Public Funding for Ai For Global Goals Ltd

AI is already having a revolutionary influence on our daily lives and business. Siri, Google Assistant and Amazon Alexa are used every day in households around the world, chatbots streamline customer service for a multitude of service-based businesses, and recommendation engines guide e-commerce customers to products they might like. And AI's influence is set to grow. For example, McKinsey estimates that AI technologies could deliver $1trillion of additional value each year, just in banking. And Google anticipates AI will add £400billion to UK GDP by 2030\. But with these benefits comes disruption to business and labour markets. AI is changing the nature of work and redrawing divisions of labour between humans and machines. By 2025, the World Economic Forum predicts AI will displace 85 million jobs, but generate 97 million new roles. Consequently, demand for AI and ML specialists is soaring, predicted to grow by 40%, or 1 million jobs, in the next five years. Yet supply does not match this growing demand---a yawning deep tech skills gap has emerged. This is why retaining talent and training, upskilling and reskilling them in AI and other deep-tech topics has become a key priority for many businesses. AI itself presents the most promising solution to providing the learning and development (L&D) necessary to build an AI-skilled workforce, combining the personalisation and high-quality learning of one-to-one tutoring with the low cost and rapid scalability of one-to-many instruction. However, implementing AI-powered L&D also comes with significant challenges---prime among them being trust. For example, Accenture found that only 35% of global consumers trust how AI is being implemented by organisations. Therefore, trustworthy systems are needed to reap the benefits of AI in L&D, and close the deep-tech skills gap. This project will develop and implement trust paradigms in a demonstrator generative AI product (ie app) offering personalised L&D called Elandi. When it enters the market, Elandi will transform deep-tech L&D, providing tech talents with rapid, personalised education in deep-tech topics such as ML and AI. Its affordability also democratises deep-tech L&D for SMEs, offering the means to train, upskill and reskill workers at all levels of a business to implement successful AI transformations. As such, Elandi will be a tool for empowering as many tech talents as possible to achieve their full professional potential, building a more educated workforce in this increasingly economically important area, and catalysing the UK's transition to an AI-enabled economy.

Today, large generative models can take simple instructions from their users and generate artefacts such as code, text, images and videos that until recently, could only be produced by humans. The success of products that rely on such models, however, requires their users/stakeholders to trust them. This is particularly important in domains such as education, finance, and medicine where the stakes are high. We are building Elandi, a product that uses the power of the latest developments in AI (particularly, generative AI) to bring affordable personalised L&D to everyone: 1. Elandi will compare a user's profile/resume with their career goals (e.g., a job description) and identify the gaps. 2. Using this gap (plus various user preferences, and more), it generates a prompt for the generative AI. 3. The generative AI will then provide the user with the right L&D content. The UX areas we are currently exploring are course planning, bite-sized tutoring, assessment, and content generation. Thanks to our successful courses since 2020, we now have a unique user community (1000s of AI talents from 100+ countries) that can the source of data and advice. Furthermore, we worked with top AI professors/lecturers, and some of the world's largest corporations in real-world AI applications. All these helped us make tremendous progress towards Elandi's product-market fit (PMF); from discovery, to conceptual design and ML prototypes. Overall, the most important learning from our discovery is that fit-for-purpose content, improved UX, personalised recommendation, and expert mentoring are in high demand. While this progress continues, we realised that there is an urgent need for a comprehensive framework for AI assurance (e.g., auditing the AI, measuring users' trust in it, and quantifying its various risks) in the L&D space. Therefore, as part of our broader ambition, through this grant we would like to kickstart an explicit work stream on this topic. Our goal is to build on the latest research (including metrics and measurements) on trustworthy AI, to provide the tools and frameworks necessary for auditing AI models and products in the L&D space. While we will be focused on the L&D use cases, we expect our results to have implications beyond L&D and in other domains.. An improved L&D offering will be of profound societal and commercial value; it will play a critical role in improving national and international productivity. It is aligned with strategic government initiatives such as "Levelling up", "Lifelong Learning", and "Skills for Life".

Today, education is offered in a one-to-many fashion: One course for many students, which results in courses that are only partially fit for students' needs. On the other hand, producing courses specific to each student's needs (i.e., a one-to-one alternative) is slow and expensive. This is not unique to course planning; other components of learning and development (L&D) systems (e.g., tutoring, content generation, assessment, and coding) can also benefit from such personalisation. We believe that AI -- including machine learning (ML) -- can help solve this. Particularly, given the recent advances in generative AI: ML models that can take simple instructions and generate artefacts such as code, text, images and videos that until recently, could only be produced by humans. In order for such ML models/products to succeed, however, they need to be trusted by their users/stakeholders. This is particularly important for generative AI models that are usually very large and hence difficult to explain to stakeholders. Therefore, this project aims to form a consortium and use its collective expertise to propose a solution for: 1. Building generative AI models for education/L&D in "ML Fundamentals", 2. Developing a framework to evaluate and gain stakeholders' trust in our model(s) 3. Validating 1 & 2 (e.g., by showing successful adoption of our L&D product). During phase-1, our focus will be on building the appropriate consortium (consisting of expertise in trustworthy AI, ML research/engineering, L&D business, and ML product/UX), and producing a technical report that outlines our proposal for the phase-2 project. According to a recent survey by PWC, 74% of CEOs were concerned about the availability of key skills they need, in the market. While a typical big company spends more than $1m/year on L&D, a typical employee engages less than once a month with them. Our surveys of the talent market shows that fit-for-purpose content, improved UX, personalised recommendation, and expert mentoring can address the current shortcomings. These are similar to the changes that AI offered in other domains. Therefore, we are on a mission to bring such a change to the L&D space. An improved L&D offering (e.g., more personalised and affordable) will be of profound societal and commercial value; it will play a critical role in improving national and international productivity. It is aligned with strategic government initiatives such as "Levelling up" and "Skills for Life".