Commonly, AI is used to understand large amounts of data more quickly than humans could. Our approach is about ‘innovating more with less’.
We couple deep artificial intelligence expertise and broad domain expertise with a spirit of unconstrained thinking.
Bring your ambition, and we’ll help you unlock more creativity, better wellbeing and more efficiency. By innovating with less data, less waste and less fear of untrustworthy use.
The way we do business has to change. Fixing the great problems of energy management, food inequity, water and global health requires nothing less than a paradigm shift. Our focus at CC is on innovation with business and societal impact that reflects the three pillars of sustainability (social, environmental and economic). This impact is about a long game that requires us to employ AI tools to unleash our collective knowledge and creativity.
CC believes great solutions demand great technology innovation founded on collaboration and consideration of unintended consequences. Having created many ‘world first’ solutions, we know what it takes – and have what it takes to create and deploy new-to-the-world services and products that change markets. We’ll innovate with you for our own collective future. One that we can flourish in, not the one that is currently at risk.
CC is actively researching and developing breakthrough techniques in human-machine understanding and interaction. Our vision is to shape a new age of empathetic AI technology where machines ‘get us’ at a psychological level. They’ll infer our internal emotions, attention, personality and health to help us make useful decisions. It’s taking us rapidly towards the realm of hybrid human-machine workforces.
Our work in human-machine understanding is founded on new designs based on human centricity and explores the intersection of AI and psychology. By detecting, perceiving and interpreting changes in the human state, AI can support its human teammates by reducing overload, distraction and fatigue. This allows machines and humans to seamlessly adapt collectively to optimize team performance and minimize risk under changing operational conditions.
The positive business and societal implications are extraordinary.
Innovation that has great positive impact is also at risk of exploitation – and AI is no exception. The challenge now is to build trusted partnerships with AI-enabled machines and protect them from misuse. It is time to confront concerns over ethics, safety and regulation, worries about AI’s impact on jobs, as well as increased inequality and extreme fears of AI getting out of control.
CC is working to facilitate responsible, trustworthy AI through our AI assurance framework. It is designed to help our clients that their AI systems operate as intended and do not suffer from unintended consequences. Based on the foundations of explainable AI, transparency and security, the framework is designed to build trust in systems, dynamically test them, and identify gaps in how they work for remediation.
Read: AI assurance – protecting next-gen business innovation.
This is where AI applications are deployed on smart, connected devices – or endpoints – at the network edge. Generation-after-next AI enabled ecosystems will be increasingly dynamic and responsive. Pervasive intelligence will be distributed closer to data sources and AIoT (the artificial intelligence of things) to infer in real time, protect privacy, improve fraud detection and security while reducing latency and cost.
The big challenge is to enable responsiveness to short-term signal variations while simultaneously evolving functionality in response to long-term contextual variations. Current edge AI systems infer trends locally, but the AI models are trained at large, high-compute, high-power data centers before being deployed. The result is a response that is, by design, not current and probably increasingly inaccurate as trends evolve.
At CC, we are actively exploring new technologies that apply deep learning algorithms at the edge. This transformative method will fundamentally redefine our approaches to communications, manufacturing, emergency services and healthcare – enabling adaptive responses based on learning local trends and performing relevant interventions. This contrasts to the current ‘one-size-fits all’ reality that is trained on an increasingly inaccurate past.
Read: The next frontier is to bring AI – and learning – to the edge.
CC researchers and technologists are working to apply the benefits of deep learning, a subset of machine learning that emulates human performance on specific tasks at scale. Deep learning works by harnessing the power of multi-layered neural networks to learn insights from large quantities of data, classify them, predict outcomes, and even create new content (generative AI). An artificial neural network is a model inspired by how the brain works.
The basic unit of the network is a node that receives multiple inputs and generates an output. To configure the parameters that describe the operation of the network, it must learn the salient characteristics (label data) of data that represents the use case of interest. This learning is achieved through training, which is broadly categorized into supervised learning, unsupervised machine learning and reinforcement learning.
Training is typically data hungry and compute intensive, and data labeling is often manual and time consuming, so hybrid approaches like semi supervised learning aim to reduce the training effort by combining the strengths of different learning methods.
Blazing the AI trail
We live in a world of rapid change. But haste is not the same as speed and we cannot afford mistakes. Thoughtful AI innovation can create relevant, lasting market-leading impact that completely outstrips brittle short-term wins. This requires deep foundational expertise at the intersection of AI and domain relevant technologies. The place where CC sits.
We are ready to support you on your AI innovation journey. Our global team of 800+ people is committed to the task of applying deep tech innovation that adds value at both business and societal level. Commercial teams, technologists and policy makers need to continue to work together to ensure efficient and responsible AI delivery and to both earn and build public trust. CC continues to work with policy makers and business leaders to do just that.
Dr Maya Dillon
Prof. David Berman
Dr Carolina Sanchez Hernandez
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