OctaiPipe, the UK’s leading Federated Learning Platform for IoT, has won Trustworthy AI funding from UKRI as part of its BridgeAI programme.
Artificial Intelligence and Machine Learning can deliver high ROI across many applications in industry, but its widespread adoption is hampered by a lack of suitable machine learning infrastructure and the need to centralise data which in turn leads to high costs, privacy risks, overdependency on cloud platforms and network availability.
To solve these barriers to AI for Industrial IoT, the team at OctaiPipe are harnessing a simple but revolutionary idea. Rather than move data from Edge devices to central cloud platforms to train algorithms, move the algorithms to the data and learn at the Edge. Intelligence is achieved by combining learning on many devices via a novel technology called Federated Learning.
OctaiPipe is “currently working to accelerate the adoption of trusted and responsible Artificial Intelligence (AI) and Machine Learning (ML) technologies by reducing bottlenecks during development and deployment of such technologies and making important steps to the realisation of the potential of AI.”
“We’re investing in our AI talent pipeline with a £54m package to develop trustworthy and secure artificial intelligence, and putting our best foot forward as a global leader in tech both now, and in the years to come.” – Chloe Smith, Technology Secretary.
During Phase 1, OctaiPipe is building a consortium of leading industry partners to deliver on the mission set by Innovate UK.
OctaiPipe is focused on enabling trustworthy edge AI using enhanced privacy and security techniques. Their goal is to reduce the data that needs to leave your devices or appliances whilst still delivering key insight and information when it’s needed most.
In addition, this approach has the potential to reduce your carbon footprint and emissions by reducing the data transmitted over the network and stored in the cloud.
OctaiPipe does this by combining federated learning, automated machine learning, and fault-tolerant Edge Machine Learning Operations (MLOps) which automates the entire machine learning lifecycle on device, connected machine or asset.
To deliver value, AI at scale must be Trustworthy, Efficient, Resilient and Sustainable. OctaiPipe believes with the growing rate of devices globally that investing in Trustworthy AI is key for any organisation to remain competitive among business and consumer adoption, similar to the growing popularity of ESG investment funds. And, regulations like the AI Act set deadlines and criteria for businesses to meet to deliver digital/connected hardware and software systems.
Federated learning (FL), or collaborative learning, is a machine learning method that allows a group of decentralised edge devices or systems to train machine learning models without needing to move or store the raw data in a central server.
OctaiPipe is currently working with industry partners to build a leading UK consortium, to deliver trustworthy AI for IoT systems.
George Hancock, co-founder of OctaiPipe: “With the disruption, novelty and eventual understanding of LLMs over the past 6 months, it’s crucial for us to take action now to build trust and secure data from the Internet of Things and deliver efficient and scalable intelligence that can benefit us all through connected systems”
Chong Shen Ng, Lead Data Scientist of OctaiPipe: “Through this funding, we’re driving forward with the mission to make OctaiPipe synonymous with trust in AI, a crucial element in the increasingly fragmented yet connected world of IoT.”
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