omenti

a dive into pizza pt. 2

Now that we know why we hold pizza the way we do, it's time to move on making a great pie.  There are dozens of varieties. I'll stick with one I have some (perhaps too much) experience with - Neapolitan.  A few  other thin crust styles like New York (which has a higher fat content in the dough) will be somewhat similar, but deep dish and other types have dramatically different baking requirements.

 

Folklore and my experience suggests a brick oven does the best job.  These are usually a set around 330°C (about 625°F).  The cooking surface is brick with a large brick dome covering the space.  Many of these use burn gas, but wood is sometimes used to impart a bit of a smokey flavor.  Much more common are commercial steel electric or gas ovens.  Chain pizza shops? I'm not a fan.

 

There are three heat transfer mechanisms: conduction, radiation, and convection.  Conduction is heat transfer by direct contact between the oven and pizza bottom. Radiation is infrared energy that largely comes from the rest of the oven - the broiler element in your oven uses this trick. Finally convection is the transfer of heat from the oven to the pie by the air.

 

Convection turns out to play only a minor role so we'll skip it.  Conduction is another matter.  In our 330°C brick oven the interface between the pizza and brick is about 210°C (410°F).¹  In about two minutes the bottom cooks nicely and the temperature at the top of a half centimeter thick pizza approaches the boiling temperature of water.

 

Most pizzerias use steel pizza ovens.  Steel is a much better conductor of  heat than brick.  With the oven set at the same 330°C,  the temperature at the bottom of the pizza is now much higher - about 300°C (570°F) and the bottom burns quickly.  Dropping the steel oven's temperature to 230°C (445°F) will give you the good 210°C crust cooking temperature, but there's another problem - what about the top of the pie?  For both brick and steel ovens we need to look at radiative heat transfer..

 

Infrared radiation from the 330°C brick walls quickly raises the temperature of the top of the pie to the boiling point of water allowing water in the cheese and toppings to evaporate so they can cook properly.  For a simple Margherita pie two minutes gives a nearly perfect "balance" with everything cooked just right.  The cooler 230°C steel oven has a much lower radiative heat transfer.  So low the top of the pie doesn't reach boiling by the time the bottom cooks  Cooking the top requires another 90 seconds or so leaving the crust overcooked.

 

You can partly get around these problems by adjusting the dough (New York style pies have a higher fat content), using a broiler element (some steel ovens have them) or physically lifting the pie from the oven floor with a wooden pizza spade for a minute or so.  A good pizza cook with a brick oven will also use the spade on a Neapolitan pie if there are a extra toppings.

 

If you're lucky enough to visit a shop with a brick oven and good cook, try to go when they aren't busy.  It is common to raise the temperature to at least 390°C during busy hours to increase throughput by turning around pies in about eighty seconds.  These fast and hot pies are difficult to judge and frequently have slightly burned crusts.  It's even harder if ask for anything more than the simplest pie.

 

You can get a ceramic or brick pizza stone if you want to back a pie a home.  Set your oven as high as it will go and experiment - it will take more cooking time.  I've tried with lackluster results .. I tend to wait and visit real pizzerias.  Alternatively you can work on types of pizza that have very different cooking techniques. 

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¹ You can calculate this with a differential equation using the thermal properties of the brick and pizza. It gets a little messy and the 210° figure could be off by a couple of degrees, but it's in the right ballpark and that's a good temperature for baking the crust.

 

a dive into pizza pt.1

Sarah and I were talking and the subject of pizza came up.  It turns out she's a big fan - particularly of Hawaiian pizza.  Rather than getting mired in the cultural war of pizza types, let's go for the basics - the mathematics of pizza.  You'll only need a piece of paper, a ball (Sarah recommends a volleyball), and a marker. 

 

Take a piece of paper and lay it flat on something.  It's flat with zero curvature everywhere.  if you draw a triangle on it and sum the angles, you get 180°.  A circle will have a circumference of 2π times the radius - 2πr - and an area of πr². All is well. Now fold it along an axis - say the long one if it's a rectangular piece.  You've introduced some curvature.   Now grab it by a corner and let it flop. Again there's curvature, but notice which one is stronger and save that thought.

 

Here's where the math comes in.  Exploring what curvature means, Carl Friedrich Gauss wondered if there was anything intrinsic about the curvature of a surface.  The sheet of paper - what is the relation between its flat shape and when you fold it?  His approach was to consider the paths you can take along the surface.    Imagine you're tiny and walking on the sheet of paper.  You can take any path you want.  When it's folded state there are a lot of paths that are concave (he called these negative curvature) and others, along the axis of the fold, with no curvature.  Gauss defined the curvature at any point on the surface as the product of the two most extreme curvatures running through that point.  In this case the red one that runs along the axis has zero curvature and the most curved green path has some negative value.  The total curvature for that point is zero times a negative number or zero.  The beautiful part is the curvature for all points on the paper are zero if it's flat or folded in such a way that doesn't stretch or tear it.  

 

Now try a ball.  The surface is convex everywhere and the curvature is always positive in any direction.  The Gaussian curvature for any point - the product of the curvature of the two extreme paths - is always a positive number times positive number or positive. (for now just consider the sign of the curvature - the actual measure is a bit beyond this post).  This is why you can't perfectly wrap a sheet of paper - or pizza - around a ball.  The paper always has zero curvature and the ball is always positive.   This also explains why can't make an accurate flat map of the Earth and have to rely on projections with distortions.

 

Many surfaces are more complicated. For fun explore points on bagel or bananas, but for now back to the pizza.  If you let it flop it still has zero curvature, but no strength.  Putting a fold down an axis gives it rigidity.  You find this throughout nature and engineering. An empty Coke can should hold your weight if you put it on the ground upright and carefully stand on it.   Lay it on its side and will crush flat under your weight. The fold in a surface of zero curvature gives a lot of rigidity in one direction.   Next go somewhere that is easy to clean and try to break an egg by squeezing it in your hand.  The positive curvature everywhere makes it surprisingly strong.  If the shell has an imperfection or if you tap it with the edge of a knife or fork while squeezing, you'll have a mess. The positive curvature was distorted and the surface failed.

 

Assuming you didn't make a mess, go back to the ball.  Draw an equator and then mark a North pole. Drawing an arc at a right angle to the equator, you'll always intersect the North pole - it's a line of longitude.  Next pick another point on the equator and make another arc at a right angle.  You've made a spherical triangle. Sum up the angles.  If you started with the points close together the sum will be just over 180° and if you go all the way around it will be nearly 540°. Our old rules of trig don't work on this non-flat surface.¹  A neat thing you can do is measure the shape of the Universe in a way that is similar to making these trigonometric measurements.  So far it looks extremely flat and that's a deep result about the structure and evolution of the Universe.

 

So get some pizza to celebrate the Remarkable Theorem its explanation of why we hold pizza the way we do. 

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¹ If you draw and measure some circles on the sphere you'll notice the circumference is less than 2πr and the area is less than πr².  If you try it on a surface with negative curvature - a saddle shape or the inner part of a bagel for example - the circumference is greater than 2πr and the area is greater than πr².

 

playing with popcorn

A minipost

 

Chatting with Sarah the topic of snack foods somehow came up.  She mentioned problems getting all of the popcorn kernels to pop and sometimes there was burning. We wandered to something else, but afterwards it struck me that proper popcorn popping was something I know how to do.  In fact a few scientific papers have been written and it's a good area for home experimentation. But first the recipe:

 

Our next door neighbor when I was in high school, had it down.

° 1/3 cup of fresh popcorn .. he'd keep it in a sealed glass jar.  

°  3 tablespoons of vegetable oil in a 3 quart heavy bottomed sauce pan over medium high head. Add 3 or 4 kernels of popcorn and wait.

° When they pop add the rest of the popcorn and lift the pot away from the heat for about 30 seconds.  Put on the cover and return to heat.   If you can it's a good idea to leave the lid slightly ajar to release some of the steam (his lid had a vent)  When the popping slows to once every second or two, dump the popped corn into a bowl and add salt and melted butter to taste.

I use an inexpensive light olive oil (not a rich or expensive one) and add a bit of salt when the popcorn goes into the oil.  I prefer white popcorn, but your taste may vary. Jolly Time comes in thick plastic bags that are fairly air-tight.  Upscale popcorns come in glass jars, but I'm cheap.  

 

Popcorn is a special variety of corn.  The kernel has a hard thin hull on the outside. Inside is the seed along with enough water and a starchy food to get it sprouting.   The trick is to get the water inside to boil.  The hull is strong enough that it takes quite a bit of pressure to get it to burst. As the starch heats it gelatinizes.  The hull bursts at a weak spot and the gelatinous starch cools and solidifies as it expands outwards. The expansion is asymmetrical starting at the weak spot where the hull burst giving the mass a kick. The sound comes from the expanding gas resonating against the expanding kernel and not the breaking hull. No one figured the sound part out until a couple of years ago.

 

If you have kids or some curiosity on your own you can figure out a few things:

 

Find an oven-safe bowl with a lid and heat your oven to about 190°C (375°F).  Add a small number of kernels - say 20.  You may have to shake the bowl around a bit as it heats.  After the popping stops count successful pops.  Now try it at about 170° C (about 340°F).. your count should be different.  If you like you can find the threshold.  You might need to calibrate your oven.  I use an infrared thermometer.   (180°C seems to be the threshold for fresh Jiffy Pop white).

 

If you have a good scale weigh 20 or 30 kernels and calculate the average weight of a kernel.  Put them in the hotter oven and pop as many as you can.  Weigh the popped kernels and calculate the moisture percentage.  I get close to the 14% moisture content good popcorn is supposed to have.

 

You can try different types and freshnesses of popcorn and find popping yields (percent popped). You can also measure the average size.  Try it at a different altitude if you can or enlist a friend.  Try slicing the kernels and see what that does to the size.  How different are the fancy varieties?  And of course you can try toppings other than butter.

 

While on the subject of snack food. Sarah told me about maple cotton candy.  It isn't easy to find in the US, but I finally did.  Expensive, but very good and addictive.  Be warned.

 

 

 

chopsticks, silver spoons and beyond.

Flavor is an extremely complex fusion of senses - taste, smell, touch (pressure and temperature), electrochemistry .. even sight and sound have been shown to be involved. A bit of academic and much more commercial work has been done indicating:

 

° heavier bowls and spoon make yogurt taste better

° rounded plates make rice pudding taste sweeter

° angular plates bring out bitterness and work well for chocolate and deserts with a lot of coffee

° cheeses are rated saltier when eaten from a knife rather than a fork or toothpick

° music and lighting can make a big difference in taste.  

° higher altitude reduces salty tastes (airline meals.. when you can get good ones these days, are exceptionally salty to balance)

° the type of music has a big impact on the taste of wine.

° and so on.. (and on and on)

 

It's really complicated.  

 

Some years ago I became fascinated by something I had thought mundane. A seriously good chef had prepared a feast.  Little had been spared by the hosts and all of us polished the silverware before we were served.  At desert what should have been an excellent Tahitian vanilla ice cream had a distinctively metallic taste.   Could it be the silver?  The lighting was low, so I did what I tend to do.  I took a drink of water, pulled out my pocket knife and had some of the ice cream from its stainless steel blade.  Excellent!  A bit more water and back to the spoon -- ugh, metallic!

 

Stainless steel makes a lot of sense.  Working on high strength steel alloys for guns just before WWI a Sheffield metallurgist noticed a shining sample in an otherwise rusting pile of older rejected experiments.  They kept careful notes and this one had some chromium.  Its hardness was nothing to write home about, but the chromium formed a chromium oxide layer a few atoms thick at the surface of the steel which prevented rusting.   Cut the steel and a new layer formed almost instantly.  He had discovered stainless steel.  The metallurgist, Harry Brearley, wondered how food would taste.  He tried it and pronounced the material unreactive with food.  Something better and less expensive than silver had been found.

 

I learned about some experiments at a materials lab in University College London and decided to experiment on my own.  Early on people probably had copper and then brass cutlery .. at least in the West.  Those materials, by their electrochemical potentials, react strongly with a lot of substances and would probably impart a horrible taste.  The Chinese figured out the right path with chopsticks,.  Single use/minimum contact or highly inert ceramics.  They were millennia ahead of the rest of the world.  

 

Iron struck me was a bad choice (oxidation) and I had read zinc, tin (certainly plated cans), chrome and - of course - gold had been used in cutlery.  I plated a set of identical small spoons with various materials.    Zinc was universally awful (note.. I'm a vegetarian so I didn't test with meat).  Copper reacted strongly with anything acidic.. grapefruit and oranges are spectacularly bad.  The silver spoon left a metallic taste with dairy and something I couldn't really identify compared to stainless.  Gold was about the same as stainless for anything other than dairy or sweet.  Sweet things tasted creamy and dairy tasted even creamier and richer.

 

Next I made a set of spoons forks and took them to a dinner with several people and a variety of foods.  In general stainless came in second to gold - everyone liked sweet and dairy with gold even though they work blindfolds. There was fish and some people preferred zinc.  They could certainly taste the zinc, but liked the combination.  Zinc, copper and tin were universally bad with the vegetables and legumes served,.

 

The folks in England found the same and more.  A few dinners were prepared to pair certain utensils with foods.  That's way above my range, but I understand the results were so-so other than gold rules and stainless is way better than silver.

 

While they aren't scientific, trials like these are easy to do and you may want to try some on your own.  The work out of London were a bit noisy other than the stainless and gold results.   My take is you should use the least reactive utensils that are practical and if some interesting combination (zine and fish) is desired, perhaps it is better approached by seasoning and cooking technique.   This does beg the larger question individual reactions and machine learning based flavors.  I have an opinion, but it is time to stop.

 

These days I eat ice cream and sweet deserts with gold plated utensils - everything else is standard stainless or chopsticks.

 

 

 

refocusing science education

° It was 1956 and the cold war was raging.  Billions of dollars were being poured into the effort, but it was becoming clear that the pipeline for scientists wasn't working very well and strong science was a national priority.  Pre-college science education was in poor shape - particularly in physics.  Textbooks were boring and classes were failing to approach problems like a physicist. A group of university professors and high school teachers came together at MIT to form the Physical Science Study Committee (PSSC) to come up with solutions. Progress was being made, but education as a system had an enormous inertia,  Then October 4th, 1957 came and a little sphere orbited the Earth.   Suddenly Washington DC was pouring rocket fuel all over the PSSC.¹

 

By 1960 a program was developed that emphasized understanding rather than memorization and created films, inexpensive laboratory kit, experiments and teaching material to provide hands-on experience and thinking.  Topics were organized to underscore overarching conservation principals.  Almost overnight high school physics teaching had been revolutionized.  Spurred by the success of the PSSC similar programs were created and successfully implemented for chemistry and biology.   Mathematicians had their own approach,  They'd focus on the beauty of math and get away from the mindless arithmetic, geometry, trigonometry and algebra that had been taught since the late 19^th century.  While the science programs succeeded, the new math failed - it made too much of a leap.  Parents and teachers alike fought and quickly killed it.

 

Science teaching, if the metric is the supply of Ph.Ds. in science, is too successful.  There simply aren't enough research jobs to match supply and the number of posts in the US is shrinking.  One might argue that a Ph.D. in physics is useful in other areas - I've been told physicists think differently and the same is probably true for all of the physical sciences - but if anything the pipeline is working .  

 

Consider just what that pipeline is.  There were probably one or two nerdy types in your high school obsessed with science or math.  My high school had two in an informal astronomy club.  One of us discovered astronomy had too much math and became a doctor and I went into physics.  It takes five high schools in the US to produce one student who makes it as far as a BS or BA in physics.   At the doctorate level more than thirty high schools come up with one who makes it all the way through. The field just isn't that big.

 

I remember the PSSC courses.   When I took physics as a junior the teacher suggested I already knew too much physics and math and let me read the books and suggested more at the local air bases's library.  To get credit I became a teaching assistant for my junior and senior year.  It was great - I became familiar with the quirks of the experiments and learned developed a profound mistrust of wires that  served me well.  He'd talk to me after class.  I began to think like a physicist.   The fire was lit in me about the time I was eleven, but this oddball  version of high school physics I had kept it burning.  By the time I went off to college I was focused.  I'm fairly convinced the PSSC series would have kept me focused had I not had any background.

 

A curious factoid mentioned in a note from the American Physical Society.  Over 90% of physics Ph.D.s never changed majors in college.  I'd be surprised if other Ph.D.s in the physical sciences were different. Folks who make it through the pipe found their focus early on.

 

Although the pipe works there's a huge problem.  What I think is really needed is to give the much larger group - those who will never major in science - a clearer picture of what science is and perhaps why it is beautiful.  It is possible to teach the tools necessary to think like a scientist.  In a way I'm suggesting something similar to my piece on physical education.  Give people a life long appreciation even though they won't become pros.  I'd never make it in sports, but I love recreational rowing and watching volleyball.  Don't get  rid of the professional track, but keep it alive and perhaps improve it a bit through separation. At the same time  make popular level science fascinating and relevant.  

 

I'll stay away from ideas of how to do this, but offer something simple.   Invert the order of the subjects.  You start out with something simple like physics, then add the complexity of chemistry and then biology branching into extremely complex systems like biochemistry qualitatively.  Memorization should be avoided at all costs.  The Krebs cycle can be cleanly explained at a qualitative level.  I've seen some very nice biophysics and biochemistry at a tenth grade level.  

 

The next Monday, when the fathers were all back at work, we kids were playing in a field. One kid says to me, “See that bird? What kind of bird is that?” I said, “I haven’t the slightest idea what kind of a bird it is.” He says, “It’s a brown-throated thrush. Your father doesn’t teach you anything!” But it was the opposite. He had already taught me: “See that bird?” he says. “It’s a Spencer’s warbler.” (I knew he didn’t know the real name.) “Well, in Italian, it’s a Chutto Lapittida. In Portuguese, it’s a Bom da Peida. In Chinese, it’s a Chung-long-tah, and in Japanese, it’s a Katano Tekeda. You can know the name of that bird in all the languages of the world, but when you’re finished, you’ll know absolutely nothing whatever about the bird. You’ll only know about humans in different places, and what they call the bird. So let’s look at the bird and see what it’s doing—that’s what counts.” (I learned very early the difference between knowing the name of something and knowing something.  - Feynman

 

Berkeley used to have a version of first year physics called Physics for Future Presidents taught to non-technical majors usually in their senior year.  Although it would be out of reach for 45, it was one of the most popular courses in the school.  I'm convinced smart people and teachers could do the same for ninth graders. 

 

Education has many more issues - I sometimes get into trouble when I mention I'm not a fan of the current STEM approach.  Not only would a discussion in this post be diverting, but I'm not smart enough to offer much clue.  But huge changes can be brought to science teaching.  

 

For fun take a look at the  physics popularization by the theoretical physicist George Gamow.  He was smart enough to know how to write about real physics for people who had curiosity, but little background.  Equations are rare and he resorted to story telling.,  It was about seventy years ago, so it's a bit corny now, but inspired teaching is possible:  the Mr Tompkins series. (the songs aren't very good, but a world where the speed of light is 10 miles per hour is fun).    Many libraries will have copies too.  There are some excellent books reflecting current physics too.

 

 As always contact me if you want a deeper discussion.  

 

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¹ Actually it wasn't Sputnik immediately.  That's a manufactured story courtesy of LBJ.. but a few months after the orbit the new story was good enough to light the fuse.  Another story...

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Recipe corner

 

Brussels sprouts are still sort of ok if you roast them.  The apples have been not so great, so roast 'em too..  Here's another variation.

 

Brussels Sprouts and Apples

Ingredients

 

° a pound of brussels sprouts halved

° 2 tbl olive oil

° a bit of kosher salt

° 2 or 3  tbl nuts broken up small ..  I used almonds because that's what I had ..  seeds, like sunflower seeds would be good

° 1-1/2 tbl apple cider vinegar

° 1 tsp whole grain mustard

° 1/2 tsp honey (or something sweet..)

° 1/3 cup apple diced small.. like 1/4 inch pieces

 

 

Technique

 

° oven to 375° F.  put bs on a baking tray, toss with 1 tsp olive oil and a bit of salt . Roast for 30 to 45 min on middle rack 'til brown, crisp outside, soft inside

° put the nuts on a sheet on a lower rack and toast for about 7 or 8 minutes until they start browning.

° whisk the extra olive oil, vinegar, honey and mustard

 ° when bs come out toss with vinaigrette and nuts and apple

from math to music and physics to phys ed

In the early 18^th century commerce had become a primary driver for education the American colonies.  Physical connectivity to move goods and information was growing rapidly.  Stores were moving beyond the basics and it was becoming necessary to let potential customers know what was available.  The amount of information was growing rapidly and with it, the necessity of connecting it with the consuming public.  Teaching as many as possible how to read and do arithmetic had become important.  

 

We've seen many periods of change in education.  Public land grant colleges, the advent of the University laboratory (one of the largest and most enduring inventions of the 19^th century), full education for women and minorities, physical education, education for it's own sake vs vocational training, the dramatic change to science education during the Cold War and the failed experiment of teaching real mathematics.  Some of these have gone through cycles and now we stand at a time where it seems clear a shift of emphasis is necessary.

 

I've written a fair amount of my experience - the gifts of play, boredom and freedom that my parents gave and two mentors separated by time and space that allowed me to focus on a core interest in such a way that I managed to learn a bit about curiosity.  I've learned and taught at a variety of levels and have some ideas on what I'd like to see change.  I also suspect many of my ideas aren't that well informed and some are controversial (in particular my views on the current STEM emphasis, how to change math and science education, precollege focus, critical thinking, physical education, and practical skills training).

 

What I want to propose is a bit of homework.  The readership here is small, but extremely diverse.    I'd love to collect ideas from as many who would like to write something and post them in a few weeks,  As a constraint, keep it to one to four ideas, for pre-college education.  Feel free to point out anything you feel is being done exceptionally well that should be more broadly applied, Anything from a paragraph or two  to a few pages if inspiration hits.  Pencils down on January 14^th.  Let me know if you want it to be anonymous or associate your name.   Feel free to include anything other than text.  I'll collect everything including my bit into a long post a few days later. 

 

Remember that we all have a different take on this education thing.  Everyone's ideas are potentially interesting, so don't worry if you're not in the business.

And if you want to make a video, that's ok too...

 

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Recipe corner

 

My mother died a year ago ..  going through some old things this recipe appeared.  It's from her mother, but makes a fine gingerbread.  Better with vanilla ice cream than whipped cream, but also good by itself.

 

 

what does dim mean and a most unusual job

You're outside on a dark and starry night wondering about those stars.  Is someone out there? You take your flashlight out and point it at the sky.  Your message in a bottle tossed into the cosmos.   I'm guessing most of you did it when you were a kid.  The problem is it doesn't work.  The light you send skyward is scattered or absorbed before it gets out of the atmosphere. At least your imagination was fired - what would it take,  There are people who point lasers beams skyward  and that leads to one of the most unusual job openings I've ever seen.  But more on that later,  there's another interesting question to ask first.

 

Our eyes are amazing at what they do.  We get reasonably detailed color images along with less detailed, but much more sensitive night vision.  We usually think about owls as having some of the best night vision eyes.  They're pretty good, but some frogs can detect a single photon of light.  So image this.  You're a frog watching a light that glows with an unwavering brightness.  If you were a space frog it might be the Sun.   You measure the brightness of the light and then start a journey outwards.  Twice as far from the source the light appears a quarter as bright.  Three times as far and it's a ninth as bright.  One hundred times and it's one ten thousandth it's original brightness.  The measured brightness at your distance goes as 1/r² where r is the distance.  The inverse square law.¹ You continue on and and the light gets dimmer and dimmer.  How did can it get and what would it look like?  I posed the question to a class of premed students I was teaching physics to..

 

The light gets dimmer to a point - the single photon point.  Rather than diminishing further it begins to flash at that brightness. .  At first are flashes most of the time, but travel further and there an increasing number fleshless regions.  Add up the energy you receive - the number of photons over some unit of time.  The frog is  bored and haven't croaked yet, so it plots the energy over some amount of time .. just the number of photons  (flashes) it sees in some length of time.  The curve is still the inverse square law.  

 

Light, as it happens, comes in chucks.  In physicsy terms it's quantized and that chunk is a photon.²

 

When first encountered the paper on the sensitivity of frog's eyes I was blown away.  For perspective a 60 watt equivalent light bulb emit's about ten billion photons a second.   The back of the envelope suggests the Sun would start to look like a blinking single photon about 1000 light years out - the range of our deep space frog. 

 

If you're sending information you have to worry about how quickly you can flash the light to begin with.  If your receiver is seeing a bright light or radio signal (radio is just longer wavelength photons), you can send information at a fast clip.  If it's very dim things get slow.  Even with huge exotic antenna arrays the science from the New Horizons spacecraft that flew by Pluto had to be recorded and sent back over a period of months,  NASA and JPL have been experimenting with deep space laser communication systems. The idea here is a laser beam can be made to not spread out much and will appear bright,  HDTV from Mars orbit can be done.   As long as you don't have to go through the atmosphere.  

 

We've almost circled  back to the beginning - the people who shoot laser beams skyward.  But the atmosphere absorbs and scatters so much.  What's going on?  

 

You can point a very bright, highly culminated beam of laser light to the sky.  Some small lasers are bright enough to travel through a few miles of atmosphere and still be focused and bright enough to temporally blind a pilot.  In fact it's a federal crime to shine a laser at an airplane.

 

There are people who shine extremely powerful lasers skyward legally.  The atmosphere can be thought of as a sea of small regions about the size of your hand. They have slightly different optical characteristics than their neighbors.  Their turbulent movement is what causes stars to twinkle. They represent an enormous limiting factor to the resolution of telescopes with mirrors larger than a half meter in diameter.

 

The trick is to look at a reference star near the object you're studying.  You know where it is so you image it, look at it's new position, do a bit of math and change the geometry of your optics in real time to adapt.  Simple in principle, but difficult in practice, it has revolutionized optical and infrared astronomy at a half dozen of the largest observatories in the world.

 

One of the problems is you need a fairly bright reference star and for about 99.99% of interesting astronomy there aren't any suitable stars that are close enough to use.   Astronomers get around that by making their own reference stars.  The standard technique is to use a very powerful pumped dye laser to create a yellow beam with a wavelength of 589nm..  

 

The laser is pulsed on for a short time - microseconds - and the beam soars skyward.  For the first 30 kilometers there's enough atmosphere to scatter most of the light so it's a visible yellow line. At about 95 km above the Earth it encounters a layer of sodium atoms - leftovers from constant bombardment of our atmosphere with micrometeorites.  589nm is a resonant frequency of atomic sodium so they get excited and then spontaneously de-excite emitting yellow light in all directions.  A little artificial guide star shines during the pulse.  Some of it's light makes it back to the telescope through nearly the same path light from the astronomy target has to travel.  Everything you need to compute your mirror changes.  All of this happens about a thousand times a second.  The process works so well that the largest 'scopes on Earth now resolve better than the Hubble Space Telescope, although it still has other advantages.

 

That upward beam is really bright.  It's only on for about a tenth of a percent of the time, but it could blind anyone on an airplane who happened to be in the way.   You get around this with one of the most exotic jobs I know of - a night watch for the 21st century.

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¹ The way to think about it is to recognize light travels in a straight line.  Image two imaginary spheres - one is  one unit of length away from from a glowing sphere and the other two units.  If you pick a region with any area on the first sphere (say 10 square inches) it covers a fraction of the first sphere that is four times larger than the fraction covered by the same area on the second sphere because the area of the spheres goes as the square of their radius 4*Π*r²

² In your physics classes you probably learned light can be a particle and a wave.  You can think of the photon as the particle of light.  Well..  and this would take too much to get into ...  the particle/wave duality model is an artifact about how people saw quantum mechanics in 20s and 30s.   I'll just state what we now know ... everything's just a wave.  But unless you are deep into physics it's probably safest to think about particle wave duality.

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Recipe Corner

 

Really easy and just right for the cooler weather

 

White Bean Toast 

 

Ingredients

 

° extra virgin olive oil

° 6 slices of crusty whole grain bread (or whatever constitutes excellent toast for you)

° 1 clove garlic, minced

° 1 tsp chopped fresh rosemary

°  a 15oz can of no salt cannellini beans drained and rinsed

° 2 tbl lemon juice

°  salt

° 2 tsp chopped parsley

° bit of red pepper flakes

 

Technique

 

° oven to 425°F.  Use 1 tbl of EVOO and brush one side of each piece of toast.  Arrange face up on a sheet and toast on top rack until brown and crisp.

° heat 2 tbl of EVOO in a skillet over medium heat.  Add garlic and rosemary when the oil is hot and cook for a minute and remove from heat.

° add beans, lemon juice and salt and mash some of the beans a bit to give a variety of textures and some whole beans.

° spread over the toast, drizzle on some good EVOO, sprinkle on parsley and red pepper flakes.

 

much more artificial than intelligent

Look at the little group of numbers on the right.  They were handwritten and aren't exactly neat and tidy.   Take the first four on the third line.  Our brain says 1 , 7, 4 , 2.   We might bring it together as 1742 and some of you might think of a year - perhaps the year Handel's Messiah was first performed.  And we do it with a brain composed of nearly 90 billion neurons each with up to ten thousand connections to other neurons.  All of this  uses just 20 watts of power and weighs about a kilogram and a half.

 

The brain's neurons are simple computational objects.  There are input 'wires' called dendrites that connect to the cell body and outputs called axons.  Signals arrive on the dendrites and the neuron sums them.  If they exceed some critical value the neuron "fires", sending a signal out through the axons to other neurons.  There's a lot of detail to the nature of the wiring and how it comes to be, but for now think of the neurons as objects that sum inputs and fire if a critical value is exceeded...  more abstractly they're just mathematical functions.

 

The reason for starting off with some numbers is one of the basic programs - the 'hello world' if you will - for machine learning is to build a number recognizer.  A very large set of handwritten and printed numbers was scanned by a 28 by 28 pixel sensor giving a 784 pixel image.  Each of these pixels has a value depending on the brightness of the number it corresponds to.  For the sake of argument say it ranges from 0.00 for black to 1.00 for bright white.  A very crisply written 1 would be a line of dark values in a sea of mostly white. The edges would have intermediate grey values.  Less crispy numbers have lot of grey regions.  

 

To make our neural network (call it a NN) each one of these numbers is put into a corresponding neuron. The number in the neuron is called the activation.  You can think of the neuron as doing something when it's activation is a high number.

 

These 784 neurons are  going to be the first layer of our NN.  We'll have 784 neurons on one side representing the brightness of each pixel of the image.  On the far side is a small layer of 10 neurons on the other side the numbers 0 through 9.  Each of these contains the NN's best guess for how much the scanned number is one of the numbers.  For example you might have 0.02, 0.14, 0.38, 0.02, 0.98, 0.22, 0.13, 0.22, 0.33, 0.07 .. the guess  that it is the number 1 is 0.02, 2 is 0.14 and so on.  In this case 0.98 stands out, so NN is jumping up and down saying 'it's 5".

 

In between are other layers .. often called 'hidden layers.'  Rather than presenting the math that describes the network and waving our hands saying "it's all in the training", let's go in more deeply and see what's going on.  The diagram shows a very simple NN intended to recognize numbers with 784 input neurons on the left (it only shows a few... use your imagination), a single hidden larger of 15 neurons and an output layer with 10 neurons..   

 

Several years ago I wrote my first machine learning program to recognize numbers from this data set.  I used two hidden layers with 20 neurons each.  Their number and sizes are not hard and fast choices - there's a lot of room for experimentation and frankly a lot of this is more art rather than engineering.

 

Consider of the numbers from the pixels in the first layer.  These activations will determine the activations in the first hidden layer which in turn determine the activations in the second hidden layer which determine those in the output layer.  A pattern in the first determines a pattern in the second which determines a pattern in the third and the final pattern tells us the guesses for each number.

 

I like to think about the hidden layers doing something rather than just saying they're a black box.  It may be something very foreign to us, but in this case it is possible that one might, say, recognize components like edges and the next may be larger components like lines or roundish structures (a 9 is a line and a roundish structure, a 7 has two lines...).  It probably isn't doing this, but it's reasonable to think of a NN breaking down problems into layers of abstraction.  Trying to understand what's going on in these hidden layers is very difficult in large problems - perhaps impossible and that becomes an important issue when machine learning is used for purposes that impact us.¹  

 

At this point the post will diverge a bit.  I'll go into some but not detail that some folks may find useful.  I'll highlight it in blue.  Feel free to skip it if you're only interested in a high level view.  Up to now we've established a neural network is just a function that takes input on one side and spits out an answer on the other.  In this case the input is the brightness values of a 28 by 28 pixel scan of numbers and the output is the NN's best guess for each digit, from 0 to 9, of how likely the input was that number. 

 

Consider the simple, but artificial,  example where the second layer is trying to look for a some kind of feature of the number - say certain types of line segments.  Each of the 784 neurons is connected each of the 20 neurons in the second layer.    Each value in the left most layer is a pixel value.  As an exercise to make things clear take out a sheet of paper and make a vertical column of circles labeling them a₁, a₂ , a₃, .... up to a₇₈₄.  (only do a few of them  ^... are your friends)  On the right draw another column with a couple of circles.  Connect each circle on the left with each circle on the right.  We multiply each of these activations by a weight - just a number - and find the weighted sum  of all of the pixels ..  labeling the n^th neuron as a_n and the n^th weight as w_n we get:  w₁a₁ + w₂a₂ + w₃a₃ + ... + w₇₈₄a₇₈₄.   In the example of a trying to find a little line segment imagine making all of the weights 0 except for those in the region right around the little segment.  Then taking the weighted sum just focuses on the values of the pixels we care about.  If we really wanted to force the issue we could add add a negative number called the bias to the sum to require the weighted average be large.  The bias surpasses other bits of signal in the vicinity of what we're interested in.  The weighted sum going into the neuron on the second row is now (w₁a₁ + w₂a₂ + w₃a₃ + ... + w₇₈₄a₇₈₄ - b),where b is the bias.  

 

We're getting closer.  This new activation for the pixel in the second layer can have quite a range. If the weights are between -1 and 1, it can be between -784 and +784 before adding the bias.  It is common to scrunch everything to a range between 0 and 1 using a sigmoid function, call it s(x).²  Now the scrunched weighted sum is   s(w₁a₁ + w₂a₂ + w₃a₃ + ... + w₇₈₄a₇₈₄ - b) for each of the 20 neurons in the second layer.  This new activation that goes into the neuron in the second column is the crude equivalent of a synapse in biology.

 

You'll note the naming convention I've used isn't good enough.  We're just looking at what goes into a single neuron of the second layer. In this example there are  19 more, each with a connection to each of the 784 neurons of the first row and each connection with it's own weight.  And there's another bias.  Do this and you have the first two rows.  Next you consider the connections between the second and third layers (the two hidden layers in this example).  After you finish that you have the connections between the third and final layer. And this is just a simple example.   Fortunately computers are really good at this kind of math and there's a natural way to easily organize this.  If you get into this but  you'll need to be on good speaking terms with linear algebra.³

 

Before getting into the "magic"of how these weights are found take a look at the complexity of this example.  There are (784 * 20) + (20 * 20) + (20 * 10) weights and 20 + 20 + 10  biases.  That's 16,300 knobs to adjust correctly - yikes!  Imagine what it's like for problems with columns millions of elements long.  This approach, and there are many variations,  did't take off until computers became powerful enough and we had interesting data - big data - to play with.   Although there are a lot of calculations, each is very simple.  You don't need a big Intel processor with it's complex instruction set.  GPU - graphics processing units - are arrays of thousands of tiny and fairly slow simple processors.  Each is something like ten or twenty times slower than a modern Intel CPU, but all of them can run in parallel..  you end up being faster by a factor of a thousand or so.  Complex machine learning problems have moved Nvidia beyond computer graphics.  It extends to smartphones - Apple's machine learning core on it's latest processors can make this happen in your phone rather than having to rely on the cloud. 

 

Onward to machine learning because we're not daft enough to adjust this by hand.  

 

What we want to do is come up with a recipe - an algorithm - that adjusts those 16,300 knobs so we're likely to get the right answer when we give the machine a scanned number.  For that we'll use some big data.. the enormous set of  scanned and labeled (what the scan really is:  7 for example)  28 by 28 pixel numbers that's in the public domain.⁴   The data is used to train the network.  You start out running some training data, check what the results are, make some adjustments to the weights and biases and keep repeating the process until the result is good enough.  Then you test your NN by giving it another set of labeled training data it hasn't seen so you can see if it's still doing a good job. The hope is the layered structure generalizes what the recognizer does beyond the training data.  This is critical and whoever implements a NN needs to be aware of the domain(s) where it's valid.

 

So each neuron is connected to all of the neurons in the previous layer and it's activation is the weighted sum of all of the all of those neurons.  You might think of the weights as the strength of each connection and the bias is an indication if that neuron tends to be active or inactive.   To start off you set all of the weights and biases completely randomly.  It will give terrible results on the first set of training data. 

 

To make it learn you define a cost function.  We know what the number should be:  4 for example.  Now look at the differences between each final digit and what the machine guessed.  Consider the square of the difference between what you expect and what you get.  If the original number was 4 we expect the output list to behave like this:  0 to have the value 0, 1 to be 0, 2 to be 0, 3 to be 0, 4 to be 1, 5 to be 0,...

 

The first column is the number in question, the second column is the expected result of a perfect NN, the third is the square of the difference of the computed expectation and the expected result (the second column) which are shown in the forth column.

0  0        0.3       (0.3 - 0)²    0.09        

1  0        0.2       (0.2 - 0⁾²    0.04

2  0        0.1       (0.1 - 0)²    0.01

3  0        0.8       (0.8 -0)²    0.64

4  1        0.3       (0.3 - 1)²   0.49

5  0        0.7       (0.7 - 0)²    0.49

6  0        0.3       (0.3 - 0)²    0.09

7  0        0.8       (0.8 - 0)²    0.64

8  0        0.5       (0.5 -0)²    0.25

9  0        0.0       (0.0 - 0)²   0.00

 

The cost is the sum of the squares of the differences... in this case  2.74. Ideally we would get 0.00 for the cost...  

 

The cost is small when the image is accurately recognized, large otherwise. .  Now look at the average of all of the costs over all of the training data...  that gives a measure of how good our NN is for that set of data.  A single number for each set of settings.

 

The trick is just calculus.  Our AI is much more artificial than intelligent.  I'll just state a method exists to find out how to turn the knobs and when to stop.  I'll use a small bit of  blue so be ready to skip if you don't know about gradients, 

 

Imagine the case where we just have one variable.  There would be some kind of curve for the weight relating that weight to the average cost.  If it was very simple, and it's not likely to be, you could just find the minimum by taking the derivative and be done with it.  A better tactic is to start somewhere on the curve and figure out which direction to move to lower the output by measuring the slope .. move left if the slope is positive, right if it's negative. You'll end up at a local minimum.  Now imagine the function with many inputs - so you have many dimensions.  Now you're looking for the direction and steepness of steepest descent ..  just the negative of the gradient of the function.

 

So compute the gradient of the function, take a small step downhill and repeat over and over until you find a local minimum.  So put all of the 16,300 weights and biases into a tall column vector. The negative gradient of the cost function of that vector tells which corrections to all of those numbers gives the most rapid decrease of the cost function.  Minimizing it means better average performance across all of those training samples.

 

A useful way of thinking about the negative gradient of the input vector is each element tells us if a weight or bias should be increased or decreased and how important it is.  so one that starts out as 0.02, -1.29, 0.33 ...  would say w₁ should decrease a little and it is relatively unimportant, w₂ should increase a lot and it is important, w₃ should decrease a somewhat.

 

This setting of the weights and biases is called backpropagation.  We're just minimizing the cost function.  It's important that this function is smooth so we can find local minimums...  that's why the activation numbers of the neurons have a smooth range rather than something more discrete like ones and zeros.  Oddly enough this is where the brain is more digital than the neural network.

 

Playing with this example I was able to recognize a set of number images I hadn't trained the NN on at about 94% accuracy.  There are more sophisticated approaches.  It is important to stress that the innards get very messy and difficult to understand.  That's an unavoidable bug as it is very easy to fool ourselves.  There's bias in the building of the neural network, bias in the training data and bias on it's application.  It is great for some applications, but when it's used in science (I have some experience with it in astrophysics), you use it as a last resort and try very hard to keep the models simple and understandable.  The opposite of what happens in machine learning on social networks. 

 

I think they're a fantastic way to approach certain classes of otherwise intractable problems  and potentially misleading and even dangerous in other domains.

 __________

¹ Like associating aspects of our life and the games we play with our likelihood of us being a good engineer or a neo-Nazi.  These biases, some on purpose and others by accident are extremely important and worth taking up separately.  Machine learning tends to be good at identifying things very similar to known sets.  There be dragons and caution is required.  

² For example s(x) = 1/(1 + e^-x).   There are a variety of functions of this class.  This one is known as the logistic function - you pick what's appropriate for the problem at hand.   In this case large negative numbers go to 0 and large positive numbers go to 1.  

³ Organize the input activations as a column vector and the weights as a matrix where the row corresponds to the connections between one layer and a particular neuron in the next layer.

 ⁴ the MNIST Database from LeCun, Cortes, and Burges.

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The first good brussels sprouts are out.

 

Roasted Brussels Sprouts with Mustard

 

Ingredients 

° 1 pound brussels sprouts halved

° 6 tbl olive oil

° kosher salt

° 1 tbl dijon mustard

° 1 tbl coarse grain mustard 

° 1 tbl cider vinegar

 

Technique

° oven to 425°F

° put brussels sprouts on a baking sheet.  drizzle on half of the olive oil and some salt and stir them (I use my hands).  Roast on middle rack for about 35 - 45 min turning them a few times until softened and browned.

° whisk the mustards, olive oil and vinegar into a dressing and add a bit of salt to taste.

° put the baked brussels sprouts in a bowl and toss with the dressing.

 

 

 

listen to the mirrors streaking overhead

 

 I've seen it rainin' fire in the sky

 

Ah the Perseids - My favorite meteor shower.  The peak falls somewhere on the 11^th or 12^th of August.  That usually means you can be out all night without a jacket.  If you're in a very dark area on a moonless night sometimes you'll see two hundred in an hour.

 

Comets are the Pig Pens of the Solar System.  Not enough mas for gravity to form into anything solid and spherical, the stuff they're made of tends to boil away as they heat up.  Warm one enough and it's dust trail thickens enough to form a tail or two .  The Tuttle-Swift comet is youthful enough to be extra dusty and the Perseid shower is just the Earth passing through it's dust path.

 

A bright moon marred this year's shower..  even in the mountains counting forty probably required exceptional eyes.  I'm happy with a few, but the sky was cloudy.  Time for Plan B.  I'd listen to them. 

 

Perseid meteors are tiny little things - usually about the size of a grain of sand - that are booking along at about 59,000 meters per second (132,000 miles per hour) when they hit the atmosphere.  They're gone in a flash, but the friction heats them up so much that they ionize - their atoms losing an electron or two. This ionized plasma trail is electrically conductive and makes a dandy radio reflector.

 

The trick is to aim an antenna at VHF or higher frequency station just over the horizon.  The signal won’t make it to you unless, say a reflector just happened to show up somewhere between you and the station.

 

 

I started listening to meteors as a kid in Montana.  At the time Great Falls only had two FM stations.  Edmonton had several including one that was well separated in frequency from anything in Lethbridge or Calgary. I’d point a directional antenna Northish towards Edmonton their frequency and wait.  You'd hear bursts of Canadian radio from one to ten seconds long.    It was clear why I never dated in high school.    

  

 

I still listen to the trails on cloudy nights.    Last night a highly directional antenna was pointed towards a very powerful 61.26 MHz transmitter in Timmins, Ontario (CITO).  The plot on the top is frequency (horizontal) with signal strength in decibels (vertical)  Under it is a scrolling time-stamped “waterfall” plot with frequency along the horizontal axis and color coding for signal strength.  The first meteor trail  was about six seconds long and dab smack at 61.260 MHz  - Timmons coming in via a Perseid!  

 

The plot just under it is a much larger meteor that left a trail lasting over over two minutes. (visually something like this could last as long as twenty seconds)  It was large enough that a bit of it may have made it to the ground.  It would have been bright - undoubtedly brighter than any star or planet. 

 

 

The idea of using mirrors in the sky to communicate isn't new.²  In the early days of satellites it was thought active communication satellites might not be good enough for video and other large bandwidth applications.  Echo was a passive communication satellite.  A metalized balloon about a hundred feet in diameter orbiting about a thousand miles up.  I remember watching it as a kid - it's large reflective surface made it a very bright object in the evening sky.  

 

 

Microwaves were bounced off Echo -- the idea being to have a series or regularly spaced balloons in orbit to allow continuous communication.  As with any technology there were some interesting problems and it wasn't a surprise that Bell Labs was involved with an interesting ground station at Crawford Hill in Holmdel, New Jersey.  

 

for the history of technology buffs  here's an old film on Project Echo

 

 

 

After the Echo program a couple of Bell Labs radio astronomers were using the antenna to  investigate some stubborn radio noise that just wouldn't go away.  They'd point the antenna anywhere in the sky, but wherever they went, there it was.  They double-checked all of their equipment including removing a layer of white organic dielectric material in the twenty foot horn antenna (bird poop). The signal's wavelength corresponded to the microwave spectrum a blackbody would give about three degrees Kelvin -- just above absolute zero.  Only one explanation was plausible.   It turned out to be a cosmic background radiation.  A microwave radiation that is everywhere - the detectable remnant of the Big Bang.  Penzais and Wilson received the Nobel Prize in Physics for the discovery.   A surplus antenna helped change our view of the Universe.

 

 

I continued watching Perseids for five hours.  It  maxed out at about 60 an hour - more than I would have seen visually without the clouds .  Not close to my record, but that's another story.  

__________

 

¹ You can figure out their composition by studying the spectrum of their final light.  Meteorites are those large enough to survive the trip to the Earth.  They are usually at least a few grams at first.  

 

² I won't go into meteor scatter communications, but it's been used in trucking and remote sensing.

 

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Recipe corner

 

Tomatoes are finally great!  I've noted this one before, but it's a keeper and someone just requested it again

 

Simple Summer Pasta and Tomatoes

Some time ago I had a very simple tomato and pasta dish. The tomatoes were
in chucks and not really cooked. It would only work if they were perfect so
I gave it a try. Here is about what I did. This would be enough for two
hungry people or four people if served as a side course. 

Two really amazingly blimpishly ripe tomatoes that are going to have great
flavor. I would go with an heirloom tomato as the new ones have so much
goodness bred out of them. My guess is I had maybe about one and a half
pounds of tomato. Chop these into half inch pieces and scape them into a
big enough bowl (how is that for precision?) be sure and get some of the
juice that is sitting on the cutting board.

Pour some good extra virgin olive oil on them. Maybe two glugs (another quality
measurement). Put a good amount of a good finishing salt (Malden is great) and freshly ground pepper on them stirring to mix. Let them sit.

Start some water boiling for the pasta and throw some salt in. Cheap salt
of course

Now chop a strong red onion or some scallions into slivers. Maybe about a
quarter cup

Chop a handful of good arugula

Cook about a half pound of pasta and try to stay away from the marinating
tomatoes

Place the onions on top of the tomatoes spreading them out, then make a layer
of arugula choppings

Now dump the pasta on top of the layered mixture in the bowl and leave it
for a two or three minutes. To deal with the frustration grate some good
cheese for a topping and top it. (I prefer it without cheese, but most people probably want it). The heat from the pasta is slightly cooking the ingredients beneath,  just barely, but perfectly enough. 

Now mix it all up and serve.

 

the burgess shale and time machines

Two days ago I found myself on the bus out of Manhattan.  The trip was a treat - I had managed a few hours with a dear friend.  As the bus crawled through commute hour traffic I wondered about the odds of deep friendships like these.  The path to this one involves great leaps of serendipity that I easily trace back to watching someone stare at the ceiling in a Connecticut auditorium fifteen years ago..  I asked them why.  If I hadn't the path probably wouldn't have appeared and I wouldn't know several people on this list.  Perhaps there would be other paths. You wonder about chance versus inevitability.   And that leads to much deeper questions

 

I spent a good deal of my teenage years in the wilderness between The Bob and Glacier in Montana and Banff and Jasper in Alberta with a few excursions to British Columbia.  Meeting two physicists in a small cosmic ray station by chance on Sulphur Mountain in Banff that was one of the sparks that defined my education, but when you're surrounded by Nature there are many possibilities.  

 

The Burgess Shale in British Columbia is so amazing so as to be borderline magic.

 

I was about fourteen when I met soft rockers - paleontologists - for the first time.  Soft rockers do fossils and this was ground zero.  Canadians are wonderful people and Canadian scientists doubly so.  A slow moving science curious teenager was all they needed.  That afternoon I watched as they opened the rock to fossils that were seeing sunlight for the first time in about 550 million years.  I had seen a few fossils in a museum at the University of Utah, but nothing like these.  In fact one of these was completely new to these experts.  They whooped like kids.  It was the first time I saw the ecstasy of discovery. I like Canadians.  

 

The neat thing about this deposit and a few others nearby is they're wonderfully preserved records of the Middle Cambrian.  Even down to soft tissued plants and critters.  You've probably heard the term 'Cambrian Explosion.'  It was a relatively short period when most of what we'd call animals appeared.  It was explosive in that the rate of diversification was far larger than anything seen on Earth.  The Burgess Shale is the best recording device for the period, so paleontologists and, more recently, paleoclimatologists are on it like flies at a picnic.

 

Soon afterward much of this diversity abruptly disappeared.  There were so many paths life could have taken - some completely bizarre.  The question that gnaws on you is what if you could press rewind on time ...  what if you when back to the middle of the Cambrian and let time run forward again.  How would life evolve?  Would we exist?

 

It's a delicious question that seems purely theoretical at first glance.  Many maintain that evolution is pure chance while others maintain there may be an inevitable structure.  But then Richard Lenski came along with a time machine.

 

He had this neat idea.  It hit him that bacteria quickly go through generations.  He decided to look for signals of evolution on a time scale humans might witness.  He started with a dozen populations of E. coli bacteria in early 1988 and watched and waited.  There were problems with funding, electrical outages and the like, but now they're around the 65,000 generation mark with the experiment is still running.  

 

The results were and are amazing.  Some evolutionary adaptations occurred in all of the populations, but others were unique to a single population.  At least one, the ability to use an odd substance as a carbon source in an aerobic environment, created a bizarrely unique form of E. coli. 

 

Other somewhat different approaches show similar results - there is some evolutionary convergence, but divergence is more common - much more common.

 

Lenski won a MacArthur for his work bringing some meaning to what the award is about. He had created something of a time machine for studying evolution. 

 

For me this is a beautiful result.  We're really unique.  That is something to treasure and nurture.  We need to make our condition better and help each other as it's up to us.  As the bus turned onto I-78 I found myself smiling thinking about those deeply improbable and completely unique friendships.

 

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Recipe corner

I've been playing around with ice cream this Summer.  I'm not eating much, but the temptation to do something different is there.    I was caramelizing some onions....  The base is an adaptation of one of the standards...

 

Caramelized Onion Ice Cream

 

Ingredients

 

° 1/2 cup of thinly sliced yellow onions

° 2 Tbl unsalted butter

° 1/2 cup white granulated sugar

° 2/3 cup red wine

° 1 orange ..  juice and grate .. set the zest aside

 ° 4 egg yolks

° 1 large egg

° 2 cups whole milk

° 1/2 cup heavy cream

° 1 Tbl balsamic vinegar (go for good here, but not the super expensive 25 year old variety:-)

 

Technique

 

° caramelize the onions in the butter and 1/4 cup of sugar using your favorite method.  Go for deep brown caramelization without burning (I put them in boiling water for about 5 minutes on similar and then drain, dry.  melt butter in a pan, add sugar and then onions,... brown for 15 to 20 minutes)

 ° add wine, OJ, zest and boil until reduced by half.  This smells good

° whisk the egg yolks and egg and remaining sugar until you get a thick uniformly colored mixture. 

° milk and cream to the onions and bring to a boil stirring along the way.  

° SLOWLY add the egg mixture to the hot mix and stir stir stir.

° pour into a new pan and cook until it coats the back of a spoon. Don't let it boil.

° strain to a clean bowl and add the vinegar

° chill to refrigerator temperatures

° churn freeze