Neptune's Pride. Explore, Expand, Exploit, Exterminate! TOKTalk.net voidhaze's Home Does life have a purpose? – Michael Ruse One of my favorite dinosaurs is the Stegosaurus, a monster from the late Jurassic (150 million years ago), noteworthy because of the diamond-like plates all the way down its back. Since this animal was discovered in the late 1870s in Wyoming, huge amounts of ink have been spilt trying to puzzle out the reason for the plates. The obvious explanation, that they are used for fighting or defence, simply cannot be true. The connection between the plates and the main body is way too fragile to function effectively in a battle to the death. Another explanation is that, like the stag’s antlers or the peacock’s tail, they play some sort of role in the mating game. Señor Stegosaurus with the best plates gets the harem and the other males have to do without. But this essay is not concerned with dinosaurs themselves, rather with the kind of thinking biologists use when they wonder how dinosaur bodies worked. Why do we still talk about organisms and their features in this way? Daily Weekly Biology
A little hand, big idea :-) by James You will need some fine settings on your printer to print the 60% scale hand. The model pictured is 100% and prints easily with .25mm layers. But for 60% I used 0.18 layer and 0.3mm extrusion width with 12-18mm/s feed rates. After the print finishes you need to cut off the support pads some are circular others are rectangular. You will need some fishing line 0.5-0.8mm dia and 1.5-2mm dia self-tapping screws. not easy to get but available, go to a hobby store. The wrist and thumb base parts I recommend screwing together for initial assembly, then when it all works well glue and screw it together. The tips are a separate print because I printed them hollow, 0.3mm wall thickness with soft pliable PLA, any printable plastic will do though. Once it’s together thread 5 lengths of fishing line through each finger and tie knots in the end to retain them (I'm working on the components to connect these lines to but for now just tie them to rings for your fingers to control the hand).
Marcus du Sautoy Marcus Peter Francis du Sautoy, OBE (born 26 August 1965)[5] is the Simonyi Professor for the Public Understanding of Science and a Professor of Mathematics at the University of Oxford. Formerly a Fellow of All Souls College, and Wadham College, he is now a Fellow of New College. He is President of the Mathematical Association. He was previously an EPSRC Senior Media Fellow and a Royal Society University Research Fellow. His academic work concerns mainly group theory and number theory. Life and career[edit] In December 2006, du Sautoy delivered the 2006 Royal Institution Christmas Lectures under the collective title The Num8er My5teries. Du Sautoy is an atheist, but has stated that as holder of the Simonyi Chair for the Public Understanding of Science his focus is going to be "very much on the science and less on religion Popularisation of mathematics[edit] He is known for his work popularising mathematics. Personal life[edit] Du Sautoy was a post-doc at the Hebrew University. Work[edit]
High-voltage engineers create nearly 200-foot-long electrical arcs using less energy than before (Update) Photos taken by the researchers show plasma arcs up to 60 meters long casting an eerie blue glow over buildings and trees at the High Voltage Laboratory at the University of Canterbury in New Zealand. A team of engineers at Canterbury University in New Zealand has developed a method to create nearly 200-foot-long electrical arcs -- visible currents of electricity traveling through air that has been broken down into electrically charged particles. Others have created longer arcs, but the traditional technique requires large amounts of energy in order to break down the air. The new technique requires much less energy. Daniel Sinars, who researches fusion at Sandia National Laboratory in Albuquerque, N.M., has also worked with exploding wires, but at a much smaller scale. "It's hard to make a plasma that size," said Sinars. The team occasionally created plasma arcs during other exploding wire experiments and pursued the new research in order to better understand how the arcs formed.
Why It's Good To Be Wrong - Issue 2: Uncertainty That human beings can be mistaken in anything they think or do is a proposition known as fallibilism. Stated abstractly like that, it is seldom contradicted. Yet few people have ever seriously believed it, either. That our senses often fail us is a truism; and our self-critical culture has long ago made us familiar with the fact that we can make mistakes of reasoning too. The trouble is that error is a subject where issues such as logical paradox, self-reference, and the inherent limits of reason rear their ugly heads in practical situations, and bite. Paradoxes seem to appear when one considers the implications of one’s own fallibility: A fallibilist cannot claim to be infallible even about fallibilism itself. What? A fallibilist cannot claim to be infallible even about fallibilism itself. When fallibilism starts to seem paradoxical, the mistakes begin. But wait. No. Consider the steps you are obliged to follow, from hearing of an ex cathedra declaration to believing its content.
Decision support system A Decision Support System (DSS) is a computer-based information system that supports business or organizational decision-making activities. DSSs serve the management, operations, and planning levels of an organization (usually mid and higher management) and help to make decisions, which may be rapidly changing and not easily specified in advance (Unstructured and Semi-Structured decision problems). Decision support systems can be either fully computerized, human or a combination of both. While academics have perceived DSS as a tool to support decision making process, DSS users see DSS as a tool to facilitate organizational processes.[1] Some authors have extended the definition of DSS to include any system that might support decision making.[2] Sprague (1980) defines DSS by its characteristics: DSSs include knowledge-based systems. Typical information that a decision support application might gather and present includes: History[edit] Taxonomies[edit] Components[edit] Classification[edit]
Expert system An expert system is divided into two sub-systems: the inference engine and the knowledge base. The knowledge base represents facts and rules. The inference engine applies the rules to the known facts to deduce new facts. Inference engines can also include explanation and debugging capabilities.[10] History[edit] Edward Feigenbaum in a 1977 paper said that the key insight of early expert systems was that "intelligent systems derive their power from the knowledge they possess rather than from the specific formalisms and inference schemes they use" (as paraphrased by Hayes-Roth, et al.) Expert systems were introduced by the Stanford Heuristic Programming Project led by Feigenbaum, who is sometimes referred to as the "father of expert systems". In addition to Feigenbaum key early contributors were Bruce Buchanan, Edward Shortliffe, Randall Davis, William vanMelle, and Carli Scott. In the 1980s, expert systems proliferated. Software architecture[edit] R1: Man(x) => Mortal(x) Truth Maintenance.
How Khan Academy is using Machine Learning to Assess Student Mastery | David Hu See discussion on Hacker News and Reddit. The Khan Academy is well known for its extensive library of over 2600 video lessons. It should also be known for its rapidly-growing set of now 225 exercises — outnumbering stitches on a baseball — with close to 2 million problems done each day. To determine when a student has finished a certain exercise, we award proficiency to a user who has answered at least 10 problems in a row correctly — known as a streak. Proficiency manifests itself as a gold star, a green patch on teachers’ dashboards, a requirement for some badges (eg. gain 3 proficiencies), and a bounty of “energy” points. Basically, it means we think you’ve mastered the concept and can move on in your quest to know everything. It turns out that the streak model has serious flaws. First, if we define proficiency as your chance of getting the next problem correct being above a certain threshold, then the streak becomes a poor binary classifier. In Search of a Better Model to this: . . . .