OKLAHOMA CITY (AP) — A body camera captured every word and bark uttered as police Sgt. Matt Gilmore and his K-9 dog, Gunner, searched for a group of suspects for nearly an hour.

Normally, the Oklahoma City police sergeant would grab his laptop and spend another 30 to 45 minutes writing up a report about the search. But this time he had artificial intelligence write the first draft.

Pulling from all the sounds and radio chatter picked up by the microphone attached to Gilmore’s body camera, the AI tool churned out a report in eight seconds.

Anyone who has dealt with ants in the kitchen knows that ants are highly social creatures; it’s rare to see one alone. Humans are social creatures too, even if some of us enjoy solitude. Ants and humans are also the only creatures in nature that consistently cooperate while transporting large loads that greatly exceed their own dimensions.

Prof. Ofer Feinerman and his team at the Weizmann Institute of Science have used this shared trait to conduct a fascinating evolutionary competition that asks the question: Who will be better at maneuvering a large load through a maze? The surprising results, published in the Proceedings of the National Academy of Sciences, shed new light on group decision making, as well as on the pros and cons of cooperation versus going it alone.

To enable a comparison between two such disparate species, the research team led by Tabea Dreyer created a real-life version of the “piano movers puzzle,” a classical computational problem from the fields of motion planning and robotics that deals with possible ways of moving an unusually shaped object—say, a piano—from point A to point B in a complex environment.

Showing how far AI engineering has come, a new aerospike engine burning oxygen and kerosene capable of 1,100 lb (5,000 N) of thrust has successfully been hot-fired for 11 seconds. It was designed from front to back using an advanced Large Computational Engineering Model.

Designing and developing advanced aerospace engines is generally a complicated affair taking years of modeling, testing, revision, prototyping, rinsing and repeating. With their ability to discern patterns, carry out complex analysis, create virtual prototypes, and run models thousands of times, engineering AIs are altering the aerospace industry in some surprising ways – provided, of course, they are properly programmed and trained.

Otherwise, it’s garbage in, garbage out, which has been the Golden Rule of computers since they ran on radio valves and electromechanical relays.