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  1. Jaime Guillermo Carbonell (July 29, 1953 – February 28, 2020) was a computer scientist who made seminal contributions to the development of natural language processing tools and technologies. His extensive research in machine translation resulted in the development of several state-of-the-art language translation and artificial ...

  2. Jaime Guillermo Carbonell, an American computer scientist and AI researcher with focus on machine learning and machine translation. He holds a Ph.D. in computer science in 1979 from Yale University and is Allen Newell professor at Carnegie Mellon University, co-founder and chairman, of Carnegie Speech Incorporated and Wisdom Technologies ...

  3. Carbonell, 66, died February 28, 2020, following an extended illness. He was the Allen Newell Professor of Computer Science and had earned the distinction of University Professor, the highest academic accolade CMU faculty can attain. He also founded and directed the Center for Machine Translation, which later became the Language Technologies Institute.

  4. 3. März 2020 · Jaime Carbonell, Founder of the CMU’s Language Technologies Institute (LTI), has passed away on February 28th, 2020, following an extended illness. The Distinguished CMU University Professor was a pioneer in Language Technologies. He foresaw a world where people could freely communicate with each other, no matter what language they ...

  5. Jaime Guillermo Carbonell was a computer scientist who made seminal contributions to the development of natural language processing tools and technologies. His extensive research in machine translation resulted in the development of several state-of-the-art language translation and artificial intelligence systems. He earned his B.S. degrees in ...

  6. 4. Jan. 2021 · Jaime was elected a Fellow of AAAI in 1991, and was a councilor from 1991 to 1994. He was a regular contributor to AAAI, with two papers at AAAI 2020: one in detecting “help speech” in social media to highlight support of Rohingyas refugees, and one in multi-view multi-task learning with scarce data.