Guest Blog – Rise of the Machines (Learning)
November 12, 2015
A really smart guy told me years ago that neural networks would prove to be the second best solution to many problems. While he was right about lots of stuff, he missed that one! Out of favor for years, neural networks have enjoyed a resurgence fueled by advances in deep machine learning techniques and the processing power to implement them. Neural networks are now seen to be the leading solution to a host of challenges around mimicking how the brain recognizes patterns.
Google’s Monday announcement that it was releasing its TensorFlow machine learning system on an open-source basis underscores the significance of these advances, and further validates Sensory’s 22 year commitment to machine learning and neural networks. TensorFlow is intended to be used broadly by researchers and students “wherever researchers are trying to make sense of very complex data — everything from protein folding to crunching astronomy data”. The initial release of TensorFlow will be a version that runs on a single machine, and it will be put into effect for many computers in the months ahead, Google said.
Microsoft also had cloud-based machine learning news on Monday, announcing an upgrade to Project Oxford’s facial recognition API launched in May specifically for the Movember Foundation’s no-shave November fundraising effort: a facial hair recognition API that can recognize moustache and beard growth and assign it a rating (as well as adding a moustache “sticker” to the faces of facial hair posers).
Project Oxford’s cloud-based services are based on the same technology used in Microsoft’s Cortana personal assistant and the Skype Translator service, and also offer emotion recognition, spell check, video processing for facial and movement detection, speaker recognition and custom speech recognition services.
While Google and Microsoft have announced some impressive machine-learning capabilities in the cloud, Sensory uniquely combines voice and face for authentication and improved intent interpretation on device, complementing what the big boys are doing.
From small footprint neural networks for noise robust voice triggers and phrase-spotted commands, to large vocabulary recognition leveraging a unique neural network with deep learning that achieves acoustic models an order of magnitude smaller than the present state-of-the-art, to convolutional neural networks deployed in the biometric fusion of face and voice modalities for authentication, all on device and not requiring any cloud component, Sensory continues to be the leader in utilizing state-of-the-art machine learning technology for embedded solutions.
Not bad company to keep!