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The Spoon

Daily news and analysis about the food tech revolution

AI

The Food Tech Show: Did The Automat Ever Really Go Away?

In this week’s episode of The Food Tech Show, we talk about those new contactless systems and compare them to a technology from long ago: the automat.

Yep, that old-school idea born in New York City a century ago is back (or maybe it never left?), showing up everywhere from restaurants to condos.

Jenn Marston waxes nostalgic about the automat and other concepts that seem to be getting a second look as the food system looks to reinvent itself in the wake of COVID-19.

We also talk about these stories in today’s podcast:

As always, you can listen to this week’s podcast on Apple Podcasts, Spotify or wherever you get podcasts. You can also download the episode direct to your device or just click play below.

Birdie Uses AI to Scour Reviews and Help Brands Understand Their Products

Thanks to our connected world, people who either love or hate a product, don’t have to keep their opinions to themselves. There is no shortage of platforms to express their thoughts.

This steady stream of opining is actually a source of fuel for Birdie, a company that uses AI to comb through product reviews and discussion forums (written in English) to surface product insights for CPGs and other other product brands.

For instance, by applying its AI to customer reviews of V8 juice, Birdie was able to show that people were often using the vegetable drink as a hangover remedy. By uncovering this data, V8 could then choose to create a specific line of drinks or marketing campaign that reaches this particular type of indulgent adult. The same idea applies to those pouches of pureed foods for toddlers. Birdie discovered that athletes and outdoors people carried these with them because they were easy to carry and loaded with nutrition.

Birdie is not a social media listening tool. It’s not just tallying up social mentions of a brand and analyzing timelines to see what is trending. Instead, the company is more focused on consumer product reviews on Amazon, Google and other places where purchases can be verified and are filled with more details about how the product arrived, how it was used, how long it lasted, etc.

“Our main differentiator is the fact that we chose to be very focused on products, and built a deep dictionary that relates to the buying journey of consumer products,” Patrícia Osório, CMO of Birdie told me by phone this week. “We capture the data related to a product attribute, or usage of the product, how they bought the product. With that, we can show our clients a detailed and easy to find view about how consumers are interacting with their brand.”

According to Osório, the number of product reviews in the U.S. has been growing quickly, with an increase of 60 percent year over year. She said there is an average of 621 new reviews written per day on food products, with an average of 342 reviews per SKU.

In a way, Birdie is like a distant cousin to Spoonshot, which applies its AI to vast datasets on food to uncover novel flavor combinations. Only in Birdie’s case it is uncovering novel uses for existing products.

Founded two and a half years ago, Birdie has raised $1.6 million in Seed funding and counts Procter & Gamble among its clients.

Birdie’s technology actually fits in with the larger hacker culture that we live in today. In addition to expressing their opinions, people love deconstructing and re-purposing existing products to fit their own needs, and sharing their findings with other people online. All this adds up to a never-ending source of data for Birdie’s algorithms, and more product insights for brands.

Plant Jammer Gets €4M Investment for its AI-Powered Recipe Platform

Plant Jammer, a four-year-old Danish startup building an AI-powered cooking assistant, is one step closer to its goal of reaching one billion people, thanks to a €4 million investment in its AI recipe algorithm and platform. The Copenhagen-based company plans to expand its presence in the digital food space by licensing its API to third parties who can build branded customized experiences for their customers.

The new injection of capital comes from Danish investment firm Vaekstfonden, German food processing company Dr. Oetker, and German appliance manufacturer Miele. Miele had previously invested in Plant Jammer in 2018.

”Plant Jammer’s combination of recipe creation with AI is both unique and functional. We expect that this technology will be a core pillar in the connected kitchen of the future. Therefore, we believe Plant Jammer has great business potential,” says Dr. Christian Zangs, Managing Director of Miele Venture Capital.

Plant Jammer’s application, already in use by 10,000 households in Europe, allows users to build customized recipes by factoring in their individual preferences and what they may have in their home or what may be on sale in the local supermarket. While the app is focused on plant-based and vegetarian creations, partners who license the platform are not limited to those options. The database also contains food choices that include animal products and dairy; the PlantJammer app chose not to surface those results allowing the company to focus its version on a select niche.

In an interview with The Spoon, CEO and founder Michael Haase explained that partners who license the Plant Jammer’s API will pay based on the number of “calls” or accesses by users. For example, a grocery chain in Sweden can use the Plant Jammer API to develop a branded application such as a chatbox, that could include such extras as a link to online shopping. Each time a user of that third-party application builds a recipe, based on ingredients, tastes, diet, or any number of factors, the PlantJammer AI-driven database would work behind the scenes to deliver the results.

“I like to think of the analogy of the gold rush,” Haase adds. “We are interested in being the supplier of the jeans and shovels that enable others to do their jobs better.”

Personalized data from commercial partners will not be shared with Plant Jammer, but those partners can pass on generalized information via tags to allow the Haase’s company to continue to innovate on its platform. There are several areas Haase hopes to develop focused around food waste and the increased use of the excess capacity of local farmers and vendors.
Initially, the company founder says, the goal is to focus on food waste in the home. Haase says that 50% of all food waste takes place in the home, so we want people to build recipes based on what they already have in their refrigerator or cupboard.

“Our declared purpose is to empower one billion people with food habits that increase their health and the health of the planet,” Haase added.

That said, Haase admits his goal is a lofty one. “Right now, we are in a world of what I would call ‘trickle-down gastronomy’,” he says. “There is a huge divide between those whose world is focused on things such as molecular gastronomy and the masses. If we can show people that you can make something great in 25 minutes with simple ingredients, that would be great.”

QCify Goes 3D for Quality Control and Fair Pricing in the Food Supply Chain

As the economy is barraged daily by some kind of pandemic-related bad news, many businesses remain closed (or serving far fewer customers), job losses continue to pile up and people all over are being more cautious about how much they spend.

The food supply chain is not immune from this belt-tightenting. Looking to save some money, food buyers may haggle more vigorously over what they pay per pound for something like almonds, costing the growers and processors money. This problem, Raf Peeters told me, is where QCify can help.

Peeters is the CEO of QCify (pronounced kew-sih-fye), which uses a combination of computer vision and artificial intelligence (AI) to perform quality control on food items at processing plants. Right now QCify inspects almonds and pistachios by running samples through a special machine that uses six cameras to capture a 3D image of each almond. The company’s AI then analyzes the image and grades the almonds based on USDA (or other) criteria such as size, color, insect damage, imperfections, etc.

All that data collected by the inspection machines are sent back to to QCify HQ, where it is incorporated into the company’s algorithms. Twice a year QCify then sends out updates to all of its installed machines, which means that even if a customer bought a QCify system a couple years ago, it will run the newest AI. “Customers feel like they have the latest and greatest,” Peeters told me by phone this week.

Qcify explainer video

The result of all this computer vision and machine learning is that nut processors can set a fair price for their wares, based on objective criteria (like the USDA grading). Right now, QCify works with almonds and pistachios, and has customers in both the U.S. and Australia. A buyer can’t argue over the quality of the almonds (and thereby demand a lower price) because the processor not only has the grade from the QCify system, but it can also produce the sample images to show exactly what quality the almonds or pistachios are in.

QCify isn’t the only company looking to remove biases from the food supply chain using computer vision and AI. AgShift and Intello Labs do much the same thing. Peeters said that QCify is different from the competition because its six-camera setup captures 3D images of the nuts, instead of just scanning the top an bottom of the food, which Peeters claims is what his competition does.

QCify was founded in 2015 and Peeters said they company has only raised an unspecified amount of angel investment money. The company sells the machines themselves and charges a monthly/annual subscription fee for updates and calibration. While he wouldn’t reveal pricing, Peeters said that customers can earn their money back within a year.

In these cash-strapped times, a faster ROI isn’t just peanuts, which, coincidentally is one of the next nut categories QCify is expanding into.

KloveChef Opens Up Voice-Guided Cooking Platform to Publishers

KloveChef, the voice-guided cooking startup cofounded by one of India’s biggest celebrity chefs in Sanjeev Kapoor, is opening up its platform this month to publishers wanting to add voice-guided cooking functionality to their recipes.

The new tool will allow anyone who has recipe content — chefs, cookbook authors, bloggers or food retailers — to upload their recipes to KloveChef’s platform via a web interface and it will convert them into a voice-guided recipes.

“We will democratize the interactive recipe creation and distribution,” said Bahubali Shété, KloveChef cofounder and CEO, in an interview with The Spoon.

Shété told me that recipe publishers will be able to use KloveChef to publish their recipes across a variety of voice platforms such as Amazon Alexa, Google Assistant, Google Home and Amazon Fire TV. To do so, they just copy the recipe URL or paste the full recipe into the web interface and KloveChef will convert into a voice-guided recipe.

Shété also said that publishers will have the option of letting users send their recipes posted on other web channels such as YouTube or Pinterest to their voice assistants for guided cooking.

KloveChef is opening up their voice platform after finding some success with their Alexa voice skill targeted primarily at home cooks in India. According to Shété, the guided cooking assistant has a total of 465,000 users and 100,000 monthly active users.

Shété says publishers can make money through KloveChef if the recipe is converted into a shopping list. The recipe-to-shopping list feature, which KloveChef has been testing through its app in India, currently has over 1 million recipes converted into shopping lists via voice search.

I have to admit, I like the idea of self-publishing recipes to voice platforms. It reminds me of the early days of ebooks, when authors would use technology from early pioneers like Smashwords to put their books into the world and on other popular platforms. Perhaps not all that surprisingly, just as like those early days of ebooks, recipe self-publishers are relying on Amazon to reach the end consumer, only instead of Kindle this time it’s Alexa.

It’s too soon to see how successful KloveChef will be in attracting cooks for its voice guided recipe assistant outside of India. In its home market, they’ve been able to leverage the large reach of Kapoor, while here in the states, Alexa tends to favor its featured partners such as Food Network or Tasty. KloveChef will have to compete with the algorithm-favored partners through attracting recipe publishers such as popular food bloggers or food retailers with built-in audiences to accrue a sizeable user base.

Looking forward, the company hopes to also attract users by making the platform better over time. One of the early features will be adapting guided cooking where users can speed up a recipe or slow it down depending on their experience. The company plans to release the new capabilities by mid-August.

How Will the Black Swan of COVID-19 Impact Data Used in AI-Based Flavor Prediction?

In order to build an effective artificial intelligence (AI) platform, you need good data. Data feeds the algorithms that go into the AI; the better your data the better your AI system will function.

In the food tech world, there are a number of startups like Spoonshot, Analytical Flavor Systems and Tastewise have built intricate AI platforms that use tons of different data to help big CPG companies identify and predict culinary and flavor trends.

But what happens when a big catastrophic black swan event occurs like, oh, I don’t know, a global pandemic, which changes the eating and buying patterns of almost everyone on the planet all at once?

For instance. In February, it was easy to buy flour and yeast at your local grocery store. Fast forward to March and suddenly store shelves were empty and you had to resort to making your own yeast. Around that same time, instead of pictures of fancy restaurant meals, social media accounts were flooded with pictures of homemade bread.

Food predicting AI systems uses data points like restaurant menus, social media mentions and consumer purchasing patterns to determine future trends. But everyone didn’t start making sourdough bread at home because it was suddenly fashionable. It was because everyone was stuck inside.

How then, will AI systems handle this shock to the data system? Sheltering in place won’t last forever (knocks on wood), and who knows how long people will actually make their own bread. The popularity of it now is an aberration, does this mean that the data surrounding it is no good? Is bread making today indicative of anything other being bored or does it foretell a bigger trend?

To get a better sense I reached out to both SpoonShot and Analytical Flavor Systems to see how they are incorporating this massive disruption to our eating patterns into their own prediction process — and got two very different answers.

SpoonShot’s AI uses more than 3,000 sources across 22 data sets including menu, social and pattern data. Kishan Vasani, Co-Founder & CEO of SpoonShot, didn’t seem to think that COVID-19-induced eating changes would impact his company’s predictive capabilities at all. “Algorithms shouldn’t be overly sensitive to black swan data,” he said, “If you think about it, AI essentially means having enough relevant and appropriate data to process and predict.”

In other words, if your AI system is worth its salt, you should be able to weather big changes like this. “Everything goes back to the data and data sources,” Vasani said, “Menu data is significantly slowed down, but that’s compensated for with cooking platforms.”

On the other hand Jason Cohen, Founder and CEO of Analytical Flavor Systems, thinks the pandemic and subsequent lockdowns are a big deal. “Companies will say, ‘no no no, we can make predictions,'” Cohen said, “I do not believe that. This is the most rapid and intense change to consumer behavior since World War II.”

Cohen believes that with quarantines already in place for more than 60 days, new habits will definitely have formed. People will still be baking bread at home. What’s important is to meet this new data where it lives, literally.

Up until the pandemic, Analytical Flavor Systems used a 50 person panel of tasters as part of its data collection. This panel would come into the office to try various on-market foods. But since lockdown, the company has moved entirely to at-home testing. “In addition to CPG products, we are asking them to taste profile their homemade bread and soups,” Cohen said, “The point is we need to see those flavors, aromas and textures they are exposing themselves to.”

Cohen doesn’t think that past data is invalidated, but rather that data needs to be collected before during and after this crisis. Something which I think Vasani would agree with.

The thing about predictions now is that we won’t know if they were accurate for a long time. SpoonShot looks out 18 months and is even considering pushing that out to two years.

Hopefully we’ll be able to eat bread at a restaurant again by then.

Intello Labs Raises $5.9M for its AI-Based Food Grading

Intello Labs, which uses a combination of computer vision and artificial intelligence to grade food quality along the supply chain, announced today that it has raised a $5.9 million Series A. Saama Capital led the round with participation from global agritech funds GROW (Singapore) and SVG Ventures THRIVE (USA), and existing investors Omnivore and Nexus Venture Partners. This brings the total amount raised by Intello to $8.3 million.

Based in Gurgaon, India, Intello Labs offers a suite of computer vision products and cloud-based AI that can be used by farmers, food packers, exporters and retailers to assess the quality of fruits and vegetables. The goal is to bring transparency and objectivity to the food buying and selling business and establish fair prices throughout the supply chain. By having AI judge food grades, you eliminate haggling between biased people over what a particular bushel of apples is worth, for example.

When we first wrote about the company in 2018, it was focused on rural farmers and only used smartphone cameras to capture images of food. Finding that smaller farmers were more apprehensive about working with technology, Intello shifted to work with bigger, more corporate clients last year.

The company offers a number of different assessment tools:

  • Intello Track uses smartphone cameras to capture images of produce, which are analyzed by Intello’s cloud to assess size, color, and defects before returning a food grade.
  • Intello Sort is a machine that separates produce based on quality.
  • Intello Pack can be used to monitor produce as it is being packed for shipment
  • Intello Deep is a handheld scanner that detects Brix , pH , TSS, dry matter, moisture, pesticide residue

Intello isn’t the only company using AI and computer vision to assist with the assessment of fresh fruits and vegetables. AgShift offers similar services and launched its Hydra scanning system for bulk inspections last year.

Quickly and fairly assessing food quality has taken on greater importance during this time of global pandemic. Mass restaurant closings and general upheaval revealed the weaknesses and inequalities in our food supply chain with farmers throwing out tons of food. Tools like Intello Labs’ can bring efficiency to the food buying process and help keep the world fed.

Expect More Restaurants to Use AI Cameras Like DragonTail’s to Show a Kitchen’s Cleanliness

Before the COVID-19 pandemic, people mainly worried about where their food came from. Was it organic, locally raised, grain-fed, etc. (It was a simpler time.) But as the virus has spread, the bigger concern for consumers is who has touched their food, and were they wearing gloves and a face mask when they did so.

This is the socially distant, contactless delivery world that we now live in, which actually makes it the perfect time for Dragontail Systems to launch its new AI-powered camera that detects the sanitary conditions of food prep areas as the food is being packaged up for delivery.

You may remember Dragontail when its camera + computer vision system debuted at Domino’s in Australia last year. Dragontail’s camera is mounted above the workstation in Domino’s back of house where pizzas come out of the oven, are sliced up and boxed.

Back then, the Dragontail system was being used to assess quality control: that the pizza was the right shape with the right toppings, and that it was cooked properly. Dragontail’s camera took pictures of the pizza and Domino’s sent those pics to the customer as a way of showing hey, your pizza order was made properly and it’s heading out to you.

Now, in addition to the existing quality control features, Dragontail announced today that its camera system can check for sanitation conditions. The camera detects things like whether gloves and facemasks being worn, or how often a workspace is sanitized. The exact parameters of what to look for are up to the restaurant, as are how violations are communicated to the worker and/or manager.

So now, in addition to a picture of the food cooked properly, restaurants can provide a customer with a checklist of steps taken to show that the food was also handled properly. The end customer, then, can feel a little more comfortable knowing the restaurant followed proper cleanliness procedures.

We actually started seeing similar technology pop up last year in, of all places, China. As we wrote then, AI systems were installed in restaurants in the Shaoxing Province of China to monitor for unsanitary conditions like improper uniforms and mixed use of cutting boards. More recently in India, home cook marketplace FoodCloud launched a Kitchen Cam, which offers customers video footage of the kitchen and cooks as they make and package food.

Dragontail’s camera is currently being used in more than 2,500 stores across Australia, Canada, Singapore, the UK and Belgium. With today’s announcement, the company is ready to expand into the U.S. and work with restaurants of all sizes. The cameras cost between $500 and $1,000 and there is a $50 monthly fee for the computer vision systems.

As we’ve written before, the COVID-19 pandemic is accelerating changes throughout the meal journey. With cameras like Dragontail’s likely becoming more commonplace, the meal journey will now include pictures.

Gousto Raises $41M for High-Tech Meal Kit Business

London, UK-based Gousto announced today it has raised another £33 million (~$41 million USD) for its meal kit subscription service, according to a press release sent to The Spoon. The round was led by Perwyn, with participation from BGF Ventures, MMC Ventures and online fitness guru Joe Wicks. It brings Gousto’s total funding to $179 million.

The new funds will go towards further developing Gousto’s technology, which is a huge component of both its customer interface and back-end logistics. On the consumer-facing side, Gousto uses an AI-powered personalization tool to recommend the most relevant meals to customers, who can select from over 50 recipes each week. Customers can choose a 2-person box or a family-size box, with recipes ranging from around $30 to $60, depending on the size of the box. According to the press release, Gousto currently delivers over 4 million meals to 380,000 U.K. households each month.

Behind the scenes, Gousto uses AI to automate parts of its supply chain and optimize overall logistics. The company also plans to launch a Next Day delivery service and build out its tech team over the coming months. 

If fundraising and headcount growth sounds a little out of place in today’s troubled economic times, remember that Gousto is a meal kit company whose primary goal is to serve people in their homes. The UK, where the service is available, is currently on lockdown in response to coronavirus that’s set to be extended until early May. That makes for a plenty of potential customers where a service like Gousto is concerned. The company told TechCrunch that demand for its business “has spiked upward” in recent weeks. In particular, Gousto has seen a 28 percent increase in family-style boxes. 

All that said, if you’re hoping to become a new Gousto customer, you’ll have to wait. A note on the Gousto website says the company is not accepting orders from new customers at this time, due to “extremely high demand.” There’s no word of how long that will last. Presumably, the new funding round and corresponding plans to increase headcount and tech development will help Gousto better reach the high demand for meal kits that will be around for some time.

CES 2020: Stratuscent’s Digital Nose Can “Smell” When Crops are Ripe or Food is Burning

Something good-smelling must be in the air at CES this week, because digital noses are becoming a bit of a thing at this year’s tech expo. Yesterday I dropped by the booth of Stratuscent, a Montreal, Quebec-based startup which is digitizing scents to detect freshness.

The company’s sensors, called eNoses, detect chemicals in the air to create a scent print — like a fingerprint for a smell. According to CEO David Wu, who gave me a tour, Stratuscent’s “secret sauce” is its superior AI and machine learning, which can quickly and accurately determine any number of complex scents, even ones too tricky for humans to smell. The company’s tech came from NASA, where it was originally used for leak detection.

The eNose is pretty simple to use. Just wave the product in question under the eNose and it will determine what it is — as well as its percentage of accuracy — in under thirty seconds. You can Wu demonstrating the technology below:

CES 2020: Stratuscent's eNose is a Digital Smelling Machine
Can Stratuscent determine this mystery smell? (Spoiler: Yes, yes it can.)

Wu told me that Stratuscent’s noses have a variety of applications, including sniffing ethylene, a chemical that indicates spoilage, in crop shipments. They’re also working with a dairy company to detect milk freshness. In the home, Wu told me that the eNose could also be integrated into smart kitchen appliances to identify cooking stages (your sauce is about to burn!) and alert users to food spoilage.

Startuscent was founded in 2017 and has raised $4.3 million thus far. Wu said that in addition to its partnership with a dairy company, Stratuscent is pushing further into the food and agriculture space, and is also in conversations to work with indoor agriculture farmers.

Stratuscent isn’t the only player digitizing smell technology (what a world). Yesterday Chris wrote about Aryballe’s new Digital Nose 2.0, which also debuted at CES this week and also digitizes scent to detect freshness, cooking smells, etc.

Regardless, the digital scent landscape is just beginning to emerge. As food safety outbreaks grow — and consumers become more conscious about reducing home food waste — I think there will be a growing market for this sort of technology. Which means there’s ample opportunity for more than one player to nose its way into the digital smelling space.

Sony Sets up AI Unit to Work on Food

Sony announced this week that is has launched Sony AI, a new organization that will research and develop artificial intelligence specifically for games, imaging and sensors, and “gastronomy.” The new initiative will have offices in Japan, Europe and the U.S.

There aren’t many details around what exactly Sony will be working on, but Sony spokesman Shinichi Tobe told AlJazeera yesterday that “AI and robotics will not replace chefs. We are aiming to offer new tools to expand their creativity with AI and robotics.”

This isn’t Sony’s first foray into food. In April of last year, Sony teamed up with Carnegie-Mellon University to work on food robots. As we reported at the time:

Sony said they were starting off with food-related robots because the complexities involved with food could later be applied to a wider range of industries. Specifically, it cited the ability to work with fragile and odd-shaped materials, as well as the ability to operate a robot in small spaces.

AI and robots are like peanut butter and chocolate with AI being the “brain” for the robot “hands.” Things like computer vision, deep learning and synthetic data help form the AI so the robot can determine objects to grab and manipulate, etc..

Sony’s motivations may also be more societal in nature as the company’s home country of Japan is facing an aging population. Robots and other forms of automation could help with a potentially diminished labor force.

Food is a popular subject for robotics and AI researchers. Nvidia’s Lab in Seattle built a kitchen to train its robots to do everyday tasks. IBM partnered with spice company McCormick to use AI to develop new food products. And Korea’s Woowa Bros. hooked up with UCLA to work on food robots as well.

Something tells me we’ll be seeing more of these types of deal throughout next year.

French Nutritional Coaching App Foodvisor Raises $4.5M

Foodvisor, a nutrition assistant app that aims to help people eat better, announced today that it has raised $4.5 million from the VC fund Agrinnovation (operated by Demeter) along with other angel investors. With this round, Foodvisor has raised $5.3 million to date.

Foodvisor creates an AI-powered food diary that allows users to keep track of what they eat. The app uses a combination of computer vision and deep learning so that all a user need do is snap a picture of the food they are eating. Foodvisor can then identify the food on the plate, serving size and create a detailed nutritional report. Foodvisor says its app can identify 1,200 different food items and is constantly learning new ones.

The company says it will use the new money to accelerate growth in the United States where, the Foodvisor CEO makes a point to say in the press announcement emailed to The Spoon, that “70% of the Americans are overweight or obese.” Foodvisor says that currently 20 percent of its user base comes from the U.S.

They say that you lose weight in the kitchen and get fit at the gym. Keeping track of what you eat is a good first step for anyone looking to shed a few pounds and get healthier. Foodvisor isn’t the only app that uses your smartphone camera to help make food journaling easier. Bite.ai offers a free consumer app as well as an API that other business can tap into to for similar food recognition services.

Foodvisor offers a free app for iOS and Android as well as premium version that offers more personalized advice and recommendations for a monthly subscription that costs from $4.99 to $6.99.