Yes. Current deep learning is powerful but fundamentally statistical. It classifies, predicts, and optimizes, but it doesn’t cognize. The assumption is that by

Yes. Current deep learning is powerful but fundamentally statistical. It classifies, predicts, and optimizes, but it doesn’t cognize. The assumption is that by scaling parameters and data, generalized intelligence will “emerge.” There is a big possibility that the fundamental framework of traditional AI is incomplete. It’s roots are inspired by Hebbian Learning, which itself is descriptive in nature. A good analogy is to think about Newton’s law of Gravity, works tremendously work, but it couldn’t explain the orbit of mercury. No matter how much we tweaked the maths, it didn’t fit. The framework was incomplete. It took Einstein’s general relativity – a new framework who eventually explained it. If the framework itself doesn’t allow for the truth we are after, we can spend trillions, it not gonna happen. It like having a design of motorcycle and somehow expecting it for fly. Currently, in AI landscape our efforts are being poured to build ramps. So that we can create an illusion that we have a flying machine. It can be argued that its also flying but claims are very misleading. At JN Research, we took a principle-first route. We built Adaptrons - artificial neurons that actually exhibit graded potentials, subthreshold states, and action potentials. When networked, even small systems (hundreds to thousands of units) show memory formation, dreaming, anticipation, and original thought generation. This is not ML, not symbolic AI. It’s a new substrate for cognition. If you’re interested, check us out here: https://jn-research.com https://jn-research.com