More details, Mark Zucklberg personally called Yann LeCun on the phone to 
be the director of AI research.

Here's an abstract from one of his latest papers:

*Learning Hierarchical Features for Scene Labeling*, scheduled to appear in 
the special issue on deep learning of IEEE Trans. on Pattern Analysis and 
Machine Intelligence. The task is to label all the pixels in an image with 
the category of the object it belongs to. This is sometimes called scene 
labeling, scene parsing, or semantic segmentation. The bottom line is that 
our system beat all previously published scene labeling systems on accuracy 
on three standard datasets: Stanford Bakground (8 classes), SIFTflow (33 
classes) and Barcelona (170 classes). It also beat the best competitors by 
a factor of 100 in speed. Our system is a multiscale convolutional network 
trained in purely supervised mode (with backprop) to label each pixel. The 
decisions are then cleaned up by a simple post-processing (the simplest one 
consisting in taking the majority category within a superpixel).


On Friday, February 21, 2014 11:02:12 AM UTC-6, Ted wrote:
>
> Sounds like the same for deep learning, heard this on NRP radio while 
> driving home yesterday:
>
>
> http://www.npr.org/blogs/alltechconsidered/2014/02/20/280232074/deep-learning-teaching-computers-to-tell-things-apart
>
> On Wednesday, February 19, 2014 11:22:11 PM UTC-6, BenGoertzel wrote:
>>
>>
>> http://www.technologyreview.com/news/524401/yahoo-launches-10-million-research-effort-to-invent-a-smarter-siri/#comments
>>  
>>
>>
>>
>> -- 
>> Ben Goertzel, PhD 
>> http://goertzel.org 
>>
>> "In an insane world, the sane man must appear to be insane". -- Capt. 
>> James T. Kirk 
>>
>


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