Natural language generation, speech recognition, virtual agents, text analysis, machine learning platforms, AI optimized hardware, emotion recognition, image decision management, deep learning platforms, biometrics, robotics automation, cyber defense, compliance, knowledge worker assistance, content creation, recognition, and marketing automation are just a few of the technologies that are being developed.

Chatbots, speech recognition, text recognition, face identification, and sentiment analysis are all examples of predictive and prescriptive models.

Accessibility of AI and analytics tools is critical with the development of citizen data scientists. By offering tools for managing the end-to-end machine learning life cycle, AI platforms assist to democratize and productize ML models. They do it by using a SaaS interface that is meant to make user interactions easier for non-technical individuals. Without these platforms, AI's impact would be limited because a larger portion of resources would be spent on developing and maintaining models.

Environment monitoring and reaction for climate change objectives, automated transportation, taking over risky occupations, robots working alongside people, enhanced senior care, cyborg (organic/bio-mechanic) creatures.