AlphaGo

Friday, January 29th, 2016

Researchers at DeepMind staged a machine-versus-man Go contest in October, at the company’s offices in London:

The DeepMind system, dubbed AlphaGo, matched its artificial wits against Fan Hui, Europe’s reigning Go champion, and the AI system went undefeated in five games witnessed by an editor from the journal Nature and an arbiter representing the British Go Federation. “It was one of the most exciting moments in my career, both as a researcher and as an editor,” the Nature editor, Dr. Tanguy Chouard, said during a conference call with reporters on Tuesday.

This morning, Nature published a paper describing DeepMind’s system, which makes clever use of, among other techniques, an increasingly important AI technology called deep learning. Using a vast collection of Go moves from expert players — about 30 million moves in total — DeepMind researchers trained their system to play Go on its own. But this was merely a first step. In theory, such training only produces a system as good as the best humans. To beat the best, the researchers then matched their system against itself. This allowed them to generate a new collection of moves they could then use to train a new AI player that could top a grandmaster.

“The most significant aspect of all this…is that AlphaGo isn’t just an expert system, built with handcrafted rules,” says Demis Hassabis, who oversees DeepMind. “Instead, it uses general machine-learning techniques how to win at Go.”

[...]

“Go is implicit. It’s all pattern matching,” says Hassabis. “But that’s what deep learning does very well.”

[...]

At DeepMind and Edinburgh and Facebook, researchers hoped neural networks could master Go by “looking” at board positions, much like a human plays. As Facebook showed in a recent research paper, the technique works quite well. By pairing deep learning and the Monte Carlo Tree method, Facebook beat some human players — though not Crazystone and other top creations.

But DeepMind pushes this idea much further. After training on 30 million human moves, a DeepMind neural net could predict the next human move about 57 percent of the time — an impressive number (the previous record was 44 percent). Then Hassabis and team matched this neural net against slightly different versions of itself through what’s called reinforcement learning. Essentially, as the neural nets play each other, the system tracks which move brings the most reward — the most territory on the board. Over time, it gets better and better at recognizing which moves will work and which won’t.

“AlphaGo learned to discover new strategies for itself, by playing millions of games between its neural networks, against themselves, and gradually improving,” says DeepMind researcher David Silver.

According to Silver, this allowed AlphaGo to top other Go-playing AI systems, including Crazystone. Then the researchers fed the results into a second neural network. Grabbing the moves suggested by the first, it uses many of the same techniques to look ahead to the result of each move. This is similar to what older systems like Deep Blue would do with chess, except that the system is learning as it goes along, as it analyzes more data — not exploring every possible outcome through brute force. In this way, AlphaGo learned to beat not only existing AI programs but a top human as well.

DarwinTunes

Monday, January 25th, 2016

Bioinformaticist Bob MacCallum explains the DarwinTunes project, which evolves pleasant tunes using simple algorithms and listener ratings:

Special Computing Subsystem 24

Friday, January 22nd, 2016

The Russian Air Force, now known as the Russian AirSpace Force, has been able to maintain a high tempo of operations in Syria, launching a high volume of precision munitions surprisingly cheaply:

Instead of mounting a kit on an old bomb and lose the kit every time, the Russians mounted a JDAM-like kit, but on the airplane.

Introducing the SVP-24:

SVP-24

SVP stands for “special computing subsystem”. What this system does is that it constantly compares the position of the aircraft and the target (using the GLONASS satellite navigation system), it measures the environmental parameters (pressure, humidity, windspeed, speed, angle of attack, etc.). It can also receive additional information from datalinks from AWACs aircraft, ground stations, and other aircraft. The SVP-24 then computes an “envelope” (speed, altitude, course) inside which the dumb bombs are automatically released exactly at the precise moment when their unguided flight will bring them right over the target (with a 3-5m accuracy).

In practical terms this means that every 30+ year old Russian “dumb” bomb can now be delivered by a 30+ year old Russian aircraft with the same precision as a brand new guided bomb delivered by a top of the line modern bomber.

Not only that, but the pilot does not even have to worry about targeting anything. He just enters the target’s exact coordinates into his system, flies within a defined envelope and the bombs are automatically released for him. He can place his full attention on detecting any hostiles (aircraft, missiles, AA guns). And the best part of this all is that this system can be used in high altitude bombing runs, well over the 5000m altitude which MANPADs cannot reach. Finally, clouds, smoke, weather conditions or time of the day play no role in this whatsoever.

Last, but not least, this is a very cheap solution. Russian can now use the huge stores of ‘dumb’ bombs they have accumulated during the Cold War, they can bring an infinite supply of such bombs to Syria and every one of them will strike with phenomenal accuracy. And since the SVP-24 is mounted on the aircraft and not the bomb, it can be reused as often as needed.

(Hat tip to Randall Parker.)

We are not smart

Thursday, January 21st, 2016

We are not smart, Joe Rogan reminds us:

That’s the scariest thing about life, it’s that dumb people are out-breeding smart people at a fucking staggering pace. And nobody ever even talks about it! We all kinda know it’s happening, and the real problem is; most of us are dumb. We don’t want to admit it, but really, how many of us are really smart? Look, I know I’m stupid. I know. I know I’m stupid, yet I’m smarter than almost everybody I meet! And the real problem with dumb people is, they don’t even know they’re dumb. That’s a part of being dumb, you’re not aware!

There should be a way to tell, like a home pregnancy test type thing. Some shit you take at home and you lick it and you go “Oh, I’m a fucking idiot! Shit! The fuck is this?! It’s broken, gimme another one!” Dudes would never believe it, idiots would have fucking boxes stacked to the ceiling. “LIAR! COCKSUCKER! NO!”

The real problem is, most of us are idiots! We just like to think that we’re not idiots because we use a bunch of shit that smart people have figured out. But how many of us understand any of that shit? Think about the technological level the world operates on, how many of us really understand that? What if everybody out there died, and we had to take over the world? How well would you think we’d do?

[Crowd starts cheering]

“Yeah, terrific! We would do awesome!” Yeah, does anybody really know how any of this shit works?

[Taps microphone]

Why’s that loud, any idea? I’ve been a comedian for sixteen fucking years, I have no idea what’s in there! I don’t know, some loud shit? I don’t know.

[Points at spotlight]

What makes that bright, bright shit? I don’t know. Think about all the stuff you need to run your life. Computers and palm pilots and cell phones, how many of you know how to make any of that shit?! I mean, if I left you alone in the woods with a hatchet, how long before you can send me an email?

We are not smart! We buy shit from smart people! I don’t have a camera on my phone because I’m smart! If you left me on an island for a fucking million years I could never figure out how to put a camera on a phone! I don’t even know what a camera is! I know that I press a button and a picture shows up. What happens between me pressing the button and the picture showing up is anybody’s fucking guess! There might be leprechauns with spray paints fucking gremlins up the ass!

All I know is “megapixel”! Yeah, you gotta say that to get the good shit. I don’t even know what a megapixel is! It’s like a noise you make with your mouth. “Megapixel! Ohhh, you’re clever! You are clever!”

Who knows of people who know that shit? Does anybody know anyone that’s invented anything? Who are they? Is anybody watching them? Making sure they’re alive? Making sure that somebody mixed kids with them? No! No one’s paying attention! I think what’s going to happen is that one day smart people are just gonna die and they’re gonna leave us with a bunch of shit we don’t understand. I think there’s gonna be no warning!

We’re just gonna be sitting around, having a good time, having a couple of drinks, power’s just going to shut off. Everybody’s gonna get out their lighters “Way to go, you fuckin’ idiots! Can’t even keep the power on, what the fuck…” And what do you do when the power goes out? I don’t know what you do, what I do usually is that I sit around and I wait. Cause I figure “There’s a guy fixing that shit. Probably working out it right now…” How long will it take before you figure out all the smart people are dead? It would take years. You would have to run out of batteries, “Dude I don’t know how to make a fucking battery, what do we do? SHIT!”

“Listen, just get together with a torch, okay? Get a torch, we’re all gonna meet in the street and we’re all gonna work this out. It’s gonna be cool.”

Standing out in the street with a torch, “What’sup, fag?”

“Dude, you know how to get the power on?”

“I thought you did!”

“No… alright, keep me posted.”

“You too!”

We’d just be sitting in our houses with out torches. That would work. ‘Till the animals realise we don’t have electricity any more and they start sneaking around, checking shit out. And they realise there’s no loud noises to scare them off any more and bears just start grabbing people.

[Imitating bear attack]

They just realise we’re fat and slow, they don’t even have to catch us. They scare us, we’ll just black out. It’s a matter of time before they start eating us! More evolution! But not me motherfucker, I’ve got guns! I’ve got bullets, I’m gonna be fine! …until I run out of bullets

“I don’t know how to make a fucking bullet, do you? SHIIIITTTT! Dude, there’s bears out there, we don’t even have any bullets, what the fuck do we do?!”

“Listen man, we’re gotta get out of the city, we’re sitting ducks. This is what we should do; we should move back into the caves!” People will live in the caves again!

“Dude, it’s safer in the caves, bro! Just fucking guard the entrance with sharp, pointy sticks!”

“GRRRR”

“AAHHHH!”

We would just get down to a core group of survivors, fighting off the bears. And within one or two generations we would forget EVERYTHING! Trigonometry, calculus, all that shit’s gone! Science, the ‘net, it’s gone! It’s never gonna happen again! It would take thousands of years, you would have to reinvent electricity… Within one hundred years would think the world’s flat and the sun is seventeen miles away. Just like the people that wrote The Bible, that’s what they thought, ain’t that funny?

“GRRRR”

“AAHHHH!”

We would just devolve to a core group of survivors and let them re-evolve and re-discover the earth. How crazy would that be? How crazy would a caveman discovering downtown Phoenix be? Just coming out of the cave with his fucking club, with his buddies.

“Dude, who fuckin’ built all this shit?”

“Bro, it had to be aliens!”

“FUCK YEAH! Fuck yeah it’s aliens! I can’t do that, you do that?”

“Neither can I! What the fuck?”

See, I think this has happened before. I think it explains the pyramids. And yeah, I was reeeallyy high when I thought this up. But it makes sense! If you ever watch a documentary on how they built the pyramids, they have no idea how they made those things.

“Well, we believe they used levers”, but this is all that you really need to know. They know they’re there, so they know somebody made them. But all you need to know about the Great Pyramid of Giza; there’s two million, three hundred thousand stones that weigh between two and 80 tonnes – some of them were cut form a quarry that was that was five hundred miles away! No machines, no trucks, no steel, they had copper tools and they were perfectly cut, you couldn’t even get a razor blade in between these rocks and they were perfectly aligned, true North, South, East and West. And if you cut and place ten of these monstrous stones a day it would take you six hundred and sixty for fucking years to make one pyramid! All brought to you by people who thought the god Ra took the sun across the sky in a canoe and returned later that evening with the moon. They had sixteen year old queens! Cleopatra was sixteen years old when she was running shit. That’s like Lindsay Lohan being Queen of the world!

And they built that?! They built that? Are you sure? Are you sure? Okay, because I have another theory. I think people used to be really, really, really fuckin’ smart! But the dumb ones just out-fucked the smart ones! That’s what I think! I think that we are all the bastard children of the idiot stone workers of Egypt! I think that at one point there was a master race and they were reading each other’s minds and they were free of ego and they were totally honest and they were mapping out the cosmos and behind them, the stone workers just fucked away.

[Mimics stone workers having sex, and giving birth to a child]

“Oh look, he look just like me! That’s my fella right there!”

They just took over. And one day the smart people just die. There’s probably no warning. Just one day the idiots show up at the pyramids “Hello?! Anybody in there?! We’re supposed to get our checks on Friday! Hello?! The boy’s have got overtime coming! The holidays’ just around the corner, have you no heart?”

Then eventually they just realise the smart people are all dead.

“What do you want to do?”

“I think for now we should just move into the pyramids then we’ll figure it all out.”

And that’s what they did, they just moved in. Then they just started lying about it. After a couple of generations, “Who built this?”

“WE DID! We’re the best, we’re number one! Egypt! Egypt! Egypt! Look at that beautiful flat wall! That’s craftsmanship, son! I think I will draw stick figures on it!

[Mimics drawing]

“This.. is a woman… she’s carrying food upon her head… that’s important to document! And this… is a man… but, he has a head of a dog! And he’s evil!”

You sure they made that? They wrote in stick figures, dude.

Modernized Nuclear Weapons

Sunday, January 17th, 2016

The US has been using precision munitions for decades, but it has only recently moved to modernize its nuclear weapons — adding not only precision guidance but also a “dial-a-yield” feature whose lowest setting is just 2 percent as powerful as the bomb dropped on Hiroshima in 1945.

B61-12 Diagram. Modernizes Nuclear Weapons, ‘Smaller’ Leaves Some Uneasy - The New York Times - Google Chrome 1122016 100707 AM

B61-12

Untangling the Tale of Ada Lovelace

Sunday, January 10th, 2016

While untangling the tale of Ada Lovelace, Stephen Wolfram eventually gets to Ada’s paper on Babbage’s Analytical Engine and what might have been:

Despite the lack of support in England, Babbage’s ideas developed some popularity elsewhere, and in 1840 Babbage was invited to lecture on the Analytical Engine in Turin, and given honors by the Italian government.

Babbage had never published a serious account of the Difference Engine, and had never published anything at all about the Analytical Engine. But he talked about the Analytical Engine in Turin, and notes were taken by a certain Luigi Menabrea, who was then a 30-year-old army engineer—but who, 27 years later, became prime minister of Italy (and also made contributions to the mathematics of structural analysis).

In October 1842, Menabrea published a paper in French based on his notes. When Ada saw the paper, she decided to translate it into English and submit it to a British publication. Many years later Babbage claimed he suggested to Ada that she write her own account of the Analytical Engine, and that she had responded that the thought hadn’t occurred to her. But in any case, by February 1843, Ada had resolved to do the translation but add extensive notes of her own.

Over the months that followed she worked very hard—often exchanging letters almost daily with Babbage (despite sometimes having other “pressing and unavoidable engagements”). And though in those days letters were sent by post (which did come 6 times a day in London at the time) or carried by a servant (Ada lived about a mile from Babbage when she was in London), they read a lot like emails about a project might today, apart from being in Victorian English. Ada asks Babbage questions; he responds; she figures things out; he comments on them. She was clearly in charge, but felt she was first and foremost explaining Babbage’s work, so wanted to check things with him—though she got annoyed when Babbage, for example, tried to make his own corrections to her manuscript.

It’s charming to read Ada’s letter as she works on debugging her computation of Bernoulli numbers: “My Dear Babbage. I am in much dismay at having got into so amazing a quagmire & botheration with these Numbers, that I cannot possibly get the thing done today. …. I am now going out on horseback. Tant mieux.” Later she told Babbage: “I have worked incessantly, & most successfully, all day. You will admire the Table & Diagram extremely. They have been made out with extreme care, & all the indices most minutely & scrupulously attended to.” Then she added that William (or “Lord L.” as she referred to him) “is at this moment kindly inking it all over for me. I had to do it in pencil…”

William was also apparently the one who suggested that she sign the translation and notes. As she wrote to Babbage: “It is not my wish to proclaim who has written it; at the same time I rather wish to append anything that may tend hereafter to individualize, & identify it, with the other productions of the said A.A.L.” (for “Ada Augusta Lovelace”).

By the end of July 1843, Ada had pretty much finished writing her notes. She was proud of them, and Babbage was complimentary about them. But Babbage wanted one more thing: he wanted to add an anonymous preface (written by him) that explained how the British government had failed to support the project. Ada thought it a bad idea. Babbage tried to insist, even suggesting that without the preface the whole publication should be withdrawn. Ada was furious, and told Babbage so. In the end, Ada’s translation appeared, signed “AAL”, without the preface, followed by her notes headed “Translator’s Note”.

Ada was clearly excited about it, sending reprints to her mother, and explaining that “No one can estimate the trouble & interminable labour of having to revise the printing of mathematical formulae. This is a pleasant prospect for the future, as I suppose many hundreds & thousands of such formulae will come forth from my pen, in one way or another.” She said that her husband William had been excitedly giving away copies to his friends too, and Ada wrote, “William especially conceives that it places me in a much juster & truer position & light, than anything else can. And he tells me that it has already placed him in a far more agreeable position in this country.”

Within days, there was also apparently society gossip about Ada’s publication. She explained to her mother that she and William “are by no means desirous of making it a secret, altho’ I do not wish the importance of the thing to be exaggerated and overrated”. She saw herself as being a successful expositor and interpreter of Babbage’s work, setting it in a broader conceptual framework that she hoped could be built on.

There’s lots to say about the actual content of Ada’s notes. But before we get to that, let’s finish the story of Ada herself.

While Babbage’s preface wasn’t itself a great idea, one good thing it did for posterity was to cause Ada on August 14, 1843 to write Babbage a fascinating, and very forthright, 16-page letter. (Unlike her usual letters, which were on little folded pages, this was on large sheets.) In it, she explains that while he is often “implicit” in what he says, she is herself “always a very ‘explicit function of x’”. She says that “Your affairs have been, & are, deeply occupying both myself and Lord Lovelace…. And the result is that I have plans for you…” Then she proceeds to ask, “If I am to lay before you in the course of a year or two, explicit & honorable propositions for executing your engine … would there be any chance of allowing myself … to conduct the business for you; your own undivided energies being devoted to the execution of the work …”

In other words, she basically proposed to take on the role of CEO, with Babbage becoming CTO. It wasn’t an easy pitch to make, especially given Babbage’s personality. But she was skillful in making her case, and as part of it, she discussed their different motivation structures. She wrote, “My own uncompromising principle is to endeavour to love truth & God before fame & glory …”, while “Yours is to love truth & God … but to love fame, glory, honours, yet more.” Still, she explained, “Far be it from me, to disclaim the influence of ambition & fame. No living soul ever was more imbued with it than myself … but I certainly would not deceive myself or others by pretending it is other than a very important motive & ingredient in my character & nature.”

She ended the letter, “I wonder if you will choose to retain the lady-fairy in your service or not.”

At noon the next day she wrote to Babbage again, asking if he would help in “the final revision”. Then she added, “You will have had my long letter this morning. Perhaps you will not choose to have anything more to do with me. But I hope the best…”

At 5 pm that day, Ada was in London, and wrote to her mother: “I am uncertain as yet how the Babbage business will end…. I have written to him … very explicitly; stating my own conditions … He has so strong an idea of the advantage of having my pen as his servant, that he will probably yield; though I demand very strong concessions. If he does consent to what I propose, I shall probably be enabled to keep him out of much hot water; & to bring his engine to consummation, (which all I have seen of him & his habits the last 3 months, makes me scarcely anticipate it ever will be, unless someone really exercises a strong coercive influence over him). He is beyond measure careless & desultory at times. — I shall be willing to be his Whipper-in during the next 3 years if I see fair prospect of success.”

But on Babbage’s copy of Ada’s letter, he scribbled, “Saw A.A.L. this morning and refused all the conditions”.

Yet on August 18, Babbage wrote to Ada about bringing drawings and papers when he would next come to visit her. The next week, Ada wrote to Babbage that “We are quite delighted at your (somewhat unhoped for) proposal” [of a long visit with Ada and her husband]. And Ada wrote to her mother: “Babbage & I are I think more friends than ever. I have never seen him so agreeable, so reasonable, or in such good spirits!”

Then, on Sept. 9, Babbage wrote to Ada, expressing his admiration for her and (famously) describing her as “Enchantress of Number” and “my dear and much admired Interpreter”. (Yes, despite what’s often quoted, he wrote “Number” not “Numbers”.)

The next day, Ada responded to Babbage, “You are a brave man to give yourself wholly up to Fairy-Guidance!”, and Babbage signed off on his next letter as “Your faithful Slave”. And Ada described herself to her mother as serving as the “High-Priestess of Babbage’s Engine”.

But unfortunately that’s not how things worked out. For a while it was just that Ada had to take care of household and family things that she’d neglected while concentrating on her Notes. But then her health collapsed, and she spent many months going between doctors and various “cures” (her mother suggested “mesmerism”, i.e. hypnosis), all the while watching their effects on, as she put it, “that portion of the material forces of the world entitled the body of A.A.L.”

How did Elon Musk learn enough about rockets to run SpaceX?

Saturday, January 9th, 2016

How did Elon Musk learn enough about rockets to run SpaceX? Rocket scientist Jim Cantrell explains:

Once he has a goal, his next step is to learn as much about the topic at hand as possible from as many sources as possible. He is by far the single smartest person that I have ever worked with, period. I can’t estimate his IQ but he is very very intelligent. And not the typical egg-head kind of smart. He has a real applied mind. He literally sucks the knowledge and experience out of people that he is around. He borrowed all of my college texts on rocket propulsion when we first started working together in 2001. We also hired as many of my colleagues in the rocket and spacecraft business that were willing to consult with him. It was like a gigantic spaceapalooza. At that point we were not talking about building a rocket ourselves, only launching a privately funded mission to Mars. I found out later that he was talking to a bunch of other people about rocket designs and collaborating on some spreadsheet level systems designs for launchers. Once our dealings with the Russians fell apart, he decided to build his own rocket and this was the genesis of SpaceX.

I knew he read textbooks, but I didn’t know exactly which textbooks: Rocket Propulsion Elements, Aerothermodynamics of Gas Turbine and Rocket Propulsion, Fundamentals of Astrodynamics, and the International Reference Guide to Space Launch Systems.

Hundreds of Flying Robots

Friday, January 8th, 2016

We don’t think about them that often, but above us are hundreds of flying robots that play a large part in our lives on Earth:

In 1957, lonely Sputnik circled the Earth by itself, but today, the worlds of communication, weather forecasting, television, navigation, and aerial photography all rely heavily on satellites, as do many national militaries and government intelligence agencies.

The total market for satellite manufacturing, the launches that carry them to space, and related equipment and services has ballooned from $60 billion in 2004 to over $200 billion in 2015. Satellite industry revenue today makes up only 4% of the global telecommunications industry but accounts for over 60% of space industry revenue.

[...]

About two-thirds of active satellites are in Low Earth Orbit. LEO starts up at 99 miles (160 km) above the Earth, the lowest altitude at which an object can orbit without atmospheric drag messing things up. The top of LEO is 1,240 miles (2,000 km) up. Typically, the lowest satellites are at around 220 miles (350 km) up or higher.

Most of the rest (about one-third) of the satellites are much farther out, in a place called geostationary orbit (GEO). It’s right at 22,236 miles (35,786 km) above the Earth, and it’s called geostationary because something orbiting in it rotates at the exact speed that the Earth turns, making its position in the sky stationary relative to a point on the Earth. It’ll seem to be motionless to an observer on the ground.

[...]

A small percentage of other satellites are in medium Earth orbit (MEO), which is everything in between LEO and GEO. One notable resident of MEO is the GPS system that most Americans, and people from many other countries, use every day. I never realized that the entire GPS system, a US Department of Defense project that went live in 1995, only uses 32 satellites total. And until 2012, the number was only 24—six orbits, each with four satellites.

How Useful Is the Theory of Disruptive Innovation?

Wednesday, January 6th, 2016

In The Innovator’s Dilemma and The Innovator’s Solution, Christensen and Raynor discuss 77 cases of disruptive innovation. Andrew A. King and Baljir Baatartogtokh interviewed experts on each of those cases and discovered something:

Many of the theory’s exemplary cases did not fit four of its key conditions and predictions well. A handful corresponded well with all four elements (notably, for example, the disruptions by Salesforce.com, Intuit’s QuickBooks, and Amazon.com). However, a majority of the 77 cases were found to include different motivating forces or displayed unpredicted outcomes. Among them were cases involving legacy costs, the effect of numerous competitors, changing economies of scale, and shifting social conditions. Discussions with our industry experts also helped us to identify the most generally applicable elements of the theory of disruptive innovation as well as to define other ways managers can guide businesses through stormy times.

[...]

Before surveying and interviewing experts on each of the 77 cases, we identified four key elements of the theory of disruption: (1) that incumbents in a market are improving along a trajectory of sustaining innovation, (2) that they overshoot customer needs, (3) that they possess the capability to respond to disruptive threats, and (4) that incumbents end up floundering as a result of the disruption.

[...]

In summary, although Christensen and Raynor selected the 77 cases as examples of the theory of disruptive innovation, our survey of experts reveals that many of the cases do not correspond closely with the theory. In fact, their responses suggest that only seven of the cases (9%) contained all four elements of the theory that we asked about.

The Rocket Builder’s Handbook of the Twenty-First Century

Tuesday, December 29th, 2015

Elon Musk and his SpaceX team threw out the Rocket Builder’s Handbook of the Twentieth Century and started from scratch with their own designs:

Instead of using a foundation of flight-tested but badly outdated designs, they designed a brand-new rocket, built using modern materials and techniques.

Instead of building high-performance rocket engines with exotic alloys to squeeze out every ounce of performance, they sacrificed some performance for a large reduction in cost.

Instead of building a larger, more powerful rocket engine for the booster stage, they designed it to use nine smaller ones.

Instead of using expensive radiation-hardened computers, they chose a low-cost design using three redundant computers built from ‘off the shelf’ components.

Instead of using flight computers in the uppermost stage alone, they used identical avionics in both stages to facilitate booster stage recovery.

Not only did using nine booster engines provide assurance that a launch would succeed even if an engine failed, it opened up options to recover the booster stages at some later date. By using virtually identical engines on both the booster and the second stage, they would also get more engine performance data per launch while reducing both cost and design complexity.

SpaceX also designed the booster stage of the Falcon 9 to have far more power than was required to launch the most common medium-weight payloads into space. This extra power provided the margin SpaceX needed to develop a recovery system without having to take a future hit on the vehicle’s payload capacity. Even as an expendable launch vehicle, the Falcon 9 is already one of the lowest-cost in its class, putting the company ahead of its competition even without reusability.

SpaceX Falcon 9 Launch Profile

The recovery system that SpaceX finally arrived at was nothing more complicated than fuel, landing legs, grid fins, and updated flight software. This system took advantage of the vehicle’s extra fuel capacity and lighter weight to create a reusable booster stage out of an expendable one. Reusability could then be achieved with minimal structural changes to the existing booster stage.

ORBCOMM-2

Tuesday, December 22nd, 2015

SpaceX’s ORBCOMM-2 mission has placed 11 satellites into orbit — and then returned to land on, well, land:

Elon Musk’s Success

Wednesday, December 16th, 2015

Elon Musk has been asked many times to explain his success, and occasionally he has tried:

He points to things he does that other people don’t do — actively seeking out negative feedback, for instance, and working really, really, really hard. But I think he knows that he’s different. This is what he said once to NPR, back in 2007, before the first Tesla car hit the road, before the first SpaceX rocket took off: “What I’m good at is, well, I think I’m good at inventing solutions to problems. Things seem fairly obvious to me that are clearly not obvious to most people. So…and I’m not really trying to do it or anything. I just, I don’t know, I can see the truth of things, and others seem less able to do so.”

Eight years later, he tries to boil down some more practical lessons for me.

The things you’ve been working on the last ten years or so, would they be where they are now yet without you?

“Which things?”

Everything you’ve been doing at SpaceX and everything you’ve been doing at Tesla.

“Would they happen without me? Um, certainly some things wouldn’t have. You know, I think probably not.”

So what is that that you’re doing to make that happen?

“Well, you’ve got to convince great people to join the companies and then get them to work together in concert toward a clear goal with a strategy that’s sensible.”

But surely there are thousands of people who are doing that. Why are you more successful than pretty much anyone else right now?

“Well, it’s really because people, they either have a strategy where success is not one of the possible outcomes — occasionally it’s that. And then they don’t change that strategy once that becomes clear, amazingly. Or they cannot attract a critical mass of technical talent, if it’s in a technology-related thing. Or they run out of money before reaching a cash-flow-positive situation. That tends to be what occurs.”

Sure, but even so, there’s other people who get over all those bars…

He laughs. “No, they don’t. There’s very few.”

You really think those hurdles are enough to stop nearly everything?

“Oh yeah, absolutely. Probably very often when a company starts out, it’s headed in the wrong direction. But it really depends on how quickly it can recognize that and take corrective action. But people tend to think that they’re right even when they aren’t right.”

An essential part of the Elon Musk tale of triumph is how close he came himself to complete disaster in the second half of 2008. “Yeah, we had some really, really hard times,” he says when I refer to this, “and very narrowly escaped death as a company, both for SpaceX and Tesla.” He has told the full story over and over — how SpaceX had failed with its first three rocket launches and Tesla was struggling to make vehicles quickly or economically enough, and how, when the wider market crash then came, both companies were only kept going by Musk committing the remainder of his $180 million fortune from PayPal, to the point where he was reduced to borrowing money off friends for living expenses. And how, at the last minute, doom was averted: The fourth SpaceX launch reached orbit, and the company was awarded a $1.6 billion NASA contract; other Tesla investors agreed to match Musk’s final $20 million investment and saw them through the most vulnerable moments.

How NASA Created a Flight Simulator for the First Astronauts Landing on the Moon

Sunday, December 13th, 2015

NASA created a flight simulator for the first astronauts landing on the moon — using Space-Age technology:

Fifty-four years ago, we tested out Project LOLA — the Lunar Orbit and Landing Approach simulator — at the Langley Research Center in Virginia. The pilot perched on a gantry, peeking out the cockpit at a close-circuit TV system that tracked along detailed lunar mosaics in response to their commands.

Project LOLA 1

Project LOLA 2

NASA constructed four models at different scales, so the cockpit could track over the murals simulating a landing. The largest was on a six-meter (20-foot) diameter sphere, simulating the lunar surface from an altitude of 322 kilometers (200 miles) so every 1 centimeter covered 5.7 kilometers (1 inch per 9 miles). The three smaller full-relief scaled sections at 4.5 meter (15 feet) by 12 meter (40 feet). The final model of Crater Alphonsus scaled to just 1 centimeter for every 61 meters (1 inch to 200 feet). The lunar surfaces were created by carefully hand-painting and airbrushing the surfaces using detailed photographs taken from earlier lunar missions.

Project LOLA 3

Guedelon Castle

Monday, November 30th, 2015

In a forest in Burgundy, a 13th-century castle is being built using only the tools, techniques, and materials available to the builders of the time:

It’s archaeology in reverse.

The Guédelon project was started in 1997 at this location, which was chosen because it was near an abandoned stone quarry, a pond for water, and in a forest that could provide wood. The whole exercise is an experimental archaeology endeavor that seeks to discover what it would have been like to create a castle centuries ago, not by making guesses from artifacts from the past, but by experiencing it in real time. Knotted rope is used to make measurements, stone is imperfectly cut to denote the station of the castle’s owner, and rock is chiseled by hand.

Guedelon Castle 2013

Guedelon Castle 2013 Opposite

Something similar is going on in Arkansas.

Sustainable Produce

Sunday, November 29th, 2015

Modern greenhouses are now in the vanguard of sustainability.

The logic of farmers’ markets begins with this: that the route from harvest to plate ought to be as direct as possible. That’s fine if farmers live round the corner from consumers. But urban land is in short supply, expensive, often polluted, and unsuitable for horticulture. And there is more. Even in a short chain from farm to table, produce can get spoiled. A fresh tomato is not dead; like all fresh products, it’s a living organism with an active metabolism, post-harvesting, that provides a fertile substrate for microorganisms and causes tomatoes to deteriorate very fast. Freshness does not in itself translate into sustainability: unless the supply chain is well?organised, losses can be considerable. And food losses come down to a waste of land, water, energy and chemicals used to produce what is ultimately discarded. This ought to be a good argument for local markets, but it is not. Everything depends on transportation, storage and speed. Poorly packed products go to waste in a matter of hours.

Thanks to decades of research, we now understand the interacting metabolisms of vegetables and microorganisms. We can design high-tech transport and storage techniques that slow down, even halt, deterioration through the use of harmless mixtures of gases. Chips fitted to containers give off signals when the gas composition and temperature need adjusting to plan ripening at the exact moment of delivery. Likewise, to minimise food losses in supermarkets, packaging techniques and materials have been developed to prolong shelf life. Surprising but true: modern treatments with biodegradable plastic bags and sealing create an optimal environment inside the package and reduce loss. So does the industrial washing of packed and cut vegetables, which also saves water, compared with household?level processing.

What then of labour? While ‘handpicked’ sounds attractive to the urban consumer or occasional gardener, this type of manual labour is backbreaking if done all day long. Remuneration is poor, job security close to zero, and only few are willing to do this kind of work. To top it all, the yield from organic farming is low. So think about the alternative: harvesting vegetables such as tomatoes with smart robots that carefully grab each fruit, after assessing its ripeness with a special camera; using smart technology to fine-tune the dosing of fertiliser to every stage of plant development. This enhances flavour and texture, and reduces the overall amount of fertiliser needed. The result is that, in greenhouses, one square metre of tomato plants produces more than 70 kilos of high?quality tomatoes, all of which make it to consumers’ kitchens.

Since we’re on the subject of freshness, consider this: ketchup might actually be better for us than fresh tomatoes – and not just because of economics (the tomatoes used in ketchup are subgrade ones that would otherwise be destroyed). While fresh tomatoes contribute to a healthy diet, human digestive systems are not tuned to extracting most nutrients from fresh tomatoes. Tomatoes are far more nutritious when cooked or processed into ketchup or paste. So, ketchup is no bad thing – unless overloaded with sugar and salt. Indeed, a growing body of evidence suggests that the discovery of fire and cooking – that is, heating food – has been essential in the evolution of the human brain because it allowed for a better absorption of nutrients. Moreover, drying and smoking promoted the preservation of perishable foodstuffs, and perhaps facilitated the emergence of a more complex diet and division of labour.

But surely, you’ll object, tomatoes grown in small-scale gardens taste better. Not so! Double-blind tasting panels have been unable to pick out the greenhouse tomatoes as lacking in flavour, or tomatoes grown without fertiliser as more tasteful. According to Dutch reports on such testing, taste is more dependent on the variety of tomato than on the way it is grown. More importantly, the context of eating determines everything. The on-the-vine tomatoes you consume with mozzarella and olive oil on a village square in Italy will never taste the same at home. It’s a matter of psychology and gastronomy, not chemistry and biology.

In complete contrast to the mantras of organic farming, modern greenhouses are now in the vanguard of sustainability. No longer net?energy absorbers, pilot schemes show that they can produce enough additional energy to heat an entire neighbourhood by storing excess heat from the summer sun in groundwater to be released during winter. Since plants use only a small part of the solar spectrum in photosynthesis, modern technology enables us to find applications for the rest of the spectrum. Greenhouses also utilise residual CO2 from industry to promote plant growth and, in the Netherlands, CO2 from natural?gas production is routinely reused in agriculture. Conceiving greenhouses as net?energy producers opens up new opportunities to build them in hot, arid climates in order to use the stored energy for cooling down the facility.

But energy is just one dimension of sustainable production. Water is equally important. Here too, greenhouses optimise resource use. Under the very best conditions, one kilo of tomatoes can be produced using just 4-6 litres of water, because evaporation from plants can be collected and reused. Meanwhile, according to a 2015 study published in Science Direct, for tomatoes grown in the open air or under open plastic, the production of the same one kilo requires as much as 60 litres of water. Just as water might be reused in greenhouses, pests can be kept out. In a controlled environment, you can minimise the use of pesticides, or opt to use biological controls in the form of predatory insects.

Agricultural science has made great strides in breeding tomatoes with resistance to disease and pests, or with longer shelf-lives and better taste; while the latest genetic and biological techniques have increased our understanding of the genetic diversity of tomatoes and enabled us to speed up the breeding process. Such techniques do not always lead to genetically modified tomatoes. For that to happen, genes from other species would need to be introduced, of the kind that lead to higher vitamin contents in sweet potatoes, for example, or that use bacteria to build resistance against fungi.

So what do we really mean by sustainability? There have been many attempts at providing an exact and measurable definition beyond the statement of the Brundtland Report (1987), which coined the term in the context of equitable development that would not endanger the livelihoods of future generations. The concept originated in 19th-century forestry science to indicate the amount of wood that could be harvested from a forest without damaging future productivity. Since then, it has evolved to mean ‘respecting people, planet and profits’, in the parlance of the UN Earth Summit of 1992 and subsequent Millennium Development Goals.