From machine learning to computer vision, reinforcement learning to virtual assistants, and autonomous vehicles to robotics, Shell has been focused on a range of technologies that have supported advances in AI. Foundational methodologies such as numerical optimization, statistics, and fusion of observed data with underlying physics are given equal importance. Businesses that harness new data sources and use AI and machine-learning technology to provide insights will be in a strong position to shape future commercial development and influence how society changes. A steep rise in the number of connected devices and an explosion in data volumes are already delivering unparalleled levels of business disruption. The recent explosion in data volumes and availability has led to a step change in the training of algorithms and provided important new insights: easy access to vast data volumes is making AI algorithms smarter.
The AI group is spread across four international locations and collaborates with universities and national research laboratories in India, UK, The Netherlands, and the USA.
Computational Science uses physics-based models to predict the behaviour of materials and systems using high performance computing. Computational science augments traditional research methods by accelerating and guiding experimental work and providing insight into processes and results. It is used across Shell’s businesses to predict everything from the chemistry of catalysts and batteries to capturing flow through reactors, pipelines and rocks. These are complex simulations which require high performance computing and algorithm optimisation.
The Computational Science Centre of Excellence is based in Bangalore, India, but the team includes staff in Amsterdam, the Netherlands, and Houston, USA. Computational science is a broad discipline. Within Shell, it draws on expertise from more than 50 technologists in disciplines such as chemistry, physics, mathematics, material science and mechanical, chemical, aerospace and metallurgical engineering. This team has strong external links and collaborates with high-profile global companies, technology start-ups and leading academic institutions.
AI Engineering is a hybrid skillset covering data engineering and data science related concepts in wider spectrum of AI paradigms such as predictive analytics, natural language processing, machine learning, machine vision, deep learning, hyper-personalisation, computer-aided decision-making, human/computer interaction, autonomous systems, goal-driven systems, patterns & anomaly detection.