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Daily news and analysis about the food tech revolution

AI

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

The Latest, But Not The First: Five Ways AI Altered The Food Industry Before ChatGPT

Generative AI has shaken the tech industry to its foundations. For the first time, Google’s search dominance looks vulnerable, while ChatGPT has elevated Microsoft’s Bing from second banana to sexy beta. Meanwhile, hundreds of new startups are creating vertically-focused SaaS offerings powered by OpenAI, and tech corporations, big and small, are evaluating how to jump on the generative AI bullet train.

In the food world, we have some early arrivers in spaces like restaurant tech software such as ClearCOGS and Lunchbox leveraging OpenAI to add additional functionality. On the content creator and influencer side, we’re already seeing recipe creators and culinary pros tap into the power of generative AI.

But if you think the arrival of ChatGPT is the first AI with the potential to have a big impact on the world of food, you’d be wrong. In fact, over the past decade, we’ve watched as artificial intelligence has started to transform significant portions of the food world. Here are five ways AI has changed food over the past decade:

AI-Generated Recipes

Over the past decade, one of the most significant milestones for artificial intelligence in the world of food is the application of IBM Watson’s general AI to recipe creation. About ten years ago, the Watson team figured it needed to do something besides beat human contestants on Jeopardy to demonstrate its AI’s powers. Before long, Watson had its own cookbook of what IBM called ‘cognitive recipes’. Eventually, CPG brands like McCormick partnered up with IBM to see how they could apply Big Blue’s AI to their business.

Novel Food Discovery and Creation

Over the past few years, a new cohort of startups using AI to accelerate the discovery of novel food ingredients or plant-based recipes have emerged, causing ripples through the consumer packaged food market as they present a direct challenge to the more conventional – and slow – way in which food companies traditionally discover new food products. Over five years ago, companies like Gastrograph started to use AI to create predictive modeling around how different consumer cohorts may react to new food products, and more recently, we’ve seen a new generation of food companies like NotCo base its entire roadmap around AI-generated recipes for its plant-forward product lineup. On the novel ingredient discovery side, companies like Shiru and Kingdom Supercultures are using machine learning to find new ingredients that can help replicate the functional and taste properties of more traditional animal-based inputs.

Alexa’s Personalized Meal Planning and Recipes

When Amazon showed off Alexa almost a decade ago, in late 2014, most thought it was a cool home-based voice interface for weather forecasts and kitchen timers. But Amazon’s AI-powered virtual assistant helped launch a new way for consumers to do everyday things, including buying food and checking on that roast in the oven. But it wasn’t long before Amazon started to help me automate and personalize our shopping lists, and eventually started to create personalized recipes based on our past behavior.

Computer Vision Is Everywhere

A little over two years after Amazon debuted Alexa, it opened its first Amazon Go store featuring its Just Walk Out technology. Powered by sensors and computer vision, the new storefront lets shoppers pick up things off the store shelves and walk out without going through checkout. Soon, a whole bevy of human-less retail startups emerged to offer grocery and convenience store operators platforms to create more friction-free shopping powered by computer vision. We also saw computer vision-powered home appliances enabling consumers to identify their food in the fridge or the oven. Computer vision has also taken off in the restaurant back-of-house for solutions that help reduce food waste and help optimize food inventory.

Food Robots

While robotics and AI are not always synonymous, many robots are deploying some form of AI to help feed us. Whether it’s Google Mineral’s farm robot modeling plant traits and phenotyping crop varieties or server robots dynamically mapping the layout of a restaurant dining room, we are seeing a proliferation of AI-assisted food robots up and down the food value chain.

As far as generative AI goes, we’ve only begun to see how it could change the food industry. Initial applications are more likely to be in restaurant marketing (like the image created for this post using DALL-E), operations, and customer service systems. But as the technology becomes more powerful and creative programmers figure out ways to integrate generative AI technology into their platforms, the impact of ChatGPT and similar AI systems holds massive transformative potential for the food industry.

If you’d like to learn more about how generative AI will change the food industry, you’ll want to attend The Spoon’s mini-summit, How ChatGPT & Generative AI Will Change the Food Biz, tomorrow. You can sign up here.