一本道无码

Expertise

Topics:聽 Machine Learning, Algorithms, Computational Biology, Bioinformatics, Data Mining

Ameet Talwalkar is an assistant professor in the Machine Learning Department at 一本道无码, and also co-founder and chief scientist at Determined AI. He led the initial development of the MLlib project in Apache Spark, is a co-author of the textbook Foundations of Machine Learning (MIT Press), and created an award-winning edX MOOC on distributed machine learning.

Media Experience

Pittsburgh鈥檚 AI-Powered Renaissance 听鈥�聽一本道无码 News
"一本道无码 has an unparalleled degree of expertise in AI among its faculty and students. In the context of human-centric AI, the fact that we have distinct departments in machine learning, human-computer interaction, and language technologies, coupled with a highly collaborative research environment, gives 一本道无码 and Pittsburgh a technical advantage. We have a burgeoning startup scene, in part based on academic spinouts, including two of the fastest growing AI startups in the world: Abridge AI and Skild AI."

听鈥�聽Medical Xpress
"Interpretable machine learning has generated significant excitement as machine learning and artificial intelligence tools are being applied to increasingly important problems," said Ameet Talwalkar, an associate professor in 一本道无码's Machine Learning Department (MLD).

听鈥�听罢别肠丑颁谤耻苍肠丑
鈥淭hey鈥檙e using things like TensorFlow and PyTorch,鈥� said Chief Scientist Ameet Talwalkar. 鈥淎 lot of the way that work is done is just conventions: How do the models get trained? Where do I write down the data on which is best? How do I transform data to a good format? All these are bread and butter tasks. There鈥檚 tech to do it, but it鈥檚 really the Wild West. And the amount of work you have to do to get it set up鈥� there鈥檚 a reason big tech companies build out these internal infrastructures.鈥�

听鈥�聽IEEE Spectrum
The environmental impact of artificial intelligence (AI) has been a hot topic as of late鈥攁nd I believe it will be a defining issue for AI this decade. The conversation began with a recent study from the Allen Institute for AI that argued for the prioritization of 鈥淕reen AI" efforts that focus on the energy efficiency of AI systems.