The third edition of the Paris-Saclay Junior Conference on Data Science and Engineering is addressed to PhD students in their first year, M2 students and third year students at Engineering schools at Paris-Saclay. It will offer these students the opportunity to present their scientific works developed at internships, or in the first year of thesis, and also to grow their critical sense thanks to a professional conference hosting prestigious invited speakers, academics and industry scientists.

The conference aims at gathering a large public of master, engineering school and PhD students, and is an excellent means of discovering the research world in Data Science and Engineering.

PhD students are also involved in the conference organization as reviewers, session chairs, organizers of networking events. 

Machine Learning -- What’s next?

For many Machine Learning (ML) problems, labeled data is readily available. When this is the case, algorithms and training time are the performance bottleneck. This is the ML researcher’s paradise! Vision and Speech are good examples of such problems because they have a stable distribution and additional human labels can be collected each year. Problems that extract their labels from history, such as click prediction, data analytics, and forecasting are also blessed with large numbers of labels. Unfortunately, there are only a few problems for which we can rely on such an endless supply of free labels. They receive a disproportionally large amount of attention from the media.

We are interested in tackling the much larger class of ML problems where labeled data is sparse. For example, consider a dialog system for a specific app to recognize specific commands such as “lights on first floor off”, “increase spacing between 2nd and 3rd paragraph”, “make doctor appointment after Hawaii vacation”. Anyone who has attempted building such a system has soon discovered that generalizing to new instances from a small custom set of labeled instances is far more difficult than they originally thought. Each domain has its own generalization challenges, data exploration and discovery, custom features, and decomposition structure. Creating labeled data to communicate custom knowledge is inefficient. It also leads to embarrassing errors resulting from over-training on small sets. ML algorithms and processing power are not a bottleneck when labeled data is scarce. The bottleneck is the teacher and the teaching language.

To address this problem, we change our focus from the learning algorithm to teachers. We define “Machine Teaching” as improving the human productivity given a learning algorithm. If ML is the science and engineering of extracting knowledge from data, Machine Teaching is the science and engineering of extracting knowledge from teachers. A similar shift of focus has happened in computer science. While computing is revolutionizing our lives, systems sciences (e.g., programming languages, operating systems, networking) have shifted their foci to human productivity. We expect a similar trend will shift science from Machine Learning to Machine Teaching.

The aim of this talk is to convince the audience that we are asking the right questions. We provide some answers and some spectacular results. The most exciting part, however, is the research opportunities that come with the emergence of a new field.

Patrice Simard is a Distinguished Engineer in the Microsoft Research AI Lab in Redmond. He is passionate about finding new ways to combine engineering and science in the field of machine learning. Simard’s research is currently focused on human teachers. His goal is to extend the teaching language, science, and engineering, beyond the traditional (input, label) pairs. Simard completed his PhD thesis in Computer Science at the University of Rochester in 1991. He then spent 8 years at AT&T Bell Laboratories working on neural networks. He joined Microsoft Research in 1998. In 2002, he started MSR’s Document Processing and Understanding research group. In 2006, he left MSR to become the Chief Scientist and General Manager of Microsoft’s Live Labs Research. In 2009, he became the Chief Scientist of Microsoft’s AdCenter (the organization that monetizes Bing search). In 2012, he returned to Microsoft Research to work on his passion, Machine Learning research. Specifically, he founded the Computer-Human Interactive Learning (CHIL) group to study Machine Teaching and to make machine learning accessible to everyone.