Abstract
Small Language Models (SLMs): thanks to innovative architectures and training techniques, SLMs are on par and sometimes even better than larger models. Because they have fewer parameters, inference requires less computing and memory, making them excellent candidates for resource-constrained environments.
About the Speaker
Mr. Moshe Wasserblat is currently Natural Language Processing (NLP) and Deep Learning (DL) research group manager at Intel's AI Product group. In his former role, he has been with NICE Systems for more than 17 years and has founded the NICE's Speech Analytics Research Team. His interests are in the field of Speech Processing and Natural Language Processing (NLP). He was the co-founder coordinator of EXCITEMENT FP7 ICT program and served as organizer and manager of several initiatives, including many Israeli Chief Scientist programs. He has filed more than 60 patents in the field of Language Technology and also has several publications in international conferences and journals. His areas of expertise include: Speech Recognition, Conversational Natural Language Processing, Emotion Detection, Speaker Separation, Speaker Recognition, Information Extraction, Data Mining, and Machine Learning.