Difference between revisions of "DT's page for Kahn Food project"
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| + | =News= | ||
| + | * François Chollet, Google, @fchollet | ||
| + | :::There's a point to be made that a neural network for food classification that can classify Tide pods as "not food" has already reached superhuman capabilities, Twitter, 6:30 PM - 20 Jan 2018 | ||
| + | <br /> | ||
| + | =Reference= | ||
| + | <br /> | ||
| + | * [https://www.smith.edu/kahninstitute/future.php Page for Kahn future projects] | ||
| + | <br /> | ||
| + | =Quotes= | ||
| + | <br /> | ||
| + | * Jacques Pepin, "It's hard to beat bread and butter when you have have very good bread and very good butter." | ||
| + | * Faith Willinger, on Chef's Table Season 1, Episode 1, about Massimo Bottura: "One of the most important ingredients, in his food, is memory." | ||
| + | <br /> | ||
=Short Presentation for Kahn= | =Short Presentation for Kahn= | ||
<br /> | <br /> | ||
| − | + | * NN + ML = branch of AI | |
| + | * Huge progress at exponential rate | ||
| + | * Example: [https://www.sciencealert.com/it-took-4-hours-google-s-ai-world-s-best-chess-player-deepmind-alphazero In Just 4 Hours, Google's AI Mastered All The Chess Knowledge in History]. AlphaZero. | ||
| + | <blockquote> | ||
| + | In a series of 100 games against Stockfish, AlphaZero won 25 games while playing as white (with first mover advantage), and picked up three games playing as black. The rest of the contests were draws, with Stockfish recording no wins and AlphaZero no losses. | ||
| + | </blockquote> | ||
| + | * ML applied to many different areas: chess, self-driving cars. Why not look at how it's been applied to food. | ||
| + | * Areas where ML is being applied to food | ||
| + | ::* Classifiation | ||
| + | ::::* Beer classification (12 parameters) | ||
| + | ::::* Classifying products as drugs or non-drugs | ||
| + | ::::* Classification of different types of honey | ||
| + | ::::* Classification of cereal grains acording to morphological features | ||
| + | ::* Recipes | ||
| + | ::::* MIT AI Recommends a recipe based on a photo of food | ||
| + | ::::* IBM Watson (winner of Jeopardy) learns all recipes from Bon Appetit and generates new recipes | ||
| + | <br /> | ||
| + | Interested in exploring, and cataloguing the different areas where ML has been applied to food. | ||
<br /> | <br /> | ||
=Good History of AI/NN= | =Good History of AI/NN= | ||
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=Recipes= | =Recipes= | ||
<br /> | <br /> | ||
| + | * [http://news.mit.edu/2017/artificial-intelligence-suggests-recipes-based-on-food-photos-0720 AI suggests recipes based on food photos].<br /> | ||
| + | <blockquote> | ||
| + | Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) believe that analyzing photos like these could help us learn recipes and better understand people's eating habits. In a new paper with the Qatar Computing Research Institute (QCRI), the team trained an artificial intelligence system called Pic2Recipe to look at a photo of food and be able to predict the ingredients and suggest similar recipes. | ||
| + | </blockquote> | ||
* [https://spectrum.ieee.org/tech-talk/robotics/artificial-intelligence/ibm-watson-learns-to-cook-from-bon-appetit-magazine IBM's Watson Learns to Cook from Bon Appetit Magazine] | * [https://spectrum.ieee.org/tech-talk/robotics/artificial-intelligence/ibm-watson-learns-to-cook-from-bon-appetit-magazine IBM's Watson Learns to Cook from Bon Appetit Magazine] | ||
<blockquote> | <blockquote> | ||
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</center> | </center> | ||
<br /> | <br /> | ||
| + | <!- ======================================================================== --> | ||
| + | <!- ======================================================================== --> | ||
| + | <!- ======================================================================== --> | ||
=Videos= | =Videos= | ||
| + | <br /> | ||
| + | |||
<br /> | <br /> | ||
{| | {| | ||
| + | | | ||
| + | Netflix's Chef's Table, Season 1, Episode 1: Massimo Bottura. | ||
| + | <br /> | ||
| + | <videoflash>1pY6IvkQm2Q</videoflash> | ||
| + | | | ||
| + | |- | ||
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<videoflash>V1eYniJ0Rnk</videoflash> | <videoflash>V1eYniJ0Rnk</videoflash> | ||
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MIT's AI System "Pic2Recipe" Predicts recipes from photos | QPT | MIT's AI System "Pic2Recipe" Predicts recipes from photos | QPT | ||
| + | |- | ||
| + | | | ||
| + | [[Image:NN_birds_blaise_aquera_y_arcas.png | 450px]]<br /> | ||
| + | [https://www.ted.com/talks/blaise_aguera_y_arcas_how_computers_are_learning_to_be_creative/up-next#t-705406 TED talk by Blaise Aquera y Arcas] | ||
| + | | | ||
| + | How computers are learning to be creative. Very good intro to how NN work, at 6 minutes 27 sec. | ||
|} | |} | ||
<br /> | <br /> | ||
| + | =Schedule= | ||
<br /> | <br /> | ||
| + | * Options | ||
| + | ** Thursday 5:30 - 8:30 p.m.? | ||
| + | ** Friday lunch + | ||
<br /> | <br /> | ||
| + | =Ideas= | ||
<br /> | <br /> | ||
| + | * MIT food researcher | ||
| + | * Michael Pollan | ||
| + | * Anthony Bourdain, Parts unknown | ||
| + | * Amherst survival center? | ||
<br /> | <br /> | ||
| + | * April 30th, May 1st, major 2-day activities for 2 current Kahn projects | ||
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| − | [[Category:Food]][[Category:Kahn]] | + | [[Category:Food]][[Category:Kahn Institute]] |
