Wallscope

Over the past years deep learning has brought a revolution in the area of artificial intelligence (AI), producing remarkable results in a variety of application domains including computer vision, natural language processing, speech recgonition, robotics, and clinical decision making. Despite the success of deep learning over a wide spectrum of real-world tasks, there is no doubt that many of the problems that are really at the core of AI are far from being solved. Reasoning, namely taking pieces of information, combining them together, and using these to draw logical conclusions or devise new information, is not a general-purpose capability for modern AI. Imagine an airplane passenger sitting in an exit row, studying the emergency guide, which is often a combination of images and text. Their brain combines visual and textual information in order to infer the intended message -- open the door in the unlikely event of an emergency. A computer system seeing the same document would first employ an image recognition model to scan the image. An Optical Character Recognition (OCR) system would read the text, and a third system would correlate the image and text to understand the complete picture. Although the fundamental principles of analyzing the world around us and the approach a machine takes to process complex information are both based on breaking down the data to its core elements, humans are instinctively better at correlating and integrating information from different modalities, and re-using previously acquired experience and expertise to transfer it to radically different challenges and domains. Today's neural networks fail disastrously when exposed to data outside the distribution they were trained on, overly adhere to superficial and potentially misleading statistical associations instead of learning true causal relations, are unable to reason on an abstract level, which makes it difficult to implement high-level cognitive functions, and are essentially black boxes with with respect to human understanding of their predictions. This fellowship aims to alleviate these deficiencies by developing a new class of neural network models which will demonstrate reasoning capabilities, a skill required to enhance many AI applications. Rather than relying on a monolithic network structure, we propose to assemble a network from a collection of more specialized modules, making use of an explicit, modular reasoning process, which allows for differentiable training (with backpropagation) but without expert supervision of reasoning steps. We will develop a theoretical framework which characterizes what it means for neural network models to reason, design various reasoning modules, and showcase their practical importance in applications which understand requests and act on them, process and aggregate large amounts of data (e.g., from multiple modalities), make generalizations (e.g., robots cannot be pretrained on all possible scenarios they might encounter), deal with changing situations and causality, manifest creativity (e.g., in writing a story or a poem), co-ordinate various agents (e.g., in game playing), and are able explain their predictions and decisions. The proposed Fellowship will have a transformative effect on AI theory and practice. It sets an ambitious agenda which unifies multiple strands of AI research, bridging the gap between the neural and symbolic views of AI and integrating their complementary strengths. It will provide the means for developing a UK skill base in AI, and wil have wide ranging impact in academia, industry, the UK economy, and society e.g., , by embedding AI in many domains of daily life and rendering tools such as neural networks more explainable.

Data-driven innovation is transforming every sector of our digital economy (DE) into a de-centralised marketplace; accommodation (AirBnb), transportation (Uber), logistics (Deliveroo), user-generated vs. broadcast content in the creative industries (YouTube). We are witnessing an inexorable shift from classical models centred upon monolithic institutions, to a dynamic and decentralised economy in which anyone is a potential producer and consumer. A gig economy, underpinned by digital products and services co-created through shorter-lived, diverse peer-to-peer engagements. Yet, the platforms that enable this DE are increasingly built on centralised architectures. These are not controlled by society, but by large organisations making commercial decisions far from the social contexts they affect. There is an urgent need to disrupt this relationship, to deliver proper governance that empowers society to take control of the DE and enables people to assert greater agency over the vast centralised silos of data that drive these platforms. We stand on the cusp of a second wave of DE disruption, driven by bleeding edge data-driven technologies (AI) and secure, distributed data sharing infrastructures such as Distributed Ledger Technologies (DLT), in which data is no longer siloed but becomes a fluid, de-centralised commodity shifting power away from tech giants to individuals and de-centralised organisations. This future Decentralised Digital Economy (DDE) enables people and organisations to work together, to trade, and ultimately to trust via frictionless digital interactions free from reliance upon centralised third parties, but often with reliance upon autonomous services. This shift in agency and power is a game changing opportunity for society to take back control over its digital economy - but we have a limited window of opportunity to get it right. We have already witnessed de-centralisation in the financial sector, where the lack of regulation and clear governance of crypto-currencies has proven a double-edged sword, allowing free exchange of value across the globe, but that is coupled with fraudulent company flotations and currency rates rigged by large mining pools. This is a consequence of technology-driven innovation unchecked by socio-economic insight; a lack of knowledge making policy makers impotent in the face of the tech giants. We are now at the tipping point of similar wide-sweeping disruption across all sectors in the DDE, a transformation that will radically redefine our models of value and how it is created, the ways in which we work, and how we use and extract value from our data. DECaDE represents a critical and timely opportunity to shape this emerging de-centralised digital economy (DDE), to develop insights that define a new 21st century model of work and value creation in the DDE, and ensure a prosperous, safe and inclusive society for all. DECaDE is a 60 month centre, comprising 21 people and building upon over 8.6 million pounds of feasibility scale UKRI/EPSRC investments in DLT and Human Data Interaction (HDI) held by the proposing team. DECaDE is a three-way partnership between the Universities of Surrey and Edinburgh, and the Digital Catapult DLT Field Labs. The latter is a full member of the consortium, through which we have co-created this research programme and with whom we will engage in further co-creation of the future DDE through diverse end-users in the public and private sector to support the competitive position of the UK