A Robot Communicates Through An Interface.

I, Robot (2004)

AI Pathfinding - Update

An update on the A* pathfinding system I’m doing for my prototype. I’ve added smoothing to the paths so I get a more natural looking movement. I’ve also added weights, which means I can add a penalty to moving on different surfaces, which the AI will take into account when finding a path.

For example if there is puddle of mud between the unit and its target location (and walking through mud slows the units speed), it will figure out if its better to walk around the mud or walk through it. Another example as shown in the GIF, the grass has a small movement penalty, making the AI prefer to walk along the road instead of straight to the target, despite the path being longer.

100 cans of spray paint, 60 hours of painting, 24 individual frames

INSA is a graffiti artist who makes gif animations out of his physical art. Here he paints and animates the beautiful original painting by James Jean, which was created for Paramount’s new movie mother!

Click here to watch INSA bring this painting to life

SP. Artificial intelligence software calculates the best pose for selling the product and demands it from the model.

Looker (1981)

Michael Conover: Information Visualization for Large-Scale Data Workflows

data geometry

memes

visual analysis of program structure

visual analysis of propaganda

compare last week’s analysis and share with colleagues

geom_bin2d rather than geom_point(alpha=...) in ggplot2

ggpairs

automated grading: in addition to unit testing, 1) parse syntax trees of submissions, 2) define edit distance between them, 3) induces a network structure, 4) identify clusters, 5) give feedback to a representative member of the cluster and cc: everyone else

Presented at SF Data Mining on Oct 9, 2013 The ability to instrument and interrogate data as it moves through a processing pipeline is fundamental to effecti… @vagabondjack reasonengine.wordpress.com

Current AI programming techniques are incompatible for creation of human intelligence

        When artificial intelligence first came about in the first computing machines, computing was restricted by technology and computing power. The easiest way to get around giving intelligent functions to a machine was to give it basic sets of rules. These finite set of rules took up small amounts of memory, and could be used dependent on the input and goal. The rules could be combined to create more complex functions, exponentially increasing the amount of total functions available.

       The current computing grew from these beginnings, now using complex algorithmic and recursive functions using basic rules to further increase the amount of computing possibilities. The search for true artificial intelligence, one comparable to our amount of intelligence and conscious awareness, is in the works. A robotic creation using transistors and circuits and algorithmic programming whilst having the conscious and cognitive abilities that humans possess is the end goal. We know so far that even the most advanced artificial intelligence makes semantic and perceptual errors about the physical world.

       The problem with creating an artificial intelligence like our own is the rule-based computing which is the seed of modern computing intelligence. The seed planted was a rule-based one, and since we used these seeds to grow modern computing, we now have this type of computing available. I strongly believe this rule-based computing will never allow for true human-based artificial intelligence to be used. Human cognition and consciousness is not a rule-based system, and rule-based systems are not able to perform the amount and type of processing that the human mind does.

       The human mind processes information in bottom-up and top-down processes by integrating sensory info and semantic knowledge in integration centers of the brain. The mind can take this info and again reanalyze in in a seemingly subjective fashion, or by applying further conscious reason to perform a reaction to the input info. The mind has the ability to consciously engage ideas in the brain in a way that doesn’t seem to obey rules.

       Humans can be argued to be mostly a tabula rasa (blank slate) at birth, with arguably some innate abilities; perhaps there are some undefined “rules”. To create a “fully grown” and “mature” robot instantaneously, as well as endow it with all the knowledge of the world and processing an adult human would possess is a disastrous thought. We can’t program a mature robot, we need to grow it. Create a robot with the ability to learn, and to perform connections by repeated pairings of stimuli. A robot would be endowed with the learning abilities of which humans possess, so that it may learn connections in the world and be endowed with human-type knowledge and ability. The way we “program” robots now with artificially intelligent algorithms does not begin to scratch the surface of human knowledge ability.

       A robotic creation as a “newborn” with very few programmed rules besides rules for stimuli pairing, feature detection, whilst integrating the perceptual info similar in fashion to how the info bonds and integrates in the human brain is essential. No need for large highly complex algorithmic programs, we set a few basic algorithms, and allow the robot to “learn” the world on its own. While this is a long process, I believe it is the closest approximation to a human-like artificial intelligence. We bare the robot, and allow it to grow and mature in the human world by interaction with the world and gaining knowledge in the fashion that we do. This is the only way to create a robot which can be perceptually and semantically comparable to a human.

This post was inspired by this video on cognitive science: https://youtu.be/0T_nOzpBYxU

https://vimeo.com/175247441

If you could talk to your childhood inspiration, what would you say?

Listen to Brian Lehrer’s full interview with LeVar Burton here. 

Bitsquare, decentralised #bitcoin exchange

Illustration for the current issue of @wireduk, in which Jürgen Schmidhuber explains why human-level AI is within our reach.