How’s my AGI idea (human-like AI)?

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My life’s goal is to build a human-like AI. I’ve been working on the AI since February of 2020.

My approach is VERY novel. I am not college-educated and I did not research AI much before I started the AI. Instead, I spent a year independently studying neurons and how they interact with eachother to make human thoughts. I came to the conclusion that neurons can do anything code can do, and replicating neural activity to create human-like thinking would be like replicating java in an attempt to make minecraft. It’s amazing for computer vision, and very specific things, and I’m sure my finished AI will use it for something, but it is not enough. I do not feel it is very human, despite its’ basis in neuroscience. I feel semantics, linguistics and psychology would be more useful in understanding how to replicate intelligence than neuroscience.

Instead of using a neural network, or deep learning, I am using semantic networks. Before I explain how the AI works, and how I’d like to develop it further, I should explain the theory behind it all. My goal is to create an AI that can understand english, so it can be told how to do things like you’d tell a human. It will store the knowledge to “skill-files”, like “how to write an essay”.txt or “personality”.txt. These files could be “written” by anybody, even people who do not know how to code, just by telling the AI how to do the various tasks and skills and things. Linguists, psychologists, nerds who want an AI gf, teachers, AI researchers, anyone who knows english would be able to add to their own AIs and share the created skill-files online for other people’s AIs to use. The skill-files could even reference other skill-files, so people could use what other people made to make even crazier skill-files. As more skill-files are made, the AIs will be able to do more and more, and this will make even more people interested in using the AI and making their own skill-files. Eventually, the AI could even be made to read books to learn new skills/things. When the problem of human-like AI is attacked by many different sorts of people, using many different techniques, I am sure the problem will be solved.

To create an AI that understands english like a human does, I have been using coded english laws, coded logic and semantic networks. The semantic network is its memory. In its networks, most concepts are connected to other concepts (for example: “[clothing] is a [noun]”, “[duck] has 2 [legs]”), and most things it reads updates its semantic network accordingly. When I work on this AI, I look for problems it cannot solve and then add to or edit the code (usually using language laws or logic, in a very general way that works for many similar situations) so that it can solve the problem. I have been doing this with word problems (ex: carlos has 2 bricks, each brick costs $5, how much will carlos make if he sells all his bricks?) and Language Arts GED questions. If I do this enough, I will end up with an AI that can understand English like a human can. Every time I edit the AI, it becomes one step closer to that goal.

The code for the AI is at https://github.com/mortefin/hedone

Here’s what it does (with some exceptions), currently, for everything it reads:

  1. Splits the message into clauses (this sometimes happens later), and preforms actions 2-5 for each clause.

  1. Turns the words into variables, editing them when needed. Plural words turn into their non-plural forms, certain words are joined and treated as if they’re only one word (example: “Wonder Woman”), words with punctuation lose their punctuation. This is done so the AI knows which concept in its semantic network each word is referencing.

  1. The AI defines each “word” by creating a list of connections for each word. It does this using its semantic network(s) (sometimes the AI will use context-specific semantic networks in addition to its main semantic network). Most concepts in its semantic network are connected to other concepts, in simple connections (X is Y) and complex connections (any connection other than X is Y, like X has 2 Y). For simple connections, the AI adds the connected concept to the word’s connection-list and also adds the connections that the connected concept has, and the connections of those concepts, etc etc. For complex connections, only the complex connection is added to the connectionlist, but all the connections of the concepts in the complex connection get added to the complex connection (except complex connections, but that’s being worked on).

  1. The AI looks at the word classes of all the words (using their connection-lists), and some other things, to determine what the subjects, adjectives, actions and objects of the clause are. It uses, mostly, coded english laws to do this (Ex: if I see a noun before a verb, it is a subject, but if it comes after, it is an object).

  1. If the clause is a question, the AI answers it by figuring out how many of the subjects is the object, or how many of the subjects (verb)s the object, and tells the user. If it’s not a question, the AI saves the knowledge it parsed to a semantic network file. It creates connection(s) from the subject(s) to the object(s), via any found verbs/adjectives. For example, “monkey is an animal” would be a simple connection from Monkey to Animal (Monkey – Animal), but “monkey has four limbs” would be a complex connection from Monkey to Limb that’d look like (monkey – has 4 – limb).

submitted by /u/Mortefin
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