Deep Learning Spreads

Deep learning is gaining traction across a broad swath of applications, providing more nuanced and complex behavior than machine learning offers today. Those attributes are particularly important for safety-critical devices, such as assisted or autonomous vehicles, as well as for natural language processing where a machine can recognize the intent of words based upon the context of a conversation. Like AI and machine learning, deep learning has been kicking around in research for decades. What's changing is that it is being added into many types of chips, from data centers to simple microcontrollers. And as algorithms become more efficient for both training and inferencing, this part of the machine learning/AI continuum is beginning to show up across a wide spectrum of use models, some for very narrow applications and some for much broader contextual decisions. "Some of this is in anticipation of what will be required in chips for autonomous vehicles," said Chris Rowen, CEO of Babblabs.