Kira Radinsky has written an algorithm that dissects old news stories and other Internet postings to look for past cause and effect, and then can alert us to possible disasters, geopolitical events, and disease outbreaks.
Not one to let Amazon get all the robots-are-our-future attention, Google’s revealed that its already got plenty of projects underway, and they’re headed up by Andy Rubin. Yep, the man who made the company’s Android operating system arguably the biggest smartphone OS in the world.
Android robot on Android operating system, a perfect fit!
Although some people might find the idea of love with a machine repulsive, experts predict that as the technology advances and robots become more human-like, we will view our silicon cousins in a friendlier light. As the future unfolds, robots will fill more roles as family caregivers, household servants, and voice-enabled avatars that manage our driverless cars, automated homes, and entertainment systems.
The goal of this new delivery system is to get packages into customers’ hands in 30 minutes or less using unmanned aerial vehicles. Putting Prime Air into commercial use will take some number of years as we advance technology and wait for the necessary FAA rules and regulations. This is footage from a recent test flight.
Amazon says that the system is ready to enter commercial operations as soon as the necessary regulations are in place. Wow. But forget about Amazon deliveries, I want one to fly myself around. Just hold up the barcode for “work” or “bar” and be on your way. I wonder what FAA has to say about that?
A discussion of the cellular automata ideas described by Stephen Wolfram (the guy behindWolframAlpha) in his book A New Kind of Science. The article provides a brief overview of the cellular automata explaining why Wolfram considers it to be so remarkable. The author of the article, Ray Kurzweil, compares the cellular automata with other computational models such as evolutionary algorithms and Turing Machine. A great read if you are not quite ready for 1200 pages of the original piece.
So what is the discovery that has so excited Wolfram? As I noted above, it is cellular automata rule 110, and its behavior. There are some other interesting automata rules, but rule 110 makes the point well enough. A cellular automaton is a simple computational mechanism that, for example, changes the color of each cell on a grid based on the color of adjacent (or nearby) cells according to a transformation rule. Most of Wolfram’s analyses deal with the simplest possible cellular automata, specifically those that involve just a one-dimensional line of cells, two possible colors (black and white), and rules based only on the two immediately adjacent cells. For each transformation, the color of a cell depends only on its own previous color and that of the cell on the left and the cell on the right. Thus there are eight possible input situations (i.e., three combinations of two colors). Each rule maps all combinations of these eight input situations to an output (black or white). So there are 28 = 256 possible rules for such a one-dimensional, two-color, adjacent-cell automaton. Half of the 256 possible rules map onto the other half because of left-right symmetry. We can map half of them again because of black-white equivalence, so we are left with 64 rule types. Wolfram illustrates the action of these automata with two-dimensional patterns in which each line (along the Y axis) represents a subsequent generation of applying the rule to each cell in that line.
Most of the rules are degenerate, meaning they create repetitive patterns of no interest, such as cells of a single color, or a checkerboard pattern. Wolfram calls these rules Class 1 automata. Some rules produce arbitrarily spaced streaks that remain stable, and Wolfram classifies these as belonging to Class 2. Class 3 rules are a bit more interesting in that recognizable features (e.g., triangles) appear in the resulting pattern in an essentially random order. However, it was the Class 4 automata that created the “ah ha” experience that resulted in Wolfram’s decade of devotion to the topic. The Class 4 automata, of which Rule 110 is the quintessential example, produce surprisingly complex patterns that do not repeat themselves. We see artifacts such as lines at various angles, aggregations of triangles, and other interesting configurations. The resulting pattern is neither regular nor completely random. It appears to have some order, but is never predictable.
Why is this important or interesting? Keep in mind that we started with the simplest possible starting point: a single black cell. The process involves repetitive application of a very simple rule 3. From such a repetitive and deterministic process, one would expect repetitive and predictable behavior. There are two surprising results here. One is that the results produce apparent randomness. Applying every statistical test for randomness that Wolfram could muster, the results are completely unpredictable, and remain (through any number of iterations) effectively random. However, the results are more interesting than pure randomness, which itself would become boring very quickly. There are discernible and interesting features in the designs produced, so the pattern has some order and apparent intelligence. Wolfram shows us many examples of these images, many of which are rather lovely to look at.
Toyota was the latest to step on-board the self-driving car hype machine this week when they announced they would offer a car with automated driving technologies by the mid-2010s.
Self-driving is a bit exaggerated here. In reality, most automakers introduce smarter cruise control and automatic parking features rather the complete self-driving experience of Google cars. On the other hand, a gradual introduction of autonomous driving makes it easier for society and laws to adapt to this kind of technology.
New type of transistors that are better suited for AI, e.g. implementation of artificial neural network models and statistical machine learning. Compared to traditional silicon transistors, it has the following advantages:
Douglas Hofstadter, the Pulitzer Prizewinning author of Gödel, Escher, Bach, thinks we’ve lost sight of what artificial intelligence really means. His stubborn quest to replicate the human mind.
This article sheds light on the question whether systems like IBM’s Watson or Apple’s Siri are intelligent or just seem to behave intelligently. It also provides a good historical context to understand the origins and fundamentals of artificial intelligence.