Nov 22, 2017 · post

Algorithmic Cookery & Happy Thanksgiving

As you are preparing for your Thanksgiving meal, just know that a robotic arm is holding the spoon at the Institute for Culinary Education (ICE); progress is relentless. “The Chef Watson cookbook is a revolutionary display of the creative collaboration of man and machine." Cognitive Cooking with Chef Watson, culinary and cognitive creativity at your fingertips. Perhaps you should try the Acorn Squash Meat Roast … with English Breakfast Tea.

Jeopardy! It is so 2011. In 2017, there is (of course) a neural network trained to generate recipes developed by Janelle Shane. Try the Pears Or To Garnestmeam or the Shanked Whipping Peanuts:

Image Credit: The Daily Dot

For the literary-minded, Janelle recommends training the neural network first on the works of your favorite author before feeding it recipes. Here is what happens when you use the works of H. P. Lovecraft:

Bake at 350 degrees for 30 to 32 minutes. Test corners to see if done, as center will seem like the next horror of Second House.

Whip ½ pint of heavy cream. Add 4 Tbsp. brandy or rum to possibly open things that will never be wholly reported.

The algorithm adds a helpful note:

NOTE: As this is a tart rather than a cheesecake, you should be disturbed.


Eager to cook your own recipes with char-RNNs (i.e., multilayer recurrent neural networks with a character-level language models)? The code is available here. Do androids dream of cooking electric pies?

If you’ve done your shopping already, take a quick picture of your haul and let the algorithm im2recipe serve you cooking instructions. The algorithm developers collected 1m cooking recipes and 800k food images, the largest publicly available collection of recipe data, and trained a neural network to find a joint embedding of recipes and images, a multi-modal embedding that puts words and images in the same multidimensional embedding space. This embedding allows to retrieve a recipe given an image and an image given a recipe. More importantly, it allows arithmetic with chicken pizza:

v(chicken_pizza) - v(pizza) + v(lasagna) = v(chicken_lasagna)

You can now compute your next meal (Image Credit: MIT).

In 2017, “we can embed that” truly is the new “we can pickle that." (Pro-tip: challenge it with your left-overs.)

In the days after Thanksgiving, perhaps try the algorithm for personalized diet meal planning or AVA, your intelligent nutrition bot focused on your happiness.

(You’re welcome!)

Finally, you will be pleased to know that the Association for Computing Machinery (ACM) holds a yearly workshop with the appetizing title Multimedia for Cooking and Eating Activities. We have high hopes for Thanksgiving 2018!

We, the entire team at Cloudera Fast Forward Labs, wish you a Happy Thanksgiving.

Cover image credit: Photo by Besjunior/Shutterstock

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Nov 30, 2017 · newsletter
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Oct 20, 2022 · newsletter

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Sep 21, 2022 · newsletter

CFFL September Newsletter

September 2022 Welcome to the September edition of the Cloudera Fast Forward Labs newsletter. This month we’re talking about ethics and we have all kinds of goodies to share including the final installment of our Text Style Transfer series and a couple of offerings from our newest research engineer. Throw in some choice must-reads and an ASR demo, and you’ve got yourself an action-packed newsletter! New Research! Ethical Considerations When Designing an NLG System In the final post of our blog series on Text Style Transfer, we discuss some ethical considerations when working with natural language generation systems, and describe the design of our prototype application: Exploring Intelligent Writing Assistance. more
Sep 8, 2022 · post

Thought experiment: Human-centric machine learning for comic book creation

by Michael Gallaspy · This post has a companion piece: Ethics Sheet for AI-assisted Comic Book Art Generation I want to make a comic book. Actually, I want to make tools for making comic books. See, the problem is, I can’t draw too good. I mean, I’m working on it. Check out these self portraits drawn 6 months apart: Left: “Sad Face”. February 2022. Right: “Eyyyy”. August 2022. But I have a long way to go until my illustrations would be considered professional quality, notwithstanding the time it would take me to develop the many other skills needed for making comic books. more
Aug 18, 2022 · newsletter

CFFL August Newsletter

August 2022 Welcome to the August edition of the Cloudera Fast Forward Labs newsletter. This month we’re thrilled to introduce a new member of the FFL team, share TWO new applied machine learning prototypes we’ve built, and, as always, offer up some intriguing reads. New Research Engineer! If you’re a regular reader of our newsletter, you likely noticed that we’ve been searching for new research engineers to join the Cloudera Fast Forward Labs team. more

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Cloudera Fast Forward Labs is an applied machine learning research group. Our mission is to empower enterprise data science practitioners to apply emergent academic research to production machine learning use cases in practical and socially responsible ways, while also driving innovation through the Cloudera ecosystem. Our team brings thoughtful, creative, and diverse perspectives to deeply researched work. In this way, we strive to help organizations make the most of their ML investment as well as educate and inspire the broader machine learning and data science community.

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