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What is Machine Learning?
Machine learning aims to produce machines that can learn from their experiences and make predictions based on those experiences and other data they have analyzed. The Machine Learning Center at Georgia Tech (ML@GT) is an Interdisciplinary Research Center that is both a home for thought leaders and practitioners and a training ground for the next generation of pioneers.
The field of machine learning crosses a wide variety of disciplines that use data to find patterns in the ways both living systems, such as the human body and artificial systems, such as robots, are constructed and perform. Whether it’s being applied to analyze and learn from medical data, or to model financial markets, or to create autonomous vehicles, machine learning builds and learns from both algorithm and theory to understand the world around us and create the tools we need and want.
Recent News
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Researchers Say AI Copyright Cases Could Have Negative Impact on Academic Research
Two years since OpenAI introduced ChatGPT, dozens of lawsuits have been filed alleging technology companies have infringed copyright by using published works to train artificial intelligence (AI) models.
Academic AI research efforts could be significantly hindered if courts rule in the plaintiffs' favor.
Desai and Riedl are Georgia Tech researchers raising awareness about how these court rulings could force academic researchers to construct new AI models with limited training data. The two collaborated on a benchmark academic paper that examines the landscape of the ethical issues surrounding AI and copyright in industry and academic spaces.
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Mathematician Molei Tao Receives Sony Faculty Innovation Award
School of Mathematics Associate Professor Molei Tao has been honored for his work on the foundations of machine learning, particularly diffusion generative models.
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The Sherlock Holmes of AI
New tool AI Psychiatry recovers compromised deep-learning models so researchers can understand what went wrong.
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New AI Tool Identifies Better Antibody Therapies
Researchers combine deep learning with advanced sequencing techniques to predict how antibodies interact with antigens.
Events
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Jan 15
ML@GT Seminar Series | The Emergence of Generalizability and Semantic Low-Dim Subspaces in Diffusion Models
Featuring Qing Qu, University of Michigan
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Apr 16
ML@GT Seminar Series | Anna Rumshisky, University of Massachusetts Lowell
Featuring Anna Rumshisky, University of Massachusetts Lowell
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Apr 2
ML@GT Seminar Series | Speaker To Be Announced
Featuring Speaker To Be Announced
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Mar 12
ML@GT Seminar Series | Lalitha Sankar, Arizona State University
Featuring Lalitha Sankar, Arizona State University
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Feb 26
ML@GT Seminar Series | Andrew Wilson, New York University
Featuring Andrew Wilson, New York University
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Feb 12
ML@GT Seminar Series | Jacob Andreas, Massachusetts Institute of Technology
Featuring Jacob Andreas, Massachusetts Institute of Technology