I’ve been soldering for a long time, and I take pride in my abilities. I won’t say that I’m the best solder-slinger around, but I’m pretty good at this essential shop skill — at least for through-hole and “traditional” soldering; I haven’t had much practice at SMD stuff yet. I’m confident that I could make a good, strong, stable joint that’s both electrically and mechanically sound in just about any kind of wire or conductor.
But like some many of us, I learned soldering as a practical skill; put solder and iron together, observe results, repeat the stuff that works and avoid the stuff that doesn’t. Seems like adding a little inside information might help me improve my skills, so I set about learning what’s going on mechanically and chemically inside a solder joint.
3D printers are the single best example of what Open Hardware can be. They’re useful for prototyping, building jigs for other tools, and Lulzbot has proven desktop 3D printers can be used in industrial production. We endorse 3D printing as a viable tool as a matter of course around here, but that doesn’t mean we think every house should have a 3D printer.
Back when Bre was on Colbert and manufacturing was the next thing to be ‘disrupted’, the value proposition of 3D printing was this: everyone would want a 3D printer at home because you could print plastic trinkets. Look, a low-poly Bulbasaur. I made a T-rex skull. The front page of /r/3Dprinting. Needless to say, the average consumer doesn’t need to spend hundreds of dollars to make their own plastic baubles when WalMart and Target exist.
The value proposition of a 3D printer is an open question, but now there is some evidence a 3D printer provides a return on its investment. In a paper published this week, [Joshua Pearce] and an undergraduate at Michigan Tech found a 3D printer pays for itself within six months and can see an almost 1,000% return on investment within five years. Read on as I investigate this dubious claim.
I had a friend who was an engineer for a small TV station. I visited him at work once, and despite the fact that he wouldn’t let me climb the 1,200′ antenna tower, I had a great time. I was working for a video production studio at the time, so there was a fair amount in common about our jobs. One of the regular chores we faced was cleaning the heads on tape machines. He had a 5-gallon pail of cleaning solution under his bench that he told me was Freon, which he swore by for head cleaning and general contact cleaning. He gave me some for my shop in a little jar.
I never knew for sure if that stuff was Freon, but it was the mid-80s, shortly before CFCs were banned, so it might have been. All I know is that I’ve never found its equal for cleaning electronics gear. With that in mind, I thought I’d look at contact cleaners that are in use today, what’s really going on when you clean contacts, and why contacts even need cleaning in the first place.
Last week we covered the past and current state of artificial intelligence — what modern AI looks like, the differences between weak and strong AI, AGI, and some of the philosophical ideas about what constitutes consciousness. Weak AI is already all around us, in the form of software dedicated to performing specific tasks intelligently. Strong AI is the ultimate goal, and a true strong AI would resemble what most of us have grown familiar with through popular fiction.
Artificial General Intelligence (AGI) is a modern goal many AI researchers are currently devoting their careers to in an effort to bridge that gap. While AGI wouldn’t necessarily possess any kind of consciousness, it would be able to handle any data-related task put before it. Of course, as humans, it’s in our nature to try to forecast the future, and that’s what we’ll be talking about in this article. What are some of our best guesses about what we can expect from AI in the future (near and far)? What possible ethical and practical concerns are there if a conscious AI were to be created? In this speculative future, should an AI have rights, or should it be feared?
The concept of artificial intelligence dates back far before the advent of modern computers — even as far back as Greek mythology. Hephaestus, the Greek god of craftsmen and blacksmiths, was believed to have created automatons to work for him. Another mythological figure, Pygmalion, carved a statue of a beautiful woman from ivory, who he proceeded to fall in love with. Aphrodite then imbued the statue with life as a gift to Pygmalion, who then married the now living woman.
Throughout history, myths and legends of artificial beings that were given intelligence were common. These varied from having simple supernatural origins (such as the Greek myths), to more scientifically-reasoned methods as the idea of alchemy increased in popularity. In fiction, particularly science fiction, artificial intelligence became more and more common beginning in the 19th century.
But, it wasn’t until mathematics, philosophy, and the scientific method advanced enough in the 19th and 20th centuries that artificial intelligence was taken seriously as an actual possibility. It was during this time that mathematicians such as George Boole, Bertrand Russel, and Alfred North Whitehead began presenting theories formalizing logical reasoning. With the development of digital computers in the second half of the 20th century, these concepts were put into practice, and AI research began in earnest.
Over the last 50 years, interest in AI development has waxed and waned with public interest and the successes and failures of the industry. Predictions made by researchers in the field, and by science fiction visionaries, have often fallen short of reality. Generally, this can be chalked up to computing limitations. But, a deeper problem of the understanding of what intelligence actually is has been a source a tremendous debate.
Despite these setbacks, AI research and development has continued. Currently, this research is being conducted by technology corporations who see the economic potential in such advancements, and by academics working at universities around the world. Where does that research currently stand, and what might we expect to see in the future? To answer that, we’ll first need to attempt to define what exactly constitutes artificial intelligence.
When a Hackaday article proclaims that its subject is a book you should read, you might imagine that we would be talking of a seminal text known only by its authors’ names. Horowitz and Hill, perhaps, or maybe Kernigan and Ritchie. The kind of book from which you learn your craft, and to which you continuously return to as a work of reference. Those books that you don’t sell on at the end of your university career.
So you might find it a little unexpected then that our subject here is a children’s book. Making A Transistor Radio, by [George Dobbs, G3RJV] is one of the huge series of books published in the UK under the Ladybird imprint that were a staple of British childhoods for a large part of the twentieth century. These slim volumes in a distinctive 7″ by 4.5″ (180 x 115 mm) hard cover format were published on a huge range of subjects, and contained well written and informative text paired with illustrations that often came from the foremost artists of the day. This one was published at the start of the 1970s when Ladybird books were in their heyday, and has the simple objective of taking the reader through the construction of a simple three transistor radio. It’s a book you must read not because it is a seminal work in the vein of Horrowitz and Hill, but because it is the book that will have provided the first introduction to electronics for many people whose path took them from this humble start into taking the subject up as a career. Including me as it happens, I received my copy in about 1979, and never looked back. Continue reading “Books You Should Read: Making A Transistor Radio”→
There are numerous examples of hardware which has latent features waiting to be unlocked by software. Most recently, we saw a Casio calculator which has the same features as its bigger sibling hidden within the firmware, only to be exposed by a buffer overflow bug (or the lead from a pencil if you prefer a hardware hack).
More famously, oscilloscopes have been notorious for having crippled features. The Rigol DS1052E was hugely popular on hacker benches because of it’s very approachable price tag. The model shipped with 50 MHz bandwidth but it was discovered that a simple hack turned it into the DS1102E 100 MHz scope. Tektronix has gotten in on this action as well, shipping modules like I2C, CAN, and LIN analyzation on the scope but requiring a hardware key to unlock (these were discovered to have a horribly insecure unlock method). Similar feature barriers are found on Rigol’s new reigning entry-level scope, the DS1054Z, which ships with protocol analyzation modules (among others) that are enabled only for the first 70 hours of scope operation, requiring an additional payment to unlock them. Most scope manufacturers are in on the game, and of course this is not limited to our tools. WiFi routers are another great example of hardware hosting firmware-unlockable features.
So, the question on my mind which I’d like to ask all of the Hackaday community is this: are unlockable features good for us, the people who use these tools? Let’s take a look at some of the background of these practices and then jump into a discussion in the comments.