State of the Farm: June, 2024

We managed to get pretty much everything planted by the end of Memorial Day weekend. This is, more or less, the traditional planting time up here in New England. Of course, global warming has brought spring in two weeks early and pushed summer out two weeks later. In addition, it has changed our location in the USDA plant hardiness map. 

 

This has some pros and cons. On the pro side, we get to start planting two weeks early and can harvest into early October. That is, if we pick the right seeds. It gets hotter now in the summer—we’re looking at a record heat wave in the coming days—which might be good or bad. The beans last year didn’t set fruit above 95F. But the melons like the heat. 

 

On the con side, the heat is a problem for many of the plants. The extended growing season also extends the opportunity for pests and diseases. We’re having record slug and rodent problems. Slugs (“They can’t hear. They can’t speak. They can’t operate machinery.”) have never been that much of a problem. But with the warmth and the wet, they are starting to be a real issue. We’re investigating a number of solutions but haven’t implemented any as of yet.

 

The rodent problem comes from multiple sources. For one thing, we’ve not had an outdoor cat for a few years now. Cats don’t just kill everything that moves, rodents and similar animals are scared away by their urine. Cats are happy to oblige. For another, we lost a large hickory last year. That served as a food source for squirrels. I think the squirrels competed with the chipmunks and helped keep the population at least a little bit down. Finally, the longer season and the mild winter no longer keeps the smaller rodents in check. 

 

We had redone the garden fence, putting in chicken wire and lining the ground with hardware cloth. This held back the mice and most of the voles. However, it didn’t stop the chipmunks one little bit. 

 

We’ve been trapping them regularly and that makes a dent but only a dent. It’s another problem we’re working on.

 

So.

 

We have three main garden areas—the turtle garden, the main garden, and the raised beds—and several fruit trees and arbors. 

 

There are three sections of the turtle garden: north, central, and south. We planted potatoes and sorghum in the north area. The potatoes are doing okay but the sorghum is a little stressed. If we continue with sorghum, we will likely move it. In the central section: more potatoes, pinto beans, and sugar beets. The beets and sorghum are part of my continuing experiments to be able to generate sugar. So far: failure. But I’m still trying.

 

The south section is fava beans and asparagus. Fava beans are another experiment. They are supposed to be a perennial up here but we will see. The asparagus bed is now close to three years old and supplies us with spears.

 

I overplanted the main garden last year and had too much competition. I reduced what I was planting this year and every plant has a clear area to grow. The list this year is:

• Basil
• Daikon radishes
• Carrots
• Sunflowers
• Goldberry
• Brussel sprouts
• White beans
• Brown beans
• Bush beans
• Cucumbers
• Melons
• Rutabagas
• Kohlrabi
• Celery
• Squash

Aaaand we’re already having issues. I planted eight lovely basil sets in the southwest corner of the garden and came in two days later and they were gone. Eaten to the root. Then, we found a hole under the fence. Too big for a mouse. Too small for a squirrel. I’m betting on chipmunks. But what rodent preferentially eats basil? We’ve never had that. Planted eight more purchased sets and they’ve been picked off, one by one. We are down to four. The brown beans came up and about a quarter of them had their tops bit off. Clearly, we have to handle this.

 

Of the four squash beds we planted, two didn’t come up at all. The peas I planted back in April had the same issue. We did notice that during COVID there seemed to be a seed germination issue. It might still be percolating through the system.

 

The raised beds have now been planted:

• Strawberries
• Potatoes
• Peanuts
• Peppers
• Basil

We’re getting strawberries. The early radishes (not listed above) didn’t really produce. The potatoes are being ravaged by potato beetles so I sprayed them with Dead Bug. For the record, we like to garden because we like to know where our food is coming from. That doesn’t mean we’re “organic” in the traditional use of the word. We’re not above using fertilizer, insecticides, or fungicides. It does mean that we’re judicious in their use and selective as to what we apply.

 

In the fruit department, the pears, apples, peaches, blueberries, and grapes look great. We lost all of the Manchurian pears—they either didn’t pollinate or the chipmunks got them. I think the former. A number of fruit trees produced huge amounts of blossoms but did not yield fruit at all or a lesser amount. This was the first time in several years where we didn’t put out orchard bees. That may be the problem.

 

The paw paws were like this. We have four trees. Three of them had blossoms. The fourth is too young. Across the three trees we only have about a dozen fruit growing. Paw paws are notoriously difficult to get pollinated. First, they cannot self-pollinate. Pollination must come from a genetically different plant. This means you can’t take a cutting and grow it next to the parent plant to get pollination. In addition, they are generally beetle pollinated and small beetles at that. Bees don’t help. There’s some evidence that a few paw paw species are pollinated by flies since the odor of the flowers resembles carrion or dung. 

 

All of this means that if you take a paw paw out of its range, you may or may not get fruit. In Europe, for example, they all hand pollinate. We might try that next year.

 

Finally, we’re attempting a grand experiment.

 

In the picture above, in the background, two items are circled. The left circle shows the use of bird tape on the strawberries and a blueberry bush. The right circle, in the background, is what looks like a tunnel. This is the blueberry house. 

 

We’re using bird tape on the lesser blueberry bushes. The blueberry house is covered with netting. 

 

We don’t like to use netting. It’s a complete PITA to deploy. It catches everything—leaves, branches, dirt. It supposed to be pulled after the harvest so leaves and snow can come in. And, sometimes, it catches birds. Some birds can be released. Others are not so lucky.

 

We saw the bird tape being used in Vermont. We’re hoping it works here. It doesn’t seem to work so well with strawberries. It’s possible the red color is so inviting that it overcomes whatever the bird tape does. Or, it’s possible we’re not using the bird tape correctly. We will see.

 

This is a good time in the garden—minus the pests. Just a little weeding is sufficient to keep control. Watering isn’t much of a problem because we’re having good rain. Not a lot of work. As the summer progresses, the work load gradually increases. Weeds get more persistent. Harvests have to be managed. Right now is good.

 

Finally, an apology. I didn’t get this out at the right time. So, in effect, it’s a day late. I’m sorry about that.

Upcoming new novel: Smilodon Country

 I have a new novel, Smilodon Country, being released on 6/25/2024. Here's the description:

 

In the ruins of Saint Louis, Lucio has been a Long Bottom Boy for as long as he can remember. He has scavenged. He has been hunted for food. He has eaten his fill of human prey. He has been both the abused and the abuser.

He's been okay with this.

One day he sees things differently. Can he stay? Can he leave? Where can he go? Who can he trust? Is there a place for him anywhere?

He does not know it but he is on his way to Smilodon Country.

 

This one has an 18+ age range.

 

The Amazon pre-release link is here. My website will have further links as they become available.

The Language of Artificial Intelligence

 

I find the AI discussions at best distracting and at worst irritating. I mean this down to even referring to it as AI. This is clearly a marketing ploy and it’s distressing that it works so well.

 

(Picture from here.)

 

Remember, AI is a Spielberg movie. And one of the most famous intelligent computers is HAL from 2001.

 

The underlying systems involve neural networks and large language models. What I think the resulting trained systems should be called deep analysis pattern determining predictive machines. I know that this isn’t as sexy as AI but it’s more descriptive. Here is my reasoning.

 

Machine learning involves deep analysis of the training data across thousands of dimensions. No, not dimensions like in the old show Sliders. I mean dimensions of analysis. 

 

Let’s say we described an object in terms of geographical location, temperature, altitude, velocity, and color. Each of these attributes can be evaluated to a value. That value can be described as being a point on a line. Altitude in meters. Velocity in meters/second. Color as a list. Etc. Each of these attributes can be referred to as a dimension. The dimensional values allow the representation of the object as a point in a multidimensional space, allowing comparisons between different points in the same space. This is analogous to plotting something on an x/y graph. The distance between two points can be determined. Similarly, “distances” between two points can be derived from their relative values in their dimensional space.

 

That’s the deep analysis. The next component is its ability to determine patterns in a huge dataset. It can be done because of the previous data representation. There are several types of machine learning used to discover patterns but the ones that most concern this discussion are unsupervised learning, supervised, learning, and reinforcement learning. Unsupervised learning derives patterns from unlabeled data. The patterns are determined solely from the system itself. 

 

Supervised learning where inputs and a desired output value are used to train a model. The training set represents data that the model would expect—radiology images, for example. The desired output is specified and associated with the input data. The goal is that when the model inputs new data it will be able to generated outputs will reflect the what the model learned in the training data. In a radiological image example, the model might be trained on lung cancer images. Then, presented with new images be able to detect lung cancer. 

 

Reinforcement learning doesn’t require labeled input/output values as in supervised learning. Instead, the model determines action from unlabeled input data and its decisions are then evaluated and positive or negative feedback is given. This is similar in the way animals are trained to do tricks. Say, you want a dog to run up a ramp and jump through a hoop. First, you might reward the dog when it approaches the ramp. Then, further reinforce the dog when it starts to walk up the ramp, and so on.

 

Prediction is when given an input they output the most likely expected material.

 

I am not criticizing these systems. These are highly useful—especially the more specialized systems such as used to determine drug discovery and other disciplines. What I object to is that the language we use to describe them gives them qualities they cannot possess. 

 

While these systems are certainly artificial and “intelligent” in the broadest sense of the term, they are not conscious. They do not understand what they are doing. Yet, we use language that implies both of these things. 

 

For example, all of the above systems have a problem with generating data that reflects their training but do not represent anything possible or desirable. These have been termed “hallucinations.” A hallucination is defined to be “is a false perception of objects or events involving your senses: sight, sound, smell, touch and taste.” (See here.) These systems do not perceive and have no senses. They have no ability to experience. 

 

Or we might say “ChatGPT tells me that there should be glue on pizza.” To tell something implies a intentional communication between two conscious entities. That’s not happening here.

 

Humans project all the time. We see two dots with a curve under them and perceive a face. Our cat rubs its head on our knee and we declare it loves us. We trust that our leaders have our best interests at heart. None of these assertions need be true—and some are provably false—but we believe them all the same. 

 

The AIs that are now in use in search engines, in offices, on the shop floor have demonstrable failures. Yet, they are still being forced on us.

 

Describing them as human is propaganda and we should recognize it as such.