I’m putting this up because I got it from reader Matt and also learned how to embed a video starting in the middle. Voilà: the story of Vincent, the bionic kitty. You can stop it at 2:04.
The bionic kitty
December 10, 2015 • 12:15 pm
Category: technology
The Red Giant—a machine that grinds up cars
February 7, 2015 • 3:45 pm
Reader Merilee sent me a link to this video, and the machine is flipping amazing—it grinds up whole cars into fragments, glass, wheels, and engines. Be sure to watch toward the end when the Beast pulverizes a bunch of engine blocks. I don’t know how the Red Giant does it, but the YouTube video comes with this information in Polish (translations welcome):
W polsce przydałaby się taka do mielenia źle zaparkowanych samochodów
Isaac Asimov’s predictions for 2014
January 3, 2014 • 4:45 am
Roughly 50 years ago, on August 16, 1964, Isaac Asimov wrote an essay in the New York Times predicting what the world would be like fifty years hence. (He was inspired by the World’s Fair of 1964, to which my sister and I were taken by our parents.) It’s a longish piece, and concentrates on increasing population pressure as well as the ability of technology to deal with that pressure and also improve our lives.
I’ll highlight just three of his predictions, one mostly right, on partly right, and one wrong.
This is what he got mostly right:
In 2014, there is every likelihood that the world population will be 6,500,000,000 and the population of the United States will be 350,000,000. Boston-to-Washington, the most crowded area of its size on the earth, will have become a single city with a population of over 40,000,000.
He was accurate on most of these. In fact, he slightly underestimated the world’s population, which is now 7.1 billion (see the U.S. and world population clock here), while the population of the U.S. is 317,309,000 and that of the Northeast Corridor is about 49.6 million.
This one wasn’t quite right:
The situation will have been made the more serious by the advances of automation. The world of A.D. 2014 will have few routine jobs that cannot be done better by some machine than by any human being. Mankind will therefore have become largely a race of machine tenders. Schools will have to be oriented in this direction. Part of the General Electric exhibit today consists of a school of the future in which such present realities as closed-circuit TV and programmed tapes aid the teaching process. It is not only the techniques of teaching that will advance, however, but also the subject matter that will change. All the high-school students will be taught the fundamentals of computer technology will become proficient in binary arithmetic and will be trained to perfection in the use of the computer languages that will have developed out of those like the contemporary “Fortran” (from “formula translation”).
That hasn’t quite come to pass, although MOOCs are making inroads into conventional teaching and most high-school students don’t know computer programming, due largely to the fact that computers are already user-friendly. And we still have conventional subjects in schools.
The next one is mostly wrong, due largely to Asimov’s inability to foresee the rise of the Internet and its ability to dispel boredom:
Even so, mankind will suffer badly from the disease of boredom, a disease spreading more widely each year and growing in intensity. This will have serious mental, emotional and sociological consequences, and I dare say that psychiatry will be far and away the most important medical specialty in 2014. The lucky few who can be involved in creative work of any sort will be the true elite of mankind, for they alone will do more than serve a machine.
Yes, psychiatric drugs like antidepressants are on the rise (20% of Americans take them), but psychiatry is not our most important medical speciality, for most of its medications are dispensed by general practitioners.
More important, the world is becoming plugged in: when you walk down the street in an industrialized country, or ride in a bus or train, notice how many people are using their cellphones, iPads, iPods, or computers. Google Glass, the wearable computer, is next. This is the way the whole world will go. (My theory, which is mine, is that eventually the whole world will be like New York City.) Connectivity has brought tremendous advantages: think of the ability to access information at your desk instead of a making a laborious trip to the library. And electronic journals and instant publication have markedly sped up the progress of science. Well, perhaps we won’t be as bored, but we may lose the skills of interpersonal communication.
Asimov made many other predictions. In general I think he did pretty well—certainly better than I would have—but it’s remarkable how many other people got stuff wrong, usually predicting a more technologically advanced or ideologically repressive society than we have now. Remember Nineteen Eighty Four (written in 1949), or The Jetsons cartoon series, which supposedly took place in 2062?
h/t: OpenCulture via reader Jim E.
Elizabeth Hasselbeck interviews (and praises) Ken Ham
December 17, 2013 • 8:23 am
This was, of course, on Fox News. Elizabeth Hasselbeck, who was the immensely annoying conservative foil on the chat show The View, and was subsequently fired, has now found an appropriate home at “Fox & Friends”, a morning chat/news show. And although she avers that she’s neither conservative nor liberal, she shows her true colors in this interview with über-creationist Ken Ham, which ran yesterday. They begin by talking about the unconscionable atheist billboards that appeared in Times Square, and Ham goes on to describe atheism as an “anti-God religion.” Isn’t that some kind of oxymoron?
From The Raw Story:
Ham said that his group, Answers in Genesis, had put up its own billboards in Time Square, including one that says, “To all our atheists friends: Thank God you’re wrong.”
“Our message to the atheists is, hey, we’re not attacking you personally but we want you to know the truth, that there is a God who created you and you are sinners as all of us are, but that God sent his son to become a babe in a manger,” he insisted.
Hasselbeck agreed that the American people “seemed to be with you” because a conservative polling organization had found that most people believed that Christmas should be more about Jesus Christ than Santa Claus.
“The atheists are only a small part of the population,” Hamm said. “And really, it’s that minority, less than 2 percent of the population, that seem to be having such say in our culture, in imposing their anti-God religion.”
“What they’re really doing, the atheists, they’re really wanting to impose their anti-God religion on us, on the culture. And so we need to stand up against that.”
Hasselbeck concluded by thanking Ham for “standing up for your faith.”
Ham also notes:
“Because they’re becoming so aggressive, I just feel that it’s really time that Christians really stood up in this culture to take on the atheists and to proclaim their message of hope. . . I mean, what’s the atheists’ message? There is no God? When you die that’s the end of you? So everything’s just meaningless and hopelessness?”
That, of course, is a main argument, and a fallacious one, against atheism. I know many nonbelievers, and none of them claim that everything is hopeless and meaningless. (Indeed, faitheists and believers would love for us to be that way!) The implicit argument, and one that sustains many American religionists, is that the only meaning comes from God. I wonder if those folks consider their work, their families, their hobbies, and their friends “meaningless.”
What we atheists stand up for reason. Our “meaning” is what we make for ourselves, rather than derive from superstition, and our hope is for humans to make this world better through reason and science.
By presenting and praising Ham, Hasselbeck is implicitly endorsing creationism. (After all, she could have gotten any number of noncreationist pastors to go after atheist billboards.) But don’t take my word for it: here’s Hasselbeck on The View espousing the old and discredited Argument from Design. She even alludes to the “fine tuning” argument and mentions the eye as an example of design! And at least one of her co-hosts agrees.
Oy vey! Thank Ceiling Cat that Whoopi Goldberg stood up for evolution, although she apparently believes in God.
Sadly, this chat pretty much represents a cross-section of educated Americans.
h/t: Chris
A Rube Goldberg pancake machine
February 10, 2013 • 4:59 am
If you’re of a certain age (an age even bigger than mine, but I read old stuff), you’ll know about the amazing and amusing inventions—most of them imaginary—of cartoonist Reuben Garrett Lucius “Rube” Goldberg (1883 – 1970). Here’s one example of his imaginary devices: an alarm clock triggered by the arrival of the early bird catching a worm:
Nowadays a “Rube Goldberg” machine refers to any excessively complicated contraption that, in the end, performs a simple task.
Here’s a real Rube Goldberg machine: a pancake-making device, cooked up by the UK’s Happy Egg Company, that begins when a free-range hen lays an egg. The YouTube notes describe it:
The Pancake-omatic took a team of four design engineers more than 200 hours to construct and a further 100 to test. The device which will go on display at the Design Museum later this month, uses a wide selection of household objects including an old-style gramophone and an electric whisk and features a luxury nest throne for the hen to lay her egg in – we think it’s the best invention to eat the most delicious pancakes!
But see for yourself:
The Happy Egg Co.: building LOLz one egg at a time.
What kind of computer does the Mars rover use?
August 7, 2012 • 4:57 am
Although we never talk about computers here, I’m absolutely sure that many readers are into them. And even I was curious to know what kind of computer was onboard the Mars rover Curiosity. Well, it turns out that, much to my delight (I’ve always been an Macintosh man), it’s an Apple “Airport Extreme”. The description of both hardware and software are at Extreme Tech, which says this:
At the heart of Curiosity there is, of course, a computer. In this case the Mars rover is powered by a RAD750, a single-board computer (motherboard, RAM, ROM, and CPU) produced by BAE. The RAD750 has been on the market for more than 10 years, and it’s currently one of the most popular on-board computers for spacecraft. In Curiosity’s case, the CPU is a PowerPC 750 (PowerPC G3 in Mac nomenclature) clocked at around 200MHz — which might seem slow, but it’s still hundreds of times faster than, say, the Apollo Guidance Computer used in the first Moon landings. Also on the motherboard are 256MB of DRAM, and 2GB of flash storage — which will be used to store video and scientific data before transmission to Earth.
The RAD750 can withstand temperatures of between -55 and 70C, and radiation levels up to 1000 gray. Safely ensconced within Curiosity, the temperature and radiation should remain below these levels — but for the sake of redundancy, there’s a second RAD750 that automatically takes over if the first one fails.
The piece also describes the instrumentation of Curiosity and how it communicates with Earth (remember that 7-minute delay).
Reader Michael, who found this piece, notes that “the base price for the BAE Systems RAD750 single board computer was $200,000 10 years ago, so I assume it’s nearer $750,000 today. Very tough. Very precisely made.”
The Wikipedia link in the previous paragraph describes the computer:
The RAD750 is a radiation-hardened single board computer manufactured by BAE Systems Electronic Solutions. The successor of the RAD6000, the RAD750 is for use in high radiation environments such as experienced on board satellites and spacecraft. The RAD750 was released in 2001, with the first units launched into space in 2005.
The CPU has 10.4 million transistors, nearly a magnitude more than the RAD6000 (which had 1.1 million). It is manufactured using either 250 or 150 nm photolithography and has a die area of 130 mm². It has a core clock of 110 to 200 MHz and can process at 266 MIPS or more. The CPU can include an extended L2 cache to improve performance.
The CPU itself can withstand 2,000 to 10,000 gray and temperature ranges between –55 °C and 125 °C and requires 5 watts of power. The standard RAD750 single-board system (CPU and motherboard) can withstand 1,000 gray and temperature ranges between –55 °C and 70 °C and requires 10 watts of power.
Original photograph from Peter Vis’ site here.
Recognizing a cat
July 7, 2012 • 2:58 am
Elebenty gazillion readers sent me this story (with diverse links) over the last ten days. Thanks to all of you, who are now too numerous to name. (See links at The New York Times, Wired Science, and the BBC, among others.)
The interesting results are encoded in scientific language in the abstract of a yet-unpublished manuscript by Le et al. (reference and link below). If you’re like me, the following won’t make a ton of sense:
We consider the problem of building high- level, class-specific feature detectors from only unlabeled data. For example, is it possible to learn a face detector using only unlabeled images? To answer this, we train a 9-layered locally connected sparse autoencoder with pooling and local contrast normalization on a large dataset of images (the model has 1 bil- lion connections, the dataset has 10 million 200×200 pixel images downloaded from the Internet). We train this network using model parallelism and asynchronous SGD on a clus- ter with 1,000 machines (16,000 cores) for three days. Contrary to what appears to be a widely-held intuition, our experimental re- sults reveal that it is possible to train a face detector without having to label images as containing a face or not. Control experiments show that this feature detector is robust not only to translation but also to scaling and out-of-plane rotation. We also find that the same network is sensitive to other high-level concepts such as cat faces and human bod- ies. Starting with these learned features, we trained our network to obtain 15.8% accu- racy in recognizing 20,000 object categories from ImageNet, a leap of 70% relative im- provement over the previous state-of-the-art.
At any rate, the tale is this: Google scientists interested in face-recognition software set up an immensely complicated artificial neural network, containing 16,000 computer processors, and fed it random images from YouTube. And what came out? Cat recognition! (Note: NOT squid recognition.) Cats, of course, are everywhere on YouTube.
The NYT notes:
Presented with 10 million digital images found in YouTube videos, what did Google’s brain do? What millions of humans do with YouTube: looked for cats.
The neural network taught itself to recognize cats [JAC: this took 3 days], which is actually no frivolous activity. This week the researchers will present the results of their work at a conference in Edinburgh, Scotland. The Google scientists and programmers will note that while it is hardly news that the Internet is full of cat videos, the simulation nevertheless surprised them. It performed far better than any previous effort by roughly doubling its accuracy in recognizing objects in a challenging list of 20,000 distinct items. . .
To find them, the Google research team, led by the Stanford Universitycomputer scientist Andrew Y. Ng and the Google fellow Jeff Dean, used an array of 16,000 processors to create a neural network with more than one billion connections. They then fed it random thumbnails of images, one each extracted from 10 million YouTube videos.
The videos were selected randomly and that in itself is an interesting comment on what interests humans in the Internet age. However, the research is also striking. That is because the software-based neural network created by the researchers appeared to closely mirror theories developed by biologists that suggest individual neurons are trained inside the brain to detect significant objects. . .
“We never told it during the training, ‘This is a cat,’ ” said Dr. Dean, who originally helped Google design the software that lets it easily break programs into many tasks that can be computed simultaneously. “It basically invented the concept of a cat. We probably have other ones that are side views of cats.”
The Google brain assembled a dreamlike digital image of a cat by employing a hierarchy of memory locations to successively cull out general features after being exposed to millions of images. The scientists said, however, that it appeared they had developed a cybernetic cousin to what takes place in the brain’s visual cortex.
The BBC notes:
The work of the team stands at odds with many image-recognition techniques, which depend on telling a computer to look for specific features of a target object before any are presented to it.
By contrast, the Google machine knew nothing about the images it was to see. However, its 16,000 processing cores ran software that simulated the workings of a biological neural network with about one billion connections.
In a similar way nerves in brains are heavily interconnected and it is believed that “recognition” involves the triggering of a specific pathway through that thicket of connections.
Pathways for particular objects, people or other stimuli are thought to be built up as organisms learn about the world. Some neuroscientists speculate that parts of the human visual system become so specialised they recognise very specific subjects such as a person’s grandmother or their cat.
As millions of images were analysed by Google’s network of silicon nerves, some parts of it started to react to specific elements in those pictures.
After three days and 10 million images the network could spot a cat, even though it had never been told what one looked like.
Although the work at first seems useless, it isn’t. We learn to recognize objects by repeated exposure to them. And it’s always been a mystery to scientists how we’re able to form and remember images of people and friends whom we repeatedly see. (When I was younger, my father used to ask me the question, “Try to imagine a face that you’ve never seen before.” Try it—it’s not easy!) Some day face-recognition software will be everywhere: identifying you before letting you into secure facilities, helping police solve crimes, and so on.
And of course it will also give us a clue about how our brain works when recognizing faces. We can easily clue in on human faces, but not so easily on the faces of individuals from different species, which may be nearly as distinct from each other as one human is from others. And there’s an evolutionary reason for that: it was crucial for our group-living ancestors to recognize not only kin but other groupmates (who helped us before, and who was bad to us?), and to discriminate group-mates from potentially hostile members of outgroups.
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Lee, Q. V. et al. 2012. Building high-level features using large scale unsupervised learning. Manuscript at Cornell University Library. (free pdf download at link).