Boeing’s B-17 was the most numerous heavy bomber of World War II, and its reputation of being nigh indestructible in the face of Messerschmidts and flak cannons is stuff of legend. The first flight of the B-17 was in 1935, and a decade later at the close of World War II, the B-17 would begin to show its age. It could only carry 6,000 pounds of ordnance; the first atomic bombs, Little Boy and Fat Man, weighed 9,700 pounds and 10,300 pounds, respectively. The Avro Lancaster notwithstanding, a new aircraft would be needed for the Allied invasion of Japan. This aircraft would be the Boeing B-29 Superfortress.
On paper, the B-29 nearly holds its own against all but the most modern bombers of aviation history. Yes, the B-29 is slow, but that’s only because jet engines were in their infancy in 1944. This bomber was a forgotten super weapon of World War II, and everyone – Japan, German, Great Britain and the USSR – wanted their own. Only the Soviets would go as far to build their own B-29, reverse engineering the technology from crashed and ditched American bombers.
There are a couple of really great things about transmitting data using light as the carrier. It can be focused so that it doesn’t spill all over the neighborhood like radio signals do — giving it both some security against eavesdropping and preventing one signal from stepping on another’s toes. And while you can modulate radio signals up nearly to the carrier frequency, the few gigahertz we normally use for radio just won’t cut it for really high bit rates. Light gets you terahertz.
The Koruza project is an open-source, “inexpensive” system that aims to transmit 1 Gb/sec over distances around 100 meters, using modulated infrared light. The intended use-case is urban building-to-building communication at speeds that would otherwise require laying fiber-optic cables. Indeed, the system piggy-backs on existing fiber-optic equipment to get the job done, but the hard part is aligning the units to get maximum signal from point A to point B.
Koruza does this by including motorized lenses on the 3D-printed chassis. You make a rough alignment with a visible green laser, and then fine-tune the IR beams from a web console where you get immediate feedback on how the received signal strength is changing. Both Koruza boxes have a Raspberry Pi inside and use normal networking for calibration and signal-strength statistics. It’s a really neat system, and it’s fully DIY’able except for the commodity fiber-optic bits.
We’ve always had a soft-spot in our heart for transmitting data over light beams. The Ronja project has been doing so since 2001, and over longer distances, with completely DIY hardware, if at a slower bitrate. And now that Li-Fi seems to be getting traction, we might see an unfocused equivalent running inside our homes.
Lining up the beam from your homebrew (or retrofitted) laser cutter doesn’t come without its challenges. For instance, how do I use my remaining eye to align an invisible beam that has enough power to burn through some objects in its path? Some of us will go through the extra hassle and expense of mixing in a visible guide that traces the path of the CO2 laser. For the penny-pinchers out there, though, [Stephen] has us covered with an inexpensive technique that will cut you down by only a few strips of masking tape.
Stephan’s technique is simple, but elegant. He covers each mirror with tape, fires the laser, and leaves a burn mark, working his way from the last mirror that the laser hits to the first. With a burn mark on each mirror, and one through a guide made from a sheet of plywood, [Stephen] has a pretty good idea where the native direction of the beam is headed. He then swaps a red dot laser in to line up with the burn marks, and then aligns the mirrors using visible, and safe, light. Phew! Now that’s a lot easier than iteratively firing the beam and replacing the tape on the mirror each time we want to tweak the mirror alignment.
With all that burnt masking tape, the process can get a bit smelly. Nevertheless, we’ve filed this one away for later when we start getting that itching, burning sensation that kicks us into building our own homebrew laser cutter.
Have you ever seen the science experiment (or magic trick?) where you get water supercooled to where it isn’t frozen, but then it freezes when you touch it, pour it, or otherwise disturb it? Apparently, ice crystals form around impurities or disturbances in the liquid and then “spread.” A similar effect can occur in metals where the molten metal cools in such a way that it stays melted even below the temperature that would usually cause it to melt.
[Martin Thuo] at Iowa University used this property to make solder joints at room temperature using Field’s metal (a combination of bismuth, indium, and tin). The key is a process that coats the molten metal with several nanolayers that protect it from solidifying until something disturbs the protective layer.
[Mark Rober] is an uncle to three nieces and nephews, and when there is snow on the ground he faces the relentless onslaught of three elite juvenile snowball aces. Lesser men would face the fact that they are over the hill when it comes to snow-based combat, but not [Mark]. He has brought technology to his aid, and with the help of his brother created a snowball machine gun capable of firing 13 snowballs at the unruly youths in half a second.
Power for his creation comes from a leaf blower, and the gun itself is made from ABS pipe fixed onto the blower outlet. Magazines made from pipe with its top section cut away are loaded via a 45 degree junction fitting, and the rate of fire is set by how fast the operator pushes the line of snowballs with a wooden block. He has made full build instructions available as a PDF, so assuming you are reading this in a part of the world where it snows, what are you waiting for! Those of us who live in paces where it rarely snows and what snow we get is wet and slushy can only look on with envy.
The video below has the full story, complete with gratuitous destruction of fruit, youngsters cowering in their snow forts, and finally [Mark] receiving his snowy comeuppance.
If you search the internet for 12 volt to mains AC inverter designs, the chances are you’ll encounter a simple circuit which has become rather ubiquitous. It features a 4047 CMOS astable multivibrator chip driving a pair of MOSFETs in a push-pull configuration which in turn drive a centre-tapped mains transformer in reverse. Not a new design, its variants and antecedents could be found even in those pre-Internet days when circuits came from books on the shelves of your local lending library.
[Afroman], no stranger to these pages, has published a video in which he investigates the 4047 inverter, and draws attention to some of its shortcomings. It is not the circuit’s lack of frequency stability with voltage that worries him, but the high-frequency ringing at the point of the square-wave switching when the device has an inadequate load. This can reach nearly 600 volts peak-to-peak with a 120 volt American transformer, or over a kilovolt if you live somewhere with 230 volt mains. The Internet’s suggested refinement, a capacitor on the output, only made the situation worse. As he remarks, it’s fine for powering a lightbulb, but you wouldn’t want it near your iPhone charger.
If this video achieves anything, it is a lesson to the uninitiated that while simple and popular designs can sometimes be absolute gems it must not be assumed that this is always the case.
We wake up this morning to the news that Google’s deep-search neural network project called AlphaGo has beaten the second ranked world Go master (who happens to be a human being). This is the first of five matches between the two adversaries that will play out this week.
On one hand, this is a sign of maturing technology. It has been almost twenty years since Deep Blue beat Gary Kasparov, the reigning chess world champion at the time. Although there are still four games to play against Lee Sedol, it was recently reported that AlphaGo beat European Go champion Fan Hui in five games straight. Go is generally considered a more difficult game for machine minds to play than chess. This is because Go has a much larger pool of possible moves at any given time.
Does This Matter?
Okay, the news part of this event has been covered: machine beats man. Does it matter? Will this affect your life and how? We want to hear what you think in the comments below. But I’m going to keep going with some of my thoughts on the topic.
You’re still better at Ms. Pacman [Source: DeepMind paper in Nature]Let’s look first at what AlphaGo did to win. At its core, the game of Go is won by figuring out where your opponent will likely make a low-percentage move and then capitalizing on that choice. Know Your Enemy has been a tenet of strategy for a few millennia now and it holds true in the digital age. In addition to the rules of the game, AlphaGo was fed a healthy diet of 30 million positions from expert games. This builds behavior recognition into the system. Not just what moves can be made, but what moves are most likely to be made.
DeepMind, the company behind AlphaGo which was acquired by Google in 2014, has published a paper in Nature about their approach. They were even nice enough to let us read without dealing with a paywall. The secret sauce is the learning process which at its core tries to mimic how living entities learn: observe repetitively while assigning values to outcomes. This is key as it leads past “intellect”, to “intelligence” (the “I” in AI that everyone seems to be waiting for). But this is a bastardized version of “intelligence”. AlphaGo is able to recognize and predict behavior, then make choices that lead to a desired outcome. This is more than intellect as it does value the purpose of an opponent’s decisions. But it falls short of intelligence as AlphaGo doesn’t consciously understand the purpose it has detected. In my mind this is exactly what we need. Truly successful machine learning will be able to make sense out of sometimes irrational input.
The paper from Nature doesn’t go into details about Go, but it explains the approach of the learning system applied to Atari 2600. The algorithm was given 210×160 color video at 60Hz as an input and then told it could use a joystick with one button. From there it taught itself to play 49 games. It was not told the purpose or the rules of the games, but it was given examples of scores from human performance and rewarded for its own quality performances. The chart above shows that it learned to play 29 of them at or above human skill levels.
[Afroman], no stranger to these pages, has published a video in which 
![You're still better at Ms. Pacman [Source: DeepMind paper in Nature]](https://hackaday.com/wp-content/uploads/2016/03/deepmind-atari2600-machine-learning.jpg)
