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Tom Dietterich

Professional Background

Tom Dietterich is a prominent figure in the field of computer science, bringing years of experience and a wealth of knowledge to his position as a distinguished professor at Oregon State University. His impressive career trajectory includes roles as Chief Scientist for several innovative technology companies where he made substantial contributions to the realms of artificial intelligence and machine learning.

With a solid educational foundation, Dietterich earned his Doctor of Philosophy in Computer Science from Stanford University, one of the leading institutions in the world, known for its cutting-edge research and technological advancements. Prior to that, he completed his Master of Science in Computer Science at the University of Illinois at Urbana-Champaign, demonstrating his commitment to the ever-evolving field of computer sciences. He also holds a Bachelor of Arts in Mathematics from Oberlin College, which has undoubtedly provided him with a robust analytical framework and problem-solving skills that he applies to his professional endeavors.

His role at Oregon State University has been particularly impactful, where he not only teaches but also continues to conduct extensive research, guiding students in their academic journeys and helping shape the future of computer science. He has also held prestigious positions as the Chief Scientist at various organizations, including BigML, Inc, Decho Corp (an EMC Company), and Pi Corporation. These roles have allowed him to lead research initiatives and product development efforts while leveraging his expertise in intelligent systems and machine learning.

Education and Achievements

Tom Dietterich's educational journey signifies a deep-rooted commitment to academic excellence and innovation. His journey began at Oberlin College, where he received his Bachelor of Arts in Mathematics. This foundational education paved the way for advanced studies at the University of Illinois at Urbana-Champaign, where he obtained his Master of Science in Computer Science, equipping him with essential skills that would later be instrumental throughout his career.

Next, he attended Stanford University, where he earned a Doctor of Philosophy in Computer Science. Stanford's rigorous academic environment and emphasis on innovative research expanded his knowledge base and cultivated his interest in artificial intelligence and machine learning. His doctoral research laid the groundwork for future contributions to the field.

Achievements

Throughout his career, Tom Dietterich has been recognized for his groundbreaking work and influential role in advancing computer science. His contributions to research have been significant, establishing him as a leader in intelligent systems and machine learning. As a distinguished professor at Oregon State University, he has facilitated numerous research projects and influenced the academic community, preparing the next generation of computer scientists to innovate and excel in their careers.

In his capacity as Chief Scientist at various companies, including BigML, Inc and Pi Corporation, he has driven the development of pioneering machine learning applications and intelligent systems, showcasing his ability to bridge the gap between theoretical research and practical implementation in industry. His roles have allowed him to mentor aspiring scientists and contribute to projects that shape the future of technology.

Tom Dietterich's work continues to inspire students, researchers, and professionals in the fields of computer science and artificial intelligence. His dedication to education, innovative research, and mentorship underscores his vital role in advancing knowledge and fostering future leaders in technology.

Highlights

Apr 17 · linkedin

The concept of "AGI" (a system that can match or exceed human performance across all tasks) shares all of the defects of the Turing Test. It defines "intelligence" entirely in terms of human performance. It says that the most important AI system capabilities to create are exactly those things that people can do well. But is this what we want? Is this what we need?

I think we should be building systems that complement people; systems that do well the things that people do poorly; systems that make individuals and organizations more effective and more humane.

Examples include

  1. writing and checking formal proofs (in mathematics and for software),
  2. writing good tests for verifying engineered systems,
  3. integrating the entire scientific literature to identify inconsistencies and opportunities,
  4. speeding up physical simulations such as molecular dynamics and numerical weather models,
  5. maintaining situational awareness of complex organizations and systems,
  6. helping journalists discover, assess, and integrate multiple information sources, and many more.

Each of these capabilities exceeds human performance -- and that is exactly the point. People are not good at these tasks, and this is why we need computational help.

We should be evaluating AI systems for these kinds of capabilities rather than giving them IQ tests. Building AGI is a diversion from building these capabilities.

Related Questions

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Tom Dietterich
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Location

Corvallis, Oregon