Patrice Simard

Microsoft Research Podcast · 078 - Machine teaching with Dr. Patrice Simard

• Why Is Machine Teaching More Effective Than Machine Learning

  In order to perform "machine learning", we must first collect a large dataset and teach the machine to distinguish that data, learning what makes for the right answer in the process, based on our guidance. We teach the machine to memorize and to pattern-match. Humans don't learn like that. Instead, we learn to ask the right questions about the data, and use those to make choices.

  If we could skip the costly dataset training and instead use the labeling directives to teach the machines to understand, it could save a lot of money and make machine learning more efficient.

  ▶ at 7:16 Share Transcript

• What Is the Difference Between Machine Learning and Machine Teaching

  Machine learning is where a computer learns to extract knowledge from data, without being told what the knowledge is. Machine teaching is all about a machine being able to learn from a human teacher, but without any other data. When humans teach another human, the process looks very different from when a human teaches a machine, but can we teach a machine like we teach another human?

  ▶ at 4:46 Share Transcript

• What Makes Research So Attractive

  If at school you struggle with getting the right answer because you can never agree on what the question is asking, research might be for you. Changing the question and trying to answer it in a slightly different way is what Dr. Patrice Simard does at Microsoft for his job.

  ▶ at 3:24 Share Transcript