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Daily news and analysis about the food tech revolution

IBM

A Decade Before The ChatGPT Recipe Craze, a Cooking Show Champ Helped IBM Train Chef Watson

By now, most everyone has tried their hand at prompt engineering ChatGPT or another LLM to create a decent recipe.

But a decade and a half ago, well before the current craze of making recipes with generative AI, IBM was trying to figure out how to make Watson start cooking. The supercomputer-powered AI, which was probably the first real-world AI most of us knew by name, had just broken into the broader American consciousness after it had beaten human players Ken Jennings and Brad Rutter in a Jeopardy tournament. Now, IBM was looking for ways to showcase how the technology could help people be more creative, and they identified cooking and recipes as the next world to conquer.

Around this time, the Watson team teamed up with the Institute of Culinary Education (ICE) to help train Watson. James Briscione, who had won Chopped season 2 a couple of years before and was the ICE’s director of culinary research, remembers those early days when IBM computer scientists filed into his kitchen.

“The first day we set up, the Watson team came to the kitchens at ICE, walked in with a laptop, flipped it open, logged into an interface that IBM was hosting, and we started parsing datasets.”

This meant going through and looking at ingredient combinations based on cuisine style, dish type, and flavor profiles of different dishes, as well as breaking down each type of ingredient into the various flavor and aromatic compounds into building blocks, which allowed Watson to then process millions of flavor combinations and recommend them to ICE chefs. During the process, the Watson team made sure the human chefs remained as ana integral and necessary part of the AI feedback loop.

“For the majority of the project, it did not give us recipes, it gave us ingredient combinations,” said Briscione. “And then I did the work then to translate that into the recipe.”

Briscione said taking Watson’s combination suggestions and combining them into a recipe helped unlock the creativity of him and the other chefs.

“As a sort of a thought experiment, it was even more interesting because then we could take an ingredient output, I would take it and interpret that ingredient output one way. Another chef could take that exact same ingredient output and interpret it completely differently. So in inspiring creativity, it was really, really powerful.”

Nowadays, Briscione is applying what he’s learned to build a new company that helps train large language models to better understand food. He will discuss this new company at the Smart Kitchen Summit next week.

You can watch the entire interview and see the transcript below. .

The Chef Who Helped Build Chef Watson: A Conversation With James Briscione

Transcript

Michael Wolf: I’m excited to have James Briscione who is a chef I’ve been following for a while. James, you do so many things. You’re an author. You’re a Food Network personality. And you’re one of those rare chefs that have been dabbling with AI longer than pretty much most people even working with AI at all. So it’s exciting to have you. Thanks for coming.

James Briscione Yeah, Michael. Excited to chat here excited about SKS coming up in June. This will be a great event and can’t wait to get there.

Michael Wolf Yeah, we’re going to hear you on stage talking about your experiences and what you’re looking forward to with the integration of AI. But for those who don’t know you, tell us a little bit about your background and what you’ve done over your career.

James Briscione As you said, I’m a chef first. I started as a dishwasher at the age of 16, worked my way up to some of the top kitchens in the country. James Beard award winning kitchens that I was at the helm of. Four Star Fine Dining in New York City. Kind of did it all. With that really elevated fine dining background, I moved into education at the Institute of Culinary Education in Manhattan and really was in the right place at the right time when IBM came knocking and said, ‘we’ve got this crazy idea. We’ve got this thing called Watson, that just conquered Jeopardy. And now we want to see if it can help people. We know it can answer questions. We want to see if it can help people be more creative.’

And they thought about music, they thought about visual arts, but you know, felt those were too subjective and culinary arts was a very objective area for this. So when they came to meet with us, they met with all the instructors, kind of talked about the process of development and creating dishes, and how you work as a chef. Having just been the first two-time champion on the show Chopped on the Food Network, the way I sort of process and put together flavors and ingredients was exactly what they were trying to build with Watson. So that started about a four-year relationship working with the core team there at IBM to develop Chef Watson, which I now know was recipe generative AI. Almost 11 years ago, before we started building it, I had no idea what those words even meant. And AI was only something you saw in Will Smith movies.

Michael Wolf So those early days, you’re helping with Watson. Are they bringing you into a kitchen at IBM headquarters? What does that exactly mean? Are they monitoring you with cameras, or are you saying, ‘hey, these are what flavors are trying to tell a computer what a flavor is?’

James Briscione First, as we talked about it, I was still in that Chopped competition mode. So I was like, ‘if I’m going to cook against this computer, I’m going to kick its ass. I’m actually going to prove that this thing can’t do it better than a human. The first day we set up, they came to the kitchens at ICE (the Institute of Culinary Education), walked in with a laptop, flipped it open, logged into an interface that IBM was, was hosting, and we started parsing datasets and going through and generating ingredient combinations based on a number of different factors based on cuisine style. so original cuisine, a type of dish and, and, and a core ingredient to inform, the flavor profile of, of the dish. So we’d say Italian grilled lobster. And then it would generate trillions of possible ingredient combinations that could be used to create a dish that were typical Italian ingredients that kind of fit in with what it knew about a grilled lobster recipe or a grilling recipe and a lobster recipe overlay. And then use that lobster to as kind of the core flavor profile to then build sort of that flavor tree off of that core ingredient, which that process, that’s how I tend to think about creating a dish, but getting down to the molecular level, understanding all of the aromatic compounds in the food, how those flavors relate to one another, why they go well together. I never looked at information that way or understood it in that form. And it was mind blowing to process tens of thousands of aromatic compounds in every dish, just like that.

Michael Wolf So it was essentially building, I don’t know if the right word is ontology, but kind of trying to dissect food at a more atomic level and then understanding what the commonalities are. You know, saying ‘lobster often goes in these types of dishes’ or ‘Hey, maybe it works with these types of dishes.’ So really trying to create the data building blocks so Watson can then say, hey, here’s a unique flavor idea, recipe idea you may not have thought of with your small human brain.

James Briscione Exactly. And, you know, for the majority of the project, it did not give us recipes. It gave us ingredient combinations. And then like, you know, it was kind of, I did the work then to translate that into the recipe. But as sort of a thought experiment, it was even more interesting because then we could take an ingredient output, I would take it, and interpret that ingredient output one way. Another chef could take that exact same ingredient output and interpret it completely differently. So in inspiring creativity, it was really, really powerful. And actually, there were some cool examples of where we would take the same generation, go to separate sides of the kitchen, and come back in the middle with our finished dish. You couldn’t even tell that they started at the same place.

Michael Wolf You’ve watched over the past decade, this expansion of folks trying to use technology to understand the way we cook better. Those early days of watching Watson were pretty seminal and informative, and that was the first time I remember seeing articles, maybe in the New York Times, saying ‘Watson beat Jeopardy, now it’s trying to cook’. So as you’ve watched this evolve over the past decade, what have you been thinking about? And what have you learned maybe about AI and its intersection with food? Is it something now you’re more excited about than ever?

James Briscione 100% more excited than ever. I think the potential here to simplify, to streamline, which to me is kind of the ultimate promise of AI, to make our lives better, to organize and streamline. I think where obviously it gets tricky, is one, it’s new. So there’s going to be some inherent distrust of it. One bad recipe, one recipe that doesn’t work and people are going to bail on it as well.

Michael Wolf Right, right. We’ve all done those bad recipes with ChatGPT. Like that just sounds awful.

James Briscione Yeah, and you know, I mean, it’s going to be interesting to watch this landscape too now because the majority of what’s out there are just some, you know, some basic GPT wrappers. And if any of these copyright lawsuits get through, a lot of these datasets, these sources, start to dry up or become more restricted. So one thing I’m starting to work into is building a new dedicated model for recipe generation with nutrition and flavor inputs that really can optimize your food specifically for you. If you want to get down as far as the genome, I think that’s some functionality that is off in the future, but generally, as an active 44-year-old male who lives in a hot climate, AI can tell me exactly what I should be eating on a day-to-day basis to optimize me for what I do.

Michael Wolf That’s interesting. And I think the startup you’re working on is called CulinAI. And so that’s exactly it. And so is this something you’re building your own large language model or you’re building something that can integrate with maybe some of the other large language models? Tell us a little bit about it.

James Briscione Yeah, so, and I’m actually working with the original developer of Chef Watson. It’s kind of a hybrid model where we are going to be employing some large language models, but also some kind of dedicated pieces that would be unique to this model, particularly the flavor science and the nutrition data input. And then, really, kind of the secret sauce is in the selection because, again, we know that the large language models can generate lots of great things that look like good recipes, but training it to then go back through those and select out the ones that are actually right is where it all comes together.

Michael Wolf Well, I’m excited to hear more about that at Smart Kitchen Summit. You are someone who works in a professional kitchen. You’ve been on TV, won awards, you have your own restaurant. But there’s also the consumer, right? Someone who, like me, doesn’t know what they’re doing. And one of the reasons I got interested in the Smart Kitchen in the early days is because I thought that maybe technology can help me become a better cook. How do you think average everyday consumers who aren’t like you can use technology tools like AI to help them cook better?

James Briscione We talked about kind of one of the biggest benefits AI is to make our lives better, to simplify processes and personalization, right? And I think that’s really where it comes in to find the right information. Even just how to get your ingredients organized at the beginning of the week to set up for, hey, ‘here’s what I’m going to, here’s what I’m going to cook for the week’, building out a meal plan that utilizes all of the ingredients that you have so that you don’t, at the end of the week, have half a pint of cherry tomatoes, three quarters of a head of celery, two onions, and half a butternut squash. It’s all just sitting there because you bought it all because you had to have it for that recipe, and now it all is just kind of like laying to waste, and you leave it there until it’s time to finally throw it away. And I think some of those, I think a lot of those things are what discourage people or kind of keep people from cooking. So, AI tools that can teach you to approach that process the way I do as a chef of not just looking at, okay, here’s what I’m gonna do for dinner for Tuesday night, but okay, as I’m doing dinner for Tuesday night, here’s how we get lunch for Wednesday ready.

Michael Wolf Right, right.

James Briscione And another chunk of dinner for Thursday, all kind of set up and set aside so that that’s easier too. And I think a lot of those tools are some of the things we’re looking at building into CulinAI, and I think those are the pieces that I’m excited about.

Michael Wolf Well, I’m excited to hear you in Seattle in June at Smart Kitchen Summit. James, where can people find out more about you?

James Briscione Most social media platforms at James Briscione. That’s probably the best way to find me, LinkedIn, all of the typical places, just right under my name, I’m there. There’s not many Brisciones around, so.

Michael Wolf All right, man, we’ll see you in a bit. Yeah, there aren’t. That’s a great, unique name. All right, James, we’ll see you soon.

James Briscione All right.

IBM Partners With McDonald’s, Acquires McD Tech Labs to Automate Drive-Thru Orders

Over the summer, McDonald’s announced that it was trialing automated ordering at 10 Chicago, Illinois locations. Now, it appears that we will be seeing automated ordering at more locations sooner than later. Today, IBM announced that it has partnered with McDonald’s to continue to develop automated drive-thru orders and acquired its proprietary McD Tech Labs. The financial details of the acquisition were not disclosed.

McDonald’s acquired Apprente, a voice technology company, in 2019, and shortly after developed McD Tech Labs based on Apprente’s tech. This technology uses artificial intelligence to process drive-thru orders and then sends the order to the kitchen, where it is fulfilled by staff.

What is the benefit of using an AI-powered ordering system? In the trial run at the Chicago locations, McDonald’s found an 85 percent accuracy rate with orders and about 20 percent of orders needing staff assistance. AI can also help reduce customer wait time and free up employees to assist elsewhere in the restaurant.

In addition to the acquisition of McD Tech Labs, IBM will assist in the continued development of the Automated Order-Taking (AOT) technology and work on additional languages and dialects.

Consumers have traditionally voiced a certain degree of hesitancy in interacting with artificial intelligence. However, in Datassential’s AI & Menus report, it found that 43 percent of consumers found voice ordering technology unappealing initially, but after experiencing it, 68 percent were satisfied with the tech.

McDonald’s operates nearly 14,000 locations throughout the U.S, and it was not disclosed when a large rollout of the AOT technology might occur. IBM stated in the press release that it will help the quick-service chain accelerate the deployment of the AOT technology across different markets.

Would You Prefer to Stand in a Virtual Line When Going to the Grocery Store?

One of the ways grocery stores have dealt with the COVID-19 pandemic is to limit the number of shoppers that can physically go into the store at a given time. As a result, people are standing in a very long lines outside some supermarkets as they are spaced a socially distant six feet apart from each other.

Even with social distancing, you’re still surrounded by other people who may or may not be wearing a facemask, depending on where you live it could be hot (or cold), and everyone else in line is probably just as on-edge about the whole situation as you are. But what if you could just stay in the comfort of your own car while you wait your turn? That’s the idea behind Safe Queue, an app borne out of the IBM Call for Code Global Challenge.

Created by Los Angeles-based developer David Chura, Safe Queue is a mobile app that creates virtual lines to gain entrance into stores. Once downloaded, a user puts in the address of the store they want to go to when they are within 1,000 feet of the store, the app places them in a virtual line. A unique QR code is sent to the user along with updates about their place in line. When its (finally) their turn to go in, they present the code to store personnel who scan it for authenticity.

Having this virtual queue could be useful for people with disablities that make it difficult for them to stand for long periods of time, or for people with small children. Or for anyone who just wants grocery shopping to be a little less stressful.

At the same time, Safe Queue, and so many other things about this pandemic, highlights issues around equity and inequality. People with smart phones can wait in their cars, people without are stuck standing in line. And also, how would it work if you have a mix of people just standing in line without the app and people who do have the app. How is the place in line determined then?

Hopefully, these are issues that Chura is working on. There are plans to release Safe Queues as an open source project as well as through the Apple and Google app stores. An IBM spokesperson told me that if enough people downloaded the app, it could be used to create a grass roots-style call for businesses and governments to adopt the virtual line technology. But that seems like a bit of a stretch to me. I’m not sure how many people would download an app that wouldn’t work until enough people adopted it. It seems like something stores would have to embed into their native apps and offer to people (after working through the equity issues).

Having said that, I applaud Chura for his creative thinking in trying to solve a problem people face in this pandemic. Now we’ll just have to see if stores and consumers line up to try Safe Queue out.

IBM Develops AI-Powered Hypertaste Tech to Identify Liquids

IBM announced over the holiday weekend that it has developed Hypertaste, an AI-assisted “e-tongue,” which can be used to identify liquids. This, according to IBM, can be helpful for situations where you want to test a substance… without actually putting in your mouth. Examples might be testing water quality at lakes or rivers, or identifying counterfeit wines.

Big Blue said that up until now, the issue with such testing was that portable sensors were too limited in what they could detect, and more powerful sensors were too big to be portable. Read the full blog post for a detailed explanation, but in a nutshell, Hypertaste works using a handheld sensor, a mobile phone app and the cloud. Put the Hypertaste device in a liquid and its electrochemical sensors (covered in polymer coatings) measure voltage in response to different combinations of molecules.

These recordings are sent to a mobile app, which shoots them up to the cloud where they are analyzed. Machine learning algorithms trained to know what that particular liquid should “look” like compare the sample, with results coming back in less than a minute. Hypertaste then can be used to test any liquid, as the algorithms just need to be trained on what to look for.

One of the applications for this technology is authenticating ingredients in food. From the IBM blog post:

…think of the supply chain safety from producer to consumer for packaged food and drinks. At present, once food and drinks are packaged, there is little ability to verify that the package actually contains what is on the label, apart from sending the product to a lab for testing. So, suppliers acting in bad faith may insert lower-quality products into the supply chain with little risk of getting caught, or counterfeiters may even fake a real product by adding the few analytes which are most likely to be tested for in a lab. Fooling a combinatorial sensing system such as Hypertaste is much harder as there is no single substance on which the identification relies, and it is more difficult for wrong-doers to access the sensor training parameters which provide the “key” to interpreting the chemical fingerprints.

E-tasting is becoming quite the cottage industry lately. The Chinese government has been using AI-powered robots to see, smell and taste food to ensure quality and authenticity. And in May, researchers at Washington State University developed an e-tongue for durable spiciness testing.

Right now, the applications for Hypertaste appear to be B2B based, but perhaps it could find its way into consumer products for home testing of water and other liquids.

IBM to Use IoT, Watson and Data to Boost Sugar Cane Production in Thailand

If you’re around my age, when you think of IBM, an image of big mainframe computers with giant rotating tape loops come to mind (I’m old). But for you young’uns, you’d be forgiven if the first thing that comes to mind when thinking about IBM is food.

Today, Big Blue announced a two-year research collaboration with the Thailand government’s National Science and Technology Development Agency (NSTDA) that will use IBM’s Internet of Things (Iot), artificial intelligence (AI) and analytics capabilities to help improve sugarcane yields in Thailand. (Thailand is the world’s second largest exporter of sugar.) The pilot will run on three sugar cane farms covering 1 million square meters run by Mitr Phol, Asia’s largest sugar producer.

IBM’s Agronomic Insights Assistant will bring together elements of IBM Watson Decision Platform for Agriculture, the IBM Pairs Geoscope and The Weather Company, which IBM purchased in 2015. The program will gather data from the fields (soil moisture, crop health, etc.) using a combination of IoT sensors and satellite imagery, which will be augmented with local data from the NSTDA and years of weather data from The Weather Company to better predict potential environmental issues like rainfall.

The IBM platform will then take all this data and run it through Watson to create a software and mobile dashboard to help Mitr Phol better assess and manage risks like pests, diseases, irrigation and pesticide/fertilizer application, with the goal of optimizing productivity and increasing crop yield.

The Agronomic Insights Assistant will start its pilot in the middle of this year, and because IBM is working with NSTDA, a government agency, the insights gained may be shared with other farmers in the region so they can apply the same tactics.

As noted earlier, IBM is a name that keeps popping up in the food tech space for us here at The Spoon. In September of last year the company created the Agropad, a cheap, paper sensor that could be used to measure acidity and chemical levels in soil. And earlier this year, Big Blue partnered with McCormick to apply its AI tech to developing new spices.

Right now, the Agronomic Insights Assistant is in the research phase, so things like pricing and availability weren’t discussed. IBM is facing a lot of competition in the data-driven-insights-for-agtech space. Arable and Teralytic both make field sensors to provide data on soil conditions, Taranis uses aerial imaging including from satellites to help farmers spot diseases early, and Hi Fidelity Genetics uses sensors, data and AI for improved crop breeding.

The advantage IBM has, of course, is that it’s IBM. It has existing sales channels, Watson is perhaps the premiere AI brand, and it can combine sensors, data, weather prediction and AI under one roof. And, of course, a younger generation of farmers unfamiliar with IBM’s roots may not have the preconceived notion of IBM’s mainframe roots.

IBM AgroPad Combines Paper, AI and the Cloud to Analyze Soil and Water

IBM unveiled its new AgroPad yesterday, which uses a combination of paper, artificial intelligence and cloud computing to help farmers easily test for acidity levels and concentrations of various chemicals in their soil and water.

What’s interesting about IBM’s approach is the combination of low + high tech. Here’s a description about how AgroPad works from the IBM announcement blog post:

A drop of water or soil sample is placed on the AgroPad, which is a paper device about the size of a business card. The microfluidics chip inside the card performs on-the-spot a chemical analysis of the sample, providing results in less than 10 seconds.

The set of circles on the back of the card provide colorimetric test results; the color of each circle represents the amount of a particular chemical in the sample. Using a smartphone, the farmer would then take a single snapshot of the AgroPad by using a dedicated mobile application and immediately receives a chemical test result.

AgroPad can measure pH, nitrogen dioxide, aluminum, magnesium and chlorine levels in samples. With this data, farmers can better understand their farmland and make adjustments to where and how they fertilize.

For the artificial intelligence nerds out there, what’s interesting is that the AI is actually running on the mobile phone. After the picture of the AgroPad is taken, the analysis happens on the device itself — not back up in the cloud. The computer vision in the software can precisely analyze the color composition and intensity better than the human eye to deliver instant results.

For the cloud computing nerds out there, each AgroPad test paper has a unique QR code. When a farmer takes a photo of the test paper, AgroPad attaches the phone’s GPS data, as well as the timestamp when the photo was taken, to the specific sample. When multiple tests with all this meta-data are uploaded into the cloud IBM stitches together all the findings to create a data-rich map of an entire farm.

As the global population increases, optimizing and maximizing farm output will be even more important. As such, there are a lot of companies working on technology to make farms more efficient and productive. Big Blue already faces a lot of competition just in the soil analysis space from a number of startups already going to market. Teralytic makes in-ground sensors that monitor pH, nitrogen and potassium. Arable‘s own sensors measure moisture, solar radiation and plant health. And CropX‘s screw-in sensors measure soil moisture and salinity.

I asked Mathias Steiner, Manager, Industrial Technology & Science, IBM Research in Brazil, how a manual process like AgroPad’s (physically going and taking samples) will be able to compete with theses sensor companies. He told me that AgroPad’s chemical indicators are not available as an electronic sensor. Presumably, AgroPad could find a market because unlike sensors, the tiny pieces of paper can’t be stolen and won’t break down. Additionally, it’s a safe bet that IBM could more easily customize AgroPad for different farms by offering different chemical analysis.

Right now, AgroPad is still in the prototype phase. Steiner said the company is looking to team up with an industrial-sized partner for expanded tests.

IBM Issued Patent For Drone-Delivered Coffee Based On Your Cognitive State

Having just arrived back from Japan a couple days ago, I grabbed an extra cup of coffee this morning to help me get over the fog of my jetlag. In the future, my extra boost of caffeine may come courtesy of a drone that knows — preemptively — when I’m feeling a little low-energy.

That’s because IBM was issued a patent last week for a system that would deliver coffee via drone based on a person’s current mood or cognitive state. The patent, called “Drone delivery of coffee based on a cognitive state of an individual”, utilizes sensor data and other indicators to know when a person is sleepy.

According to the patent, data  from wearables, cameras and analysis of past behavior could be used to determine “sleep cycle, bed time for the last N days, a joint measure of the characteristic for two people (e.g. husband and wife), and current blood pressure, or heart rate.” The patent also describes utilizing contextual information like a person’s calendar to determine whether they’re in need of additional alertness. Have a meeting that morning? Keep eye out for coffee drone!

The drone system described in the patent could also deliver coffee to an office or other room full of people and dynamically decide who is most in need of a little caffeine. From the patent: “…if it is known that a meeting participant is meeting with a senior supervisor, and the participant prefers coffee when under stress, the confidence level L can be increased” that someone may want a cup of coffee.

While this patent seems like a strange one for IBM, the company is notorious for its prolific patent filing and, not unlike others, sometimes creates interesting scenarios no one else has yet envisioned and file a patent for it. That said, the concept of preemptive delivery is one that seems to be gaining steam. The Spoon uncovered a patent issued to Amazon (another prolific accumulator of patents) in February for predictive restaurant ordering.

So are we headed for a world where products just show up before we even push a button or know ourselves we’re in need of a pick-me-up? Maybe — but I can see how some consumers would find this type of preemptive determination a bit too pushy, particularly if the system is expecting payment for these products.

On the other hand, utilizing data to understand where there is high demand for a product is something that companies like Zume are already doing today, and I can see how preemptive delivery to areas with groups of people where there might both an undisclosed and unsatiated demand makes sense.

Either way, I don’t expect to see coffee delivering drones showing up unannounced at my house for some time.

Now excuse me while I go put another pot of coffee on.

From Safety to Savings, Blockchain Technology Will Disrupt the Food Scene

Behold the blockchain. The disruptive new technology promises to make traditional paper ledger-based transactions obsolete, replaced by digital ledgers. Headlines appear every day heralding how blockchain technology will revolutionize financial services markets, which remain burdened by unwieldy paper trails and costly proprietary software applications. But blockchain technology will also have a transformative impact on the food industry and many other industries. From cost savings to removal of intermediaries to more efficient and precise tracking of contamination, the food business will derive many benefits from blockchain.

None of this is lost on titans in the food industry and technology giants are focused on blockchain, too. IBM has announced a blockchain collaboration with food giants including Walmart, Unilever and Nestle. Big Blue has announced that it will help global food businesses use its blockchain network to trace the source of contaminated food instantly.  Because blockchain uses digital means to track transactions and trace the flow of food, contamination trails can be solved by data-centric means rather than paper-based ones. Officials from Walmart have demonstrated how this kind of contamination tracking can take place in under three seconds.

According to the World Health Organization, one in ten people will fall ill every year due to contaminated food. Children under 5 years of age are at particularly high risk, with 125,000 children dying from foodborne diseases every year. Blockchain technology will have a giant impact on these problems. The full list of food companies signed on to work with IBM’s blockchain network is as follows: Dole, Driscoll’s, Golden State Foods, Kroger, McCormick and Company, McLane Company, Nestlé, Tyson Foods, Unilever and Walmart.

In this video, Walmart’s VP of Food Safety discussed the huge impact that blockchain will have:

According to Forbes: “By using blockchain, when a problem arises, the potential is to quickly identify what the source of contamination is since one can see across the whole ecosystem and where all the potential points of contamination could be using the data to pinpoint the source. As such it is ‘ideally suited’ according to IBM to address these challenges because it establishes a trusted environment for all transactions.”

IBM has already conducted several pilots focused on food safety in order to demonstrate the ways in which blockchain can benefit global food traceability.

All participants in the global food supply chain stand to benefit from blockchain technology, ranging from growers to suppliers and distributors. Beyond tracking contamination, blockchain promises to usher in much more efficient, trusted financial transactions that can remove many types of intermediaries. According to a new market intelligence report by BIS Research, titled ‘Blockchain Technology in Financial Services Market – Analysis and Forecast: 2017 to 2026′, cost savings of $30 to $40 billion per year will be achieved in trade finance.

The move to blockchain does not necessarily mean buying into expensive proprietary platforms, either. While IBM’s blockchain network resides on the IBM Cloud platform, The Linux Foundation’s Hyperledger project is squarely focused on keeping blockchain open source and blockchain solutions free. Many powerful companies are partners on the project, and are committed to keeping patent wars and proprietary shenanigans out of the blockchain ecosystem.

“Blockchain technology enables a new era of end-to-end transparency in the global food system – equivalent to shining a light on food ecosystem participants that will further promote responsible actions and behaviors,” notes Walmart VP Frank Yiannas. “It also allows all participants to share information rapidly and with confidence across a strong trusted network.”

6 Food Science Books That Will Change the Way You Look at Food

In Austin, where I’m from, barbecue pitmasters debate the Maillard reaction as often as they tuck into a plate of brisket and ribs. In other words, the best chefs have long known that science is the secret to their success, but over the past few years, science has become sexy to regular folks too.

Now you don’t have to go to the Institute of Culinary Education or the Massachusetts Institute of Technology to understand all of those chemical reactions that make food taste a certain way, or to learn how to make it taste even better. There are cookbooks for that. Here are a few of my favorites.

On Food and Cooking: The Science and Lore of the Kitchen, by Harold McGee

Written back in 1984, this is a serious food science bible. Every professional chef has a dog-eared copy and can probably recite word for word sections about her favorite ingredient, cooking technique, and science behind why it works. Get ready for an intense discussion at the molecular level, including a chemistry primer.

The Science of Good Cooking, by Cook’s Illustrated

Cook’s Illustrated and America’s Test Kitchen pioneered the idea of cooking with the scientific method in order to develop foolproof recipes (they totally changed the way I make baked potatoes, for example). This easy-to-read book walks you through 50 experiments and more than 400 recipes that will soon become your new favorites.

The Food Lab: Better Home Cooking Through Science, by J. Kenji Lopez-Alt

Harold McGee has some competition, as J. Kenji Lopez-Alt’s new book might just be the bible for a new generation, especially the home cook. His accessible tone, funny anecdotes, and step-by-step photos are the icing on the cake of delicious recipes, developed with the exhaustive scientific method seen in The Science of Good Cooking. I pretty much made all of his Thanksgiving meal suggestions and couldn’t have been happier.

Neurogastronomy: How the Brain Creates Flavor and Why It Matters, by Gordon M. Shepherd

If you want to know not only how to make that stuffing for Thanksgiving but also why it tastes so good, this is your jam. Be prepared for a super nerdy analysis of the mechanics of smell as well as how the brain processes flavor in terms of emotion, food preferences, cravings, and memory.

Cognitive Cooking with Chef Watson, by IBM and the Institute of Culinary Education

In the 21st century, cooking isn’t limited to humans. A few years ago, IBM teamed up with the Institute of Culinary Education to create a cognitive cooking technology called Chef Watson that could discover new ingredient combinations and recipes that humans would never think of. This book details those recipes (think Hoof-and-Honey Ale), as well as how they did it.

Liquid Intelligence: The Art and Science of the Perfect Cocktail, by Dave Arnold

And where would the best meal be without a good drink to go with it? Dave Arnold has put together more than 120 cocktail recipes using the most cutting-edge techniques and hard-core science, guaranteeing you the knowledge you need to make the most amazing milk-washed vodka cocktail of your life.