Sapiens Prime Directives

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?

The Chatbot Controversy

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.

Sapiens and Chatbots

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?

Aspirational AI

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.

The Hidden Structure of Knowledge

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.”

Artificial Neural Networks

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.