Archive for the ‘Software’ Category
Paprika Recipe Manager is offered on a variety of platforms, including smartphones, and can use the cloud to keep the database in synch across your devices. But I’m an old fuddy-duddy and just use a Macbook, so I paid $20 to get it.
The problem, as TYD pointed out, is that I have scores if not hundreds of Recipes in Word documents, including a couple of documents that are recipe collections, though many of them are single-recipe documents: a page that I print when I want to make it.
So how to do the conversion? Paprika does have an import capability, but I didn’t even look at that. I’m sure it has specific format requirements, etc. So here’s how I’m doing it: When I discover a new recipe, I capture it into Paprika Recipe Manager. I no longer keep a Word document (though I still have those old ones), so the Paprika recipe is it. I immediately edit it to assign a category and make any changes I want. Then, when I want to cook it, I use the very nice print function on Paprika to print it.
So all new recipes go into Paprika directly. And then each time I make an old recipe, I first enter it into Paprika (and with copy-paste it’s a cinch) and then print it from there. So old recipes are gradually brought over, and in a logical order: popularity.
Sometimes I take a food photo, since Paprika can show a photo with recipe title. I did that when I brought over Shari’s Chicken Marinade (which I’ve blogged).
I’m very happy with the program. It’s by far the best recipe database I’ve used. They finally got it right: memetic evolution in action.
UPDATE: You create the categories to which a recipe can be assigned, and the categories are check boxes, not radio buttons, so you check all the categories to which a recipe might belong. A single recipe thus might be in the categories “Snack,” “Lunch,” “Halloween,” “Uncle Ted,” and “Low-carb.” When you click “All recipes” you see it once; if you click any category, you see all recipes in that category.
Here’s my current main page in Paprika. You’ll note the recipes are in alphabetic order. BTW, when the menu mentions a time (“simmer 10 minutes,” for example), Paprika highlights the time (“10 minutes”) and if you click it, a countdown timer (set at 10 minutes in this example) pops up. Not so useful on my computer, but nice if you’re using an iPad or smartphone.
Julia Angwin and Surya Mattu report in ProPublica:
One day recently, we visited Amazon’s website in search of the best deal on Loctite super glue, the essential home repair tool for fixing everything from broken eyeglass frames to shattered ceramics.
In an instant, Amazon’s software sifted through dozens of combinations of price and shipping, some of which were cheaper than what one might find at a local store. TheHardwareCity.com, an online retailer from Farmers Branch, Texas, with a 95 percent customer satisfaction rating, was selling Loctite for $6.75 with free shipping. Fat Boy Tools of Massillon, Ohio, a competitor with a similar customer rating was nearly as cheap: $7.27with free shipping.
The computer program brushed aside those offers, instead selecting the vial of glue sold by Amazon itself for slightly more, $7.80. This seemed like a plausible choice until another click of the mouse revealed shipping costs of $6.51. That brought the total cost, before taxes, to $14.31, or nearly double the price Amazon had listed on the initial page.
What kind of sophisticated shopping algorithm steers customers to a product that costs so much more than seemingly comparable alternatives?
One that substantially favors Amazon and sellers it charges for services, an examination by ProPublica found.
Amazon often says it seeks to be “Earth’s most customer-centric company.” Jeffrey P. Bezos, its founder and CEO, has been known to put an empty chair in meetings to remind employees of the need to focus on the customer. But in fact, the company appears to be using its market power and proprietary algorithm to advantage itself at the expense of sellers and many customers.
Unseen and almost wholly unregulated, algorithms play an increasingly important role in broad swaths of American life. They figure in decisions large and small, from whether a person qualifies for a mortgage to the sentence someone convicted of a crime might serve. The weightings and variables that underlie these equations are often closely guarded secrets known only to people at the companies that design and use them.
But while the math is hidden from public view, the effects of algorithms can be vast. With more than 300 million active customer accounts and more than $100 billion in annual revenue, Amazon is a shopping giant whose algorithm can make or break other retailers. And so ProPublica set out to see how Amazon’s software was shaping the marketplace.
We looked at 250 frequently purchased products over several weeks to see which ones were selected for the most prominent placement on Amazon’s virtual shelves — the so-called “buy box” that pops up first as a suggested purchase. About three-quarters of the time, Amazon placed its own products and those of companies that pay for its services in that position even when there were substantially cheaper offers available from others.
That turns out to be an important edge. Most Amazon shoppers end up clicking “add to cart” for the offer highlighted in the buy box. “It’s the most valuable small button on the Internet today,” said Shmuli Goldberg, an Israeli technologist who has extensively studied Amazon’s algorithm.
Amazon does give customers a chance to comparison shop, with a listing that ranks all vendors of the same item by “price + shipping.” It appears to be the epitome of Amazon’s customer-centric approach. But there, too, the company gives itself an oft-decisive advantage. Its rankings omit shipping costs only for its own products and those sold by companies that pay Amazon for its services.
We found that the practice earned Amazon-linked products higher rankings in more than 80 percent of cases. Amazon’s offer of the Loctite glue, a respectable No. 5 on the comparison list, dropped to the 39th best deal when shipping was included. (The prices Amazon shows are ranked correctly for those who pay $99 per year for Amazon’s Prime shipping service and for those who are buying $49 or more in eligible items.) . . .
And read the whole thing for a survey of what the shaving world refers to as “shady business practices.”
I’m make this recipe, which made me discover that diced salt pork is a substitute for diced pancetta and about 1/4 the cost. UPDATE: Wrong! Salt pork is way too salty to eat: when the recipe says discard what’s left after rendering the salt pork (which takes longer than you might think), do it.
And I’m using Patrika Recipe Manager (multi-platform), which I’m liking a lot. With the edit mode I can fix recipes: making changes to the on-line version. For example, in this recipe it calls for 4 tablespoons of Pommery mustard, but it turns out that you use 1 Tbsp at one point and 3 Tbsp at another, so I changed the ingredients list to reflect that, and putting the revised entries in the list of ingredients in order of use in the recipe, so in fact the two are well separated.
And that brings me to Pommery mustard. It’s just whole-grain mustard, though a particular brand that’s been made for centuries. But I think any good whole-grain mustard would work as well. I suppose I’ll find out: I still have a lot of Pommery mustard left to use.
UPDATE: The trick in this recipe is to know when to add which mustards when. I suggest measuring out everything in advance and group by step. I want to make it again to get mustard sequence right. I also added the first 2 Tbsp (1 oz) butter too early: it should be added after onions are removed.
After reading this recommendation, I got the Paprika Recipe Manager, and I have to say I’m impressed. It includes a browser with a lengthy list of links to recipe sites, and when you find a recipe you like, you can save it directly to Paprika Recipe Manager, where it is nicely formatted for reading and can be printed as a shopping list (ingredients organized by category) or as a recipe to use in the kitchen.
Recommended. Here’s where to get it.
Interesting note from Kevin Drum: even if Hillary Clinton would have used a state.gov email account, it is not a secure account and should not include classified information. Worth reading to understand more whether there is anything more to the Hillary Clinton email “scandal” than there was to the Benghazi “scandal” (which was zero).
Kaz Sato has a very interesting report of using Google’s open-source machine-learning library TensorFlow:
It’s not hyperbole to say that use cases for machine learning and deep learning are only limited by our imaginations. About one year ago, a former embedded systems designer from the Japanese automobile industry named Makoto Koike started helping out at his parents’ cucumber farm, and was amazed by the amount of work it takes to sort cucumbers by size, shape, color and other attributes.
Makoto’s father is very proud of his thorny cucumber, for instance, having dedicated his life to delivering fresh and crispy cucumbers, with many prickles still on them. Straight and thick cucumbers with a vivid color and lots of prickles are considered premium grade and command much higher prices on the market.
But Makoto learned very quickly that sorting cucumbers is as hard and tricky as actually growing them. “Each cucumber has different color, shape, quality and freshness,” Makoto says.
In Japan, each farm has its own classification standard and there’s no industry standard. At Makoto’s farm, they sort them into nine different classes, and his mother sorts them all herself — spending up to eight hours per day at peak harvesting times.
“The sorting work is not an easy task to learn. You have to look at not only the size and thickness, but also the color, texture, small scratches, whether or not they are crooked and whether they have prickles. It takes months to learn the system and you can’t just hire part-time workers during the busiest period. I myself only recently learned to sort cucumbers well,” Makoto said.
There are also some automatic sorters on the market, but they have limitations in terms of performance and cost, and small farms don’t tend to use them.
Makoto doesn’t think sorting is an essential task for cucumber farmers. “Farmers want to focus and spend their time on growing delicious vegetables. I’d like to automate the sorting tasks before taking the farm business over from my parents.”
Makoto first got the idea to explore machine learning for sorting cucumbers from a completely different use case: Google AlphaGo competing with the world’s top professional Go player.
“When I saw the Google’s AlphaGo, I realized something really serious is happening here,” said Makoto. “That was the trigger for me to start developing the cucumber sorter with deep learning technology.”
Using deep learning for image recognition allows a computer to learn from a training data set what the important “features” of the images are. By using a hierarchy of numerous artificial neurons, deep learning can automatically classify images with a high degree of accuracy. Thus, neural networks can recognize different species of cats, or models of cars or airplanes from images. Sometimes neural networks can exceed the performance of the human eye for certain applications. . .
If you have young children, this science-education game might be of interest. The author describes how his daughter took to it.