Autonomous journalism with artificial intelligence and sensory data processing

A system configured for autonomous content generation and delivery, comprising:

• a processor;
• memory coupled with the processor;
• and
• instructions stored in the memory and executable by the processor to cause the system to:
• receive event data associated with an event, the event data being received from one or a plurality of content sources and in a plurality of information formats;
• generate structured content from the event data by:
• applying machine learning to recognize patterns in the event data;
• verifying factual content of the event data;
• detecting bias and/or editorial influence in the event data;
• and
• transforming the event data into the structured content based on verification and the bias and/or editorial influence so that the structured content comprises factual, bias-reduced, and/or representationally balanced content;
• generate multimodal output instructions based on the structured content, the multimodal output instructions comprising synthesized speech and/or gesture parameters for a virtual character, the multimodal output instructions being configured to cause the virtual character to deliver the structured content;
• cause the virtual character to deliver the structured content in a digital environment based on the multimodal output instructions;
• generate a dynamic content update in response to an update to the event data and further processing of the update comprising applying the machine learning, verifying the factual content of the update, detecting the bias and/or editorial influence in the update, and transforming the update into the dynamic content update;
• and
• cause the virtual character to deliver the dynamic content update.

A non-transitory computer-readable medium storing code for autonomous content generation and delivery, the code comprising instructions executable by a processor to:

• receive event data associated with an event, the event data being received from one or a plurality of content sources and in a plurality of information formats;
• generate structured content from the event data by:
• applying machine learning to recognize patterns in the event data;
• verifying factual content of the event data;
• detecting bias and/or editorial influence in the event data;
• and
• transforming the event data into the structured content based on verification and the bias and/or editorial influence so that the structured content comprises factual, bias-reduced, and/or representationally balanced content;
• generate multimodal output instructions based on the structured content, the multimodal output instructions comprising synthesized speech and/or gesture parameters for a virtual character, the multimodal output instructions being configured to cause the virtual character to deliver the structured content;
• cause the virtual character to deliver the structured content in a digital environment based on the multimodal output instructions;
• generate a dynamic content update in response to an update to the event data and further processing of the update comprising applying the machine learning, verifying the factual content of the update, detecting the bias and/or editorial influence in the update, and transforming the update into the dynamic content update;
• and
• cause the virtual character to deliver the dynamic content update.