spice


AI and spices: Would you put cumin on a pizza?

BBC News

What do Tuscan Chicken, Bourbon Pork Tenderloin and New Orleans Sausage all have in common? They're all new spice mix flavours that have been developed by the world's biggest spice firm using artificial intelligence (AI). But with taste such a subjective experience, can machines really do the job better than humans? And what does this mean for cultures that see spice as a clear token of identity? Spice giant McCormick, which sells spices to consumers but also develops flavours for the food industry, says it spent four years crunching through more than 40 years of flavour-related data, using machine learning to come up with new flavour combinations that human scientists might not have considered.


AI is even discovering new spices for our meals

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The world's largest spice maker is hoping AI will help it come up with some new flavours in order to, uh, spice things up. McCormick & Company has been around for 130 years and makes a wide range of seasonings, spices, and condiments. However, after that many years, it seems the firm's human minds are running out of ideas. "McCormick's use of artificial intelligence highlights our commitment to insight-driven innovation and the application of the most forward-looking technologies to continually enhance our products and bring new flavours to market. This is one of several projects in our pipeline where we've embraced new and emerging technologies."


How McCormick and IBM will use AI to create the next big spice

Engadget

Nobody hops out of bed in the morning, thinks to themselves, "Today, I'm going to invent the next Oreo," and actually follows through on it. Even training in the skills necessary to become a professional food product developer can take the better park of two decades, much less creating and testing the thousands of flavor iterations needed to dial in on the perfect taste that will finally unseat Cool Ranch Doritos. But thanks to IBM's Philyra AI, spice manufacturer McCormick & Company's R&D the team is leveraging machine learning to cut the time it takes to develop new flavors by up to 70 percent. Last October, IBM Research unveiled the Philyra AI as a tool to accelerate the creation of new and novel scents for the fragrance industry. "It is a system that uses new and advanced machine learning algorithms to sift through hundreds of thousands of formulas and thousands of raw materials," Dr.


McCormick hands over its spice R&D to IBM's AI

Engadget

McCormick might be a brand name you recognize from its herbs and spices, French's Classic Yellow Mustard or even "edible" KFC-flavored nail polish. For more than 40 years, it's recorded reams of data on product formulas, customer taste preferences and flavor palettes. Over the last four years, McCormick has worked with IBM on an AI platform called ONE. Product developers have so far used insights it gleaned from the data to create Tuscan chicken, bourbon pork tenderloin and New Orleans sausage seasonings -- they'll be in US stores by the end of the spring. McCormack says its aim with ONE is to create family-friendly seasonings you can use on both proteins and vegetables, while AI has helped it speed up product development by up to three times.


Tinder adds GIF-like video loops to spice up your dating profile

Engadget

If you're a dating app regular, you know that a photo only says so much about yourself. But do you really want to go to the trouble of recording a whole video for people who could swipe left before you've even spoken a word? Tinder thinks there's a better balance between the two. It's launching a Loops feature that (surprise) adds two-second looping videos to your profile alongside the usual still shots. You just have to trim an existing video to portray yourself as a fun-loving party person or tender romantic.


At This New Boston Restaurant, The Meals Are Prepared By Robots

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Forget about the stereotype of the short-fused chef barking orders to a team of frazzled cooks. At Spyce Food Co., the robot kitchen is wired to achieve culinary perfection without making much of a peep or breaking a sweat. The fast-casual, yet futuristic restaurant, which opened its doors in Boston's Downtown crossing Thursday, is the robotic brainchild of four MIT grads and a Michelin-starred chef. The menu consists of seven bowl-style options including Indian, Latin, and Thai -- all "internationally-inspired and vegetable-centric," according Spyce CEO and cofounder Michael Farid. Meat, fish, and vegan options are all available.


Flipboard on Flipboard

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There are more than 8,000 online courses out there. These are some of the best. More than 50 million students signed up for one this year. When scientists announce they've made a breakthrough, they usually promise we'll see the full effects of those discoveries--anything from a better understanding of how the universe works to a drug ready for use in patients--in about five years. TULSA -- Tom Coomer has retired twice: once when he was 65, and then several years ago.


One smart cookie: Artificial intelligence helps perfect a gluten-free treat

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Coming up with the ultimate cookie recipe can take even a seasoned baker hours of trial and error, all the more so if her target audience has specialized needs -- say, a diet that's vegan or gluten-free.


PornHub launches new sex toy line for couples to spice up holiday shopping season

Mashable

I don't care what the calendar says, it's the holiday season and that means it's time to start thinking about presents -- even the sexy kind. PornHub has rolled out a new line of sex toys just in time. SEE ALSO: Can artificial intelligence revolutionize porn? The new line is produced in conjunction with famed sex toy retailer Ann Summers and aimed specifically at couples. The line includes vibrators, cock rings, and pretty much anything else under the sun you could want.


Tuning Free Orthogonal Matching Pursuit

arXiv.org Machine Learning

Orthogonal matching pursuit (OMP) is a widely used compressive sensing (CS) algorithm for recovering sparse signals in noisy linear regression models. The performance of OMP depends on its stopping criteria (SC). SC for OMP discussed in literature typically assumes knowledge of either the sparsity of the signal to be estimated $k_0$ or noise variance $\sigma^2$, both of which are unavailable in many practical applications. In this article we develop a modified version of OMP called tuning free OMP or TF-OMP which does not require a SC. TF-OMP is proved to accomplish successful sparse recovery under the usual assumptions on restricted isometry constants (RIC) and mutual coherence of design matrix. TF-OMP is numerically shown to deliver a highly competitive performance in comparison with OMP having \textit{a priori} knowledge of $k_0$ or $\sigma^2$. Greedy algorithm for robust de-noising (GARD) is an OMP like algorithm proposed for efficient estimation in classical overdetermined linear regression models corrupted by sparse outliers. However, GARD requires the knowledge of inlier noise variance which is difficult to estimate. We also produce a tuning free algorithm (TF-GARD) for efficient estimation in the presence of sparse outliers by extending the operating principle of TF-OMP to GARD. TF-GARD is numerically shown to achieve a performance comparable to that of the existing implementation of GARD.