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Soulskill

vinces99 writes "Small electrodes placed on or inside the brain allow patients to interact with computers or control robotic limbs simply by thinking about how to execute those actions. This technology could improve communication and daily life for a person who is paralyzed or has lost the ability to speak from a stroke or neurodegenerative disease. Now researchers have demonstrated that when humans use this brain-computer interface, the brain behaves much like it does when completing simple motor skills such as kicking a ball, typing or waving a hand (abstract). That means learning to control a robotic arm or a prosthetic limb could become second nature for people who are paralyzed."

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Does anyone know any good articles/tutorials that talk about using neural nets in games? I haven't been able to find very much. Here are a couple of specific problems I'm having:

1) I don't know what to do with range inputs. Like, if I have health from 0-100, should I have each health state be it's own input node, or should I have one input node that maps the 0-100 to -1,1? Currently I have 10 input nodes for the 100 health(ie one for 0-10, one for 10-20, etc) that return a boolean -1 or 1 depending on if the current health is in that node's range or not.

2) I'm trying to do backpropagation training from the player's inputs. The problem I'm having is that the majority of the time, the player isn't actually doing anything. Even if the player is constantly hitting buttons, there's still like 30 frames happening between button presses where he's technically doing nothing. It ends up quickly training the AI to do nothing. I semi-fixed it by just adjusting the learning rate to be lower if a button isn't being pressed, but I'm wondering if there is a better way to handle this.

Thanks for any help, and sorry if any of that didn't make sense. I'm pretty new to neural nets.

submitted by Dest123
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I'm writing an introductory paper to the field of AI.

What are the major paradigms? What are the major methods?

I'm having a hard time finding a framework for which to approach these topics. It seems to me (a relative outsider) that there are many disparate groups of AI research that overlap while others don't come close at all.

Methods I have so far are neural networks, genetic algorithms, classical (symbolic) AI, expert systems.

Paradigms I would possibly consider are a top-down approach (modelling cognition) and a bottom up approach (genetic algorithms?).

In what way can I group or classify approaches to AI as to help make more sense of this mass? Thank you.

submitted by arohn
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An anonymous reader writes "Google director of research Peter Norvig and AI pioneer Judea Pearl give their view on the prospects of developing a strong AI and how progress in the field is about to usher in a new age of household robotics to rival the explosion of home computing in the 1980s. Norvig says, 'In terms of robotics we’re probably where the world of PCs were in the early 1970s, where you could buy a PC kit and if you were an enthusiast you could have a lot of fun with that. But it wasn’t a worthwhile investment for the average person. There wasn’t enough you could do that was useful. Within a decade that changed, your grandmother needed word processing or email and we rapidly went from a very small number of hobbyists to pervasive technology throughout society in one or two decades. I expect a similar sort of timescale for robotic technology to take off, starting roughly now.' Pearl thinks that once breakthroughs are made in handling uncertainty, AIs will quickly gain 'a far greater understanding of context, for instance providing with the next generation of virtual assistants with the ability to recognise speech in noisy environments and to understand how the position of a phrase in a sentence can change its meaning.'"


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I'm entering a PhD program in the fall (scientific computing/bioinformatics) and am taking the summer off to travel. As such, I feel like I'm going to have a lot of free time for reading. I'm looking for suggestions for books that I should read that will make me a better computer scientist. I'm not interested in textbooks, since I'll be reading enough of those in the Fall and would prefer topics that I likely wouldn't get exposed to in a class. Also, everything I plan on reading I'm going to have to carry with me for the whole summer, so lighter and smaller is better.

So far I've compiled the following list based off of previous similar discussions:

  • The Soul of A New Machine - Tracy Kidder
  • COMPLEXITY: THE EMERGING SCIENCE AT THE EDGE OF ORDER AND CHAOS - M. Mitchell Waldrop
  • The Society of Mind - Marvin Minsky
  • Gödel, Escher, Bach: An Eternal Golden Braid - Douglas R. Hofstadter
  • Computer Power and Human Reason - Joseph Weizenbaum

What else is there anything else that I definitely should add?

EDIT: Thank you all for your suggestions. I'm definitely going to have a lot of good choices this summer.

submitted by easypeas
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