby Technologies, can travel 20 miles on a charge, and are equipped with a camera, headlights and all-wheel drive to handle rough or wet terrain. Once it arrives, you simply release the lid, grab your snacks and close it to complete the sale. The app presumably takes care of the security and dispensing end of things.
Forget vending machines, PepsiCo is testing a way to bring snacks directly to college students. The firm says it will start making deliveries with self-driving robots at the University of the Pacific in Stockton, California. Students will be able to order Baked Lay's, SunChips or Bubly sparkling water on an app, and then meet the six-wheeled robot at more than 50 locations on campus. The Snackbots: PepsiCo says it will start making snack deliveries with the robots on Thursday. Students will be able to order Baked Lay's, SunChips or Bubly sparkling water on an app, and then meet the six-wheeled robot at more than 50 locations on campus.
Snackbot is the first snack-delivering robot in the U.S. to be backed by a major food and beverage company. The snackbot is an outdoor, self-driving robot. Students at University of the Pacific are about to have a futuristic dream come true: a robot that delivers you snacks. PepsiCo's Hello Goodness brand, which was created in 2015 to provide healthier snacks and beverages to consumers on the go, partnered with the San Francisco Bay Area-based Robby Technologies to bring this self-driving snack robot -- or "snackbot" -- to life. From 9 a.m. to 5 p.m., students at the private university in Stockton, California, can order food and drinks to one of more than 50 locations across campus through the snackbot app.
More than 80 Amazon scientists and engineers will attend this year's International Conference on Machine Learning (ICML) in Stockholm, Sweden, with 11 papers co-authored by Amazonians being presented. "ICML is one of the leading outlets for machine learning research," says Neil Lawrence, director of machine learning for Amazon's Supply Chain Optimization Technologies program. "It's a great opportunity to find out what other researchers have been up to and share some of our own learnings." At ICML, members of Lawrence's team will present a paper titled "Structured Variationally Auto-encoded Optimization," which describes a machine-learning approach to optimization, or choosing the values for variables in some process that maximize a particular outcome. The first author on the paper is Xiaoyu Lu, a graduate student at the University of Oxford who worked on the project as an intern at Amazon last summer, then returned in January to do some follow-up work.