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The Spoon

Daily news and analysis about the food tech revolution

AI

With the Launch of Samsung Food, Samsung Hypes AI & Consolidates Food Features Acquired Over the Years

Over the years, Samsung has acquired and launched several products in an effort to become the king of the tech-powered kitchen.

First, there was the launch of the Family Hub refrigerator, the company’s attempt to create a smart fridge built around the company’s own operating system and packed with technology like fridge cams to identify food and help you with your shopping.

Then, there was the acquisition of Whisk, an intelligent food and shopping app that helped pioneer the shoppable recipe space. Whisk had not only amassed an extensive food database, which would ultimately become a foundation for some of Family Hub’s (now Bespoke Family Hub) shopping and recipe capabilities, but it also served up the foundational ‘Food AI’ that is now being pushed to the forefront by Samsung.

Then, there were various attempts to use AI through automation in the kitchen, as the company announced (and never released commercially) different cooking and kitchen-task robots at CES.

And we can’t forget that Samsung also took some of the smart home technology from its SmartThings smart home group (another Samsung acquisition) and paired it with Whisk’s recipe intelligence to create SmartThings Cooking, a guided cooking app.

This leads us to this week, in which Samsung announced what amounts to packaging up this collected knowledge and technology – save for (at least for now) the robotics – into a newly expanded app and platform called Samsung Food. Samsung Food, which the company describes as “a personalized, AI-powered food and recipe platform,” looks like a significant step forward for the company’s efforts to build a centralized digital food management app. It also is a logical move to consolidate much of the collected efforts under the Samsung brand after the company had collected a variety of platforms that served as a foundation for what we see today.

Let’s take a look at precisely what the company unveiled. In the announcement, Samsung detailed four primary areas of activity for Samsung Food: Recipe Exploration and Personalization, AI-Enhanced Meal Planning, Kitchen Connectivity, and Social Sharing.

For recipe exploration, Samsung looks like it’s essentially using what was an already somewhat evolved feature set in Whisk. Samsung says that it can save recipes to a user’s digital recipe box anytime and from anywhere, create shopping lists based on their ingredients, and is accessible via Family Hub. In addition to mobile devices, users can access Samsung Food with their Bespoke Family Hub fridges, which will provide recipe recommendations based on a list of available food items managed by the user and shoppable recipe capabilities.

With the Personalize Recipe function, Samsung Food looks like it builds on the personalization engine created by Whisk and plans to take it further through integration with Samsung Health. According to the announcement, by the end of this year, Samsung plans to integrate with Samsung Health to power suggestions for diet management. This integration will factor in info such as a user’s body mass index (BMI), body composition, and calorie consumption in pursuit of their health goals and efforts to maintain a balanced diet.

The AI-Enhanced meal planning feature looks like a longer-view planning feature that consolidates personalized recipe recommendations, and it will no doubt similarly benefit from the integration of Samsung Health.

With Connected Cooking, Samsung has rebranded and extended the features of the SmartThings Cooking app, adding new devices like the BeSpoke oven and incorporating some of the same guided cooking features.

And, of course, a consolidated food-related platform from Samsung wouldn’t be complete without a social media component. My guess is the Social Sharing feature – which will allow users to share with their community – is the least necessary addition to the app and will ultimately not be all that successful, as consumers will continue to use mainline social apps (TikTok, Instagram, Facebook) for their food-related social sharing.

The company also teased expanded computer vision capabilities in 2024 in the announcement. The company’s Vision AI technology “will enable Samsung Food to recognize food items and meals photographed through the camera and provide details about them, including nutrition information.”

Overall, I’m impressed with the overall cohesiveness and trajectory of what I see in Samsung Food. I think it’s a sign that Samsung – despite having the occasional misstep and strategic vagueness around their food vision – looks like they remain committed to becoming the leader of the future kitchen, something that they started way back in 2016 with the launch of the Family Hub line.

I Attended a Workshop on the Impact of AI on The Food World. Here’s What We Discussed

Last month, I headed down to San Luis Obispo to participate in a National Science Foundation-funded project analyzing the impact of automation and AI on the food system. I’d been invited to participate in a workshop headed up by Patrick Lin and Ryan Jenkins, professors at Cal Poly and the project leads.

The workshop was the first for the four-year project exploring the social and ethical impacts of automation and artificial intelligence in kitchens. The project endeavors to draw out the wide-ranging implications of this technology, exploring both the impact on commercial environments like restaurants and how automation could impact the longstanding tradition of home cooking and family meals.

“This project will help to draw out the hidden and very broad impacts of technology,” said Lin at the time of the project’s announcement. “By focusing on the trend of robot kitchens that’s just emerging from under the radar, there is still time for technical and policy interventions in order to maximize benefits and minimize harms and disruptions.” 

The two-day workshop included a cross-section of academic types, chefs and food service professionals, journalists, and technology experts. It was the first of three workshops across continents to gather insights and work towards producing a report and academic curriculum centered around the intersection of food and automation and AI.

The workshop, structured as a giant whiteboard session, included expert presentations and facilitated conversations. During and after each presentation, the participants shared their thoughts on potential impacts – both direct and cascading effects – that could result from the introduction of AI in its various forms over time. While much of the conversation focused more heavily on AI in the form of automation – i.e., cooking robots – AI in other forms, such as generative AI, was also discussed.

Below are some of the key themes discussed during the two days, as well as a few of my thoughts now that I’ve had time to think through the issues since the workshop.

I’d also love to hear your thoughts on this critical topic, so please send them along!

Finally, we’ll be discussing many of these same issues at the Food AI Summit on October 25th. If this is an issue critical to you and your company, make sure to join us!

Atrophying Cooking Skills

One of the concerns raised during the workshop was the potential loss of cooking skills and culinary knowledge as we rely increasingly on automation and AI to make our meals. While it was generally recognized that robotics could take over repetitive and tedious cooking tasks, some wondered if handing over the cooking process to machines could lead to a general loss of competency in culinary arts and a homogenization of meals produced by highly automated cooking.

It’s easy to see how highly automated food prep would be extremely popular; some would hand the entire process over to the machine. However, there’s a good chance that handing off the mundane parts of cooking would give home cooks, chefs, or food workers more time to focus on creating the special touches that often make a meal great. As we have seen with the advent of digital design and art tools, there’s a possibility that those who love making food could use technology to take their work to the next level.

The Loss of Together Time

Another concern raised across the two days was the impact on shared family time by handing over meal prep and cooking to robots. Parents and other caregivers often use time in the kitchen to share lessons to help children develop motor skills, understand their heritage and develop self-confidence. Over-automation of cooking could disrupt this transfer of knowledge. Cooking has also shown many positive mental health benefits for those involved.

I think these are valid concerns, as there is a real risk of losing some of the benefits of the shared cooking process due to automation. After all, there’s no replacement for a grandchild spending time with their grandma learning how to make her special cookies and the sharing of family history that comes along with such an activity.

However, a few counterpoints. First, no one says the act of hand-making that special recipe has to be a victim of technology, and, in some ways, I think the kitchen will prove to be one of the areas where some families will insist on preserving the art and act of doing the actual cooking themselves.

And as the world becomes more digital and automated, kitchens may be a refuge for many who find the hands-on nature of making food therapeutic and fulfilling. In other words, the kitchen may be the last true ‘maker space’ left in our homes, and many will look to protect and preserve that.

Finally, average meal times shrank 5% between 2006 and 2014, a much smaller decline than we’ve seen in meal prep times as the advent of ready-to-eat meals has become more popular over the past few decades. While automation may result in faster meals, people could spend nearly as much time – or maybe more – sitting around the dinner table.

A Loss of Authenticity, Creativity, and Happy Accidents

With AI, there’s a chance recipe creation algorithms may rely too heavily on existing data patterns and therefore lack originality. There was also the concern that AI systems may limit opportunities for spontaneous creativity and the type of “happy accidents” that often lead to new recipes. One workshop participant gave an example of mistakes leading to important new dishes, like the croissant.

There was also concern that using AI to generate meal plans or recipes could result in over-standardization and homogenization, particularly if the AI systems rely too narrowly on popular recipes, which could also reduce culinary diversity.

It’s a valid concern that AI systems will generalize based on limited data sets, often creating recipes or meal plans based on popular or trending food concepts. Anyone who listens to algorithm-generated playlists by Spotify or Pandora can attest to some off-note song recommendations, and I can see how that could easily be the case with food and recipe generation. However, good technology products allow humans to reject recommendations and fine-tune algorithms, which may allow for more personalized recommendations based on a particular user’s preferences.

There’s also a real possibility that AI could lead to new and intriguing food combinations. Chef Watson and other AIs have been able to create unexpected but interesting recipes based on intelligence built into the algorithms around flavor compounds. If a restaurant or home chef can leverage heretofore inaccessible deep insights based on science and flavor research built into AI systems to create their next masterpiece, the results could be exciting.

As for the impact on cultural diversity, I think it’s important to recognize that AI systems are known to have bias problems, often hewing more closely to the worldviews of their creators and their preferred datasets. Because the world of food is one of the most important pathways for under-represented voices to connect with broader audiences, it will be critical for us to guard against the loss of accessibility and equality in the culinary world as AI and automation tools become more commonplace.

However, food AIs could be built to emphasize unique and emerging food cultures, which could be a savvy move since millennials and younger generations celebrate new food discoveries, often from cultures outside their home markets. Also, many of the creators of new food automation technology are often from markets outside our own, emphasizing food types different from our traditional fare.

This is just a few of the themes discussed during the workshop. Other themes, such as job loss and the economic impacts of automation, were also explored in detail, and I’ll have more thoughts on that later this week.

Innit Debuts FoodLM to Power More Contextually Relevant Answers from Generative AI Platforms

Today Innit, a startup best known for its shoppable recipe and smart kitchen software solutions, announced the release of FoodLM, a software intelligence layer that helps power more contextually relevant food-related answers from generative AI large language models (LLMs).

The new platform, which itself is not a new LLM, is instead a software intelligence layer built to plug into existing LLMs to do pre and post-processing of queries to help provide better answers around a variety of food-related topics.

From the announcement:

FoodLM enables powerful semantic search for retailers to go beyond keywords and understand intent. Brands can provide consumers with highly personalized AI assistance from product selection through preparation and cooking. For health providers supporting patients with chronic diseases such as type 2 diabetes, FoodLM provides powerful science-backed assistance for healthy eating and food as medicine.

Innit CEO Kevin Brown described FoodLM as a “vertical AI” expert layer that can integrate into popular LLMs such as OpenAI’s GPT4 or Google’s PaLM. Brown compared FoodLM to what Google has done with Med-PaLM, which is Google’s medical knowledge layer that provides focused answers that are so contextually smart around medical information that it has started to pass the medical exams.

“You’re going to need the pairing of an LLM with expert training and expert systems to narrow it down for certain functions where it’s essential to be accurate,” Brown said.

The biggest concern with LLMs today is their tendency to hallucinate. Brown says that integrating with a vertical knowledge layer increases the likelihood of more relevant and accurate answers, ultimately leading to more trust in these systems.

“Food queries are one of the top use cases for LLMs, helping with tough problems like helping to manage people’s diets,” said Brown, “But only if you can trust them. If you can trust these systems and ensure they reflect key dietary and health factors, it becomes much more valuable.”

According to the company, answers are pre-processed and post-processed through FoodLM’s focused computation models, which it calls validators. The different validators within FoodLM include:

  • Nutrition & Diets: Analyzes more than 60 diets, allergies, lifestyles, and health profiles to provide detailed recommendations tailored to individual needs.
  • Health Conditions: Provides dietary guidelines, product scoring, and content specifically designed for conditions such as type 2 diabetes or hypertension.
  • Personalized Shopping: Automated grocery purchases, incorporating personalized scoring and selection of over three million grocery products worldwide.
  • Culinary & Cooking: Advanced logic to ensure that AI-generated recipes follow culinary guidelines and are cookable. Seamlessly integrates with smart kitchens, featuring automated cooking programs.

For now, Brown says FoodLM will be used by its partners through custom integrations via API. Over time, he sees the system as having a more approachable user interface where the system is used via a SaaS model.

From my perspective, FoodLM makes lots of sense for Innit. While we’ve already seen similar moves from some data-service and SaaS providers in the food space, Innit’s offering goes further and has more granular breakouts to provide specific contextualized offerings to power food-related services for their CPG, appliance, and health/wellness industries.

If you’re interested in the intersection of food and AI, make sure to check out The Spoon’s Food AI Summit, which is on October 25th in Alameda, California.

SEERGRILLS Unveils the Perfecta, an ‘AI-Powered’ Grill That Cooks the ‘Perfect Steak’ in Two Minutes

AI is seemingly everywhere nowadays, so it was only a matter of time before it would show up at the backyard BBQ to help us cook the perfect steak.

That’s the vision of a UK startup named SEERGRILLS, which debuted the Perfecta this week, which the company describes as the world’s first AI-powered grill. The grill combines high-temperature infrared cooking with its AI system called NeuralFire, which automates the cooking process.

According to SEERGRILLS CEO Suraj Sudera, the AI works through a combination of sensor data, cook preferences inputted by the user, and intelligence built into the software around different food types.

“The device will capture the starting temperature of, say, chicken breast and adjust the cooking in line with the preferences you’ve inputted in the device,” said Sudera. “Whether it’s a three-inch or five-inch chicken breast, it doesn’t matter. It will be whatever adjustments it needs, just like your cruise control on your car will adjust to keep you at the preferred speed.”

When a cook is done, users can rate the quality of the cook, which informs and optimizes the NeuralFire algorithm for the next cook. Suraj says that SEERGRILLS is also constantly updating its food database, so if, say, a new type of steak from Japan becomes popular, the AI engine will be updated to optimize the cook for that meat type. The company says its AI will also optimize to reach each type of meat’s sear and doneness, as well as help to perfect the Maillard reaction.

The hardware itself is somewhat unique compared to other infrared grills on the market in that it cooks meat vertically. The user puts the meat in a holder, which will sense the temperature and thickness of the meat. Once inserted, both sides are cooked simultaneously using infrared heat, powered by propane, which SEERGRILLS says can reach 1652ºF. According to the company, the grill can cook three ribeyes in one minute and fifty seconds, six burgers in a minute and thirty seconds, and four chicken breasts in two minutes and thirty seconds.

In addition to the grill itself, the company is also building accessories such as a rotisserie module, a pizza module, and a grill station. The company will start taking preorders in July and plans to begin shipping the Perfecta by the end of this year. Pricing for the grill and its accessories has not yet been disclosed.

🚀 Introducing Perfecta™ - The World’s First AI Powered Grill. 🚀

Shiru Used AI To Discover Its First Novel Ingredient in 3 Months. The Next One Will Go Even Faster

This week, novel ingredient discovery startup Shiru announced they have commercially launched their first ingredient, OleoPro, a plant-based fat ingredient the company says doesn’t have the environmental costs or health consequences of animal fat. As part of the announcement, the company disclosed that the company’s first commercial partner is Griffith Foods, a commercial food ingredient manufacturer.

As readers of The Spoon know, Shiru is part of a cohort of startups using AI to discover new ingredients more quickly than traditional methods. Unlike many first-generation synthetic bio products, OleoPro was developed using machine learning, enabling a multifold acceleration of the discovery and testing phase according to the company.

The company’s discovery timeframe for OleoPro took less than three months. According to the announcement, “Shiru’s biochemists and computational biologists used AI to scan and select nearly 10,000 formulations” in that time frame, and “then they determined the precise molecules that would combine to form an ingredient with the unique oil-holding protein scaffold of animal fat.” The entire discovery and commercialization process took 18 months from the project’s start, much shorter than the multi-year process typical of classical synthetic biology workflows.

And now, according to Shiru CEO Jasmin Hume, that time frame for discovery will compress even more now that the company has built out its machine learning model. Finding a new novel protein or functional ingredient will take “eight to 10 weeks is like what we’re comfortable with,” Hume told me in a recent interview. “And what that means is, it’s not just digital, but at eight weeks, we have up to half a dozen proteins that we’re making at a couple of grams. And so we go from totally digital to pilot-produced ingredients, not one but a couple that can work, in about eight weeks.”

“Instead of a half decade and more than a quarter billion dollars in R&D to ship a viable product, Shiru used AI to dramatically reduce the cost and time to market of an essential ingredient of plant-based meat to a matter of months and a few hundred thousand dollars – and the cost of protein discovery at Shiru continues to decline,” said Dr. Ranjani Varadan, Shiru Chief Scientific Officer, in the announcement. Varadan, who sat down with The Spoon last summer, was previously VP of R&D at Impossible Foods.

Podcast: Talking AI & Food With Evan Rapoport

In this week’s podcast, we talk food and AI with Evan Rapoport.

Over the past decade, Evan has led teams in Google Research and other organizations, looking at how AI could impact biodiversity and change. During our conversation, we talked about a project called Tidal, in which he and Google used AI technology like computer vision and applied it to aquaculture, and discussed the impact of AI more broadly on the food system and how Evan thinks newer technology, like generative AI, might have an impact sooner than we think on the world of food.

You can find the full conversation on Apple Podcasts, Spotify, or wherever you get your podcasts, or just click play below!

Eating Disorder Org’s AI Blunder is a Cautionary Tale About Embracing Tech for Fundamentally Human Roles

One of the ongoing debates in tech circles and beyond is how fast AI will replace humans in certain lines of work. One role where we’ve already seen organizations embrace the technology is in customer support, deploying AI-powered customer interfaces to act as the first line of contact to handle inbound queries and provide critical information to customers.

The only problem? Sometimes the information they provide is wrong and potentially harmful to an organization’s customers. To illustrate this, we need to look no further than this week’s news about efforts by the National Eating Disorder Association to use AI-powered chatbots to replace human workers for the organization’s call helpline. The group announced a chatbot named Tessa earlier this month after the helpline workers decided to unionize and, just a couple of weeks later, announced they would shut the chatbot down.

The quick about-face resulted from the chatbot giving out information that, according to NEDA, “was harmful and unrelated to the program.” This included giving weight loss advice to a body-positive activist named Sharon Maxwell, who has a history of eating disorders. Maxwell documented the interaction in which the bot told her to weigh herself daily and track her calories. In doing so, the bot went off-script since it was only supposed to walk users through the organization’s eating disorder prevention program and refer them to other resources.

While one has to question the decision-making of an organization that thought it could replace professionals trained to help those with sensitive health and mental wellness challenges, the example of NEDA is a cautionary tale for any organization eager to replace humans with AI. In the world of food and nutrition, AI can be a valuable tool to provide information to customers. However, the potential cost savings and efficiency the technology provides must be balanced against the need for a nuanced human understanding of the sensitive issues and the potential damage bad information could cause.

NEDA saw AI as a quick fix to what it saw as a nuisance in the form of real human workers and their pesky desire to organize a union to force change in the workplace. But unfortunately, in swapping out humans for a computer simulation of humans, the organization lost sight of the fact that serving their community requires a fundamentally human form of expression in empathy, something AI is famously bad at.

All forms of customer interaction are not created equal. An AI that asks if you want a drink with your burger at the drive-thru is probably going to be suitable in most scenarios, but even in those scenarios, it’s perhaps best to tightly guardrail the AI’s knowledge set and build in offramps to the system where customers can seamlessly be handed over to an actual human in case they have a specialized question or if there’s any potential for doing more harm than good during the interaction.

Recipe for Disaster? ChatGPT is Tasked to Create Unique, Tasty Dishes and Fails Miserably

So you think your newfound ability to prompt ChatGPT for AI-generated recipes could result in a culinary masterpiece?

Hold that thought, advises the World of Vegan, a popular wellness website focused on vegan living. The site recently undertook an intriguing experiment powered by generative AI, where they prompted ChatGPT to conjure over a hundred diverse recipes. The group prompted the AI bot to whip up new and innovative recipes for a variety of occasions ranging from date night dishes to brunch and dessert ideas. From there, the site’s chef team tested each recipe to see how they tasted.

The result? Not good.

All this prompting led to what the group described as “hilariously pitiful results.” With many of the recipes, the chef team at World of Vegan spotted ingredient formulations that “would clash right away and where the mishaps would occur.” The team also felt the recipes were largely “deceptive,” seeming ordinary at first glance but often described as “rich” and “decadent” when they were quite the contrary.

“I had a feeling ChatGPT would struggle with recipe development, since developing recipes is such a delicate mixture of fine art and science,” World of Vegan founder and chef Michelle Cehn told The Spoon. “But I was shocked by just how difficult it was to find a single spring recipe written by ChatGPT that worked with a passing grade. This is a crucial warning for both food bloggers seeking shortcuts and home cooks looking for quick recipes. You’ll save yourself a lot of trouble (and wasted time, energy, and money) by bypassing ChatGPT and opting for a trusted blogger’s highly-rated recipe instead.”

Image credit: Erin Wysocarski

One of the biggest fails cooked up by the World of Vegan team was a vegan scalloped potato dish (pictured above), which the recipe’s chef said had an ingredient list and cooking instructions that were out of order. The resulting dish had an off-putting color, a pungent sauce, and tasted bad.

According to World of Vegan, out of the 100 or so recipes the team cooked up, only one – a cauliflower taco dish – resulted in an appetizing result.

Cehn believes the resulting 1% success rate might be due to ChatGPT’s reliance on what is essentially flawed data, namely millions of subpar recipes drawn from the Internet. With this as its foundation, things are destined to go poorly once the bot is tasked to create a unique recipe.

“A human brain can’t access all that information, so people are likely independently (and unintentionally) creating duplicate recipes online. Since ChatGPT must create a truly unique recipe, it has to get a little weird to create one that’s not plagiarized.”

While one might expect a site focused on creating recipes to be skeptical about AI filling its shoes, I don’t doubt the poor results are that far off from what others may find if they conducted a similar experiment. Good recipes often result from lots of experimentation and applied knowledge, something that you don’t get when a bot freewheels up a new dish idea out of thin air.

And while a more specialized AI trained on the compatibility of various culinary ingredients – something akin to a chatbot based on Chef Watson – might yield better results, we don’t have that, at least not yet.

Bottom line: human-powered recipe creators are still necessary…for the time being.

2023 Restaurant Tech EcoSystem: Nourishing the Bottom Line

In collaboration between TechTable and Vita Vera Ventures, we are pleased to share an updated 2023 Restaurant Tech Ecosystem map.

We all saw that the pandemic brought a wave of experimentation in the restaurant tech space, but we also know that tech-driven change is not always linear. 

In early 2022, we made bold predictions about the restaurant tech environment in 2023, as we anticipated numerous acquihires ahead (acquisitions primarily driven by tech talent vs strategic tech value). This was due to the tight tech labor market (at the time) and the increasingly challenging funding and interest rate conditions. 

However, with the recent wave of macro tech layoffs, the tech labor market is no longer tight, and we believe more restaurant tech companies may be forced to shut down rather than finding a soft landing through acquisition. We’ve already seen a strong reset on requirements for capital efficiency and valuations of startups in the sector. This macro shift may create potential for rollup opportunities, but many early-stage assets across the sector are overfunded single-point solutions and still subscale.

This is ironic as the need for tech-driven solutions has never been stronger, but companies without the right growth metrics will likely struggle to survive. The inflationary environment is also forcing harder decisions for operators, which may further dampen their willingness to engage with new solutions.

With that in mind, we are pleased to share our 2023 Restaurant Tech Ecosystem, which serves as a current heat map of the broader ecosystem within the US (and is clearly not exhaustive). 

Click here to enlarge/download image of map. Click here for downloadable PDF.

The Journey from Point Solutions to Comprehensive Tech Stacks

While single-point solutions for things like online ordering, loyalty programs, and delivery were popular during the pandemic, we have reached a moment now with perhaps too many point solutions in the market. 

Tech stacks that require too many logins are now in fact creating a cognitive burden for employees, rather than the intended promise of efficiency and ease of use. As a result, operators are beginning to seek integrated systems and smaller tech stacks that can do more. (See commentary in the previous section about rollup opportunities!) 

Restaurant tech advisor David Drinan succinctly identifies the near-term priority for most operators: “The restaurant industry is thirsty for technology innovation that will deliver high margin, incremental revenue.”

On the operational side, managers are still struggling with certain areas such as scheduling and inventory management. These tasks can be time-consuming, especially for independent restaurant owners who have limited resources. As a result, we have seen a growth category of solutions that can automate these functions and provide real-time data to help operators make informed decisions.

Help *Still* Wanted   

The labor shortage in the restaurant industry has been a major challenge for operators in recent years, and labor optimization is still at the top of every operator’s mind. The pandemic caused many workers to permanently leave the hospitality industry, leaving restaurants short-staffed. 

According to the National Restaurant Association, almost two-thirds of US restaurant operators say they do not have enough employees to support existing demand. Instead of replacing this lost workforce, many operators are turning to tech to automate more functions and reduce the need for human labor. 

From digital menus and ordering kiosks to automated kitchen equipment, there are many ways that technology can help restaurants operate more efficiently with fewer employees. By automating basic tasks such as taking orders and processing payments, operators can free up their staff to focus on more complex tasks that require human expertise, such as customer service and food preparation.

Another trend the restaurant industry is grappling with is the changing expectations of younger workers when it comes to the employer/employee relationship. With more emphasis on work-life balance, career development, and job satisfaction, younger workers are looking for more than just a paycheck. 

To meet these expectations, operators are looking for workforce management solutions that can help to improve engagement, development, and rewards for their employees. This includes tools for tracking and managing schedules, as well as innovative solutions for tip outs and other compensation mechanisms. By investing in these solutions, operators can not only attract and retain top talent but also improve the overall efficiency and productivity of their workforce.

Finally, it is worth noting that basic scheduling and labor management tools can have a significant impact on profitability by reducing labor costs and improving operational efficiency. By automating scheduling and timekeeping, for example, restaurants can reduce the likelihood of overstaffing or understaffing, which can be costly in terms of wasted labor or lost sales opportunities. 

In the end, the ability to leverage technology to optimize labor is critical for restaurants to remain competitive in a challenging operating environment. While kiosks and text ordering have shown promise in the QSR space, there are many other opportunities for technology to make a positive impact on the industry as a whole.

Ghost Kitchens: It’s Even More Complicated

In our 2021 restaurant tech retrospective, we had a lot to say about this growing subsector, including the challenges for success (a.k.a. profitability) within the confines of a ghost kitchen business model.  

Now, as the concept of virtual and ghost kitchens continues to evolve even further, it’s important for operators to understand the complexities involved and navigate these challenges to build successful ghost kitchen operations.

One major obstacle has been the potential for tension between virtual brands and existing businesses, where adding virtual brands can lead to direct competition with their own existing businesses. Finding the right tech and operational partner to balance between these two is key.

Additionally, ensuring food safety and maintaining quality standards across multiple brands can be a challenge. Many of the generic virtual brands have lacked distinct value or clear taste standards, leading to underwhelming food quality issues and removal from the major third-party delivery platforms.

Last Mile Magic

Making the economics work for restaurant delivery is a growing priority for the industry. This includes better interoperability between POS/Kitchen systems and delivery providers, better routing and batching systems, localized kitchens, and of course even the mode of transportation for delivery.

We are tracking over 20 companies in the North American unattended last mile category, but it is still early days with most (all?) of the solutions operating in limited geographies and customer trials. So we have left this slice off the infographic for 2023, but don’t forget to keep your eyes on the sky, as we’ve seen recent growth of backyard drone delivery companies which are proving to be faster and better for the environment (if they can outweigh the noise and regulatory concerns).

GenAI on the Menu

Tech entrepreneurs have long dreamed of personalized food recommendations, but few have succeeded in creating true personalization beyond dietary concerns, allergens, or ingredient likes/dislikes. 

However, we have now reached a unique moment where new technologies like ChatGPT will be able to create meaningful and personalized interactions with guests. This has always been the premise of a variety of AI-driven restaurant tech startups, but the ability to leverage the underlying data to engage and interact with guests in a truly personal and conversational manner is game-changing. 

By using data from previous orders and interactions alone, ChatGPT can help to create a more tailored experience for guests, from recommending menu items to offering personalized promotions. ChatGPT can become a critical part of a restaurant’s marketing team by creating content, with the ability to easily translate to different languages as well. This could give operators a crucial competitive advantage as consumers demand more personalized experiences. We have only begun to see the capabilities of ChatGPT with free templates being offered to restaurant operators already.

Moreover, conversational AI like ChatGPT can also be a valuable tool for restaurant operators seeking to understand their own operating metrics. By integrating ChatGPT into their tech stack, operators can ask natural language questions and receive real-time responses, empowering them to make informed decisions about their operations.

Emerging Restaurant Tech Concepts to Watch

  • Chat/AI across marketing and operations
  • Tech-enabled employee support and training (for example, personalized perks, tip-out options, or language choices) 
  • AI for scheduling to free up managers
  • Dynamic pricing
  • Reusable containers + tech-driven circular economy for foodservice 

Looking ahead –  As always, we welcome your thoughts and reactions, and look forward to continuing to follow this sector together in the coming years. Reach out to us: Brita@vitavc.com and hello@techtablesummit.com. 

John Deere’s Robotic Tractor is The Result of Years of Investment in AI-Powered Farming

When John Deere debuted its first-ever autonomous tractor at CES 2022, it signaled a new era of AI & robotic farming would soon be upon us. While other companies have been talking about autonomous tractors for some time, it’s an altogether different matter when the U.S.’s biggest manufacturer of farming equipment signals that this is the future.

Still in the trial phase, early versions of the 8R are now being tested by what the company describes as its “paying test cooperators.” But since it won’t be long before the final production model of the autonomous tractor is rolling off the production line, I thought it would be a good time to sit down with one of the company’s computer vision leads, Chris Padwick, the  Director of Computer Vision and Machine Learning at Deere’s Blue River Technology division, to get an idea of the how the company got to this point.

According to Padwick, since its acquisition by John Deere in 2017, Blue River has helped accelerate the farming equipment giant into precision agriculture with its “see and spray” computer vision technology. The technology, which enables a farmer to make highly targeted applications of herbicide to weeds in row crops, first debuted in John Deere’s See and Spray Select in 2021.

The system made it possible to perform green-on-brown application, which is the application of non-residual herbicide to crops in the “pre-emergence” phase. This use of precision application of herbicide allowed farmers to transition away from the blanket application of herbicide to crops using older technology such as cropdusters to more precise application that can reduce the amount of herbicide used by 77% or more.

But it wasn’t until the latest incarnation of the technology, which uses green-on-green technology, that the benefits of Blue River’s investment into deep learning-powered computer vision (which the company began researching in 2016) were fully realized. With the See and Spray Ultimate, farmers can do in-season herbicide spraying for various crops, which is powered by advanced neural network-powered computer vision that can differentiate between similarly colored weeds and crops.

“If plants are touching together, then all of your traditional computer vision techniques for image processing – like morphology and erosion dilation and template matching – kind of break down,” Padwick said. “It’s really not possible to build a system without these that can operate at 95 or above percent accuracy.”

While Blue River helped John Deere accelerate its move into AI-powered farming, Padwick pointed out that the farm equipment company had already invested significantly in the technology even before Blue River’s arrival. By 2019, the company was processing 5 to 15 million measurements per second and had even begun to use computer vision to evaluate grain quality. Much of that work, according to Padwick, was based on work John Deere had performed before Blue River had arrived.

And today, all of the combined competencies and data gathered across John Deere’s various AI efforts are helping the company create its first 8R autonomous tractor.

“In general, in all of our machine learning projects, we tried to embrace the idea that all data is good data,” Padwick said. “We might have sprayer data from See and Spray collected from cameras that can be useful to train the autonomous tractor. The autonomous tractor has different cameras, different geometry, and they can collect data with different kinds of modality and different sensors, but that data that’s collected from other projects can also be useful in that training.”

When asked if he thinks farmers will embrace autonomous tractors, Padwick believes the answer is yes. He points to the rapid acceptance of See and Spray as an illustration of how quickly farmers will adopt new technologies that help them do their job quicker and at a lower cost.

“I remember every time we would do a demo for customers of See and Spray where we’d invite several growers out to a field, and they can watch the machine in action and give us feedback on the results. The overwhelming sentiment was, ‘Wow, I knew this was coming, but I thought it was about ten years away. You guys are showing me the future today.’ So I think the customer sentiment has been fantastic and very hungry for the innovations.”

Padwick says that once early adopters start using innovations like the 8R autonomous tractor, word will spread among farmers, leading to more adoption.

“What I think is going to happen here is you’ll see some people are going to be really excited about the technology and adopting it, and then word of mouth in the coffee shops is going to spread,” Padwick said. “That’s how a lot of these products get sold, not by flashy marketing, presentations, or cool videos on YouTube; it’s coffee shop conversations. And if folks see that other farmers are starting to use the autonomous tractor and getting good value from it, that will naturally drive adoption. Because really, it’s a trust network.”

How Oliver Zahn Beat AI’s “Cold Start Problem” to Make Plant-Based Cheese That Tastes Like the Real Thing

In big data and artificial intelligence, one of the most well-recognized challenges to success is the “cold start problem.”

The cold start problem refers to when a lack of data hobbles recommender systems in machine learning models. Much like a cold car engine that causes a car to sputter and jerk along as a driver starts their journey, an algorithm built to discover and make accurate recommendations can’t perform well when it starts cold with a foundation of little to no good data.

And it’s this problem – a lack of foundational data around which to build a machine learning model – that often deters scientists, entrepreneurs, and companies across various fields from adopting new technology such as artificial intelligence.

The cold start problem is something Climax CEO Oliver Zahn was well-familiar with. As a world-recognized astrophysicist who worked for Google and SpaceX building complex data science models, Zahn knew that getting over this initial hurdle was one of the reasons established companies didn’t embrace machine learning and continue using the status quo – whatever that may be – to build new products.

So when Zahn decided he wanted to build a future food company using AI, he knew the initial challenge of building a dataset that could be mined to find new and promising building blocks in the world of plants would be his biggest hurdle. Still, it was a challenge he knew was worth taking.

“Traditionally, a lot of the big food companies around today pursue sort of a trial and error approach,” Zahn told me recently when we sat down for our conversation on The Spoon Podcast. “They use human intuition to guess what might work. But that often misses things that are less obvious.”

Zahn knew that the less obvious things could be the key to unlocking food building blocks that could power new types of food. Those building blocks, which come from the hundreds of thousands of different plants – many of them inedible – could then be combined in millions of different ways to provide new functional or sensory features to create something like a plant-based cheese. The only way to get there was to use machine learning, cold start problem or not.

“It’s a huge combinatorial screening problem,” said Zahn. “Even the largest food labs on Earth, if they all joined forces, would not be able to explore all combinations and millions of years.”

He knew AI could if he could get past those initial hurdles. But to do that, he knew Climax would have to begin not by gathering lots of data first on plants but on animal products.

“We started by interrogating animal products really deeply to try and understand what makes animal products tick the way they do,” said Zahn. “Why do they have their unique flavor profile texture profiles? Their mouthfeel? Why do they sizzle? Why do they melt and stretch when you eat them?”

You’d think that a lot of that data would already exist, but according to Zahn, it didn’t. The reason for that, he explained, was there had never been a business reason to build those datasets. But as the environmental impact of animal-based products became more apparent in recent years, there was a business motivation to start understanding how these products ticked so they could then be replicated using more sustainable inputs.

The data the company gathered by interrogating animal products allowed them to create labels for their machine-learning models to describe and characterize a food product accurately. With that in hand, Zahn said the company set about building data sets around plant-based building blocks.

“We built a lot of data sets on plant ingredient functionalities and the different ways of combining them. We then found these trends that can recreate animal products more closely, and sometimes in very non-obvious ways.”

Zahn says the process of creating accurate models can often take a very long time – up to 20 years – particularly if those building them don’t have the good intuition that comes with experience in machine learning.

“From the perspective of somebody starting a food company, that (long time horizons) can be scary, right? Because you need to get to market at some point. And so unless you have a very good intuition and have a lot of experience, in my case, a couple of decades, of trying to derive meaning from messy, large data sets, people don’t even start.”

For Zahn and Climax, the models they have built have already started yielding impressive results, enough to help them begin making what will be their first product – cheese – using artificial intelligence. What helped them get there so quickly was Zahn’s experience in building these models that told him to start with trying to understand and describe certain features of animal products – be it taste, mouthfeel, or nutritional benefit – and then find combinations of plant-based building blocks that achieved the same result.

“To look in the plant kingdom for something that is chemically identical to the animal ingredient, like a protein that you might be after, is a little bit of a red herring,” said Zahn. “Because it doesn’t need to look identical microscopically, or the sequence doesn’t need to be identical, for it to behave the same. There could be other ways to accomplish the same functionality.”

Now, after just two and a half years, Climax is ready to start rolling out its first products, a lineup of cheese that includes brie, blue cheese, feta, and chèvre (goat cheese) made from plant-based inputs. It’s an impressive feat, partly because, as a first-time entrepreneur, Zahn also faced the challenge of learning how to build a company, in itself another “cold start problem.”

If you’d like to hear the full story of Zahn and Climax Foods’ journey to building plant-based dairy products, you can do so by listening to our conversation on this week’s episode of The Spoon podcast. Click play below or find it on Apple Podcasts, Spotify, or wherever you get your podcasts.

Verneek Launches Generative AI Platform to Assist Food Shoppers

Today Verneek, a New York-based generative AI startup, came out of stealth with the debut of its first product, Quin Shopping AI. The product is the first to utilize the company’s proprietary AI platform called One Quin.

The company, which was co-founded by the husband and wife team of Omid Bakhshandeh and Nasrin Mostafazadeh, spent the last two years developing the One Quin AI engine, which Mostafazadeh describes as a ‘consumer experience AI platform.’

“What we’ve done is that we’ve built a system which has many orchestrated modules of different transformer technology or non-transformer technology that has been trained to answer incoming questions,” said Mostafazadeh.

According to Mostafazadeh, the Shopping AI was trained with anonymously aggregated consumer query data gathered through the company’s initial partners (which she says she can’t reveal at this time) and synthetically-generated data sets based on these consumer queries.

Mostafazadeh said that the One Quin Shopping AI differs from other generative AI systems, such as ChatGPT, because it is vertically targeted around the specific use case of the consumer shopping experience.

“One Quin is AI plus curated knowledge in a box, whereas likes of ChatGPT is a general AI where knowledge is not curated.”

One benefit of this vertical focus is that, according to Mostafazadeh, their product will not suffer from the hallucination problems that plague general generative AI systems. General-purpose generative AIs like ChatGPT will sometimes produce answers that, while seemingly plausible, can be factually wrong or non-sensical. In contrast, One Quin is anchored by specific parameters within a confined topic set and is architected in a way in which it produces reliable answers.

“We’ve literally spent the last two years to mitigate that (hallucination),” said Mostafazadeh. “What is very unique about what we’ve created One Quin to sit on top of data. So it doesn’t generate off the wild. Instead, through very sophisticated inner machinery, it points to data that it sits on top of.”

Mostafazadeh said that because the One Quin engine is pointed to specific data, it can respond to specific questions tailored around parameters consumers use when searching for a product. For example, suppose a customer has a question about a food or nutrition product that fits a specific price range. In that case, One Quin can access this data and produce a tailored response specific to a retailer’s product inventory.

“What Quin can do, for example, is answer a question like ‘what is the healthiest snack I can buy for my kids that costs under $5?'” said Mostafazadeh.

I asked Mostafazadeh how her AI can determine whether a product fits criteria like healthiness, which can sometimes be arbitrary. She told me they had created something akin to a “health score” based on nutritional research. For other more arbitrary criteria, she told me the system is designed to anchor the answers with data points they believe act as a good proxy.

“For tastiness, Quin is basing it on the rating that the items have,” said Mostafazadeh.

Over time, however, Mostafazadeh says they could develop criteria to score a product for something like tastiness more accurately. However, one challenge with that, for now at least, is that the system is currently architected to answer questions without knowledge about the shopper.

“Right now, we have decided to make the barrier to entry basically zero. We don’t even ask the shoppers to log in. We don’t track them, and hence it’s a blank slate.”

That could change, said Mostafazadeh, who admits adding personal shopper contextualization would be very powerful.

“We would love to know that you are vegan without you telling me you’re vegan in your query. I would love to know that you hate cilantro because it tastes soapy, and by default, I will show you all recipes that don’t have cilantro in them.”

Mostafazadeh said that another advantage of Open Quin is that it can sit on top of any compute engine, whether it’s Microsoft Azure, AWS, Google Cloud, or in-store edge computing architecture. She said this makes it more affordable than other generative AI systems and gives retailers – who can be very specific about what cloud or computer system infrastructure they tie into – more flexibility.

“You probably know that retailers don’t like AWS (Amazon’s cloud). They don’t want anything of their world that touches anything of Amazon’s world.”

Mostafazadeh said that Quin Shopping AI could be deployed using various user interfaces. For example, she said retailers could deploy it in an app, on a website, via a chatbot, or on a consumer kiosk.

The company has raised a $4.2 million pre-seed funding round, and its website went live today.

Introducing One Quin, Consumer Experience AI Platform