Intel’s Haswell chip made an appearance at this year’s CES although it wasn’t the star of the show. The chip maker wasn’t keen on playing show and tell with the CPU itself but we did get a pretty solid look at what the integrated graphics on Haswell (codenamed GT3) are capable of courtesy of a comparison video recently posted by the Tech Report.
In the video below, one system was running Intel’s Haswell CPU with integrated graphics while the other system was packing a Core i7 Ivy Bridge chip and a discrete Nvidia GeForce GT 650M graphics card. Dirt 3 was running at 1080p resolution with all of the details set to high and as you can see for yourself, it’s tough to determine which system is which.
We are reminded that the GeForce GT 650M is a midrange graphics card with 384 ALUs, a 128-bit path that dips into dedicated memory and clock speeds as high as 900MHz. The part is Nvidia’s fastest mobile GPU under the GT banner and is used in Apple’s 15-inch MacBook Pro with Retina display.
The publication also points out that drivers have been a sticking point with Intel graphics for some time but they are now releasing updates on a quarterly basis. The fact that Intel has released a new QuickSync SDK will help to ensure developers will have compatible software by the time the chip launches. At last check, Intel is planning to launch Haswell by the end of Q2 2013.Explore More
It's easy for humans to identify faces in pictures on Facebook, but the method isn't as simple for computers. Sure, Facebook has a suggested prompt that predicts who you're trying to tag, but now the company is working on a technology that promises "near-human accuracy" so you won't have to do it yourself in the future.
Facebook's API Group is developing software called DeepFace, which maps 3D facial features and creates a colorless model to narrow in on specific characterizations. The accuracy of the method is 97.25%, which is just under the 97.5% accuracy that a human can identify, according to the group.
"We present a system (DeepFace) that has closed the majority of the remaining gap in the most popular benchmark in unconstrained face recognition, and is now at the brink of human level accuracy," researchers said in a report released by Facebook API Group. "It is trained on a large dataset of faces acquired from a population vastly different than the one used to construct the evaluation benchmarks, and it is able to outperform existing systems with only very minimal adaption."
To develop the technology, Facebook looked at 4.4 million tagged faces from 4,030 of its users to help the system learn how to better identify features specific to each person. The report also reveals that Facebook looks at modern face recognition in four phases: detect, align, represent and classify.
"We revisit both the alignment step and the representation step by employing explicit 3D face modeling in order to apply a piecewise affine transformation, and derive a face representation from a nine-layer deep neural network," the company notes on its DeepFace page.
Although we might not see the updated approach on Facebook just yet, the site is expected to present it at the IEEE Conference on Computer Vision and Pattern Recognition in June, according to MIT Technology Review.