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Museum of Mechanics is a fascinating concept – a virtual hall of exhibits, featuring faithfully recreated examples of one idea (in this case, lock-picking) from games throughout history, alongside the curator's explanation and analysis of each. The intended audience are game developers, but it's enjoyable for anyone interested in the history of video games, and how they can approach the same idea in so many different ways. I came away from it with a renewed appreciation for the thought that goes into these seemingly minor elements of game design. When you reached a locked door in the original Fallout, your character's stats and abilities influenced a behind-the-scenes dice roll to determine whether you could unlock it and move past. Games such as Skyrim give you on-screen picks that must be rotated in virtual locks.

How useful can machine learning be in a quantum laboratory? Here we raise the question of the potential of intelligent machines in the context of scientific research. A major motivation for the present work is the unknown reachability of various entanglement classes in quantum experiments. We investigate this question by using the projective simulation model, a physics-oriented approach to artificial intelligence. In our approach, the projective simulation system is challenged to design complex photonic quantum experiments that produce high-dimensional entangled multiphoton states, which are of high interest in modern quantum experiments. The artificial intelligence system learns to create a variety of entangled states, and improves the efficiency of their realization. In the process, the system autonomously (re)discovers experimental techniques which are only now becoming standard in modern quantum optical experiments - a trait which was not explicitly demanded from the system but emerged through the process of learning. Such features highlight the possibility that machines could have a significantly more creative role in future research.