There has been a quantum leap in the performance of conversational AI. From speech recognition to machine translation and language understanding, deep learning made its mark. However, scaling and productizing these breakthroughs remains a big challenge—while implementing a cutting-edge algorithm using an open source deep learning framework is certainly doable, creating a scalable product that encapsulates these amazing algorithms and runs them at large scale is difficult.
Yishay Carmiel shares techniques and tips on how to take advantage of large datasets, accelerate training, and create an end-to-end product.
Yishay Carmiel is the founder of IntelligentWire, a company that develops and implements industry-leading deep learning and AI technologies for automatic speech recognition (ASR), natural language processing (NLP), and advanced voice data extraction, and is the head of Spoken Labs, the strategic artificial intelligence and machine learning research arm of Spoken Communications. Yishay and his teams are currently working on bleeding-edge innovations that make the real-time customer experience a reality—at scale. Yishay has nearly 20 years’ experience as an algorithm scientist and technology leader building large-scale machine learning algorithms and serving as a deep learning expert.
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