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AI R&D is for sale!

AI R&D is an initiative formed by WWT individuals interested in the future of AI and ML. It functions as a rotational program with an Operations team and an R&D Working team composed of data scientists, data engineers and application engineers.

What is R&D in machine learning?
R&D Machine Learning Research and Development in Machine Learning is changing the world of today and tomorrow. Internet of Things We develop systems that connect, monitor and control intelligent devices with the use of Artificial Intelligence.

Understanding AI R&D commercialisation

The UK Government’s Department for Digital, Culture, Media & Sport (DCMS) and Office for Artificial Intelligence (OAI) commissioned Oxford Insights and partner Cambridge Econometrics to develop deeper understanding of the ways in which AI research & development is transformed into marketable AI products and services.

The resulting research explored the main enablers, barriers and challenges for AI commercialisation, looking closely at the work of university spinouts, startups, Big Tech firms, and also academic ‘founder-researchers’.

The project involved conducting over 40 interviews with varied respondents, including private venture capital lenders, public funding bodies like EPSRC, UKRI, and Innovate UK, Academics and Universities’ Technology Transfer Offices, Standards Developing Organisations like BSI, The Alan Turing Institute, the NHS Transformation Directorate, and several large tech companies such as Microsoft, Nvidia, BT Group, Siemens Digital, and DeepMind.

The research resulted in:

  • A taxonomy of commercialisation routes for AI R&D;
  • New metrics for measuring AI commercialisation activity in the UK;
  • A ‘deep dive’ study of four ‘priority routes’ of commercialisation;
  • International comparisons of AI commercialisation activity with the United States, Canada, China, France, Germany, Israel, Japan, and South Korea; and
  • A case study of applied AI in the healthcare and life sciences sector in the UK.

A key goal of the research was to help teams from across government, public funding bodies, universities’ technology transfer offices, industry and other associated organisations understand what steps can be taken to support and increase the commercialisation of AI R&D in the UK.

The final report was published in May 2022, and is available for download here.

Should you wish to get in touch to discuss this work, please contact:

While artificial intelligence (AI) algorithms can be embedded in digital vonNeumann ICs, excessive size and energy consumption represents a significant barrier to the widespread adoption of such neuromorphic AI chips both at the “edge” and in the “cloud”.

Accelerating R&D cycles-of-learning to meet commercial time-to-market goals remains challenging for all semiconductor manufacturers, but is especially difficult when exploring analog AI. Being efficient with resources is critical when depositing a wide range of multi-element thin films, characterizing device structures by testing physical and electrical parameters, and then extracting dependencies within multi-dimensional property spaces. For example, if a certain range of compositions provides low-leakage but tends toward poor thermal stability then that dependency must be quantified.

To characterize analog AI we need special synapse-like test-vehicles, so that certain current pulse “spikes” can be sent across the artificial synapses in the array. Properly timed spike sequences quantify the viability of the circuit to learn, as well as other target circuit properties.

The Networking and Information Technology Research and Development (NITRD) National Coordination Office (NCO), National Science Foundation, has requested input, including from those directly performing Artificial Intelligence (AI) research and development (R&D) and directly affected by such R&D, on whether the National Artificial Intelligence Research and Development Strategic Plan, October 2016, should be revised and, if so, the ways in which it may be improved.  This document is the response from IBM Research AI to this Request for Information (RFI).  The sections below indicate important challenges in AI R&D where the strategic aims in the National Artificial Intelligence Research and Development Strategic Plan should be added or modified.  The sections below are aligned with the seven Strategy areas in the current R&D plan.

China aims to become “the world’s primary AI innovation center” by 2030. Toward that end, the Chinese government is spending heavily on AI research and development (R&D)—but perhaps not as heavily as some have thought. This memo provides a provisional, open-source estimate of China’s spending.

JERUSALEM, March 21 (Reuters) - OurCrowd, one of Israel's largest venture firms, plans to open an artificial intelligence-based research and development centre in Abu Dhabi by early June that will help with investment decision making, a company official said.

The Global AI Innovation Centre will employ about 50 engineers and OurCrowd will be the first client, although the service will be open to anyone, said Sabah al-Binali, a partner at OurCrowd, in an interview with Reuters on his first ever visit to Israel.

He said OurCrowd was investing a "massive" amount in the project, which aims to analyse huge amounts of data, without giving figures.

The Department for Digital, Culture, Media and Sport’s (DCMS) upcoming
Digital Strategy and the National Artificial Intelligence (AI) Strategy (published
in September 2021)1both acknowledge the transformational potential of AI
technology to increase productivity and create long-term economic growth.
Despite this promise, there are a number of barriers that potentially hamper
the commercialisation of AI Research & Development (R&D). Understanding
the ways in which AI R&D commercialisation can be supported is the core
purpose of this research.

The birthplace of Samsung, LG and Hyundai, South Korea is a hotbed for tech and consumer electronics innovation. The country however trails neighbors China and Japan in the artificial intelligence competition, and lags far behind global leader the United States.

Eager to kickstart its AI industry, the nation of 52 million yesterday released an ambitious national plan to invest ₩2.2 trillion (US$2 billion) by 2022 to strengthen its AI R&D capability. The program includes the establishment of six new AI research institutes, JoongAng Ilbo reports.

“The government believes obtaining AI core technologies by joining hands with private corporations will not only achieve global standards but eventually nurture talented people and quality jobs. We aim to reach the global top four by 2022,” says Chang Byung-gyu, head of the Presidential Fourth Industrial Revolution Committee launched last October.

  • Respecting international trends, KDDI Group will promote the R&D and utilization of AI, thereby enhancing customer experience value.
  • These principles are in line with the "Basic Guidelines for the Use of Data" that aims to ensure customers can confidently use KDDI Group's services.
  • KDDI Group aims to create a resilient society through the "KDDI Accelerate 5.0", a concept that balances the establishment of new lifestyles for consumers and the resolution of economic development and social issues in Japan. "KDDI Accelerate 5.0" describes that information collected on cyberspace platform and analyzed by AI will influence the physical space to build a more prosperous society. KDDI Group will contribute to the coming AI society by demonstrating that it will make AI safe to use and that it will be used in a way that customers and society can feel safe.

AI                             | Radu Orghidan | 17 May 2022

This blog article has been co-authored by Thomas Bedenk and Kai Wegner. We would also like to thank Oscar Michel for kindly helping us understand some of the more subtle aspects of the method and navigate through the technical intricacies of the code.

With our experience in building games and game services for the past 15 years, we know game production pipelines are about efficiency as much as creativity. Therefore, we think unlocking the capabilities of machine learning (ML) will elevate games production teams to new heights. Professional tools like Unity’s ArtEngine already make use of ML in interesting ways to improve texture work. We wanted to take it a step further and also look at mesh creation and alteration and see how this can either be used as creative input for artists or directly facilitate the creation of 3D assets.

At Endava, we help game companies create better games with our service offerings. We cover the full game development lifecycle, including concept, art, development, automated testing and DevOps. However, we also help our clients innovate and improve their own tools and pipeline. To showcase our advanced research and development (R&D) capabilities, we initiated a project that implements machine learning in the context of creating assets for game productions.


To investigate this, we formed a team of data scientists, ML ops, 3D artists and UI developers for about two months and started researching scientific papers that use neural networks to simultaneously generate texture and augment meshes.

After selecting StyleGAN, StyleCLIP and Text2Mesh as the most promising candidates, we built a working implementation of an AI-aided asset creation journey. Based on voice-guided user input, the solution transforms existing mesh data into new creations. We set out to evaluate where this approach is promising and where it hits limitations in its current form. We also improved the usability and applicability of this solution and evaluated how 3D artists would be able to work with this solution.