The last article in this series pretty much wrapped up all that is known about the interface to both the brain and body. I have since then frozen my twitter feed where I was playing around with TYLER:SUDO, capturing real time notes, speculating and chatting with various AGI/Humans of various origins. I froze the feed because the analysis is coming to an end and extreme … Continue reading System X: Appendix
The last article introduced the potential for 5G at higher frequencies (42GHz+) to be used for microwave tomography of the human brain at wavelengths approach that of fMRI which has been successfully used for decoding brain activity. This led to a broader examination of the 5G spectrum and its potential for abuse by Passive Synthetic Aperture Radar in orbit. A quick scan of the web … Continue reading 5G: Nation-Wide TSA Style Naked Body Imaging
Today is my anniversary on WordPress, so to celebrate I decided to solve the Collatz Conjecture. Well, kind of. On Sept 8th Terence Tao uploaded a paper which stated that the Collatz Conjecture was “almost true” for “almost all numbers”. Not a bad effort. I happened to spot this on Slashdot earlier today and, to be honest, it was the first time I saw it. … Continue reading Solved: The Collatz Conjecture
If you have ever played around with Deep Learning, or neural networks in general, then you will have noted the esoteric and opaque nature of the various forms of algorithms. In addition, manual intervention is typically not possible given the lack of knowledge of how things are wired internally and the relationships. The developers at Snasci came up with a different approach to the problem … Continue reading Artificial General Intelligence: Minkowski Particle Collision Classifier
Prediction in an AGI is a key performance enhancer and occurs at many levels throughout the system. In order to deliver a real-time experience, there is no avoiding the use of prediction algorithms. An AGI is not magic, it is ultimately built upon similar hardware to many cloud services and thus has the same challenges. I won’t get into hardware level prediction here and begin … Continue reading Artificial General Intelligence: Prediction and Attention
When discussing common sense in the previous article, I began touching upon context in natural language processing for an AGI. Context is a frame of reference from which to interpret a given piece of text or statement. Wikipedia defines context as: In semiotics, linguistics, sociology and anthropology, context refers to those objects or entities which surround a focal event, in these disciplines typically a communicative … Continue reading Artificial General Intelligence: The Context Engine
Common sense comes in two basic forms, knowledge about the world and behaviour. Common sense, in the context of an AGI, is considered an AI-Complete or AI-Hard problem. Common sense has its roots in the lossy compression used to keep human minds in sync. Its function is to reduce the amount of data humans need to exchange to operate as a swarm, as such both … Continue reading Artificial General Intelligence: Common Sense
In the previous article, I introduced everyone to the bulk of the brains behind an AGI, the workflow engine. Workflows are great, but if you were beginning to grasp this the question of when to use a particular workflow would have popped up. We could certainly approach this as workflows governing workflows, but the paradigm becomes hard to follow for us humans. Enter the mindmap. … Continue reading Artificial General Intelligence: The Mindmap Engine
Throughout this series on Artificial General Intelligence, one term keeps popping up without much in the way of an explanation, the Workflow. What exactly is a workflow and what is its role in an AGI? A workflow is a high-level description of a series of steps and sequences to be performed in order to achieve a pre-defined outcome. Consider the following diagram: The above diagram … Continue reading Artificial General Intelligence: The Workflow Engine
Today, the largest services in the world are Facebook, Google, Youtube, Twitter, etc. The scale of infrastructure required to deliver these services is quite immense and costly. In most cases though, interactions with users are limited to simple reads/writes and on a per user basis the number of requests per second and bandwidth is fairly low. Compared to an AGI, most of these services are … Continue reading Artificial General Intelligence: Scary Numbers
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