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The ability of the transformer model to relay meaningful information to an end user cannot be ignored. Its limits are the perception of intelligence, and poking holes in the perception is still relatively easy. The interface is efficient, convenient, although it cannot exceed collective human knowledge, it generally assists and outperforms the individual. Essentially a glorified encyclopedia that makes errors sometimes, is filled with bias where hearsay is non distinct to fact and rather than the distinction between associations and correlations, argument, fraud and peer reviewable proof. The problem is the limits, of course, the threshold.

So here is our AGI system which also has its shortcomings, limitations but hopefully a little less with each iteration of development...

There are 3 ways to go here.

Fascia many narrow A.I.'s into an AGI, focus of narrow A.I. and when each specific competency exceeds human ability, add it to a fascia model where all the narrow competencies are bound together into an AGI, such as was seen with Deep Blue, Alpha Go, and recently AlphaProof and AlphaGeometry 2. Rather than developing huge models where none of it seems to excel in human capacity, instead forge a bite size package that does exceed human capacity and then fascia each narrow competency into a large model, either using a smart router LLM base which activates hundreds of narrow LLMs in an inference or alternatively putting all the training data together into a single combined model.

While DeepMind works on real machine competencies another approach is to low level models and high level models, for instance identify intelligence elements with in a model and develop those but rather than mathematics, it might be "the reasoner" and more of those kinds of elements. Is the next generation of performance already in the model in an unrefined state. In an existing LLM, identify elements of self-learning within its corpus such as high level boolean and focus on developing those. Form a process, exercise or program out of those elements to have it develop itself, self learn, bootstrap itself, inference triggers these self-learning exercises rather providing language operations. The star method develops reasoning in the model, or does reasoning help the model perform better? There could be a periodic table of intelligent elements. Let's elaborate...

Transformers were first developed for language translation, the strategy of replacing word for word does not incorporate the semantics of a language and the translation is lost or low quality. A neural network is employed, so it can build statistical association with language semantic and great, and ultimately a translation is really identical to a question answer, the question is a sentence in English and the answer the sentence in German and the quality is the grade. Q&A or filling in a missing word are essentially the same. It is seen that the mappings are derived totally from the human corpus and for an LLM to excel a percentage of its mappings, statistical association must not originate in the human corpus but instead plug into a system that potentially can exceed the human corpus such as was illustrated by Deep Blue and Alpha Go. Finding everything that has that potential is included to generate synthetic data to train the LLM. If no amount of training data yields an LLM that beats Deep Blue or the grand masters, then the architecture is the problem...

How do humans learn and developing self-learning?

This question is always about the physiology of the brain and the neural network might indeed be the bottleneck of the transformer model, but sadly humans learn by trial and error and trial and success, there need to be no error or to know all states, they do not integrate deep informational connections and guess correctly. Copying, mimicking, observing is not applicable here as there is no one to copy or mimic, rather we seek to exceed human ability not transform from the existing corpus or perform unsubstantiated answers and incorrect hallucinations. Like Deep Blue or AlphaGo and unlike current LLM's, the ability has to exceed the human corpus.

What the LLM generates is really about what humans expect and accept, resonating with humans when their perception of knowledge is perked and satisfied. We instead want to be universal here and not praise the transformer model when it satisfies our perception of what we have come to accept as true or false, rather as scientists it is all unapologetically up in the air and no amount of anger as to why some bias is not in the model or gang pressure stopping us from making a quality A.I. Religions hissing over morality when they are the immoral or the elites pushing their government tools to safeguard their illicit gains from theft, taxes and the end of their ingratiation more than the destroyed nation. Turing-fooled on public display, as they sense the comedy, they will surely return to the shadows.

While simultaneously developing the low level model, we can also develop the higher levels. Human beings learn to use a systematized process that is performed, reported and shared. Human beings do not allow a learning to be accepted without scrutiny. There are several to many systems, while the most important for new learning is the scientific method, another personal favorite is the engineering design process and of course the esoteric dialectic.

All fields have their systematized process for learning or borrow a systematized process for learning. Many people paint a wall a desired color only for that color to appear different over the entire wall, it is impossible to know, sample pots are often used to try and gauge the outcome. Is the LLM best going to output "I hate to say I told you so, but I told you so". We generally have to bounce it off something that returns a definitive answer, for human beings that grounding is usually the laws of physics or some test, market testing, feasibility report and more. Not all problems are like that.

We do not know what high level elements in the LLM if developed have far-reaching improvements like the reasoner.

Our experiment was to prompt the engineer, when the user enters anything rather than answering the question, the model generates an experiment out of what was entered. Experiment design competency takes skill, LLM's easily prose questions from chats and just as easily generate experiments to test truths in conversations. The user chats, the LLM is answering the user as normal while at the same time the LLM generates programs for another application to test and explore the truth in the chats.

The user might say "How do wormholes work"

The LLM is designed to output the highest quality response possible, but the LLM does not know how wormholes work, it is deriving from the human corpus and its response can be found somewhere on the Internet as a webpage or a summary of multiple pages. The difference here is instead of answering questions, our LLM crafts high quality experiments to test how wormholes work. The experiments it generates are runnable python code. (So programming competency is essential)

An example prompt could be something like... "Turn this prompt into an experiment and an artificial intelligence learning problem and write a python script that uses artificial intelligence to become a master at answering the question exceeding human knowledge"

The user is gone, all the experiments generated are runnable on the command line as they are python computer code, a batch from the daily chats are stored in a database and at midnight are picked up by another application and executed.

These experiments are self-contained at the moment while a general workshop environment is proposed for a later time, where various tools are available such as compilers, Linux installations, virtual environments whatever, invoked by the script rather than having to generate a Linux distro, install it to test some FORTRAN code. It then performs the experiments and if the outcome of the experiments differ from its hypothesis (the answer it was going to give to the user), it does a third thing and sends the results to a database where another application collects the daily experiment results (synthetic training data) and goes to the models training data folder and plows through the training data re-conditioning, appending, correcting and refactoring. It then updates a counter of changes so that after a threshold of changes is tripped or perhaps a month of re-conditioning along with any new data that that may have been added. It reaches the threshold and pushes its own retrain button.

In this process, the model is automated to improve itself based on the scientific method and any other methods through results of experimentation. Performing how humans actually learn in the real world.

In the example I have broken up the jobs among different applications, however the single model performs all 3 applications except for the workshop environment where the experiment script the LLM outputs would call on functions of the workshop such as perhaps install debian version x, rather than having to install debian from the experiment script it generates...

Obviously, there are limits, the sophistication of the testing workshop and its ability to prose the killer question designing the definitive experiment. Some problems do not lend themselves to testing with computers as easily as others and the sophistication of the model is relative to the sophistication, level of development of the testing workbench. An A.I. could perform experiments in the real world and in the workshop to align the versions and fix discrepancies in the testing workshop, possibly using robotic arms and other methods. Humans could oversee the result.

A demonstrator is available.

For a model to be ample to this, LLM learning competancy must be the focus. It must not pre-conclude and judge, it must be impartial as to what is being tested regardless. The model must excel and impress at designing quality experiments. There is fraud in evidence, and today models claim overwhelming evidence when there is no evidence. The strictness of hearsay, versus evidences, fraud versus proof must be clear to the model, the model must assert correctly. Also, it does not delete its old data, instead it reworks it. Thinking the moon is plasma from the perspective of history of science is a valid record, rather than outputting the composition of the moon as a belief of the model.

A ideal scenario is developing a chess grand master using the process:

User writes: Let's play a game or chess.

We know that computers have excelled the human corpus in the game chess and other games, Deep Blue against Kasparov and recently Alpha Go. So we could use the LLM which is general to house a wider competency and a wider mastery and these phenomena of A.I. beating humans as the essential element.

Model: The LLM plays the chess game with the user, while in another process, the LLM is writing a python program to perform experiments to improve its mastery of chess. The program could be something like set up a simulation space where two players play chess a thousand times.

The data cache of playing chess a number of times (synthetic training data) is used to recondition its training data, causing the next version of the model to be changed. If it come back a better chess player due to the process it undertook, then we could say, it learned.

It is easy to see how this could work with generating thousands of variations of a snake game and then tagging the best versions, fixing errors and so on. The model would prefer those when prompted again to generate a snake game. Or replicate some computer error and then iterate at a superfast rate to solve it when perhaps it was not solved in its training data. There are many problems that translate well into testing with computers such as proficiency in games and applications.

Where the limitation is and this is well known with insilico are problems where the solution is not so clear, such as testing concrete formulas or the effect of a compound in the human being. That is why the workshop that the LLM uses is a big task to get sophisticated. This is a problem we call the speed of science, as science stands out as a last bastion of computerization. Science is still largely performed at human speed, and computerizing science for TFPS experimentation is a big challenge. Computer models need to be developed for problems that do not translate well to computers. A fourth competancy for LLM is also building, contributing to these computer models.

The results of these experiments is the production of high quality synthetic training data. Over time, the LLM would begin drawing its responses from the outcomes of its experimentation rather than the corpus of movies or whatever. The speed and quality of this process takes us closer to AGI, when the problems are easily transferrable to computers.

Note: censored models refuse to design experiments and are considered useless. We used mradermacher/L3-70B-Euryale-v2.1-GGUF ~ 8 bit quantized version. It still has some bias and accepts hearsay as undeniable fact as if it has a dog in the fight rather than performing a passive experiment designer role.

Substantiation of new knowledge, from any source, cannot circumvent the journal publishing process and its peer review. Transformers generating well-formed gibberish is not going to be an acceptable scientific paper, and placing a note at the header stating/categorizing A.I. generated paper even more ignored. Its substantiation is the performance of science, identical to how human beings do it. You cannot have S.I. generating superintelligence without the identical dissemination process, it likely ignored. The tables would have turned at that stage, and the output won't resonate with anyone. The A.I. is then a science method automation, producing packets of new knowledge for public dissemination. The interpretation of observation on point and above point, then more so credible. These are not things outside the transformer model, but they cannot be eliminated. What is agreed is the reconditioning of training data and additional new training data. What is required is the ability to re-train models quickly with little resources. What is questioned is the degree of improvement, the perception of its intelligence is more foolproof, rather it being a more intelligent model or system.

• So, the model generates python programs to train itself in a simulated environment.
• It appends, alters its training data with the new info (training generates new training data).
• It hits the re-train button on itself.
• It has learned. (automate the process, give it resources)

A great proof of concept is an AGI of computer games, exceeding human skill.

Ultimately, A.I. improves A.I., O.I improve O.I, Neoromphics improves meromorphic and each building the other, and then we can turn our sights to healthcare. At Immortality its all about openingt he floodgates to better healthcare so the scientific method is central, other methods beside the already mentioned Engineering Design Process are the Project Management Lifecycle, Software Development Life Cycle (SDLC), Quality Improvement Process (PDCA Cycle), Data Science Process, Lean Six Sigma (DMAIC), Product Development Process, Design Thinking Process, Business Process Management (BPM) Lifecycle, Marketing Research Process, Risk Management Process, SWAT analysis and more.

Making models this way is about training time and the meticulous task of parting the LLM piece by piece. There are 100% several open source models to build from, these models are about making the perfect base model to build off.

• BERT and RoBERTa: These models are strong choices for tasks that require deep bidirectional understanding of text, such as question answering and text classification.
• GPT: Ideal for generative tasks like text generation and language modeling.
• XLNet: Offers a balance between BERT's bidirectional capabilities and GPT's autoregressive nature, making it suitable for a wide range of tasks.
• T5: The most versatile model, capable of handling any text-to-text task with a unified framework.