Collaborate Disseminate
Prosthetic limbs have been slow to evolve from simple motionless replicas of human body parts to moving, active devices. A major part of this is that controlling the many joints …read more Continue reading Deep Learning Enables Intuitive Prosthetic Control
The current wave of excitement around machine learning kicked off when graphics processors were repurposed to make training deep neural networks practical. Nvidia found themselves the engine of a new revolution and seized their opportunity to help push frontiers of research. Their research lab in Seattle will focus on one …read more
Continue reading Nvidia Teaching Robots To Master IKEA Kitchens
Deep Neural Networks can be pretty good at identifying images — almost as good as they are at attracting Silicon Valley venture capital. But they can also be fairly brittle, and a slew of research projects over the last few years have been working on making the networks’ image classification less likely to be deliberately fooled.
One particular line of attack involves adding particularly-crafted noise to an image that flips some bits in the deep dark heart of the network, and makes it see something else where no human would notice the difference. We got tipped with a YouTube video …read more
Humans are very good at watching others and imitating what they do. Show someone a video of flipping a switch to turn on a CNC machine and after a single viewing they’ll be able to do it themselves. But can a robot do the same?
Bear in mind that we want the demonstration video to be of a human arm and hand flipping the switch. When the robot does it, the camera that is its eye will be seeing its robot arm and gripper. So somehow it’ll have to know that its robot parts are equivalent to the human parts …read more
Continue reading Neural Networking: Robots Learning From Video
Around the Hackaday secret bunker, we’ve been talking quite a bit about machine learning and neural networks. There’s been a lot of renewed interest in the topic recently because of the success of TensorFlow. If you are adept at Python and remember your high school algebra, you might enjoy [Oliver Holloway’s] tutorial on getting started with Tensorflow in Python.
[Oliver] gives links on how to do the setup with notes on Python versions. Then he shows some basic setup operations. From there, he has the software “learn” how to classify random points that either fall into a circle or don’t. …read more
In case you didn’t make it to the ISCA (International Society for Computers and their Applications) session this year, you might be interested in a presentation by [Joel Emer] an MIT professor and scientist for NVIDIA. Along with another MIT professor and two PhD students ([Vivienne Sze], [Yu-Hsin Chen], and [Tien-Ju Yang]), [Emer’s] presentation covers hardware architectures for deep neural networks.
The presentation covers the background on deep neural networks and basic theory. Then it progresses to deep learning specifics. One interesting graph shows how neural networks are getting better at identifying objects in images every year and as of …read more
“Essentially, we believe that further erosion of privacy is inevitable.” Continue reading A Frightening AI Can Determine Whether a Person Is Gay With 91 Percent Accuracy
Hardware that responds to voice commands is already out there and probably in your hand or house right now. Whether it’s a smartphone, smart speaker or wearable, it has to connect to the cloud to deliver answers to your questions. Now, a startup called Mythic (formerly known as Isocline) is launching a chip and software that will change all that, putting voice control, computer vision… Read More Continue reading Mythic launches a chip to enable computer vision and voice control on any device
Using neural networks and shows like “The Office” and “Big Bang Theory,” CSAIL’s system was able to predict how actors were about to greet each other. The post An MIT Algorithm Predicts the Future by Watching TV appeared first on WIRED. Continue reading An MIT Algorithm Predicts the Future by Watching TV
An algorithm developed at MIT creates realistic audio tracks for videos. The post MIT’s New AI Can (Sort of) Fool Humans With Sound Effects appeared first on WIRED. Continue reading MIT’s New AI Can (Sort of) Fool Humans With Sound Effects
