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The Nature of Code

The Nature of Code
“You can’t process me with a normal brain.” — Charlie Sheen We’re at the end of our story. This is the last official chapter of this book (though I envision additional supplemental material for the website and perhaps new chapters in the future). We began with inanimate objects living in a world of forces and gave those objects desires, autonomy, and the ability to take action according to a system of rules. Next, we allowed those objects to live in a population and evolve over time. The human brain can be described as a biological neural network—an interconnected web of neurons transmitting elaborate patterns of electrical signals. Figure 10.1 The good news is that developing engaging animated systems with code does not require scientific rigor or accuracy, as we’ve learned throughout this book. 10.1 Artificial Neural Networks: Introduction and Application Computer scientists have long been inspired by the human brain. Figure 10.2 Reinforcement Learning —A strategy built on observation.

Neuroevolution: an alternative route to Artificial Intelligence If you were to ask a random person what the best example of Artificial Intelligence is out there, what do you think it would be? Most likely, it would be IBM’s Watson. In a stunning display of knowledge and accuracy, Watson blew away the world Jeopardy champions Ken Jennings and Brad Rutter without blowing a fuse, and ended with Jennings proclaiming, “I for one welcome our new computer overlords.” IBM’s Watson represents the current popular approach to AI: that is, spending hundreds of hours hand-coding and fine-tuning a program to perform exceedingly well on a single task. Most people in the field of AI call machines like Watson an expert system because they are designed to be experts at a single task. However, imagine how hard it would be to hand-code a system that could do everything the human brain is capable of. What is Neuroevolution? Neuroevolution, or neuro-evolution, is a form of machine learning that uses evolutionary algorithms to train artificial neural networks. Related

Neural networks and deep learning The human visual system is one of the wonders of the world. Consider the following sequence of handwritten digits: Most people effortlessly recognize those digits as 504192. That ease is deceptive. In each hemisphere of our brain, humans have a primary visual cortex, also known as V1, containing 140 million neurons, with tens of billions of connections between them. And yet human vision involves not just V1, but an entire series of visual cortices - V2, V3, V4, and V5 - doing progressively more complex image processing. The difficulty of visual pattern recognition becomes apparent if you attempt to write a computer program to recognize digits like those above. Neural networks approach the problem in a different way. and then develop a system which can learn from those training examples. In this chapter we'll write a computer program implementing a neural network that learns to recognize handwritten digits. Perceptrons What is a neural network? So how do perceptrons work? is a shorthand.

Artificial Intelligence - system, model, type, company, business, system Photo by: Athanasia Nomikou Artificial intelligence (AI) refers to computer software that exhibits intelligent behavior. The term "intelligence" is difficult to define, and has been the subject of heated debate by philosophers, educators, and psychologists for ages. Nevertheless, it is possible to enumerate many important characteristics of intelligent behavior. Intelligence includes the capacity to learn, maintain a large storehouse of knowledge, utilize commonsense reasoning, apply analytical abilities, discern relationships between facts, communicate ideas to others and understand communications from others, and perceive and make sense of the world around us. Thus, artificial intelligence systems are computer programs that exhibit one or more of these behaviors. AI systems can be divided into two broad categories: knowledge representation systems and machine learning systems. Neural networks simulate the human nervous system. Neural networks are trained with a series of data points.

Basic Neural Network Tutorial : C++ Implementation and Source Code | Taking Initiative So I’ve now finished the first version of my second neural network tutorial covering the implementation and training of a neural network. I noticed mistakes and better ways of phrasing things in the first tutorial (thanks for the comments guys) and rewrote large sections. This will probably occur with this tutorial in the coming week so please bear with me. Introduction & Implementation Okay so how do we implement our neural network? Our neuron valuesOur weightsOur weight changesOur error gradients Now I’ve seen various implementations and wait for it… here comes an OO rant: I don’t understand why people feel the need to encapsulate everything in classes. The other common approach is to model each layer as an object? I also tend to be a bit of a perfectionist and am firm believer in occam’s razor (well a modified version) – “the simplest solution is usually the best solution”. So below is how I structured my neural network and afaik it’s as efficient as possible. The Training Data Sets

How Will Artificial Intelligence Affect Our Future Cities? This is a community post, untouched by our editors. As technology is advancing to new heights and the computer age becomes more apparent, Artificial Intelligence in our cities is starting to gain prominence. From the manufacturing machines in our industries to the prospect of automated driverless automobiles, our future cities will be equipped with robots for almost all tasks. Even the buildings themselves “will resemble a functional “living organism” with a “synthetic and highly sensitive nervous system,” all created and maintained by robots.” Industries We can already see machines replacing human employment in many fields. Some industries in particular will be dramatically affected by AI. Transport There’s hype about automated transportation or self driving cars since Google announced its driverless car early this year. “Consider buses. This will not only save a lot of time but also accommodate many more people than we see today. Infrastructure Arup’s model of Future City Buildings

15 Steps to Implement a Neural Net – code-spot (Original image by Hljod.Huskona / CC BY-SA 2.0). I used to hate neural nets. Mostly, I realise now, because I struggled to implement them correctly. Texts explaining the working of neural nets focus heavily on the mathematical mechanics, and this is good for theoretical understanding and correct usage. This tutorial is an implementation guide. I tried to make the design as straightforward as possible. To keep the implementation simple, I did not bother with optimisation. The brief introduction below is a very superficial explanation of a neural net; it is included mostly to establish terminology and help you map it to the concepts that are explained in more detail in other texts. Preliminary remarks and overview What we are doing The problem we are trying to solve is this: we have some measurements (features of an object), and we have a good idea that these features might tell us in which class the object belongs. We can control the speed with a parameter called the learning rate. 1. 2.

How will artificial intelligence affect our lives in the next ten years? | WebDevFAQ The primary focus of this essay is the future of Artificial Intelligence (AI). In order to better understand how AI is likely to grow I intend to first explore the history and current state of AI. By showing how its role in our lives has changed and expanded so far, I will be better able to predict its future trends. John McCarthy first coined the term artificial intelligence in 1956 at Dartmouth College. At this time electronic computers, the obvious platform for such a technology were still less than thirty years old, the size of lecture halls and had storage systems and processing systems that were too slow to do the concept justice. Today artificial intelligence is already a major part of our lives. One of the main issues in modern AI is how to simulate the common sense people pick up in their early years. So far I have only discussed artificial systems that interact with a very closed world. In recent times there has also been a marked increase in investment for research in AI.

Unsupervised Feature Learning and Deep Learning Tutorial Problem Formulation As a refresher, we will start by learning how to implement linear regression. The main idea is to get familiar with objective functions, computing their gradients and optimizing the objectives over a set of parameters. Our goal in linear regression is to predict a target value starting from a vector of input values . Our goal is to find a function so that we have for each training example. To find a function where we must first decide how to represent the function . This function is the “cost function” for our problem which measures how much error is incurred in predicting for a particular choice of . Function Minimization We now want to find the choice of that minimizes as given above. The above expression for given a training set of and is easy to implement in MATLAB to compute for any choice of . Differentiating the cost function as given above with respect to a particular parameter gives us: Exercise 1A: Linear Regression The data is loaded from housing.data.

Blueprint for an artificial brain: Scientists experiment with memristors that imitate natural nerves Scientists have long been dreaming about building a computer that would work like a brain. This is because a brain is far more energy-saving than a computer, it can learn by itself, and it doesn't need any programming. Privatdozent [senior lecturer] Dr. Andy Thomas from Bielefeld University's Faculty of Physics is experimenting with memristors -- electronic microcomponents that imitate natural nerves. He will be presenting his results at the beginning of March in the print edition of the Journal of Physics published by the Institute of Physics in London. Memristors are made of fine nanolayers and can be used to connect electric circuits. Like synapses, memristors learn from earlier impulses. Andy Thomas explains that because of their similarity to synapses, memristors are particularly suitable for building an artificial brain -- a new generation of computers. Thanks to these properties, synapses can be used to reconstruct the brain process responsible for learning, says Andy Thomas.

Cognitive technologies: Demystifying artificial intelligence Overview In the last several years, interest in artificial intelligence (AI) has surged. Venture capital investments in companies developing and commercializing AI-related products and technology have exceeded $2 billion since 2011.1 Technology companies have invested billions more acquiring AI startups. Press coverage of the topic has been breathless, fueled by the huge investments and by pundits asserting that computers are starting to kill jobs, will soon be smarter than people, and could threaten the survival of humankind. Consider the following: Amid all the hype, there is significant commercial activity underway in the area of AI that is affecting or will likely soon affect organizations in every sector. Artificial intelligence and cognitive technologies The first steps in demystifying AI are defining the term, outlining its history, and describing some of the core technologies underlying it. Defining artificial intelligence10 The history of artificial intelligence Moore’s Law.

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