The course will be taught by Sebastian Thrun and Peter Norvig. The course will include online lectures by the two, and according to the course website both professors will be available for online discussions. And according to the video embedded below, students in the online class will be graded on a curve just like regular Stanford students and receive a certificate of completion with their grade.
One of the interesting things here is that you can, for the most part, get the full education of the course. You just don’t get the course credit. But maybe students at other universities could take the class and then test out of their own school’s AI course? What impact would it have on professors if universities accepted certificates like this to count towards credit toward a degree at their school?
This is a presentation by Marshall Brain, founder of How Stuff Works. He’s written more extensively on the subject in an essay called Robotic Nation, which I haven’t read yet.
I think Brain might be overestimating the ability of machine-vision and natural language processing to supplant human intelligence, but the general trend towards fewer and fewer jobs is real one that I’ve written about a lot lately.
What’s interesting is that this doesn’t seem to be a result of “swarm intelligence” – individual bees can somehow make these calculations:
Scientists at Queen Mary, University of London and Royal Holloway, University of London have discovered that bees learn to fly the shortest possible route between flowers even if they discover the flowers in a different order. Bees are effectively solving the ‘Travelling Salesman Problem’, and these are the first animals found to do this.
The Travelling Salesman must find the shortest route that allows him to visit all locations on his route. Computers solve it by comparing the length of all possible routes and choosing the shortest. However, bees solve it without computer assistance using a brain the size of grass seed. [...]
Co-author and Queen Mary colleague, Dr. Mathieu Lihoreau adds: “There is a common perception that smaller brains constrain animals to be simple reflex machines. But our work with bees shows advanced cognitive capacities with very limited neuron numbers. There is an urgent need to understand the neuronal hardware underpinning animal intelligence, and relatively simple nervous systems such as those of insects make this mystery more tractable.”
An introduction to the concepts and problems with reverse engineering the human brain:
The ongoing debate between PZ Myers and Ray Kurzweil about reverse engineering the human brain is fairly representative of the same debate that’s been going in futurist circles for quite some time now. And as the Myers/Kurzweil conversation attests, there is little consensus on the best way for us to achieve human-equivalent AI.
That said, I have noticed an increasing interest in the whole brain emulation (WBE) approach. Kurzweil’s upcoming book, How the Mind Works and How to Build One, is a good example of this—but hardly the only one. Futurists with a neuroscientific bent have been advocating this approach for years now, most prominently by the European transhumanist camp headed by Nick Bostrom and Anders Sandberg.
While I believe that reverse engineering the human brain is the right approach, I admit that it’s not going to be easy. Nor is it going to be quick. This will be a multi-disciplinary endeavor that will require decades of data collection and the use of technologies that don’t exist yet. And importantly, success won’t come about all at once. This will be an incremental process in which individual developments will provide the foundation for overcoming the next conceptual hurdle.
A first for the game, the replicator demonstrates how astounding complexity can arise from simple beginnings and processes – an echo of life’s origins, perhaps. It might help us understand how life on Earth began, or even inspire strategies to build tiny computers.
The Game of Life is the best-known example of a cellular automaton, in which patterns form and evolve on a grid according to a few simple rules. You play the game by choosing an initial pattern of “live” cells, and then watch as the configuration changes over many generations as the rules are applied over and over again (see “Take two simple rules”).
The rules of the game were laid down by mathematician John Conway in 1970, but cellular automata first took off in the 1940s when the late mathematician John von Neumann suggested using them to demonstrate self-replication in nature. This lent philosophical undertones to Life, which ended up attracting a cult following.
Life enthusiasts have since catalogued an entire zoo of interesting patterns, such as “spaceships” that travel across the grid, or “guns”, which constantly spawn other patterns. But a pattern that spawned an identical copy of itself proved elusive.
