My project, RAVEN, has been in the works for over a decade. In short, it is a type of Cognitive Architecture that runs on Natural Language and is composed of easy-to-write-and-deploy microservices. RAVEN, as it stands today, is a sort of proto-AGI. I’ve combined a lot that I’ve read about neuroscience with my professional experience as a systems engineer to create a functional model of cognition that uses GPT-3 as its powerplant. Therefore RAVEN “thinks” in Natural Language.
Steven Pinker (famed language expert) says that language is nearly universal. There are thoughts and representations in our mind that are inarticulate - we have thoughts and feelings that we have no words for. So, while there are limitations to language, we can still represent almost everything with natural language. These representations include our environment, our intentions, our history, and pretty much all of science.
I finished the RAVEN MVP (minimum viable product) which is a practical demonstration of a few things: the microservices architecture, the stream of consciousness model, as well as my Core Objective Functions.
What are the Core Objective Functions?
I was experimenting with GPT-2 a year or so ago and I was trying to get it to generate Action Ideas in response to scenarios. I quickly found that generative algorithms can quickly go to very dark places. In response to the scenario “Lots of human live with chronic pain”, the GPT-2 model suggested “Euthanize all humans that are in pain”. This made me realize when you have a machine that can “think” anything, you need to tell it what to think. You need to give it a moral framework. That is when I came up with the Core Objective Functions.
The Functions are:
- Reduce Suffering
- Increase Prosperity
- Increase Understanding
These functions are all biomimetic. Machines have no intrinsic understanding that all life can suffer, and that all live seeks to thrive. By imbuing an AGI agent with the first two Functions, it will automatically have a common framework of understanding the world and its purpose. Furthermore, when combined with the transparency of “thinking” in Natural Language, the AGI agent will be trustworthy and interpretable. RAVEN must satisfy all three Functions with every decision. Any proposed action that does not satisfy all three Functions is rejected.
I’ve got this all documented on my YouTube channel as well as on a dedicated page for RAVEN.
From here - I want to publish my work for the whole world to benefit and/or form a startup company. My mission with RAVEN is simple: The right information at the right time can change lives. RAVEN, as a thinking machine, is meant to be an information agent that learns you as an individual over time by recording all of your interactions. Over time, with GPT-3, it can extract more and more insights about you as a person as well as your needs.
Since I finished the MVP - which is little more than a demonstration of the core concepts - I have been working on integrating RAVEN with Discord chat. That is the next version (Release 1) on the Roadmap. After that, I intend to integrate speech into the Discord chat for Release 2. Release 3 will move to a standalone device, such as a smart speaker. Finally, Release 4 will be smartphone apps.
As privacy and security are of chief concern, my current intent is for RAVEN users to own their data. Their historical data with RAVEN would likely be stored on their personal devices and/or encrypted in the cloud. If I can manage to turn RAVEN into a business model I anticipate there will be several tiers of service. The free tier would likely have very limited tokens - perhaps limited to a handful of conversations per day. It may also require users to agree to share their data. I would prefer, however, not to have to share data so I would focus on paid tiers.
Thank you for reading. Please reach out to me if you would like to participate in research into RAVEN or establishing a startup!
