Eye on AI - June 28, 2019
Welcome to Aigora's "Eye on AI" series, where we round up exciting news at the intersection of consumer science and artificial intelligence!
This week, the theme is AI and food! This theme takes three forms - food production, food creation, and food delivery and disposal.
Jumping right in with food production, we start with an excellent Nexus Media review article written by Jeremy Deaton on precision farming. Featured prominently in the article is Daniel Koppel, CEO of Prospera. According to Deaton:
Koppel believes that artificial intelligence will usher in the next great agricultural revolution. Previous technological advances — irrigation, mechanization, synthetic fertilizers, genetic engineering — have allowed humans to grow more food with less work. He said that artificial intelligence is going to allow growers to be even more efficient by taking the guesswork out of farming.
The article also includes some meditations on the social and ethical implications of the use of AI in agriculture and is worth a read.
Next, supporting precision agriculture are advances in "soft robots," which are robots with the ability to interface gently with the world. Such capability is essential for harvesting many foods. Science Daily provided a look at one advance in soft robotics, the development of the first "soft ring oscillator." The article may be a bit technical for some readers, but it's good to be aware of the technological advances that could fundamentally change points upstream in the supply chain.
Finally, rounding out the food production section of this article, TechCrunch reported this week on the business partnership between Farmwise and Roush to build autonomous weeders.
We begin the food preparation section of this blog post with a piece that bridges the gap between production and preparation. This piece, published in Forbes, is by one of my favorite AI influencers, Bernard Marr. Marr, the author of the recently published "Artificial Intelligence in Practice," which we reviewed here, reported on lab-grown meat and cell-based seafood. According to Marr,
"Since the world’s population is expected to reach 9 billion by 2050, there is renewed focus on how to create a food supply that can sustain this amount of people without destroying our planet. According to the U.N. Food and Agriculture Organization (FAO), industrialized agriculture is responsible for a significant portion of our air pollution, biodiversity decline, climate change and land degradation. Growing food in labs is one solution that creates more food with less space and damage to the environment if we can find a way to produce it cost-effectively at scale."
Marr also discusses "Smart Food," which is nutritionally optimized along with 3D printed meat in this eye-opening article. During our lifetimes, expect to revise our entire view of what "food" means.
In other food preparation news, researchers at MIT have developed a deep neural network called "PizzaGAN" that has learned to optimize pizza construction, based only on visual information. Such a development is especially significant when one considers recent advances in pizza-making robotics, as we covered in last week's "Eye on AI." Perhaps most exciting is the generalizability of the technology. According to ZDNet:
"Though we have evaluated our model only in the context of pizza, we believe that a similar approach is promising for other types of foods that are naturally layered such as burgers, sandwiches, and salads," MIT says. "It will be interesting to see how our model performs on domains such as digital fashion shopping assistants, where a key operation is the virtual combination of different layers of clothes."
In related news, Facebook recently announced their own AI-based technique for reverse engineering recipes from food pictures, a technique they call "inverse cooking." Again, its the generalizability of the technique that's exciting - according to Facebook:
"Additionally, this kind of training can be used for any problem that requires predicting long structured text from an image and predicted keywords. The first part of the pipeline (ingredient prediction) could be applied to address broader problems, such as image-to-set prediction."
For a slightly more "digestable" report on Facebook's contribution, see this write-up by Hongxi Li in SynchedReview.
We wrap up this week with a look at food delivery and the various technological advances that are revolutionizing it.
For starters, Amazon was back in the news for its use of simulations based on highly detailed real-world data to train its autonomous delivery robots. According to Wired:
"Amazon’s mapping and simulation technology is not just a research tool. It could also help Amazon deploy the robots to new neighborhoods when they’re ready for general use, by first testing them in simulations. “We've built it such that we can scale up to an entire city,” Scott says. By the time Scout rolls into a new town for the first time, its control system will have likely “seen” every seam in the pavement thousands of times before."
Next, Wired also reported on Nuro's pizza delivery robot delivering pizzas for Domino's - together with the news we've already seen this news means that robots can design, make, and deliver your pizza now. According to Wired:
"Later this year, Nuro’s robot will start delivering Domino’s pies and cheesy breads to customers in the Houston area. .... It’s made exclusively for carrying goods—there’s nowhere for a human to fit, let alone drive. Since last year, it has been moving groceries for Kroger in Scottsdale, Arizona, and in Houston."
Domino's hopes that the delivery robot will help them with their 10,000 person driver shortage nationwide, perhaps providing another example of how fears that robots are going to take jobs away from humans are overblown. Moreover, since recent survey results from The NPD Group indicate that the majority of food delivery customers won't use a service again if their order arrives at the wrong temperature, the fact that the Nuro delivery robots can contain built-in ovens is a significant benefit.
Finally, at the end of our article, we consider the endpoint of far too much food in the world - waste. In what can only be considered a noble goal, a company called "Phood" is leveraging AI to reduce food waste. Learn more by watching this segment at Cheddar.
L’Oréal is using AI to help fight acne.
A robotic arm detects chemicals using engineered bacteria.
Walmart and Amazon want to enter your house to deliver packages. The catch is they will collect data from inside your house that they get to keep in the process.
Walmart is using AI to reduce shoplifting at self check-out.
AI is helping small retailers fight back against Amazon and Walmart.
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