An ongoing timeline of computer technology in the form of blog posts by Sinclair Target (that’s a person, not a timeslipping transatlantic company merger).
This strikes me as a sensible way of thinking about machine learning: it’s like when we got relational databases—suddenly we could do more, quicker, and easier …but it doesn’t require us to treat the technology like it’s magic.
An important parallel here is that though relational databases had economy of scale effects, there were limited network or ‘winner takes all’ effects. The database being used by company A doesn’t get better if company B buys the same database software from the same vendor: Safeway’s database doesn’t get better if Caterpillar buys the same one. Much the same actually applies to machine learning: machine learning is all about data, but data is highly specific to particular applications. More handwriting data will make a handwriting recognizer better, and more gas turbine data will make a system that predicts failures in gas turbines better, but the one doesn’t help with the other. Data isn’t fungible.
If only all documentation was as great as this old manual for the ZX Spectrum that Remy uncovered:
The manual is an instruction book on how to program the Spectrum. It’s a full book, with detailed directions and information on how the machine works, how the programming language works, includes human readable sentences explaining logic and even goes so far as touching on what hex values perform which assembly functions.
When we talk about things being “inspiring”, it’s rarely in regards to computer manuals. But, damn, if this isn’t inspiring!
This book stirs a passion inside of me that tells me that I can make something new from an existing thing. It reminds me of the 80s Lego boxes: unlike today’s Lego, the back of a Lego box would include pictures of creations that you could make with your Lego set. It didn’t include any instructions to do so, but it always made me think to myself: “I can make something more with these bricks”.
The transcript of a terrific talk on the humane use of technology.
Instead of using technology to replace people, we should use it to augment ourselves to do things that were previously impossible, to help us make our lives better. That is the sweet spot of our technology. We have to accept human behaviour the way it is, not the way we would wish it to be.
This 1993 article by Mark Weiser is relevant to our world today.
Take intelligent agents. The idea, as near as I can tell, is that the ideal computer should be like a human being, only more obedient. Anything so insidiously appealing should immediately give pause. Why should a computer be anything like a human being? Are airplanes like birds, typewriters like pens, alphabets like mouths, cars like horses? Are human interactions so free of trouble, misunderstanding, and ambiguity that they represent a desirable computer interface goal? Further, it takes a lot of time and attention to build and maintain a smoothly running team of people, even a pair of people. A computer I need to talk to, give commands to, or have a relationship with (much less be intimate with), is a computer that is too much the center of attention.
The title is pure clickbait, and the moral panic early in this article repeats the Toyota myth, but then it settles down into a fascinating examination of abstractions in programming. On the one hand, there’s the problem of the not enough abstraction: having to write in code is such a computer-centric way of building things. On the other hand, our world is filled with dangerously abstracted systems:
When your tires are flat, you look at your tires, they are flat. When your software is broken, you look at your software, you see nothing.
So that’s a big problem.
Bret Victor, John Resig and Margaret Hamilton are featured. Doug Engelbart and J.C.R. Licklider aren’t mentioned but their spirits loom large.
Anecdotes about the development of Apple’s original Macintosh, and the people who made it.
Like a real-life Halt And Catch Fire.
The Long Now Foundation has been posting some great stuff on their blog lately. The latest is a look at orreries, clocks, and computers throughout history …and into the future.
A fascinating bit of technological archeology tracing some of the oldest still-running software, from a COBOL program at the Pentagon to the firmware on the Voyager probes.
How computers work:
One day, a man name Alan Turing found a magic lamp, and rubbed it. Out popped a genie, and Turing wished for infinite wishes. Then we killed him for being gay, but we still have the wishes.
Then we networked computers together:
The network is ultimately not doing a favor for those in power, even if they think they’ve mastered it for now. It increases their power a bit, it increases the power of individuals immeasurably. We just have to learn to live in the age of networks.
We are all nodes in many networks. This is a beautiful description of how one of those networks operates.
A wonderful presentation by time-traveller Bret Viktor.
A look at the depiction of computer hardware and peripherals in sci-fi movies over time.
Wallow in nerd nostalgia and experience the Proustian rush of rebooting old operating systems.
This looks like an excellent event: learn about programming without being a programmer.
The bottom-up appeal of netbooks in all their cheap, crappy glory.
The entire text of this seminal work is online in HTML, licensed under a Creative Commons Attribution-Noncommercial 3.0 Unported License.
Judging from the research information collected on Delicious, Flickr and Last.fm, this book proposalâ€”tying together informatics, music and gamesâ€”could blossom into a great read.
Richard Feynman and The Connection Machine.