This is Woggle, the portable Raspberry Pi J.A.R.V.I.S wannabe assistant. You can chat, ask questions and if you start to play Minecraft on the same network as Woggle, it’ll follow you into the game to allow you to interact with it still.

A copy of the cobbled together code can be found here: It’s just a bit of fun remember. I would live to see what improvements you make to Woggle.


Facebook’s Caffe2 AI tools choose iPhone, Android, and also Raspberry Pi

Your cell phone may soon have the ability to identify things in photos free of gaining access to the cloud

New intelligence can be included in cellular devices just like the iPhone, Android OS products, and low-power computer systems just like Raspberry Pi with Facebook’s new open-source Caffe2 deep-learning framework.

Caffe2 can be used to program artificial intelligence features into smartphones and tablets, allowing them to realize images, movie, text, and speech and be more situationally aware.

It is important to realize that Caffe2 is not an Artificial intelligence program, but a tool allowing AI to be programmed into smart phones. It takes only a couple of lines of code to write learning models, which could then be incorporated into apps.

The introduction of Caffe2 is critical. It indicates users will be able to get image recognition, natural language processing, and computer vision straight on their cell phone. That job is normally offloaded to remote servers in the cloud, with smart phones then connecting to it.

Mobile products are having more artificial intelligence features. 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 smart phones are due to get the Bixby voice assistant, which should make using the devices much easier.

Caffe2 can function within the power constraints of mobile products. It works with mobile hardware to speed up AI applications and create neural networks.

Caffe2 employs the computing power of cutting edge mobile hardware to quicken deep-learning jobs. For instance, in smart phones, Caffe2 will harness the computing power of Adreno GPUs and Hexagon DSPs on Qualcomm’s Snapdragon mobile SoC.

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