Neanderthals are humans

More evidence  that Neanderthals were people like us. Doesn't look like  much, eh? It's a piece of string.

It takes a lot of insight to make string. You have to understand that twisting fibres together makes a stronger product. You have to see that string is useful for tying stuff. You have to have stuff that needs tying. And so on. Quite an achievement!

Now I know why I save string.

David Feldman. When Do Fish Sleep? (1989) Second in Feldman’s series of “imponderables”, which attempt to answer those nagging questions that  our high school classes didn’t cover. Such as the title question. Do fish sleep? Well, they do exhibit episodes of near-zero activity, which I suppose could be seen as sleep. Wrasses cover themselves in a thick blanket of mucus, not to keep warm, but to obliterate their odour, which would attract predators.
     A nicely done potato chip book, with an index, which makes it a useful reference for the times when you can’t be bothered to start up your device and search online. Online searching for fishes’ sleep patterns offers so many hits that deciding which one to open may be more trouble than opening the index in this book and finding the answer on page 161 to 162.
     I like these books (and many others like them, for example the urban legend compendiums), hence ***

The Seven Wonders of the Ancient World

    (J. R. Green) The Wonders of the Ancient World (1983) Well done pamphlet about the 7 Wonders. Quick now, can you recite their names? I couldn’t either, still can’t. Anyhow, Green, Assistant Professor of Archaeology at Sydney University (Australia) writes well, packs an immense amount of information into very few words, and manages to get across what these Wonders meant to the Greeks, who made up the list. The illustrations assembled from many medieval and later sources by the Reader’s Digest team (RD take credit for the booklet) suit the text very well, and show how absence of data does not deter people from creating detailed pictures of things they have never seen. Certainly out of print by now, but that’s not the only reason this is a keeper. Lovely little reference, the kind that can settle friendly arguments. ****

Artificial Intelligence: a few musings

2005-06-20
“If it looks like a duck, and walks like a duck, and quacks like a duck, then it’s a duck” (Ancient wisdom)

Unless it’s a model of a duck.

Artificial Intelligence is model building – we want autonomous machines, but the best we can do is build models of autonomous machines.

Eg, an artificial ant – could be made to behave like an ant in many ways, but not as an ant in an anthill, or capable of making more ants.

2015-10-21
It’s probably possible to make an artificial ant that behaves like an ant in anthill. We may even be able to make an artificial ant that can reproduce in some way.

However, “behave like an ant” is not well defined. There are too many behaviours, and some are obviously easier to mimic than others. Nevertheless, it will soon be possible to make an ant-size robot that can navigate like an ant, climb vertical surfaces like an ant, etc.

But it will always be a model of ant, and therefore its behaviour will in some respect will not be antlike, and in other respects will be a bad imitation of ant behaviour. That’s simply the nature of models.

2016-05-15
Intelligence is even less well-defined than “ant behaviour”. We can mimic some intelligent behaviours, eg, sorting, learning correlations, recognising patterns, and so on, which are useful to augment human tasks such as diagnosis of a fault or illness, or finding the data we want. If a task is well enough defined, we can build a machine to do it.

But that’s the problem: “Intelligence” is simply not well enough defined. My notion of it is the ability to apply and adapt existing knowledge and insight to unanticipated problems. Every term in that definition is fuzzy and vague. Anyhow, some people (including me) would argue it’s more of a definition of creativity than intelligence.

Is consciousness part of  “intelligence”? Many people would say it is. A machine that merely solves problems isn’t intelligent, it’s just an algorithm. It’s not enough to know how to do long division, you have to be able to recognise when and why you should do it. And that you are doing it.

“Understanding” is another component of intelligence. Isn’t it? Well, it does have something to do with learning: an intelligent person is one who can make sense of new explanations. “I don’t get it” at one extreme means “I haven’t figured it out yet”, at the other it means “I can’t figure it out”. The latter is a measure of intelligence.

And that’s just three attempts to make sense of “intelligence”. We’re long way from knowing exactly what we mean by “artificial intelligence”. Far enough that we may not even recognise it when we see it.

The Limits of Knowledge

 Theory, Model, Algorithm, and the Limits of Knowledge

Three terms that are often used interchangeably. They do have something in common, we’ll see what it is after an attempt to differentiate them, by describing how what they refer to differs.

Framework: The world we live in is “reality”. We interact with it in various ways. As we grow from infancy to adulthood, we develop various methods of predicting how reality works so that we can get what we need and want. Explicit ideas about how reality works are the theories on which we base our actions. We reason about the state of reality right now so that we can change it to suit ourselves. For example, we plant seeds when we figure the weather is favourable so that we get tomatoes a couple of months later. We add fertiliser and soil conditioners and water to ensure that the tomatoes will grow. Those actions are based on a bundle of ideas and observations that form a more or less coherent theory about how tomatoes grow from seeds.

Theory: An explanation of how something works the way it does. It’s what you get when you test a hypothesis, which is a more or less speculative explanation of some observation(s). A good hypothesis links the observation(s) to some existing explanation, and predicts additional observation(s). If those predictions are proved true, then the hypothesis is confirmed and becomes a theory. A good theory implies or suggests further hypotheses, which in turn imply new observations. When a theory is applied to some practical problem, we get a model. That, and the desire to just figure things out, are what drives science and engineering.

Model: An explanation that can be used to predict how some part of reality will work. We use this term because a conceptual model about growing tomatoes is analogous to a physical model of, say, a steam locomotive. A scale model is not a replica, it is something that looks like, and in a limited way works like its prototype. The model locomotive may operate on steam as the prototype does, but even so, there will be compromises. E.g., the thickness of the boiler shell will not be to scale for that would make it too weak to contain the necessary steam pressure. And so on.

We use both models and theories to plan what to do so as to get some desired result. The difference is subtle. We test a theory’s predictions in order to discover its limits, so that if necessary we can modify it or even replace it. We use a model within its limits to control some aspect of reality as much as possible. We may use a model to test a theory; an experiment is a model constructed from that part of a theory that we wish to test. It’s not easy to derive a model from a theory: models also have to be tested.

Both models and theories are true insofar as they work. When a model becomes a precise set of rules, it becomes an algorithm.

Algorithm: A set of procedures applied to some inputs that will produce outputs in a predictable way. Thus, “long division” is an algorithm because it describes how to manipulate the input numbers (divisor and dividend) to get the answer (quotient). A recipe for a toasted cheese sandwich is an algorithm because it describes how to manipulate the inputs (ingredients and  heating device) so as to get an output (tasty sandwich). And so on.

Algorithms are everywhere. They are especially handy for determining future values of present states. In this sense, an algorithm is a knowledge machine: input information about “this thing here and now”, turn the crank, and you get information about “this thing somewhere, somewhen, somehow else”.

If the above comments make sense, we may see a model as a set of interrelated algorithms, and a theory then becomes a set of validated and interconnected models.

And that brings us to what they have in common: All three are modes of gaining new knowledge. All three operate on the same fundamental principle: “If you do this, you will find out that”. None of them “describe reality”. They describe only how we may observe certain aspects of reality. Which ones? Those that the theory or model or algorithm “is about.” What “is about” means is not easy to say. An example will explain (as far as the example applies, that is):

We may use Newton’s laws of motion to build a model that calculates the course of a rocket launched towards Jupiter. If we know its mass and its velocity, the varying gravitational forces of the Moon and Mars, the trajectory of the rocket, etc, we can calculate, and recalculate, its course to whatever precision we like. But the model will tell us nothing about the health of the crew, if any. If we want to know that, we need another (and more complicated and less certain) model. The model cannot tell us what the rocket “really is”, only how it interacts with gravitational fields and the reaction forces of its engines. If we want to know other things about it, we must use other models. What’s more, even to monitor the course of the rocket, we have to use other models.

Thus all theories, all models, all algorithms are knowledge engines. They are epistemological devices. But they are limited. They can’t tell us what some entity really is, only how we can interact with it, and what will happen when we do so. Even the notion of “entity” is fundamentally epistemological: An entity is a more or less consistent bundle of expected interactions. If any of them are missing or unexpected, we doubt that we are interacting with that entity. It may be an hallucination, or a dream, or a fake, or merely an image of the entity

Kant was right, I think: There is no way to know reality in itself. That doesn’t mean there is no reality “out there”. It just means that we can know only our interactions with it. That we can know even that much is, I think, an even greater puzzle than what it is that we can’t know.

(c) 2016-04-07 Edited 2025-10-20

Freight Cars of the 40s and 50s

     Jeff Wilson. Freight Cars of the 40s and 50s (2015) Kalmbach has a long history of publishing railway history and reference books. This is the latest iteration of its histories of North American freight cars. It doesn’t pretend to be complete, but it is comprehensive. It deals with freight car technology, then with the different types. The illustrations are all high quality. Wilson has compiled statistics by type and year, a useful guide for the modeller/operator who wants a representative collection on his layout. An good read for anyone interested in railways or technology generally, as well as modellers. ***

The Spiral Tunnels and the Big Hill (2009)

    Graeme Pole. The Spiral Tunnels and the Big Hill (2009) A nicely done compilation of text and photos covering the history of the Spiral Tunnels that carry the CPR up the Kicking Horse Pass. It begins with the original surveys that resulted in the terrible 4.2% grade over the Kicking Horse Pass and follows with the construction of the tunnels to bring the grade down to a manageable 2.2%. The information is comprehensive and interesting, with a lot of incidental human interest and juicy economic details. The construction of the CPR really was one of the engineering feats of the 1800s.In the 20th and 21st centuries, much larger works have been undertaken, so that the audacity of building that railroad across four mountain ranges is hardly appreciated. Pole’s narrative gives us a glimpse of the difficulties, which reminds us that while modern engineering works may be bigger, in proportion to the available resources the 19th century achieved much more.
     Pole repeatedly mentions what can still be seen from the highway and the trails, and adds a summary guide to the sights at the end, which makes this a handbook as well. The photo-reproduction varies, but is generally good, and there are a few too many typos. The maps don’t use standard  graphics, which makes them a little difficult to interpret. A fold-out map to a larger scale would be nice touch, but I suppose the costs of providing one were too high. Recommended. **½

Sand Wars (2015)

     Sand Wars (2015) Our civilisation is built on sand. We have used so much of it that it has become a scarce commodity, worth stealing and smuggling. Almost all land-based sources have been used up. Australia exports huge quantities of it. I don’t know if Canada does, but I wouldn’t be surprised. This documentary shows that removing  sand from beaches and the sea floor is causing unexpected and dangerous consequences. You can find the doc here:
     http://tvo.org/video/documentaries/sand-wars
     Watch it. Sand is an example of how our taking the environment for granted prevents us from seeing what we are doing. “Selective inattention” is the psychological term for this phenomenon. ***

Solar energy and the Stirling engine.

The Guardian reports that a combination of mirrors to focus the sun's heat and a Stirling engine to drive a generator could be the game changer for solar energy. The Stirling engine works by moving gas between a heated cylinder and a cooled one, or by moving the gas between a heated and a cooled end of a single cylinder. Any source of heat will work. Wikipedia has an article about it here. Stirling Builder is dedicated to the engine, and has some free instructions on how to build one. The main advantage of  Stirling's invention is that it can exploit much smaller temperature differences than the steam engine.

Alan Weisman. The World Without Us (2007)

     Alan Weisman. The World Without Us (2007) Suppose every human being would disappear from the face of the Earth? Maybe in a moment, maybe over a few hours or days, but complete disappearance. What would happen to the Earth and the traces of human occupation?
     That’s the thought experiment Weisman runs in this book. He begins by considering how the natural world would take over from us, by rotting and crumbling our homes, our subways, our roads,  and so on. He deals with the effects of our industrial legacy, and considers how the artificial chemicals we’ve dumped into the biosphere might influence future evolution. Finally, he talks about what may remain of our works of the mind the imagination.
     The answers are sobering. Natural processes begin to destroy our artefacts as soon as we stop maintaining them. Subways will flood. Houses will rot away. Bridges will sag and fall. The foundation of skyscrapers will rust, the buildings will lean and then fall. Trees, vines, grasses will grow in and on our works and will crack and split and crumble them. Our corpses will decay, although some of their containers will survive a few hundred years or so. Highly stable molecules will be recycled through the biosphere until some microbes evolve to eat them. Plastics will degrade into flakes, then into nanometre particles, by which time something may have learned how to extract the energy locked up in those molecules. Ceramic tile and pottery will survive thousands of years until geologic processes bury and metamorphose them.
     And Pioneer I and II, and Voyager 1 and 2, and assorted other probes will drift through space and may at some time fetch up in a star system. But the odds that any sentient, intelligent life form will find and decode their significance is vanishingly small.
     Well then, what, if any, traces of our existence will survive us, and for how long? The answer is, lots, but not what you expected. Plastics, ceramics, earthworks and radioactive trash will survive the longest on Earth. The space probes and radio waves will survive longest of all, drifting through space until space dust abrades the probes and radio waves attenuate so much that they can no longer be distinguished from background radiation.
     The subtext of this book is anther question: Can we survive our own successes? Technology is gift that we’ve used to procreate excessively and mine the riches of the planet. Doing that, we’ve changed it, and it will never revert to its pre-human state. In this, we are like all other successful top-level predators. But like any other creature, we will eventually become extinct, either by making our habitat lethal to ourselves, or by evolving into something else. Kurt Vonnegut imagined the latter scenario in Galapagos. Weisman’s book implies that if we don’t do something to at least partly reverse our reconstruction of Earth, a few of us may survive when the inevitable collapse occurs, and those few will become one among many species competing to survive on a planet that begins to reclaim its own. Our continued success is not guaranteed.
     Even if we manage to stumble and muddle our way through the catastrophe that’s already moving through the biosphere, eventually the Sun will destroy us. Weisman doesn’t mention the hope that others have expressed, that homo sapiens terrestris may become homo sapiens stellaris, but even if that remote possibility becomes reality, the Earth and humans as we know them will have ceased to exist.
     An oddly exhilarating book, despite the depressing and gloomy forecasts and implications. Read it. ****

Human Cantilever Bridge

 

Boing-Boing republished the above old photo, which I first saw in a Wonder Book, a series of compendiums first published in England sometime in the 1920s, and continued into the 1950s: Human cantilever bridge