Neural networks are computer systems that are vaguely inspired by the construction of animal brains, and much like human brains, can be trained to obey the whims of the almighty domestic cat. [EdjeElectronics] has built just such a system, and his cat is better off for it.
The build uses a Raspberry Pi, fitted with the Pi Camera board, to image the area around the back door of the house. A Python script regularly captures images and passes them to a TensorFlow neural network for object recognition. The TensorFlow network returns object type and positions to the Python script. This information can be used to determine if there is a cat in the frame, and if it is inside or outside. If the cat remains in position for ten consecutive frames, a text message is sent via Twilio, indicating to the owner to let the cat in or out, as the case may be.
Thirty years ago, object classification was a pie-in-the-sky technology, but now you can run it on a $30 computer to figure out where your pets are. What a time we live in! A similar solution to this problem may be a cat door that unlocks via facial recognition. Video after the break.
[Thanks to Baldpower for the tip!]
Continue reading “Neural Network Knows When Cat Wants To Go Outside”
We’ve been talking a lot about machine learning lately. People are using it for speech generation and recognition, computer vision, and even classifying radio signals. If you’ve yet to climb the learning curve, you might be interested in a new free class from Google using TensorFlow.
Of course, we’ve covered tutorials for TensorFlow before, but this is structured as a 15 hour class with 25 lessons and 40 exercises. Of course, it is also from the horse’s mouth, so to speak. Google says the class will answer questions like:
- How does machine learning differ from traditional programming?
- What is loss, and how do I measure it?
- How does gradient descent work?
- How do I determine whether my model is effective?
- How do I represent my data so that a program can learn from it?
- How do I build a deep neural network?
Continue reading “Machine Learning Crash Course From Google”
Prostheses are a great help to those who have lost limbs, or who never had them in the first place. Over the past few decades there has been a great deal of research done to make these essential devices more useful, creating prostheses that are capable of movement and more accurately recreating the functions of human body parts. At Georgia Tech, they’re working on just that, with the help of AI.
[Jason Barnes] lost his arm in a work accident, which prevented him from playing the piano the way he used to. The researchers at Georgia Tech worked with him, eventually producing a prosthetic arm that, unlike most, actually has individual finger control. This is achieved through the use of an ultrasound probe, which is used to detect muscle movements elsewhere on his body, with enough detail to allow the control of individual fingers. This is done through a TensorFlow-based neural network which analyses the ultrasound data to determine which finger the user is trying to move. The use of ultrasound was the major breakthrough which made this possible; previous projects have often relied on electromyogram sensors to read muscle impulses but these lack the resolution required.
The prosthesis is nicknamed the “Skywalker arm”, after its similarities to the prostheses seen in the Star Wars films. It’s not [Jason]’s first advanced prosthetic, either – Georgia Tech has also equipped him with an advanced drumming prosthesis. This allows him to use two sticks with a single arm, the second stick using advanced AI routines to drum along with the music in the room.
It’s great to see music being used as a driver to create high-performance prosthetics and push the state of the art forward. We’re sure [Jason] enjoys performing with the new hardware, too. But perhaps you’d like to try something similar, even though you’ve got two hands already? Try this on for size.
Continue reading “AI Prosthesis Is Music To Our Ears”