How branding and transparency help charity: water stand out. The final guest speaker at our recent 37signals HQ meeting was Scott Harrison from charity: water. charity: water is a non-profit organization bringing clean and safe drinking water to people in developing nations.
Harrison’s personal journey is compelling and it was interesting to hear how CW’s unorthodox approach, especially when it comes to branding and transparency, has helped it stand out from other charities. Branding CW’s messaging emphasizes cool visuals, striking videos, and from-the-field reportage. It’s more like what you’d expect from a business or publication than a charity. For example, the image of the yellow jerrycan — often used to carry water in third world countries — has become an iconic symbol used in CW’s advertising and videos. This image of a baby bottle filled with dirty water is also used frequently by CW: Below, a couple examples of the stunning field photography used by CW. CW’s videos are sharp too. CW also works hard to document the results of donations. NeuroSolutions: What is a Neural Network? What is a Neural Network?
A neural network is a powerful data modeling tool that is able to capture and represent complex input/output relationships. The motivation for the development of neural network technology stemmed from the desire to develop an artificial system that could perform "intelligent" tasks similar to those performed by the human brain. Neural networks resemble the human brain in the following two ways: A neural network acquires knowledge through learning. A neural network's knowledge is stored within inter-neuron connection strengths known as synaptic weights. The true power and advantage of neural networks lies in their ability to represent both linear and non-linear relationships and in their ability to learn these relationships directly from the data being modeled. The most common neural network model is the multilayer perceptron (MLP). Researchers Create Artificial Neural Network from DNA. 5inShare Scientists at the California Institute of Technology (Caltech) have successfully created an artificial neural network using DNA molecules that is capable of brain-like behavior.
Hailing it as a “major step toward creating artificial intelligence,” the scientists report that, similar to a brain, the network can retrieve memories based on incomplete patterns. Potential applications of such artificially intelligent biochemical networks with decision-making skills include medicine and biological research. The researchers predict that, eventually, neural networks could be developed that operate within cells to gather information for disease diagnosis. More details from Caltech: Consisting of four artificial neurons made from 112 distinct DNA strands, the researchers’ neural network plays a mind-reading game in which it tries to identify a mystery scientist.
Check out these YouTube videos describing the research: Neural Network. Neural network gets an idea of number without counting - tech - 20 January 2012. AN ARTIFICIAL brain has taught itself to estimate the number of objects in an image without actually counting them, emulating abilities displayed by some animals including lions and fish, as well as humans.
Because the model was not preprogrammed with numerical capabilities, the feat suggests that this skill emerges due to general learning processes rather than number-specific mechanisms. "It answers the question of how numerosity emerges without teaching anything about numbers in the first place," says Marco Zorzi at the University of Padua in Italy, who led the work. The finding may also help us to understand dyscalculia - where people find it nearly impossible to acquire basic number and arithmetic skills - and enhance robotics and computer vision. The skill in question is known as approximate number sense. A simple test of ANS involves looking at two groups of dots on a page and intuitively knowing which has more dots, even though you have not counted them. Artificial neural network. An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain.
Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one neuron to the input of another. For example, a neural network for handwriting recognition is defined by a set of input neurons which may be activated by the pixels of an input image. After being weighted and transformed by a function (determined by the network's designer), the activations of these neurons are then passed on to other neurons. This process is repeated until finally, an output neuron is activated.