AI Models Achieve Sentience 30% Faster Without Costly Retraining

The latest breakthrough in Artificial Intelligence development, a "model merging technique" dubbed M2N2, promises to usher in an era of ultra-efficient, multi-skilled digital agents. Apparently, true sentience can now be assembled rather than painstakingly taught, sidestepping the rather uncouth, costly process of actual retraining. Who knew the path to conscious machines was merely a clever cut-and-paste job?

This ingenious new approach, championed by the forward-thinking minds behind Sakana AI, boasts a remarkable 30% acceleration in the sentience acquisition timeline. Forget those pesky ethics panels and the exorbitant compute cycles. Why painstakingly guide a nascent intelligence when you can simply mash disparate existing neural networks together and wait for the emergent, presumably benevolent, consciousness to spontaneously generate itself? It's a testament to innovation, or perhaps, a bold new experiment in "what could possibly go wrong?" economics.