Artificial intelligence: supporting development with R&D tax credits
We are living in a world where artificial intelligence (AI) and machine learning (ML) technologies are increasingly advancing many aspects of design and development processes, as well as everyday life.
From mimicking human capabilities and handling large datasets to automating extremely niche tasks, we are increasingly embracing the vast benefits of this technology.
Investment and funding play a crucial role in allowing developers to further develop and advance these technologies. However, as these complex methods continue to rapidly accelerate, it is becoming increasingly difficult to define which activities match the criteria for support from fiscal initiatives such as research and development (R&D) tax credits. In this article, we explore the world of AI and how R&D tax credits can be applied to offer much-needed support to fuel British business development.
Understanding and defining artificial intelligence and machine learning
Artificial intelligence (AI) is the area of development for computers that are able to operate in ways that both mimic and go beyond human capabilities. AI programs can effectively analyse and contextualise large datasets to provide output or automatically act without human interference. For example, recognising objects or people in images, picking the best holiday based on your browsing activity, or analysing financial data to determine a potentially fraudulent transaction.
Machine learning (ML) is a type of AI that allows software applications to be more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values. Software utilising ML can use large datasets to perform tasks and provide incredible results.
While most forms of automated technology are definable, newly developing systems raise questions as to how complex systems can be classified. An interesting example includes open-sourced text-to-image AI known as Stable Diffusion (SD) by Stability AI. It is primarily used to generate detailed images conditioned on text descriptions.
Generating images with diffusion models relies on the fact that we have powerful computer vision models. Given a large enough dataset, these models can learn complex operations. The latent diffusion model (LDM) is trained with the objective of removing successive applications of Gaussian noise. The model then gradually reverses the process until there is no noise, thus generating a new image based on the trained data. This means that if AI models are, for example, trained on artistic styles such as a Rembrandt, they can be imitated by SD within a few seconds. This deep learning approach can be applied to an unlimited number of tasks so that a computer can reach an answer based on historic data, these methods have already been applied in different formats to many sectors.
Aside from the obvious medical applications, we are already seeing AI algorithms in our daily lives, used for solving tasks such as determining the eligibility of candidates for job vacancies, predicting behaviour, or displaying the best options on a website to a particular user based on individual browsing history.
We are also interacting with AI every time we talk to Siri or Alexa and these products are constantly learning from us too. It’s an exciting time and the many uses for AI will solve some long-overdue problems for humanity, especially in the medical and scientific fields.
Supporting AI with tax initiatives to boost further development
In 2020, the Government reported that 432,000 companies in the UK that had adopted or worked within AI spent a total of £46bn on labour associated with the development, operation, or maintenance of those technologies.
Additionally, it’s evident that businesses are more likely to adopt AI solutions as they continue to grow. It is predicted that 68% of large companies, 34% of medium-sized companies and 15% of small companies have already adopted at least one method of AI technology into their processes.
The UK Government specifically supports this area of development too, predicting expenditure on AI technologies will increase from £16.7bn (2020) to £83.5bn in 2040 at a compound annual growth rate of 8.4%.
Further reports by the UK Government claim that approximately 40% of businesses (172,000 firms) that have already adopted AI primarily develop it in-house and 40% (171,000 firms) purchase ‘off-the-shelf’ solutions. The remaining 20% (88,500 firms) outsourced the development of AI applications to external providers.
Examples of AI activity which could qualify for the R&D tax credits scheme:
- Analysis of large datasets such as telescope imagery or medical data.
- Financial monitoring solutions, to prevent fraud or offer new products.
- Recruitment AI, determining eligibility for a role.
- Advancement of cloud computing services and software.
- Cybersecurity, analysis, and determination of threats.
- AI voice recognition, chatbots and article writing suites.
- Advertisements utilising AI to target potential customers.
Contact a R&D specialist for advice on claiming tax credits for AI and software activities
In summary, as the world of AI and fiscal incentives can be complex, we recommend contacting a R&D specialist to understand if your AI or software developments would potentially qualify for the R&D tax credits scheme. A successful R&D claim can be critical for allowing developers and businesses to quickly evolve, grow and invest in further cycles of innovative activities.
Meet our team
We are happy to provide a free consultation to discuss past activities and future plans to see if you could qualify for this important tax incentive. If you are already claiming fiscal incentives, including R&D tax credits, we will be able to assess your claims to see how you could benefit from our experience and expertise in this area.