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The Spoon

Daily news and analysis about the food tech revolution

machine learning

Intello Labs Uses AI to Help Farmers Get a Fair Price for Their Crops

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. A poor, rural, farmer may not be educated on price points or what “fresh” produce means to a buyer. As a result, they may want to sell tomatoes at a dollar per tomato, but buyers may scoff, refuting the quality of those tomatoes, and only offer fifty cents. How are they to know how much the literal fruits of their labor are actually worth?

Intello Labs is working to help balance these scales through a combination of computer vision and artificial intelligence. Using their mobile phone app, the tomato farmer could take a picture of a bushel of tomatoes and upload it into Intello’s system. The company’s algorithms would examine the photo of the tomatoes and gives it a rating based on a set of government (i.e. USDA) or other criteria. With this objective, algorithmic rating in place, each party in the negotiation now knows the quality of the tomatoes being sold — and they can be priced accordingly.

The company started with commodities like tomatoes and potatoes, but according to Sreevidya Ghantasala, Intello Labs Head of U.S. Operations, the company’s core technology can be customized for almost any food. It could be used to rate products like seafood and chicken, or even as a tool for plant disease identification. “We have a pest and disease application for six or seven different crops,” said Ghantasala, “Our system is highly customizeable. If there’s something we don’t see on our library, we can update it in 2 to 3 months.”

Intello, which is headquartered in Bengaluru, India, has already gone live elsewhere in that country at the farmer’s market in Rajasthan to work with 10,000 farmers there for wheat and grain analysis. The company has also worked with the Reliance Foundation in India to help 100,000 farmers with pest and disease detection for crops.

Pricing for Intello’s software is subscription based, and Ghantasala wouldn’t provide specific numbers. She said that cost was dependent on what was being analyzed, and what users want to use it to detect. The company was founded in May 2016, and has raised money through friends, families and various different accelerator programs. It now has 30 employees across offices in Bengaluru, Stockholm, Sweden and Plano, Texas.

Intello isn’t the only one using computer vision and AI to generate objective food ratings. Here in the U.S., AgShift is using a similar mobile phone app to provide better data for food buyers in the supply chain to help reduce food waste. And grocery giant Walmart has implemented its own machine learning-based Eden technology to assess food freshness.

But according to Ghantasala, Intello’s ambitions go beyond food altogether. The company is working with gas and oil companies in Sweden to apply its computer vision to parts identification, and they want to expand its vision into hyperspectral imaging for more in-depth analysis.

Intello, it seems, wants to use its AI to change the world. But for now, it’s changing the world for one farmer at a time.

Motorleaf Uses AI to Predict Crop Yields for Indoor Farmers & Greenhouse Growers

Between unpredictable weather, pests, and degrading soil quality, farming is an extremely difficult way to make a living. Indoor farming, though less weather-dependent, carries its own set of burdens.

Montreal-based startup Motorleaf wants to lighten the lift for indoor farmers by improving crop yield predictions and optimizing growing conditions. The company hopes that their software, which CEO and co-founder Ally Monk likens to a “virtual agronomist,” will take the uncertainty out of farming.

To do this, Motorleaf first gathers data on the grow environment through machine vision, agricultural sensors, and historical information. It then applies algorithms and AI to help farmers determine the adjustments they need to make to the indoor grow environment to optimize their crop. Which means farmers can monitor CO2 levels, light spectrum, and other atmospheric conditions remotely through wireless devices or laptops.

Customers can opt to install Motorleaf’s own hardware (a suite of IoT-enabled sensors), though they can also just connect the Motorleaf’s software to the farm’s existing pre-installed hardware to measure and remotely adjust environmental inputs. Its interoperability makes Motorleaf an easy tool for larger greenhouse operations, ones who already have their own monitoring hardware in place, to install.  “At the end of the day, we are a software company,” said Monk.

Motorleaf isn’t the only company helping indoor farmers help manage the lifecycle of their crop. In fact, it’s not even the only company which sees itself as a “virtual agronomist.” What sets it apart, however, is its ability to predict crop yield. Monk claims that motorleaf is the first company to use AI and machine learning to increase the accuracy of yield estimations.

This is a lot more important than an average person (read: the author) might think. Commercial greenhouses pre-sell produce before their harvest based on estimations given by agronomists — though they’re not always accurate. It’s extremely difficult to guarantee the quantity or quality of their crop before it’s harvested, and miscalculations can lead to loss of profits for both the buyer and producer, and also generate huge amounts of food waste.

Motorleaf claims that their software can cut yield prediction error by more than half — at least for some crops. Monk explained that each plant needs its own specialized software for yield prediction, likening farming to a recipe. “Maybe they think there’s a right recipe to growing kale; they need this many nutrients, this much light,” he explained. “We very strongly disagree with that. We think that any farming protocol needs to be dynamic, because if something happens in a greenhouse — which happens all the time — why would you stay rigid? You have to adapt.”

So far, their AI has only been proven to work for estimating tomato yield. However, they’re also deploying algorithms for peppers and silently developing technology for five other crops.

Photo: Motorleaf.

I was surprised to learn that indoor farming environments could be so volatile. After all, that’s the whole point of bringing them indoors, right? Apparently, not so. Monk explained that variable factors like sunlight, outside air temperatures, and human error can all affect greenhouse conditions. Even the plants themselves can do unexpected things that can affect their climate change.

Motorleaf got $100,000 Canadian dollars from the FounderFuel accelerator in the summer of 2016, and later that month Motorleaf raised $850,000 (US) for their seed round of funding. The startup is currently working with clients in Canada, USA, South Africa, South America, Mexico, Holland, Poland, New Zealand and the UK, and aims to be in Spain, France and Germany by early 2019.

Monk concluded our call with what he called “a crazy thought,” one he had when he saw celebrity-branded color palettes. “Why can’t I have a Jamie Oliver taste palette? Why can’t I buy a radish that’s the exact kind he likes to cook with?“ he asked. Farmers could use Motorleaf’s software to manipulate crops into having a certain taste and look, one that would be specific to, and branded by, celebrity chefs. Consumers could purchase produce that had the same taste profile as those preferred by their favorite chefs, and even integrate them into those chef’s recipes.

In the age of celebrity-branded meal kits and baking mixes, this idea isn’t too far-fetched. We’ve even seen companies like Bowery use AI to tweak the flavor, taste, and color of fruits and vegetables.

Motorleaf hasn’t started developing any of this technology yet, but Monk used it chiefly as an example to show how AI can open up “a whole slew of possibilities” for farming. He hopes that one of its applications will be to take the unpredictability out of farming, and put the power back in the hands of the growers.

Ingest.AI Unifies Disparate Data to Run Restaurants more Efficiently

If you learn one thing while covering restaurant software companies, it’s that there are a lot of restaurant software companies. Payment systems, HR, inventory management. Not to mention all of the software applications built on top of those like GrubHub, OpenTable, and a host of others.

The problem is that none of these systems talk to one another, so useful data sits in silos, unable to integrate and deliver holistic, business-wide insights for restaurants. The result can be inefficiencies that cause wastes of human capital and food.

To solve this, Kenneth Kuo founded and is CEO of Ingest.AI, a software layer that plugs into all these disparate restaurant systems, uses machine learning to extract data from all of them, and unifies them into a single platform.

“We clean, classify and aggregate all the data to prep it for our second set of machine learning,” said Kuo.

Because Ingest.AI accesses and combines data from every part of the store, it can then tell a manager what will happen at a restaurant at any given time slice with “upwards of 90 percent accuracy.” This allows the manager to properly order the right amount of food and schedule staff accordingly.

And proper staffing can be a huge headache for a manager, especially in states that have high minimum wage and punitive overtime laws. Ingest.AI can make dynamic staffing suggestions to deliver alerts when workers are nearing overtime, and it can schedule around that or ensure there aren’t too many or too little servers at any given time.

With its predictive analytics, Ingest.AI can also help in the back of the house with proper ordering. It knows when a particular ingredient is running low as well as how long it takes a vendor to make deliveries. With this info, Ingest.AI can automate the ordering so restaurants have enough inventory on hand in anticipation of busy times.

Ingest.AI can also make smaller tweaks throughout the dining experience to increase incremental revenue. It will know, for example, that when parties of six or more people sit down, the first thing they do is order beer. The software will send out a notification to servers to suggest that the first thing they say to customers is “Hello, what kind of beer can we get for you, here’s what we have on tap…”

Restaurant managers aren’t typically data scientists, and connecting data from every aspect of the house all at once could quickly set them adrift in a sea of numbers. But Kuo is cognizant of this, and says he basically wants to answer two questions for the restaurant manager: “1. Did I make money? 2. Am I going to go under in the next week?”

You’d be forgiven for thinking was all too good to be true. A magical AI layer that can talk to and predict just about anything in your restaurant saving you time and money. It has a whiff of software snake oil. But Kuo has bona fides when it comes to artificial intelligence: Prior to his startup, he worked on IBM Watson using natural language to deliver personality insights.

There are two things that stand out for me when thinking about Ingest.AI. First, it has the capacity to replace a lot of other restaurant software startups out there. Ordermark unifies orders from different delivery services and Gebni provides dynamic pricing on menu items — but that’s all they do. Ingest.AI does those bits plus a lot more.

And second, honestly the food industry could be just the beginning for Ingest.AI. Every company I’ve worked for uses multiple software applications (Slack, Salesforce, Braintree, Workday, etc.) that don’t talk directly with one another. If Ingest.AI works as promised, there’s no reason it couldn’t expand beyond restaurants into any vertical.

But that is further down the road. Right now Ingest.AI is bootstrapped, based in Manhattan and was just inducted into the latest Food-X cohort. The company has nearly twenty customers paying anywhere from $150 – $250 per month for the service. Kuo says that it has a few deals in the pipeline and after that they will begin looking for a $1 – $1.5 million round of funding around November of this year.

So sure, there are a lot of restaurant software startups out there, but Ingest.AI seems like one to watch.

AgShift Raises $2M, Reveals RJO as First Client

Today AgShift, a startup that uses computer vision and deep learning to reduce food waste, announced its first client: RJO Produce Marketing. This news comes just days after AgShift raised $2 million in its first seed round

Agshift uses its technology to attack food waste generated in the supply chain. Right now food inspection is done manually at different points along the food system, with workers literally eyeballing product to assess its quality using their own judgment, which can vary.

“The food supply chain is fragmented,” said Miku Jha, Founder and CEO of AgShift. “Inspections are done by different people at different points.” The results, according to Jha, are “subjective and inconsistent.” One person’s Grade A is another person’s Grade B.

Jha wants to take the subjectivity out of this process with — what else? — a mobile phone app. Instead of just looking at a piece of fruit, inspectors at wholesalers and distributors hold the produce up to the phone’s camera and take a picture (like depositing a check via mobile app, the software guides you for proper positioning). AgShift’s software in the cloud analyzes that image to quantify its bruising, color distribution, average size, mold, etc. to determine its quality.

Using the USDA’s produce guidelines (or a customized set of specifications), AgShift says its software can objectively give fruit its proper rating, and provide precise reasons why it made that choice. According to Jha, this level of consistency throughout food supply chain will deliver higher-quality produce to consumers.

It will also reduce food waste on multiple fronts. AgShift analyzes color distribution better than the human eye. So if, for example, it sees some strawberries that are 90 percent dark red at a shipping point, it can tell suppliers that it is more ripe. Then the suppliers can divert those riper berries to closer destinations, rather than running the risk of them getting spoiled on a cross-country trip.

AgShift can also reduce food waste by removing human judgment from the equation. Right now, vendors and buyers might dispute the rating of a food shipment. This can lead to canceled orders and food getting thrown out. With a computer generated rating, lower quality food can be assessed earlier in the supply chain and re-sold at a lower price or re-purposed, rather than discarded.

Industry watchers may note that AgShift sounds a lot like the Eden technology Walmart recently rolled out at its grocery stores to prevent food waste. Walmart is obviously a giant and a master of the supply chain, but Eden appears to be Walmart-specific. AgShift’s agnostic platform will give it a broad range of potential customers.

AgShift’s platform is already in trial use by a number of companies, the first of which to be publicly announced is RJO Produce Marketing. According to the press release, RJO provides “quality assurance inspections, in-depth market analysis and category management services for key perishable commodities.”

While the two million raised by AgShift is a rounding error for a company like Walmart, it’s just the start for this startup. The Sunnyvale-based company was founded two years ago and currently has 12 people working in their California and India offices. Jha said the money will be used to fund R&D and expedite the product.

Jha’s mission with AgShift is a global one. As she points out, we spend a lot of time talking about growing more food for a growing population — but a good first step is reducing the amount of food we waste right now.

Farmstead Raises $2M, Releases FreshAI for Retail Food Management

It’s only Tuesday and already it has been a busy week for Farmstead, the startup that uses a combination of machine learning and small food hubs to enable home grocery delivery. On Monday, the company announced it had raised a $2 million seed round, and today, Farmstead opened up its FreshAI predictive analytics software platform to external food companies.

Farmstead is trying to create an entirely new model for grocery stores by limiting its physical footprint and using AI to intelligently curate its inventory. If successful, Farmstead Co-Founder and CEO, Pradeep Elankumaran, believes his company can drastically reduce the huge food waste problem in this country (where 40 percent of our food is never eaten).

Traditional grocery stores are massive buildings stocked to the gills to provide shoppers with a ton of options. Farmstead, on the other hand, uses food “microhubs” that are only about two to three thousand square feet and carry only about 1,000 items across all shopping categories.

It can get away with carrying such a small inventory because its AI platform predicts the items customers will want and orders just the right amount. To do this, Farmstead combines historical sales data and current trend data with consumer recommendations and external factors, such as holidays and product sell-by dates. Its machine learning processes these data points to assess which items to carry and how many it should stock at any given time.

“The system gives us a very fine prediction for stock,” Farmstead Co-Founder and CEO, Pradeep Elankumaran told me. “We want to have just enough on the shelves so no new customers walk away disappointed, but not so much that product gets wasted.”

Because Farmstead microhubs are small, they can be implemented closer to residential neighborhoods rather than warehouse districts to facilitate faster home delivery. Farmstead currently operates in the Bay Area and offers one-hour delivery from 8:00 a.m. to 9:30 p.m. for people in San Francisco, as well as rolling delivery hours during the morning, afternoon and evening for those in the elsewhere in the Bay. You can also drive to the Farmstead hub in San Francisco or San Mateo and a worker will run out and put your groceries in the car.

Elankumaran said that Farmstead is now making 1,500 – 1,600 deliveries per week. With this $2 million round, Farmstead has raised a total of $4.8 million so far. It will use the new money to scale its delivery service and expand beyond the Bay Area and will build out its core AI technology.

Which brings us back to today’s news about FreshAI. Farmstead is opening up its predictive analytics platform to supermarkets, cafeterias, restaurant chains and other companies that work with food at scale, allowing them to use Farmstead’s automated inventory management system. Farmstead says that using FreshAI has reduced its perishable food waste to sub ten percent as opposed to “35 – 40 percent average perishable food waste for typical supermarkets.”

For now, FreshAI is a pilot program, and companies can apply to take part on Farmstead’s site. If accepted, companies will upload operations data, and Fresh AI will provide daily and weekly order recommendations.

Farmstead is part of a new generation of startups such as Spoiler Alert, Ovie, and LeanPath that are using technology to combat food waste. It’s also reminiscent of CommonSense Robotics, which is using a combination of robots and micro-fulfillment centers. But Elankumaran said Farmstead is still a ways from using robots in their hubs or to fulfill deliveries. Instead, he said that they’re focusing on getting the best possible customer experience.

If Farmstead can deliver on that experience as it scales out, the busy weeks won’t be ending anytime soon for Elankumaran or his company.

SomaDetect Uses AI to Help Dairy Farmers Improve their Milk

There is a milk glut in the U.S.. Technology has allowed dairy farmers to produce more milk than ever, but all this abundance has caused milk prices to plummet. It’s getting so bad that some farmers face selling off their cows.

While technology helped create this crisis, perhaps SomaDetect‘s technology can help struggling dairy farmers get out of it.

SomaDetect uses a combination of optical sensors and machine learning to help dairy farmers analyze the milk each cow produces to determine its quality. As SomaDetect CEO Bethany Deshpande explained it to me, farmers attach a small sensor box to the milking hose, which shines a light through the milk as it flows. Based on the scatter pattern of that light coming through, SomaDetect’s software can analyze what’s in the milk.

The company measures fat and protein as well the reproductive status of a cow and any residual antibiotics. More importantly, SomaDetect can look at somatic cells to detect Mastitis, a serious bacterial infection of the udder that is the most common disease among dairy cows and the number one cause of their early death. By quickly identifying cows with high somatic cell counts, farmers can better target treatment and to help prevent the spread of Mastitis.

Additionally, one cow can throw off somatic counts for an entire batch of product. By removing high-somatic cows, the farmer can lower the overall somatic counts of their milk and earn more money: “Farmers with low somatic cell counts get a bonus from the milk processor,” said Deshpande.

This type of deep inspection of milk has only recently become possible. According to Deshpande, the optical technology has “been around for a hundred years,” but advances in computer vision and machine learning means SomaDetect’s software can analyze vast sums of information in ways that were not possible even five years ago.

SomaDetect isn’t the only company using light to help dairy farmers. EIO Diagnostics uses multispectral imaging for Somatic cell counting, and Consumer Physics is putting its handheld SCiO device to use on farms to detect levels of dry matter in cow feed, which can also impact milk production.

SomaDetect was founded in 2016 in New Brunswick, Canada. The company won a 43North startup competition, earning them $1 million in funding and office space in Buffalo, NY. SomaDetect makes money by selling the equipment to farmers and charging $5 per month per cow for the software. The company is currently running pilot programs and is searching for seed funding as it looks to expand into New York state.

Deshpande says she came from the dairy side of the industry and wants SomaDetect to work closely with farmers. With milk prices expected to stay low throughout this year, dairy farmers could use all the help they can get.

You can hear about SomaDetect in our daily spoon podcast.  You can also subscribe in Apple podcasts or through our Amazon Alexa skill. 

Aquabyte Casts its Machine Learning to Improve Fish Farming

It was estimated at one point that Dwayne “The Rock” Johnson ate upwards of 821 pounds of cod per year. He’s certainly an outlier, but as global demands for seafood increase, fish farms are rising to meet that challenge, with the aquaculture market projected to reach $219.42 billion by 2022. Already, half the seafood eaten in the U.S. is farmed, and a startup called Aquabyte is using machine learning and computer vision to make those farms more efficient and productive.

Using cameras mounted in fish farm pens, Aquabyte’s software monitors data such as the fishes’ biomass, feed consumption and sea lice counts. Armed with this data, fish farmers can better understand their inventory, optimize the feed process and maintain regulatory compliance to reduce harmful impact on the surrounding environment.

Aquabyte takes the data from the pen cameras and applies it to models developed by fish nutritionists to determine the optimal amount of feed to distribute, which is a percentage of the fishes’ size. Aquabyte’s software can better detect the biomass of the fish in pens, and can also “watch” the pellets fall through the water to determine how much of them the fish are eating.

Using its software, Aquabyte claims that farmers can save money by not overfeeding, and also promises to eliminate over and underselling by giving them a better sense of how many fish they will actually produce for sale.

Aquabyte’s computer vision and machine learning can also yield a positive impact on the local environments of fish farms. One of the ways it does this is through quantifying sea lice, which can be a destructive force in the close quarters of a fish farm. Not only can they destroy a farmer’s inventory, but sea lice can get out and attach themselves onto passing wild fish, eventually killing the native fish populations. This problem has gotten so bad that Norway has enacted regulations forcing farms to control the number of sea lice in their pens or face heavy penalties.

Traditionally, sea lice quantification is done manually by netting fish out of the water and counting the lice by hand. Aquabyte’s software, however, can automate this process to keep fish farms within regulatory compliance, without requiring anyone to hand-count sea lice.

Aquabyte was founded just under a year ago and has offices in San Francisco and Bergen, Norway, where its software is currently running on a few fish farms. (Between this and Hatch, Bergen is becoming a hotbed of aquaculture tech.)

Aquabyte is targeting salmon farming in Norway to start, and the company just raised a $3.5 million seed funding round last week. They will use the money to hire out a team of software engineers in San Francisco as it works to bring the commercial version of its software platform to market.

Future versions of the software will work in other parts of the world and with other fish such as trout, sea bass, and hopefully–assuming The Rock’s appetites don’t diminish–cod.

You can hear about Aquabyte in our daily spoon podcast.  You can also subscribe in Apple podcasts or through our Amazon Alexa skill. 

Dishq Uses Machine Learning for Bespoke Food Recommendations

This is embarrassing to admit, but whenever I go to a Thai restaurant, I just order tofu phad thai. Always. Yes, that is totally generic, but I don’t get to eat out that often, so I don’t want to take a chance on something that I might really dislike. I know what I’m getting with the phad thai, so I settle.

It’s that settling for the same-ole that Dishq is looking to improve by using artificial intelligence. Based in Bangalore, India, Dishq provides APIs for food service companies like restaurants, corporate cafeterias or food delivery services, so those companies can implement AI-powered, customized food suggestions for their customers.

During an interview, Dishq Co-Founder and CEO Kishan Vasani told me there are four parts of his company’s offering:

  • A database of more than 100,000 dishes that are broken down into 26 different attributes including ingredients, cuisine style and cuisine origin
  • Anonymized customer behavior analytics data
  • A machine learning algorithm
  • Food science research from around the world that feeds into Dishq’s algorithm

Vasani said that the collaborative filtering used by Amazon or Netflix to make suggestions won’t work for food because meals are such a personal experience. “Too many things go into it,” said Vasani, “What you like, where you grew up, who you’re with.” He says that Dishq’s deep data-driven approach allows for truly bespoke recommendations because it understands food at the flavor compound level as well as transaction history.

Where the customer encounters those recommendations are up to the company using Dishq’s API. It can be used at the menu level to surface suggestions or email notifications. It depends on whether that client is looking for more conversions, increased average order value, or just creating a better customer experience.

Right now Vasani says that Dishq has 6 clients with two million recommendations generated every month, and that clients see an 11 percent uplift in revenue with Dishq.

Sadly, Dishq can’t offer a universal taste passport that travels from restaurant to restaurant. So what you like at Domino’s would help them determine what you might like at Dunkin’ Donuts. The reason for that, Vasani says is data protection and restaurants not wanting to play nice with one another.

Founded in 2015, Dishq has 13 employees and has raised $160,000 to date, with $120,000 of that coming from the Hong Kong-based AI accelerator Zeroth.ai. Vasani says he plans to start looking for new funds in Q2 of this year.

Vasani is also looking to expand beyond food service clientele and into the consumer packaged goods. He refers to the forthcoming product as “Taste analytics as a service,” and would allow CPG companies to react more quickly to food trends as they are happening. For example, if avocados were suddenly appearing everywhere on Instagram and social media mentions around Nashville, Dishq’s data could help a CPG company spot and understand those trends to quickly ramp up some avocado-related product for that location.

Until then, however, Dishq just wants to make eating out more pleasurable. Vasani wants Dishq’s recommendations to “shift people’s experience from a 6 out of 10 to an 8 or 9,” said Vasani. If Dishq works as promised, that could mean a lot less phad thai for me in the future.

Tyler Florence has Written His Last Cookbook (Because Smart Kitchen!)

Cookbooks are a thing of the past for Tyler Florence. Though the celebrity chef has penned 16 cookbooks (and 20,000 recipes), Florence proclaimed “I believe recipes are dead” from the stage of our recent Smart Kitchen Summit.

Instead, Florence believes that the connected kitchen, along with machine learning, will usher in a new era of “micro cooking content” that in effect turns the recipe inside out, and he’s joined up with tech company Innit to make that happen.

Traditionally, recipes are top down dictating what you need to get in order to make a standardized meal. Florence and Innit have been stealthily working on an app (due out this December) that instead starts with what you already have and customizes the cooking for you.

In the app, you’ll be able to select the protein you have, the sauce you want, the vegetable you have as well as a carbohydrate. From there, Florence has created thousands of hours of video cooking footage and the app algorithmically searches through all this content and stitches together a guide on demand.

You should listen to his full talk (presented here) from the session, as Florence seems to really get how kitchen tech can fundamentally shift the way everyday people can become better cooks. He’s also forward thinking, musing about the role voice assistants and artificial intelligence will play in crafting meals that are tailored to your tastes without demanding too much of your time.

More Than Hot Dogs: Pinterest & Google Image Recognition AI Make A ‘Shazam For Food’ Possible

In this season of Silicon Valley, one story line has housemate and programmer Jian-Yang developing a food recognition app called ‘See Food.’ Since the idea was born out of a spitballed pitch for a “Shazam for Food” by Yang’s landlord Erlich Bachman, it’s not altogether surprising that when Jian-Yang finally gets around to hacking together the app, it’s only good for one thing: telling us whether whatever is in front of the camera is a hot dog or not a hot dog (and yes, the once-fake app is now a real fake app you can now download for real in the app store).

Silicon Valley: Not Hotdog (Season 4 Episode 4 Clip) | HBO

While a “Shazam for Food” pitch seems like the perfect sendup concept for a satirical show about the tech world, the truth is there has been significant advances in machine vision and learning in the past few years that make food recognition a very real and potentially useful application.

These advances were on display this month by both Google and Pinterest as they both touted image recognition services called “Lens”.  While Pinterest has been working on image search since at least 2015, they rolled out their Lens this past February. In a blog post, Pinterest CEO Evan Sharp highlighted a food use-case as an example of how Lens could work.

“You can also use Lens with food,” wrote Sharp. “Just point it at broccoli or a pomegranate to see what recipes come up.”

And this week, after Google launched a similar feature with the same name, Pinterest apparently felt they needed to emphasize the food recognition capability of their Lens offering in the form of a new blog post that repeated what Sharp told us in February: users can use Lens to serve up recipe suggestions.

“Our visual discovery technology already recognizes objects in more than 750 categories, and people have been busily pointing Lens beta at everything from lemons to strawberries to find new recipes to try. And now we’re rolling out a way for you to Lens an entire dish and get recipes to recreate the meal.”

Google introduced its Lens image recognition technology last week at its annual developer conference, Google I/O. Not that Google is new to image AI or even food recognition. The company has been working on image search for probably close to a decade, and in 2015 introduced an app called Im2Calories that gave calorie estimates of food based on image analysis of food.  And while Google didn’t highlight any specific food use cases for their version of Lens at I/O, there’s no doubt that the company and its partners will explore using image AI to surface information such as recommendations for recipes like Pinterest or nutritional information (like Im2calories).

Of course, all of this follows Amazon’s recent push into image AI with the debut of its own camera-enabled Echo devices and the continued maturation of its AWS AI service, Rekognition. My guess is that as big players double down on voice assistant services, image recognition and analysis has reached a maturation point that makes it ready for consumer applications.

While clumsy efforts at food recognition like the Smart Plate  – as well as two big companies launching an image recognition service with the same exact name – make food image recognition a ready topic for satire, the reality is the technology is reaching a point of maturity and usefulness that maybe – just maybe – we’ll soon have a Shazam for Food that consumers really want.

Food AI Startup Gets A Boost From Bono

The celebrity turned investor trope isn’t a new story; from Leo DiCaprio to Magic Johnson, the rich and famous have often turned to the tech world either as an investor and sometimes as a founder. Recently, we’ve seen some high-profile investments in the food tech space – including actor Ashton Kutcher and his investment in Keurig-like juice startup Juicero.

The latest celebrities to jump on the food tech train are none other than U2 frontman Bono and the band’s beloved drummer The Edge. The lucky startup? Irish AI and DNA food startup Nuritas, a company working to find combinations of elements within our food and develop supplements that could act as cures to common diseases.

Nuritas joins a wave of companies launching with the plan to study and use DNA analysis to help people live healthier lives. Startups like Habit take a similar approach but use biological data to deliver customized health and dietary plans for users, tackling their nutritional needs at the molecular level.

Nuritas is currently awaiting patents for its technology which uses a combination of artificial intelligence and DNA analysis to dive deep into billions of molecules and extract components that can benefit human health. The company calls these compounds “bioactive peptides” and claims they can be used to manage a host of issues, including inflammation and potentially blood sugar levels in diabetics.

The U2 frontmen are joined by CEO and founder of Salesforce Marc Benioff along with tech entrepreneur Ali Partovi as early investors in Nuritas’ seed round of funding. Bono is no stranger to successful tech investment, having poured money into both Facebook and Dropbox in their early days.

Artificial intelligence is one of the hottest areas of tech – 2016 was a huge year for machine learning and the technology is finding its way into every vertical. The potential for AI influence on our food – from the ways we eat and cook to the types of food we consume along with the commerce, storage and healthcare implications that accompany these changes – is enormous.

Read more about Nuritas’ plan for expansion and the investment from U2.