Information and Communication Technologies (ICT) are constantly producing advancements that translate into a variety of societal changes including improvements to economy, better living conditions, access to education, well-being, and entertainment. The widespread use and growth of ICT, however, is posing a huge threat to the sustainability of this development, given that the energy consumption of current computing devices is growing at an uncontrolled pace. Within ICT, machine learning is currently one of the fastest growing fields, given its pervasive use and adoption in smart cities, recommendation systems, finance, social media analysis, communication systems, and transportation. Apart from isolated application-specific attempts, the only general solution to tackle the sustainability of computations in machine learning is Google's Tensor Processing Unit (TPU), which has been opened to general use through a cloud system in mid-February. This is an interesting and effective direction to push a transistor-based technology to address some of the issues above pertaining to the sustainability of computing for machine learning, and it is inspiring other companies and start-ups to follow this trend. ECO-ML's ambition is to radically change this and to propose a novel angle of attack to the sustainability of computations in machine learning. The starting point of ECO-ML is the realization that current approaches for inference and prediction with Gaussian Processes (GPs) and Deep Gaussian Processes (DGPs) are competitive with popular Deep Neural Networks (DNNs), while offering attractive flexibility and quantification of uncertainty. In the last year, we have come across the work that the French company LightOn has done on the development of novel Optical Processing Units (OPUs). OPUs perform a specific matrix operation in hardware exploiting the properties of scattering of light, so that in practice this happens at the speed of light. Not only this is the case, but the consumption of OPUs is much lower than current computing devices, while allowing for the possibility to operate with large Gaussian random matrices, orders of magnitude larger than current computing devices. GP and DGP models are perfect candidates to benefit from the principles behind OPUs, but there is need to make advancements on the design and inference of these models for this to become a reality. We expect to produce and release the first implementation of GPs and DGPs using OPUs, and to demonstrate that this leads to considerable acceleration in model training and prediction times while reducing power consumption with respect to the state-of-the-art. We expect to advance the state-of-the-art in GP and DGP modeling and inference by developing novel model approximations and inference tailored to exploit OPU computing, but that will also trigger advances in the theory of approximation of GPs and DGPs. Finally, we expect to showcase a variety of modeling applications in environmental and life sciences, demonstrating that our approach leads to competitive performance with the state-of-the-art, while achieving sound quantification of uncertainty and fast model training and prediction times in a sustainable way. Similarly to the success that Graphical Processing Units (GPUs) enabled in the deep learning revolution, we envisage that OPUs will be a key element in making GPs the preferred choice for future large-scale modeling and accurate quantification of uncertainty tasks.

Our speech is being collected by various devices with voice-driven interactive services and transmitted over unsecured public networks to be stored and processed on vulnerable cloud-based infrastructure. With always-listening functionality, these devices result in ongoing energy consumption challenges and significant privacy concerns due to the potential for data interception by malicious actors. This scenario is particularly concerning since speech data is inherently personal, containing far more information than most people realise and can be misused for nefarious purposes. In light of the above, ensuring that voice data are private and minimising energy consumption are critical and urgent issues that require immediate action. Ultimately, P-SPIKE will accomplish this vision within the context of speaker verification in realistic conditions, while maintaining individual privacy protection by harnessing the potential of energy-efficient spiking neural networks for the processing of speech signals, a largely unexplored research domain with immense potential.

Human History is composed of a continuous flow of events. Each of them can impact subsequent events and contribute to the evolution of human knowledge. Knowledge Graphs try to encode the information about facts and events, often falling short when representing the temporal evolution of this knowledge and tracking cause-effect flows. kFLOW aims to propose strategies for representing, extracting, predicting and using the information about event relationships and knowledge evolution. For achieving these goals, a Knowledge Graph of interconnected events and facts will be realised. This graph will be populated and exploited through developing specialised strategies for data modelling, information extraction, link prediction, incorrect triple detection and automatic fact-checking.

I-VESSEG aims to close the gap hindering the use of 3D vessel segmentation tools to assist clinicians in angiographic clinical routines. The project will build on learning-based techniques and will address their limitations regarding the need for large, fully annotated training sets and their poor generalization. I-VESSEG will use interactive learning to allow continual training from weak annotations provided by the user, as data becomes available. To facilitate data access for training, I-VESSEG will be formulated in a collaborative federated learning paradigm that enables learning without the need for sensitive data sharing or centralized storage. Finally, by relying on domain adaptation and generalization techniques, I-VESSEG will be applicable in a transparent manner to any cerebrovascular imaging modality. Through a unique collaboration with a network of international excellence partners in neuroimaging, the translational value of this project will be demonstrated on two use cases of primary societal impact: 1) the diagnosis of multiple sclerosis; and 2) the detection of intracranial stenosis, a risk factor for stroke.

This proposal addresses a pressing need in data science applications: besides reliable models for decision making, we need data that has been processed from its original, raw state into a curated form, a process referred to as “data cleaning”. In this process, data engineers collaborate with domain experts to collect specifications, such as business rules on salaries, physical constraints for molecules, or representative training data. Specifications are then encoded in cleaning programs to be executed over the raw data to identify and fix errors. This human-centric process is expensive and, given the overwhelming amount of today’s data, is conducted with a best effort approach, which does not provide any formal guarantee on the ultimate quality of the data. The goal of InfClean is to rethink the data cleaning field from its assumptions with an inclusive formal framework that radically reduces the human effort in cleaning data. This will be achieved in three steps: (1) by laying the theoretical foundations of synthesizing specifications directly with the domain experts; (2) by designing and implementing new automated techniques that use external information to identify and repair data errors; (3) by modeling the interactive cleaning process with a principled optimization framework that guarantees quality requirements. The project will lay a solid foundation for data cleaning, enabling a formal framework for specification synthesis, algorithms for increased automation, and a principled optimizer with quality performance guarantees for the user interaction. It will also broadly enable accelerated information discovery, as well as economic benefits of early, well-informed, trustworthy decisions. To provide the right context for evaluating these new techniques and highlight the impact of the project in different fields, InfClean plans to address its objectives by using real case studies from different domains, including health and biodiversity data.