ARA-C01 basics - SnowPro Advanced Architect Certification Updated: 2024

“It’s too early,” said the character Lucy Van Pelt in “A Charlie Brown Christmas.” “I never eat December snowflakes. I always wait until January.”

Well, here we are in January and a string of snowstorms is predicted to drop several inches over the next ten days. Time to enjoy those snowflakes.

As the snow falls, the curious mind may wonder, “How do all of those flakes form?”

Snow is frozen water. Water is made up of two hydrogen atoms and one oxygen atom, and thus creating the chemical symbol of H2O for water. It is the same for snowflakes.

These three atoms naturally and always align into a V-shape to create the water molecule.

But there is more to making a snowflake than just frozen water.

According to the National Oceanic and Atmospheric Administration, the beginnings of a snowflake include a piece of pollen or dust in the sky. When a very cold water droplet freezes onto this particle, an ice crystal forms.

As the crystal falls to the ground, additional ice crystals attach to it and begin to create the snowflake. Each of those crystals maintain the V-shape alignment of the atoms mentioned above and form a hexagon as they continue to evenly attach to the flake.

Because of this V-shaped alignment, snowflakes always have six sides. As the flake continues to fall, more crystals will attach but always in the V-shape, six-sided pattern, creating a larger hexagon shape rather than an unbalanced random shape. This process is called “crystallization.”

NOAA further explains that it is the temperature at which the crystal forms that creates the basic shape of the snowflake. The amount of humidity in the air also plays a part in the appearance of the flake. Long, needle-like crystals form at 23 degrees Fahrenheit while very flat, plate-like crystals form at 5 degrees Fahrenheit.

As a result of this combination of types of particles, amount of moisture in the air, rate of falling and temperature, no two snowflakes are ever alike because no two combinations of these factors are ever the same, creating one-of-a-kind prisms, needles, plates, columns and lacy patterns.

Here are some other unique features about snowflakes to ponder as you enjoy a few of the January flakes.

A snowflake falls through the air to the ground at a rate of one to six feet per second. The rate is dependent upon size and shape of the flake.

According to Tom Skilling, chief meteorologist at Chicago’s WGN-TV, snowflakes start their descent at about 10,000 feet in the atmosphere in a typical winter storm. Using a fall rate of 3.5 feet per second, the flake would take about 45 minutes to reach the ground.

Of course, that is in Chicago, where the city sits at an elevation of 597 feet. In Estes Park, where the ground is much closer to 10,000 feet, the time for that flake to reach the ground would be considerably shorter.

According to Met Office, the national meteorological office in the United Kingdom, snowflakes fall at a rate of 1 to 4 mph. They further explain that supercooled water may fall as fast as 9 mph while most flakes fall at a rate of 1.5 mph. They too state it takes about an hour for a flake to fall to the ground.

Snow is not white. Although our eyes see it that way, the flakes are actually clear. The ice crystals are not completely transparent, however, so light diffuses through the crystals in the whole spectrum of colors, creating the appearance of white.

According to NOAA, the snow to rain ratio is 10 to 1, generally. There is actually much more involved in the simple statement of how much water it takes to make one inch of snow, including temperature, wind speeds and humidity levels. The general rule of thumb is that 0.1 inches of water creates one inch of snow.

With those facts in mind, take a look at a few of the fresh flakes this week to see if you can spot the hexagon shape, find the piece of pollen or see how the crystals are clear. Or just have fun playing in the piles of flakes.

program details

Our main objective is to train volunteers to assist Purdue Extension with home horticulture education in local communities. Purdue EMG’s receive training in horticulture to equip them to fulfill  this educational role through volunteering in a variety of projects approved by their local EMG County Coordinator (Purdue Extension Educator). The requirements for Purdue EMG certification include acceptance into the training through an application and screening process, payment of registration fee, completion of the EMG Basic Training which includes passing the open-book final test with a score of 70% or higher, and contributing at least 40 hours of volunteer service approved by the local EMG County Coordinator within two years. Purdue Extension Master Gardeners are also required to complete at least 12 volunteer and 6 continuing education hours every year in order to stay active. 

This class runs from August 31st to November 30th on Thursday nights from 6pm to 9pm. We are currently accepting applications for this class.

Please note that we will meet on Tuesday, November 21st for the week of Thanksgiving.

1. Those interested submit their completed EMG-1 application.
2. Conduct an ID check to verify identity (Available via Zoom)
3. Consent to a standard volunteer background check (handled by our office)
4. Complete the final registration link via CVENT (this is where the course fee is payed)

COST

• $183.00 for an individual registration with print version of Purdue EMG Manual

APPLICATION PROCESS

Limited to the first 35 to complete registration

1) Complete this application and submit to Carey Grable

APPLICATION

2)  After your application is received and  approved, you will be contacted to complete registration and payment.

ADDITIONAL INFORMATION

Note:  Purdue is committed to making all programs accessible to participants. If you require auxiliary aids or services, or if you have other program-related concerns, please contact Carey Grable (cagrable@purdue.edu) at least 2 weeks prior to the program.

To learn more about the Purdue Extension Master Gardener Program, visit https://www.purdue.edu/hla/sites/master-gardener/

Numerous observers have predicted that 2024 will be the year enterprises turn generative AI such as OpenAI's GPT-4 into actual corporate applications. Most likely, such applications will begin with the simplest kinds of infrastructure, stringing together a large language model such as GPT-4 with some basic data management.

Enterprise apps will start with simple tasks such as searching through text or images to find the match to a natural-language search.

Also: Pinecone's CEO is on a quest to supply AI something like knowledge

A perfect candidate to make that happen is a Python library called SuperDuperDB, created by the venture capital-backed company of the same name, founded this year. 

SuperDuperDB is not a database but an interface that sits between a database such as MongoDB or Snowflake and a large language model or other GenAI program.

That interface layer makes it simple to perform several very basic operations on corporate data. Using natural language queries in a chat prompt, one can query an existing corporate data set -- such as documents -- more extensively than is possible with a typical keyword search. One can upload images of, say, products to an image database and then query that database by showing an image and looking for a match.

Likewise, moments in videos can be retrieved from an archive of videos, by typing themes or features. Records of voice messages can be searched as a text transcript, making a basic voicemail assistant. 

The technology also has uses for data scientists and machine learning engineers who want to refine AI programs using proprietary corporate data. 

Also: Microsoft's GitHub Copilot pursues the absolute 'time to value' of AI in programming

For example, to "fine-tune" an AI program such as an image recognition model, one has to hook up an existing database of images to the machine learning program. The challenge is how to get the image data into and out of the machine learning program, and how to define variables of the training process, such as the loss to be minimized. SuperDuperDB offers simple function calls to simplify all those things.

A key aspect of many of those functions is to convert different data types -- text, image, video, audio -- into vectors, strings of numbers that can be compared against one another. Doing so allows SuperDuperDB to perform "similarity search," where the vector of a text phrase, for example, is compared to a database full of voicemail transcripts to retrieve the message most closely matching the query. 

Mind you, SuperDuperDB is not a vector database like Pinecone, a commercial program. It's a simpler form of organizing vectors called a "vector index."

Also: Pinecone's CEO is on a quest to supply AI something like knowledge

The SuperDuperDB program, which is open-source, is installed like a typical Python installation from the command line or loaded as a pre-built Docker container. 

The first step to working with SuperDuperDB can either be setting up a data store from scratch, or working with an external data store. In either case, you'll want to have a data repository such as MongoDB or a SQL-based database. 

SuperDuperDB handles all data, including newly created data and data fetched from the database, via what it calls an "encoder," which lets the programmer define data types. These encoded types -- text, audio, image, video, etc. -- can be stored in MongoDB as "documents" or in SQL-based databases as a table schema. It's also possible to store very large data items, such as video files, in local storage when they exceed the capacity of either MongoDB or the SQL database.

Also: Bill Gates predicts a 'massive technology boom' from AI coming soon

Once a data set is chosen or created, neural net models can be imported from libraries such as SciKit-Learn or one can use a very basic built-in inventory of neural nets such as the Transformer, the original large language model. One can also call APIs from commercial services such as OpenAI and Anthropic. The core function of having the model make predictions is done with a simple call to a ".predict" function built into SuperDuperDB.

When working with a large language model or an image model like Stable Diffusion or Dall-E, the neural net will seek to retrieve answers from the database by performing the vector similarity search. That's as simple as calling a ".like" function and passing it the query string.

It's possible to make more complex apps by assembling multiple stages of functionality with SuperDuperDB, such as using similarity search to retrieve items from a database and then passing those items to a classifier neural net. 

The company has added functions that make an app more of a production system. They include a service called Listeners that re-run predictions whenever the underlying database is updated. Various functions in SuperDuperDB can also be run as separate daemons to Improve performance.

Also: How LangChain turns GenAI into a genuinely useful assistant

This year will witness a great deal of evolution in programs such as SuperDuperDB, making them more robust still for production purposes. You can expect SuperDuperDB to evolve alongside other important emerging infrastructures such as the LangChain framework and commercial tools such as the Pinecone vector database. 

While there's a lot of ambitious talk about enterprise use of GenAI, it probably starts right here, with the kinds of humble tools that can be picked up by the individual programmer.  

If you'd like to get a quick feel for SuperDuperDB, head over to the demo on the company's Web site.

You don't need professional barista training to make cute latte art on the coffees you drink at home. Using foamed milk and freshly poured espresso, you can create Instagram-worthy snowflakes on top of your next mug of coffee. Simply use a teaspoon to drop dots of foamed milk onto the surface of the poured crema in a star formation and connect each droplet using a toothpick or skewer to draw a milky snowflake design in your cup. You'll want to make sure the milk foam you use is finely textured and silky so that the wintery design holds its shape.

Snowflake patterns make for the kind of design that doesn't require any sort of perfect pouring pattern or fancy skills. Keep the milk droplets aligned in a circular form so that the marks you draw with either a toothpick or skewer can easily line up and it looks like a snowflake. After a few tries, your picture-perfect snowflake designs may become the talk of your household.

Read more: 26 Coffee Hacks You Need To Know For A Better Cup

After you've mastered the technique of making snowflakes on top of your lattes and cappuccinos using droplets of microfoamed milk, you can try sprinkling snowflakes on top of your favorite milky coffee recipes with powdered cinnamon or cocoa. Use the handle of a spoon to help you connect up elements of your designs, or make cut-outs with parchment paper or aluminum foil that you can place gently over your coffee cups as a stencil.

Snowflake designs can provide an endless source of inspiration, and depending on how complex you'd like your latte art to be, can keep you busy as you work to perfect the shapes and styles of your latte art. In time, you may find yourself enjoying more coffee drinks at home instead of heading to the nearest coffee shop for a pretty pour. With enough practice, you may even supply your neighborhood baristas some stiff latte-art skills competition.

Read the original article on Tasting Table.