Project description
New algorithms to make machine learning sustainable
Machine learning is they key component behind the recent successes of intelligent systems and data analytics engines. Machine learning algorithms trained on data can perform impressive tasks, but often at the expense of massive computational resources. Lessening these requirements is the goal of the EU-funded SLING project. SLING will develop a new generation of resource-efficient algorithms for large-scale machine learning solutions, readily applicable to real-world scenarios. The solutions developed in the project will make machine learning more accessible and sustainable, greatly boosting the prospects of developing truly scalable intelligent systems.
Objective
This project will develop and integrate the latest optimization and statistical advances into a new generation of resource-efficient algorithms for large-scale machine learning. State-of-the-art machine learning methods provide impressive results, opening new perspectives for science, technology, and society. However, they rely on massive computational resources to process huge manually annotated data-sets. The corresponding costs in terms of energy consumption and human efforts are not sustainable.
This project builds on the idea that improving efficiency is a key to scale the ambitions and applicability of machine learning. Achieving efficiency requires overcoming the traditional boundaries between statistics and computations, to develop new theory and algorithms.
Within a multidisciplinary approach, we will establish a new regularization theory of efficient machine learning.
We will develop models that incorporate budgeted computations, and numerical solutions with resources tailored to the statistically accuracy allowed by the data. Theoretical advances will provide the foundations for novel and sound algorithmic solutions. Close collaborations in diverse applied fields
will ensure that our research results and solutions will be apt and immediately applicable to real world scenarios.
The new algorithms developed in the project will contribute to boost the possibilities of Artificial Intelligence, modeling and decision making in a world of data with ever-increasing size and complexity.
Programme(s)
Funding Scheme
ERC-COG - Consolidator GrantHost institution
16126 Genova
Italy