Public Funding for Braintree Limited

Creative content is the undisputed driving force and focal point of the majority of online consumer activity today. However, a lot of the current and past commercial value is lost by not allowing for image and video content to be uniquely and robustly linked to copyright and ownership details. Further, in the professional media industry, supply chains encounter complex rights issues which cause 80% of all content not to be 'rights ready', or commercially viable. While at a first glance this seems like a problem that can be addressed with off-the-shelf components, deliberate obfuscations or accidental variations in content and rights (collectively called "uncertainty"), do not allow for conventional online search tools to work well for this problem. This leads to the current situation where rights discovery for online content is a manual, error prone and cumbersome process, with substantial effort required to remain within the law, and virtually no effort (and minimum risk) for those that wish to violate copyright law. The LUCID Rights project brings together an interdisciplinary team of internationally-leading experts in machine learning, knowledge discovery, high-performance image & video engineering, copyright law, and business & content licensing models in order to address this important challenge. The key objective is to create a unique solution for rights discovery and indelible signature creation for the content properties and copyright information that will be robust to noise or uncertainty in the rights description. The confluence of standards for machine-readable contracts and compact signature generation allows for this to also be trialed within standard-compliant mechanisms, thereby enabling openness, and commercial traction within short timeframes. This will allow for the first time to apply advanced machine learning to disrupt the domain of digital content rights, thereby unlocking the potential for new markets and services within the UK and internationally.

Companies are generating and managing an exponentially increasing amount of data and desperately need tools to analyse and act upon the data. AI tools can be used to improve efficiency and productivity, however, many companies cannot leverage AI because: 1) Developing AI requires expensive specialist expertise. 2) AI algorithms are currently developed to solve specific problems and must redeveloped for each new application, which is inefficient and expensive. 3) Data is increasingly stored in graph data structures, which existing AI algorithms cannot natively process. Graph data must currently be translated into text before processing, which is extremely slow, inefficient and is infeasible with massive datasets. In the GAMMA project Braintree Limited and UCL will prototype a unique modular graph-based AI platform which: 1) Integrates AI into graph database structures, enabling deep complex AI analysis of massive datasets in real-time. 2) Allows anyone to implement a complex AI solution with minimal expertise. 3) Incorporates an AI module marketplace, enabling AI developers to sell compatible modules and academics to exploit their AI research commercially. Graph-native AI is a step change in capability and offers Braintree a major competitive advantage