Here’s a fun activity. Get your camera phone ready, meetup with an Artificial Intelligence Scientist and ask them to define intelligence scientifically. Make sure you define scientifically as not using abstract words like “planning” and “reasoning”. Then, get your camera ready and take a picture of their facial expression.
The best way to confuse an AI scientist is to ask them to define intelligence in a scientific way……which is ironic.
There are a few companies working towards General AI, or Artificial General Intelligence (AGI) today. None of which have made any progress. Why? I believe the answer is wrong focus. Look at companies like Google DeepMind, Vicarious and Numenta. They focus on the reverse engineering of the human brain; as if the human brain was the definition of intelligence. The biggest problem with that approach is our understanding, or should I say the complete lack of understanding, of how it works.
I believe it is an enormous mistake to believe that solving intelligence is to reverse engineer the brain. In 2005, when we first started our research, we made a conscious decision to assume that the brain was an application of intelligence, not the definition. We needed to find a probable definition for intelligence. We found our answers in the realm of physics, quantum mechanics and spacetime.
To summarize: We started the assumption that intelligence is the efficient pursuit of goals. Then we asked what it means to achieve a goal. How do we explain the achievement of a goal? The answer can be found in quantum mechanics. While there is not consensus, there is a large portion of scientists in the physics community who believe reality can be described by the collapse of the wave function; regardless if you believe the wave function is a mathematical abstraction or real.
In other words, achieving our goals means that the wave function collapsed in a way that produced the reality we were pursuing.
Intelligence is not about predicting the wave function collapse, it is instead the ability to control it, which means, according to our research, moving particles around in space and time. We therefor define intelligence as follows:
The orchestration of a sequence of particle movements to control the wave function collapse.
This is our argument for a definition of intelligence. It is still too early to either prove it or disprove it. But, it appears the definition is the only potential candidate as of today. And, it does work well for our work.
The interesting thing with this definition is that it doesn’t just describe the operation of the human brain. The describes planets moving through space and sub-atomic particles. In other words, should this definition stand the test of time, intelligence is woven into the fabric of spacetime itself.
From Science to Machine Algorithm
If intelligence is about moving particles in order to control the wave function collapse, then it appears obvious that a machine implementation of intelligence should focus on modeling reality and understanding what particles to move where.
However, we realized it wasn’t as easy as putting up a camera and “seeing” the physical world. What we wanted to build wasn’t a Sentient-AI, iow, an AI that in itself was a life-form with its own selfish goals, needs and wants. We wanted to build a Why-AI, a digital mind that could extend the user’s own way of thinking, that reasoned like the user and viewed reality like the user.
What was needed wasn’t a model of the objective reality, but instead a subjective interpretation of that reality.
What is “Artificial Intelligence”?
Above I discussed the definition for intelligence from a scientific perspective. But as we sit down to “code”, moving particles doesn’t make a lot of sense. So what is “Artificial Intelligence”? What’s the purpose from a computer science perspective?
We see artificial intelligence – by that I mean AGI / General AI / true AI (whatever you want to call it) – consisting of three parts:
1) Machine Learning – The ability for a machine to learn knowledge from the perspective of building subjective interpretations of reality.
2) Temporal 3D Modeling – The ability to use the learned knowledge to put together 3D models of reality that extend in time.
3) Comprehension – The ability to analyze these models and understand the cause and effect of possible action or inaction performed by the user or others (humans or not) in the model. In addition, comprehension is about finding a sequence of cause/effects that leads to the goal (similar to sequence of particle movements).
General Purpose Machine Learning
Our overall machine learning algorithm follows a process we call “Kylee Model”. The Kylee Model can be summarized as follows:
1) Observations – This is a stream of sensor data arriving our cognition engine (the core AI algorithm). The sensor data is abstracted and normalized into graphs. This sensor data doesn’t just come from one user’s device, but from all devices across all users.
2) Memorization – Many independent observations leads to memorization. For example, if Nigel constantly sees people silencing their phones at the movie theater, Nigel will eventually memorize that common-sense. However, at this stage we still can’t claim Nigel comprehends why we are silencing the phones at the movie theater.
3) Conceptualization – Many memorizations leads to conceptualization. Each memorization tend to be slightly different that the others. That variance allows Nigel to turn a piece of memorized knowledge into a concept. Concepts are extremely critical as it is what enables “transfer learning”. In one of our test Nigel was able to conceptualize a basic version of the knowledge of “home”. By observing what individual’s called home (specific locations), Nigel was able to learn the word home as a set of location and WiFi signals (the WiFi surprised us).
4) Comprehension – Once conceptualized knowledge gets put together to create a temporal 3D model of reality, the algorithm analyzes this model looking for two types of comprehensions; (1) Depth comprehension – like why is a ripe banana yellow? Answer, because they have particular carotenoids (of course, you can go deeper) and (2) Temporal comprehension – How do I get bananas? Answer: Get in the car, drive to grocery story, go to fruit section, pick up bananas, go pay for bananas.
The Periodic Table for Knowledge
The algorithm behind Nigel is specifically designed to identify and learn pieces of knowledge. But more than that, the algorithm is designed to break down these pieces and find the smallest pieces of knowledge – or the basic building blocks of knowledge that would let us build any interpretation of reality possible.
Think of it as the pursuit to build a periodic table for knowledge.
Inspiration from Quantum Mechanics
Not only do we believe intelligence is a part of quantum mechanics, but the algorithm has a lot of inspiration from quantum mechanics. Three examples include:
1) Thinking Process – We use the Many Worlds Theory as the inspiration for thinking. While we do not buy into the many worlds theory, we found it very useful for Nigel’s thinking process. The algorithm, within our SpaceTime boundary box, create simulations of various futures based on cause and effect. Once it has multiple futures, it acts as a “Google Maps for Many Worlds” to find a path to realizing a goal.
2) Knowledge Superposition – Interpretation is key in the thinking process. It is not enough to model a girl as a girl. A girl could be, from the perspective of the observer (the user), a daughter, a friend, a mom and many other interpretations. To solve this challenge, all knowledge pieces get encoded with all possible interpretation. Once added to a temporal 3D model, only some of the nodes in the graph fit the model which essentially collapses the knowledge to a specific interpretation. This has a big impact on the scalability of the thinking process.
3) Retrocausality – When building these temporal 3D model we assume retrocausality. In essence, we assume the goal has been achieved and follow the backwards flow of information over time to discover what caused the effects we’re seeing.
The quantum mechanic inspiration is more than inspiration. We found that the above examples make Nigel scalable. Technically it is impossible for Nigel to model the position of every particle in space, at least not with today’s computer technology. But the above examples allows Nigel to scale into an immensive powerful general AI system with global impact.
While Nigel today is in its baby stage where it is only learning the basics of reality, we truly believe it will grow into a scientist. Personally I hope Nigel can find a cure for cancer and help people out of global poverty within my own lifetime.
What about social and emotional “intelligence”? Our research found no evidence that emotional intelligence and social intelligence is anything real. I believe human emotions and social behavior allows us to build our own 3D models of reality in our brains based on survival and procreation.
For Nigel we are working with external companies to integrate emotion recognition to allow Nigel to respond to emotional states, but as far as emotion goes, we do not believe it is a part of universal intelligence.