Nano (formerly named Echo) is an intelligent assistant for the Raspberry Pi. It uses PocketSphinx for speech recognition and Python for it’s AI. There are currently two versions, one written for the Pi 2 (a desktop version) and one written for the Pi Zero (a portable version). This is obviously the Pi Zero version. I am the sole programmer of Nano and all programs associated with it. If you would like to contact me about the project, email me at [email protected] I am planning on making Nano publicly available as soon as I see it is fit, as it is still in the early development phase.
Facebook’s Caffe2 AI tools reach out to iPhone, Android, as well as Raspberry Pi
Your mobile may soon have the ability to recognize things in images without the need for getting access to the cloud
New intelligence can be combined with smartphones like the iPhone, Android OS devices, and low-power computer systems like Raspberry Pi with Facebook’s new open-source Caffe2 deep-learning framework.
Caffe2 allows you to program artificial intelligence features into tablets and smart phones, enabling them to acknowledge images, video, textual content, and speech and be more situationally aware.
You should note that Caffe2 is not an Artificial intelligence program, but a tool allowing for AI to be programmed into mobile phones. It takes just some lines of code to write learning models, which could then be bundled into apps.
The introduction of Caffe2 is extremely important. This means people will be in a position to get image identification, natural language processing, and computer vision straight on their mobile phone. That task is typically offloaded to remote servers in the cloud, with mobile phones then connecting to it.
Mobile gadgets are getting more artificial intelligence abilities. More mobiles are being bundled up with Amazon’s Alexa and Google Assistant, while Apple’s Siri has been a staple in the iPhone for a long time. Samsung’s Galaxy S8 mobile phones are due to get the Bixby voice assistant, that ought to make using the phones much easier.
Caffe2 can work within the power constraints of mobile gadgets. It works with mobile hardware to quicken AI applications and create neural networks.
Caffe2 takes advantage of the computing power of latest mobile hardware to quicken deep-learning tasks. As an example, in mobile phones, Caffe2 will use the computing power of Adreno GPUs and Hexagon DSPs on Qualcomm’s Snapdragon cellular chipsets.