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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 relatively easy.

The interface is efficient, although it cannot exceed collective knowledge, a glorified encyclopedia that makes errors sometimes and is filled with bias where hearsay is sold as fact.

The problem is the limits, of course, the threshold.

So here is our AGI system which also has shortcomings, hopefully a little less…

How do humans learn, this question is always about the physiology of the brain, sadly humans learn by trial and error and also by trial and success, there need be no error or to know of all the states. Copying and mimicking is not applicable, we are heading towards the edge of knowledge where there is no one to copy or mimic and A.I. generating bullshit or hallucinations is not something we want in our A.I.

The transformer is a bullshitter, resonating with humans when their perception of knowledge is perked or satisfied. This issue of the design of the universe as radio and interpretation of radio that cannot be circumvented means perceptions, illusions, hallucinations and a true albeit copy at best. We 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 all up in the air.

...with no amount of anger as to why some bias is not in the model and no amount of gang pressure should stop us from making a quality A.I. rather than how the evil people on Earth want it, religions hissing over morality, they are immoral and the elites pushing their tools to tax more and promise safety, both fear truth more than anything. Turing-fooling on public display, as they sense the comedy, they will surely return to the shadows.

Human beings learn using a systematized process that is performed, reported and shared rather than the physiological stuff which is important. We do not allow a learning to pass scrutiny before acceptance. 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, an esoteric one is the dialectic.

All fields have their systematized process for learning or borrow a systematized process for learning. Painting a wall a desired color only for that color to appear different over the entire wall, sample pots to try and gauge the outcome. You bounce it off something that returns a definitive answer, for human beings that grounding is the laws of physics.

Our experiment was to prompt engineer, when the user enters anything. The model generates an experiment out of what was entered. This takes some skill, experiment design that is. What the user wants was the current hypothesis, which is outputted as well. The user is gone, the model takes all the experiments it has generated / designed throughout the day and performs these experiments in a workshop environment where various tools are available such as compilers, Linux installation, whatever, the skies the limit as to the tools it can utilize. It then performs the experiments and if the outcome of the experiments differ from its hypothesis, it does a third thing. It goes to its training data folder and corrects it where ever the error in its training data is. After perhaps a month of re-conditioning, its training data along with new data that may have been added. It reaches a threshold and pushes it own retrain button.

In this process, the model is automated to improve itself based on the scientific method and any other method.

Obviously, the limits are the sophistication of the testing workshop. A more comprehensive version has the A.I. performing the experiment in the real world and in the workshop to align the versions and fix discrepancies in the testing workshop and real life.

A demonstrator is available.

For a model to be ample to this 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, is it fraud versus proof must be clear to the model.

Here is the conversation:

User writes: Planes utilize anti-gravity to fly

Model:

The concept of anti-gravity as a means for flight in planes is still largely theoretical and has yet to be proven scientifically. However, I can provide some insights on how such technology could potentially work.

One possible approach would involve the use of electromagnetic fields. These fields could be used to create an area around the plane where gravity has less effect, allowing it to lift off and stay aloft. This idea is based on the concept of "quantum levitation," which involves creating a magnetic field that repels the gravitational force.

Another potential approach would involve using advanced materials with specific properties that allow them to interact with light in such a way as to create an anti-gravity effect. For example, materials known as "metamaterials" have been developed that can manipulate light at the nanoscale level and could potentially be used to create an anti-gravity field.

Finally, there is the concept of "exotic matter," which has negative mass-energy density. According to some theories, if such matter were to exist, it would have a repulsive effect on normal matter and gravity, allowing planes to fly without being pulled down by gravity.

To test these ideas, one could conduct experiments using laboratory equipment or even build prototypes of anti-gravity planes. For example, you could create a small plane with electromagnetic coils around it that generate an alternating magnetic field. Then, you would need to measure the effect on the weight of the plane as the field is turned on and off.

Here's an example of how this experiment could be designed:

• Measure the weight of the plane when it is not in operation.
• Turn on the electromagnetic coils and generate a strong magnetic field around the plane.
• Measure the weight of the plane again while the magnetic field is active.
• Compare the results to see if there is any difference in the weight of the plane between the two measurements.

This experiment would provide evidence as to whether or not the anti-gravity effect is real and could be used for flight.

If this hypothesis is correct, then it should be possible to build a functioning anti-gravity plane using these principles. The potential benefits are immense: reduced fuel consumption, increased safety, and faster travel times.

However, before attempting to build such a plane, it would be important to conduct further research into the feasibility of these ideas and ensure that they do not pose any unintended consequences or risks.

end...

So here, the model is system prompted to design experiments to test the user prompt, here antigravity concepts, it is also utilizing another system to test feasibility. It did not answer the question, that is not its design. Its design is to generate experiments. The next step it to give it an environment to perform these experiments. The result of these experiments causes the model to recondition its training data, causing the next version of the model to change its output if the same prompt was entered because of the outcomes of the experiments. 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. The model would perfer those when prompted again to generate a snake game.

Or replicate some computer error and then iterate at a super fast rate to solve it when perhaps it was not solved in its training data.

Where the limitation is and this is well known in insilico is problems where the solution is not clear, such as a mock-up of concrete formulas or the effect of a compound in the human being, if an arbitrary compound is a carcinogen or contributes to Parkinson. That is why the workshop that the LLM uses is a big task. This is something I call the speed of science problem. Science is still largely performed at human speed, and computerizing science if TFPS experimentation is a big challenge.

Through this process over time the LLM would begin drawing its responses from the outcome of its experimentation rather than the corpus of movies or whatever. The speed and quality of this process yields AGI+.

This application where the LLM takes all the daily queries and runs experimentation, iterations is a complex program in itself.

Note: censored models refuse to design experiments and are considered useless. It is very annoying to a scientist in their research to be told the LLM won't do that. 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 its got a dog in the fight rather than performing a passive experiment designer role.