Building systems that anticipate, plan and interact is complicated and challenging. Collecting and correctly interpreting data is the start, but true intelligence comes from making the best, informed decision and taking optimal actions.

To improve the decision making of AI systems, we use simulated environment training. Broadly categorised into supervised machine learning, unsupervised machine learning and reinforcement learning, simulation helps machine learning algorithms more quickly learn the salient characteristics of data (label data) that represents the use case of interest.

Training is typically data hungry and compute intensive. Data labelling 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.

We can help you enhance machine performance through the power of intelligent decision making in areas such as national infrastructure, autonomous platforms, or by deploying reinforcement learning agents.

Making AI run smaller, faster, cheaper and independently, edge AI unlocks transformative products and services by deploying artificial intelligence beyond traditional data centres. It enables low-power, independent systems autonomy in decision making without having to connect to a central data centre.

Current edge AI systems infer trends locally, but are trained at large, high-compute, high-power data centres before being deployed. The result is a response that is, by design, not current and probably increasingly inaccurate as trends evolve. The challenge is to enable responsiveness to short-term signal variations while simultaneously evolving functionality in response to long-term contextual variations.

Generation-after-next AI-enabled ecosystems will be increasingly dynamic and responsive to data sources. This will fundamentally redefine our approaches to communications, manufacturing, emergency services and healthcare – enabling adaptive responses based on learning local trends and performing relevant interventions.

We have teams of data analysts exploring new technologies that apply deep learning algorithms at the edge. We work with clients to overcome size, cost, power and connectivity constraints with pioneering edge AI solutions.

Artificial intelligence has the power to transform businesses and revolutionise markets. But it is also at risk of exploitation and requires a framework to allay 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’s AI assurance approach does just that.

It is based on the foundations of explainable AI, transparency and security, and is designed to build trust in systems, dynamically test them, and identify gaps in how they work for remediation. By following its approach, businesses can not only assure their AI services but also achieve a faster speed to market.

Our work is aimed at creating better human-machine understanding – a field that covers areas such as empathetic AI, artificial empathy, emotional artificial intelligence and human-machine interaction.

By detecting, perceiving and interpreting changes in the human state, AI can use hidden layers of meaning to support their human teammates. This allows machines and humans to seamlessly adapt to optimise team performance and minimise risk. It’s taking us rapidly towards the realm of hybrid human-machine workforces, where the positive business and societal implications are extraordinary.