Later Terminator: We’re Nowhere Near Artificial Brains | The Crux I can feel it in the air, so thick I can taste it. Can you? It’s the we’re-going-to-build-an-artificial-brain-at-any-moment feeling. It’s exuded into the atmosphere from news media plumes (“IBM Aims to Build Artificial Human Brain Within 10 Years”) and science-fiction movie fountains…and also from science research itself, including projects like Blue Brain and IBM’s SyNAPSE. For example, here’s a recent press release about the latter: Today, IBM (NYSE: IBM) researchers unveiled a new generation of experimental computer chips designed to emulate the brain’s abilities for perception, action and cognition. Now, I’m as romantic as the next scientist (as evidence, see my earlier post on science monk Carl Sagan), but even I carry around a jug of cold water for cases like this. The Worm in the Pass In the story about the Spartans at the Battle of Thermopylae, 300 soldiers prevent a million-man army from making their way through a narrow mountain pass. Big-Brained Dummies Blurry Joints Instincts
Sphider - a php spider and search engine GeeksforGeeks IBM simulates 530 billon neurons, 100 trillion synapses on supercomputer A network of neurosynaptic cores derived from long-distance wiring in the monkey brain: Neuro-synaptic cores are locally clustered into brain-inspired regions, and each core is represented as an individual point along the ring. Arcs are drawn from a source core to a destination core with an edge color defined by the color assigned to the source core. (Credit: IBM) Announced in 2008, DARPA’s SyNAPSE program calls for developing electronic neuromorphic (brain-simulation) machine technology that scales to biological levels, using a cognitive computing architecture with 1010 neurons (10 billion) and 1014 synapses (100 trillion, based on estimates of the number of synapses in the human brain) to develop electronic neuromorphic machine technology that scales to biological levels.” Simulating 10 billion neurons and 100 trillion synapses on most powerful supercomputer Neurosynaptic core (credit: IBM) Two billion neurosynaptic cores DARPA SyNAPSE Phase 0DARPA SyNAPSE Phase 1DARPA SyNAPSE Phase 2
How to Create Login Page in PHP and MySQL with Session « Frozenade’s Zone ##########################NEW UPDATE!!! PHPMySimpleLogin 0.3 Download full source code: ########################## The production of login page using PHP and MySQL is actually very simple. I assume that you use local web server connection (Apache and PHP) and your MySQL database configuration use ‘localhost’ as hostname and ‘root’ as username with blank password. Run XAMPP Control Panel on desktop. Start Apache and PHP modules. Let’s create a database with PHPMyAdmin. Create database ‘phpmysimplelogin’. Click ‘Create’. Now, let’s create a table, name it ‘user’ with ‘2’ (two) number of fields. Click ‘Go’. First field, name it ‘username’, type ‘varchar’, lenght/values ’25’. Click ‘Save’. After that, we will fill the table. It means, you fill ‘username’ field with string ‘admin’ and ‘username’ field with an encryption string of ‘admin’. Okay, now let’s prepare the web pages. <? // Inialize session session_start(); ?
PlantUML PlantUMLis a component that allows to quickly write : Diagrams are defined using a simple and intuitive language. This can be used within many other tools. Images can be generated in PNG or SVG format. It is also possible to generate ASCII art diagrams (only for sequence diagrams). This example is working thanks to the online demo server. Q-learning Q-learning is a model-free reinforcement learning technique. Specifically, Q-learning can be used to find an optimal action-selection policy for any given (finite) Markov decision process (MDP). It works by learning an action-value function that ultimately gives the expected utility of taking a given action in a given state and following the optimal policy thereafter. Algorithm The problem model, the MDP, consists of an agent, states S and a set of actions per state A. , the agent can move from state to state. The algorithm therefore has a function which calculates the Quality of a state-action combination: Before learning has started, Q returns an (arbitrary) fixed value, chosen by the designer. where is the reward observed after performing in , and where ) is the learning rate (may be the same for all pairs). ) trades off the importance of sooner versus later rewards. An episode of the algorithm ends when state is a final state (or, "absorbing state"). Note that for all final states .
CSS tutorial: Create a dynamic horizontal navigation bar These days it's almost a standard to use an unordered list element for your page navigation. While a vertical list in some sidebar is some easy peace of work, is the horizontal navigation a little bit more complicated. Preview and example navigation We need to float the list elements to get them in the horizontal order. There are many great examples on this CSS resource site and also our example here is based on one of the tutorials listed on the suggested site. Generating the html snippet The next snippet has for the first list item the attribute id "current". First we create an array for the links and link labels we want to use in our navigation. While the navigation is very simple (just using one level) we need only some style sheet for the unordered list elements. Don't forget that we are working with a block type element and all those elements are having a 100% width by default. At last but not least we need some extra style for the current state in our navigation bar:
Model Suggests Link between Intelligence and Entropy +Enlarge image A. Wissner-Gross/Harvard Univ. & MIT A. A pendulum that is free to swing through all angles in a plane can be stabilized in the inverted position by sliding the pivot horizontally, in the same way that you can balance a meter stick on your finger. The smallest disks, subjected to causal entropy forces, tend to work in a synchronized fashion to pull down the largest disk, in what the authors present as a primitive example of social cooperation. The second law of thermodynamics—the one that says entropy can only increase—dictates that a complex system always evolves toward greater disorderliness in the way internal components arrange themselves. Entropy measures the number of internal arrangements of a system that result in the same outward appearance. Hoping to firm up such notions, Wissner-Gross teamed up with Cameron Freer of the University of Hawaii at Manoa to propose a “causal path entropy.” –Don Monroe Don Monroe is a freelance science writer in Murray Hill, New Jersey.
Beginning Game Development: Part I – Introduction | Coding4Fun Articles Part I – Introduction Welcome to the first article of an introductory series on game programming using the Microsoft .NET Framework and managed DirectX 9.0. This series as aimed at beginning programmers who are interested in developing a game for their own use with the .NET Framework and DirectX. The goal of this series is to have fun creating a game and learn game development and DirectX along the way. Game programming and DirectX have their own terms and definitions that can be difficult to understand, but after awhile, you’ll crack the code and be able to explore a new world of possibilities. I will keep things as straightforward as possible and decode terms as they appear. In this series, we are going to build a simple game to illustrate the various components of a commercial game. Tools: Before we start writing our first game we need to talk about the tools we will use. The most important tool for any developer is the Integrated Development Environment (IDE). Our Game idea: Visual C#
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