Computer Learning in Automated Manufacturing Processes (CLAMPS) demonstrates the integration of predictive machine learning to drive improvements and minimise variability within a composites manufacturing process. A collaborative project between the Centre for Modelling & Simulation (CFMS) and the National Composites Centre (NCC), highlights the digitalisation and automation steps necessary to ensure consistently high-quality parts, ultimately reducing costs. Together, CFMS and NCC have brought together a combination of traditional virtual manufacturing simulation and artificial intelligence technology to detect and control defect formation.

It is believed that at the turn of the 20th and 21st centuries, there were three distinct waves of AI technological development. The first wave was an attempt to create general intelligence with the help of the formal rules of logical thinking. The second was the development of expert systems. Now we are on the crest of the third wave, the main feature of which, computer learning, is already successful in helping artificial intelligence bypass top professional experts in the effectiveness and quality of decision making on a massive scale. Researchers have learned how to create systems that improve the algorithms themselves and then receive new data. This invention has created a new industry.

Google has cool technology to recognize images and speech, and IBM's hardware can diagnose diseases and beat humans in Jeopardy. Combine the two, and you get a powerful computer with serious brains. IBM is merging Google's artificial intelligence tools with its own cognitive computing technologies, allowing deep-learning systems to more accurately find answers to complex questions or recognize images or voices. Google's open-source TensorFlow machine-learning tools are being packed into IBM's PowerAI, which is a toolkit for computer learning. The two can be combined to improve machine learning on IBM's Power servers.