Software engineering of systems that learn in uncertain domains
Building reliable, robust software is hard. It is even harder when we move from deterministic domains (such as balancing a checkbook) to uncertain domains (such as recognizing speech or objects in an image). The field of machine learning allows us to use data to build systems in these uncertain domains. Peter Norvig looks at techniques for achieving reliability (and some of the other -ilities).
| Talk Title | Software engineering of systems that learn in uncertain domains |
| Speakers | Peter Norvig (Google) |
| Conference | O’Reilly Artificial Intelligence Conference |
| Conf Tag | |
| Location | New York, New York |
| Date | September 26-27, 2016 |
| URL | Talk Page |
| Slides | Talk Slides |
| Video | |
Building reliable, robust software is hard. It is even harder when we move from deterministic domains (such as balancing a checkbook) to uncertain domains (such as recognizing speech or objects in an image). The field of machine learning allows us to use data to build systems in these uncertain domains, but the field mostly concentrates on accuracy of results. Peter Norvig looks at techniques for achieving reliability (and some of the other -ilities).
