2 research outputs found
ICE: Enabling Non-Experts to Build Models Interactively for Large-Scale Lopsided Problems
Quick interaction between a human teacher and a learning machine presents
numerous benefits and challenges when working with web-scale data. The human
teacher guides the machine towards accomplishing the task of interest. The
learning machine leverages big data to find examples that maximize the training
value of its interaction with the teacher. When the teacher is restricted to
labeling examples selected by the machine, this problem is an instance of
active learning. When the teacher can provide additional information to the
machine (e.g., suggestions on what examples or predictive features should be
used) as the learning task progresses, then the problem becomes one of
interactive learning.
To accommodate the two-way communication channel needed for efficient
interactive learning, the teacher and the machine need an environment that
supports an interaction language. The machine can access, process, and
summarize more examples than the teacher can see in a lifetime. Based on the
machine's output, the teacher can revise the definition of the task or make it
more precise. Both the teacher and the machine continuously learn and benefit
from the interaction.
We have built a platform to (1) produce valuable and deployable models and
(2) support research on both the machine learning and user interface challenges
of the interactive learning problem. The platform relies on a dedicated,
low-latency, distributed, in-memory architecture that allows us to construct
web-scale learning machines with quick interaction speed. The purpose of this
paper is to describe this architecture and demonstrate how it supports our
research efforts. Preliminary results are presented as illustrations of the
architecture but are not the primary focus of the paper
MLSys: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoption. However, designing and implementing the systems that support ML models in real-world deployments remains a significant obstacle, in large part due to the radically different development and deployment profile of modern ML methods, and the range of practical concerns that come with broader adoption. We propose to foster a new systems machine learning research community at the intersection of the traditional systems and ML communities, focused on topics such as hardware systems for ML, software systems for ML, and ML optimized for metrics beyond predictive accuracy. To do this, we describe a new conference, MLSys, that explicitly targets research at the intersection of systems and machine learning with a program committee split evenly between experts in systems and ML, and an explicit focus on topics at the intersection of the two