By David Stephen

An indicator of superintelligence, for AI, would be the solution to a major scientific problem. To achieve this, what may be necessary would be scientific data, a new memory architecture, and extensive math models. Already, large language models [LLMs] can answer several scientific questions. But they remain limited in theorizing fundamentals answers. How does AI get there? This can become a trajectory for general intelligence from niche, transcendent intelligence. Take the brain as an example, to explain a mental state, say of an emotion or a feeling, how would AI be able to say something absolutely original and quite accurate? Also, arriving at this capability, how will alignment be maximized to ensure safety against misuse?

How close are we to AI Superintelligence?

Storage Layering

The first step towards a mini-superintelligence, for science, or specifically brain science, is storage. Data will not be stored with the current memory architecture. There will be an absolutely new model for data storage that collects patterns at the store. In mechanistic interpretability, concepts [with relationships] are often adjacent. It will be necessary to have a similar model for memory. This would be like having a hard drive partition for just neurons. Though, the form of storage would be much different because of [say] specifications [with data] solely for neurons. So, everything [established in empirical brain science] about neurons will be a store. Then electrical signals as a store as well, then chemical signals as a store.

Another store would be neurons in clusters - nuclei and ganglia. Then electrical and chemical signals as a collection. While there would be different architectural explorations for these storages, the target would be to store data like the human brain. The human brain, it is conceptualized, has thick sets [of electrical and chemical signals]. A thick set collects whatever is common between two or more thin sets. So, door is a thick set representing all doors, so is fence and so forth. There are unique elements that may remain thin sets, but most interpretations use thick sets. It is what makes it easier for the brain to store much more for less space [and energy] than computers. There are rarely repetitions, so access is faster, learning requires new fewer examples and so forth. Therefore, the partitioning or specificity of storage would provide an opportunity to directly mimic human memory.

The next step will be to layer those storages. Such that, instead of say sectors on a disk, they are like layers, for patterns to match what is common in the binary data. The objective is to ensure that storage is prepared for intelligence. Not just to have intelligence use repetitive memory, like what is obtainable at present. For example, the text 'door' is stored differently from the image or the video. Also, there are all kinds of videos, image types and so forth. Then there are several information about doors, as well. In the brain, door is a thick set, containing the text, images, videos, physical structure and so forth. In this new memory architecture for computing, whatever is common in the binary segments of door would be collected. So, instead of storing it alone, it is stored together and accessed in that collective form, as layers, so that creativity meshes are probable.

For this new memory for superintelligence, after storing specific data and collecting binary commonalities, then layers would be added, so that more binary segments would be collected. The technical details and likelihood of these can be explicated and estimated. However, to really have superintelligence that can be original enough beyond prediction, or basic reasoning, it may not begin with deep learning but with a new storage architecture.

Mathematical Models

Existing math models in deep learning would accelerate progress further, given new storage layers. Since patterns would already be available from the source, there can be...