In recent blog updates, I emphasized the potential of IBM’s TrueNorth computer chip that comes close to emulating the brain in its architecture. But Elon Musk and Mark Zuckeeberg invested in Vicarious FPC, a company that is writing algorithms that work with traditional computer architecture rather than the more brain-like architecture of TrueNorth. Let’s see if we can understand why they chose to invest in Vicarious FPC.
The current thinking is that the brain works in a novel way. Over time, it acquires generalized knowledge. This knowledge is very abstract, but can be applied to a wide group of specific data inputs. When the brain gets a specific input about something, it queries its generalized knowledge base to see if there is a possible identification match. Is there a match that is compatible with both the generalized knowledge and the input data? Like is an out-of-focus image seen in the distance compatible with its generalized knowledge about animals or rocks? The specific input could be based on as little as one sample. This enables the brain to quickly zero in on a probable identification rather than just accumulating massive amounts of data on every possibility and only then making an identification determination. This probability approach is one of the primary reasons that the human brain is so efficient.
To emulate this in software requires innovative algorithms, but not new computer architecture. So the existing zillions of computers could conceivably use some variation of this approach, and become much faster and more efficient. It would not require new computers and new computer languages as TrueNorth does.
So we basically have three approaches to AI. First, the computer Watson approach, which requires huge amounts of input data and very powerful computers with traditional architecture. This approach is well on its way and has the advantage of not requiring any new innovations. The second direction is the Vicarious FPC approach, which mimics the brains use of generalized knowledge coupled with limited input requirements. And the third approach is the IBM TrueNorth computer chip that emulates the brain’s architecture. Watson has already made its mark in Artificial Narrow Intelligence (ANI). Vicarious will probably take us closer to human Artificial General Intelligence (AGI). But the marriage of all three approaches will probably be needed to get true human level thinking AGI followed quickly by Artificial Super Intelligence (ASI).
It is worthwhile to take a closer look at the advantages and risks of the Vicarious FPC approach. In humans, we are obviously affected by the quality of the Generalized Knowledge that we accumulate in our brains. For example, if when we are young we are exposed to very strong religious beliefs, we may disregard any input that is not compatible with our generalized knowledge in this area. We will not be able to truly make an independent judgment on our religious beliefs. The same would be true for any generalized knowledge we may accumulate as to race, morality, and so on. So whoever is programming the Generalized Knowledge areas for Vicarious FPC will have to be very careful not to build in prejudices, either directly or inadvertently by the way the software is written? Otherwise we will be duplicating one of the human weaknesses we presumably would not like to replicate.
In a much earlier update, I indicated had I had a novel coming out that gives face to the AI issue. Although it is fiction, it is a possible scenario that could occur as AI develops. I will give you more detail once the book is available.