Mark Bolda's Blog
Below is a picture of what hail can do to strawberry. A friend forwarded me these pictures of what a freak hailstorm a few days ago over his field left behind. Shredded leaves, pitted and bruised fruit right down to pretty undeveloped green ones, even on the flower. This storm represents a huge setback for his farm.
I'd have a hard time getting up and facing the day after a loss like this, but nevertheless you growers do it as a matter of course.
Bless you for the work you do and the food you grow for us in spite of the curveballs Mother Nature can throw at you.
I wouldn't assume many of the readers of this space are enthusiasts of the game of Go, a game popular in Asia that has some pretty simple rules but is remarkably complex partly because of the large board and consequently a huge number of possible moves. What is interesting about all of this is that top players of the game, perhaps because of the vast number of moves, cannot tell you why they are playing a certain way or deploying a given strategy. In other words, following a theme I've brought up here before, is that by being intuitive in their skill, these players know more than they can tell.
The inability of top players to articulate why they are playing Go a certain way means that unlike chess or checkers, a good Go playing computer confounded the programmers who wanted to build a mechanical rival. So the computer programmers at Google DeepMind took a different tack in 2014 and rather than programming the computer how to play, set it up so that the computer learned the game on its own. It was given some 30 million Go board positions from a repository and told basically to use those to figure out how to master the game and win. The machine also played a whole bunch of games by itself, and ended up generating yet another 30 million possible positions.
This self learning machine was ready for human competition in 2015 and already that year bested the European champion 5-0, and in the next year went to beat the world Go champion Lee Sedol 4-1.
What happened? A lot, but a remarkable thing here is how the the self taught DeepMind machine played the game. One of the machine's opponents remarked that its play style was "alien", making moves that were counterintuitive in play that was like something out of another dimension. It learned the game on its own, free of any human influence or bias, and so perhaps it should not come as a surprise that it was no longer behaving like a person either.
Which brings us to the philosopher Wittgenstein and his lion, who said that even if a lion could speak we could not understand him. Why? As a species human beings have the same perceptual and conceptual apparatus, and because of this we can share what we experience with a language in common. Does a lion share this perceptual and conceptual apparatus with us? Probably not, since it learns about the world in a reality utterly different from our own, with a completely different mind to interpret it. As the lion does not share this same conception of the world with us, it also would not be able to describe it to us even if it was using words we are supposed to understand
So too a self learning machine. Clearly it does not have the brain of a human and so therefore, setting aside the whole concept of consciousness and self awareness, a machine which is shaping perceptions and conceptions of the world via its own learning, independent of human input and programming, very likely, much like Wittgenstein's lion, would result in something no longer understandable by us.
Interesting farm call here that at first piqued my attention as a possibly serious disease situation but in the end turned out to not be.
See the photos below. Affected strawberry plants presented with discolored leaves and stunted growth typical of nutrient deficiency or viral infection. On being removed from the soil, root growth was also limited, but the crown was not discolored as it would be for many soil pathogen infections.
We did also note that there was a lot of water saturation around the affected plants, not always but especially marked in low lying areas of the field. We had the impression that maybe there was less saturation around those plants that were healthy (cool thing was that the grower let me pull up healthy plants for comparison, provided that I put them back in again).
To be thorough, I submitted a number of sick plants to the diagnostic laboratory at TriCal, and they came back negative for disease, leaving us to make the conclusion that the plants are indeed suffering from the water saturation quite possibly exacerbated by the recent rains. In other words, the roots of these plants are being asphyxiated by the excess of water around them. The discoloration of the leaves comes from nutrient uptake being compromised by the lack of air in the soil, also not coincidentally resulting in a stunted plant.
Advice to grower was to watch water use in those affected areas for the next couple of weeks, and to look at ways of aerating the soil around the plant and bed.
Waaaay too early to be seeing Fusarium infecting strawberry plants, but here it is.
I was contacted two weeks ago by the grower who was seeing some plant collapse in his field (Photo 1) and had him submit samples and then went out personally to check it out. Granted, this field has a history of Fusarium infection, but what makes this disturbing is that the field was flat fumed this past fall with chloropicrin at 350# per acre under TIF and the plants are being managed exceedingly well (Photo 3) by a very experienced grower.
Normally, we would be seeing these symptoms when the plants load up with fruit and it gets hot in late May and early June which puts the strain on a compromised root and crown system. Not now, when the plants have nary a fruit, are small and the temperatures are cool.
Not happy about this at all.
Diagnosis of the Fusarium by Steven Koike, Trical Diagnostics.
Doing some more weekend reading in "Machine Platform Crowd" the popular and well quoted tract on what we are looking at this second phase of the machine age.
Some of you might be familiar with restaurant Eatsa in San Francisco where all the food is ordered, paid and received without any human contact. Seems like sort of a lonely place to eat, but no matter, everything is automated on the customer service side, with the key points being precise analysis of throughput, speed of service, predictive ordering and inventory optimization by store.
Now Eatsa wants to take it to the next level and automate the preparation of the food itself.
This is where is gets interesting folks. Because while a lot of the food preparation in the kitchen is going the way of automation, the company still hires humans for handling all the food items that are not regularly shaped and not hard. So, the avocados, tomatoes and eggplants are all still handled by people. All three of which are still firmer than strawberries and a whole lot firmer than a mature raspberry or blackberry.
The answer of why this is so is really instructive to those who are thinking through the problem of automating fruit picking.
The challenge is that a lot of what people do isn't programmable, not even by a long shot. Think about it, when you pick up something, not even necessarily a soft fruit, can you explain how or even why you are doing it? That you are picking up the object up is clear, but can you articulate all of the sense data and muscle mechanics that is making this most ordinary event happen? No, you can't, and so how would you even start to instruct a computer or robot on to do it?
Here from the book, on the same point on why a robot can't do what most of us take for granted without ever knowing how (italics mine):
"This is because robot's sense of vision and touch have historically been quite primitive - far inferior to ours - and proper handling of a tomato generally entails seeing and feeling it with a lot of precision. It's also because it's been surprisingly hard to program robots to handle squishiness - here again we know more than we can tell - so robot brains have lagged far behind ours, just as their senses have."
People just aren't that easy to replace - might want to think about having robots work with us, rather than instead of us.