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Accelerating AI Adoption With AI Bricks

What are AI Bricks?

AI Bricks are a collection of open source AI products and tools built based on industry needs and feedback. They provide a set of reusable components or tools which can be configured to solve common business problems. This lowers the barrier to AI adoption and allows organisations to integrate AI into their workflows more rapidly. Various engineering teams in AI Singapore are hard at work designing and implementing these AI Bricks across different fields:

  • Robotic Process Automation (RPA)
  • Federated Learning
  • Computer Vision (CV)
  • Natural Language Processing (NLP)




Robotic Process Automation (RPA)

Robotic Process Automation (RPA) is a form of business automation where well defined sets of human-computer interactions are programmed to be executed by software known as robots. TagUI, first released in 2017, is a free RPA tool supported by AI Singapore which is easy to use and works on Windows, Mac and Linux.

Users of TagUI include working professionals in organisations such as EY, Accenture, ECCO Shoes, Mercedes-Benz, Bank of Brazil, Ministry of Education, as well as students in various higher education institutions working on their Masters or PhD projects.

TagUI comes in different flavours to suit users of different backgrounds and tastes. There is a human language version that works with over 20 human languages, a Python version, a C# version, an upcoming Java version, and a Microsoft Word version to create RPA robots easily in your favourite editor, language and OS. Due to its flexible yet stable architecture, most of the different flavours of TagUI are created by the community for their own communities.

Get started with RPA using TagUI by visiting the TagUI homepage or joining the Telegram chat.




Federated Learning

Machine learning often requires substantial amounts of data. This data is often distributed across different parties. In theory, if the data could be shared, this would lead to better machine learning models. In practice, however, data privacy concerns often means that simple sharing is not a viable option. For example, this is the case in the finance and healthcare industries.

Federated Learning is a technique that enables a machine learning model to be trained using data held by different parties without the need for the data to leave where it is held. Instead, models are trained locally and only the changes in models are shared. The privacy preserving manner of model training involves the coordination by a trusted third party in order to execute.

Synergos is a platform that houses the trusted third party and the necessary infrastructure around it. The goal is to make Federated Learning user-friendly and easily adaptable across industries. The team is working towards the launch of Synergos in the second half of the year.

In the meantime, learn more about the work done here.




Computer Vision (CV)

AI models in Computer Vision (CV) have come a long way, with object detection, segmentation and pose estimation from images now a reality. For example, the team worked with HP at the start of the COVID-19 pandemic to rapidly deploy a solution to monitor social distancing on the factory floor by estimating human skeletal points together with distance projection heuristics. Work is also in progress with other clients on use cases such as footfall analytics, physical exercise feedback, just to name a few.

Also currently in development is a modular CV inference framework, that is flexible and can be configured to suit different CV applications. It will contain carefully curated models, use cases, and comes with configurable input/outputs. With this, users will be able to create a POC very quickly, by selecting the appropriate modules for their application. All this coming soon in the second half of the year.




Natural Language Processing (NLP)

Combining rich fundamentals and research with modern computing power and model architectures, the field of Natural Language Processing (NLP) has seen great developments in the last decade in achieving new heights in language understanding and end applications.

The team is involved in several projects to build products not yet available, with the goal of making them accessible to anyone or any company wishing to utilise the power of language processing. In addition, local research and regional Southeast Asian languages are put at the forefront of tool development. Some of the projects expected to come to fruition in the second half of the year include:

  • SEACoreNLP –  aims to promote the development of NLP in Southeast Asia and be the central hub for it. The current focus is on “core” NLP – tasks such as part-of-speech tagging, syntactic parsing or semantic role labeling etc. for Southeast Asian languages.
  • SG-NLP – aims to bridge the gap between the industry and Singapore-based research groups to accelerate the growth of applied and translational research in NLP. 
  • SenseMaker – tackles the problem of text annotation using weak supervision and active learning approaches.
  • Hasky – Information Retrieval and Question Answering system builder that uses self supervised training techniques to significantly reduce the need for text annotation.
  • Beagle – developed for Information Retrieval with an emphasis on gradual data accumulation while monitoring model development and controlling “data drift”. More information here.




If you are interested in any of the AI Bricks, you can contact us at https://www.aisingapore.org/home/contact/ for more information.

Author

  • Basil is the technical community manager and editor at AI Singapore, committed to bringing Singapore's AI ecosystem to new levels by working through communities, teams and individuals. Dream big or go home!

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