Recently researchers finished mapping the central nervous system (CNS) connectome of not just the female Drosophila melanogaster (i.e. fruit fly) brain, but also that of the male D. melanogaster for a comparative analysis. Here the sexually dimorphic changes turned out to induce specific mating behavior that ensures that there will only be smooching between genetically fit D. melanogaster males and females, while the rest of the connectome remained effectively the same.

Of course, with this connectome in hand it led some people to ask themselves what else one can do with this connectome graph of about 160,000 neurons other than make a fruit fly into a fruit fly. So far we have seen [Nftechie] turn this connectome into a crypto stock trader with the Stonkfly project that uses the connectome’s reward circuits to potentially make profitable trades, though [Nftechie] says that they haven’t verified yet how good a fruit fly is at trading stocks, only that it does said stonks.

Over at [PC Gamer] they summarized a number of things that people have also done, including trying to make the connectome control a game of DOOM and Beat Saber. Each game frame stimulates sensory neurons, with the generated outputs then mapped to game controls, with dopamine-producing reward circuits wired in for reinforcement learning.

Although the D. melanogaster brain is only the merest fraction of the size of the human brain, it does provide us with a glimpse of what actual artificial intelligence research may lead to, as we unravel how even a 160,000 neuron connectome is enough to make these terrors of rotting plant matter do their wonderful things.

It’s technically true that any piece of software can be reproduced in hardware, although modern software’s size and complexity generally makes this a non-starter. But if we go far enough back in time, older software becomes easier and easier to implement in hardware. The original DOOM from 1993 might one day be recreated in full this way, but that day is not today. Instead, [Nick] has recreated the original music from that game, playing the opening track in nothing but 555 timers.

The circuit starts with a 555 timer that acts as a system clock with a rate of just over 7 Hz. These pulses feed a binary counter which in turn feeds a decoder, giving the circuit 15 positions. Each output of the decoder feeds to a diode matrix which stores information about what pitch the circuit should play. The circuit only needs to play six pitches so the diodes effectively connect each moment in time to one of these six notes. From there the circuit feeds into a set of switches which select different resistor networks of another 555 which is actually responsible for producing sound. The resistor networks have different values to adjust the timing of the 555 to produce different pitches.

Of course this entire exercise is largely academic as almost any microcontroller would be able to be programmed to play this chiptune quite easily, but it’s not a bad idea to get down into the weeds of digital logic from time to time in order to refine one’s skills and knowledge about what’s really going on in the inner workings of circuits. Or, go even deeper than that and build the logic gates themselves from first principles.

Large language models (LLMs) are generally thought of as machines that accept textual prompts and spit out textual content. However, if you’re creative in the way you interface with them, you can get them to do a wider range of tasks. For example, [Andrea Ricci] figured out how to get one to play DOOM.

For this project, [Andrea] began by porting the game to the SCINTIX P4. It’s a rather interesting device, being a single board designed in the Raspberry Pi CM4/CM5 form factor, but carrying an ESP32-P4 and an ESP32-C6 instead. The game runs on the P4 and is displayed on a 1024×600 MIPI DSI panel, but it’s only stepped through a few frames at a time. These frames are then passed to Claude Sonnet via a WebSockets setup. With only the same information as a human player would get, the LLM has to figure out what it’s looking at, and then respond with movement and fire commands to play the game.

It’s quite interesting to watch the system play—the LLM mostly accurately describes the game world, navigates down corridors, opens doors, and shoots at enemies. There is a bit of work behind the scenes to enable it to see and understand the game world—namely, using a depth fan across the field of view so it can figure out where walls are and how not to bang into them. There’s also an ASCII automap used to allow the system to keep track of where it has already been. But fundamentally, the LLM is playing the game without any other sort of additional assistance.

We’ve seen some other great ways in which AIs have been whipped up to play various games, like Trackmania.