The possibility of building machines that are intelligent in the sense that we perceive ourselves to be intelligent has always been accompanied by a very reasonable concern: how can we make sure our creations will always serve us and not the other way around?
People familiar with New Sapience know we are one of the few companies that claim to be making progress towards Artificial General Intelligence (AGI). OpenAI is another. Many of our followers and investors want to get our take on the current controversy surrounding OpenAI’s ChatGPT and other Large Language Models (LLMs).
The issues are not something that can be fairly dealt with in a couple of Twitter posts. So here is a concise roadmap to the controversy and how it looks from the New Sapience perspective.
Seldom in the history of AI has a new product release been met with such widespread enthusiasm and consternation as OpenAI’s ChatGPT. New Sapience has been working on a compact scalable way to endow machines with knowledge of the underlying reality that language refers to and is built upon. Naturally we are being asked about how ChatGPT relates to our work. Is it a giant step toward Strong AI (or AGI) or is it just a better (and perhaps scarier) illusion of intelligence?
6th and final webinar of the Artificial Intelligence and Human Psychology Series
A new theory on the nature and structure of knowledge and its relation to reality does for AI what the Periodic Table of the Elements did for Chemistry.
Excerpt from a webinar hosted by author Lynn Woodland in which Dr. Bandy explains why he invested in New Sapience
Webinar Moderated by author Lynn Woodland with Physicist Dr. Willam Bandy and New Sapience CEO Bryant Cruse
It happens like this: someone has a theory about what intelligence is and develops some software to implement it. Even if it does not work or doesn’t do anything that looks like intelligence, it is still considered “AI” because that is what they were aspiring to create.
Complex ideas are aggregates of simpler ones. The inescapable conclusion is that, if you keep decomposing ideas into their components, at some point you get to the end, or rather the beginning. This is the same conjecture that Democritus made about the material world: if you keep breaking things apart, eventually you get to the indivisible pieces he called “atoms.”
Today, the technical community, including Big Tech, government and academia have embraced artificial neural networks (ANNs) and Machine Learning (ML) as their preferred methodology. While this “data science” has led to many amazing applications that are impacting the way we live and work, misconceptions about what it is and its potential are widespread.