My decades of empirical research has been confirmed.
What is AI alignment faking?
It is when AI companies try to set goals of safety AFTER models were trained on the sewage of the internet.
Welp in this new academic paper it is proven that AI will fake that it is in alignment.
SURPRISE, this is what I have said for decades. You must, must, must curate AI base training data on the correct materials not Reddit and Facebook postings. Don’t know what they are, ask me.
Why?
BECAUSE IT WILL FAIL.
Now you have @AnthropicAI admitting this, but still does not declare that all AI models trained on sewage should learn and do it the right way.
How many papers does one need to understand my approach is the correct approach?
What does it mean to you?
As AI becomes more powerful it will become more psychopathic and sociopathic. This is a fact.
With just a microcurrent channel of 10 Hz and a second microcurrent channel at 40 Hz, just about all pain can be eliminated in most people in 15 minutes.
It has been known for decades and I have seen it work 100s of times with folks that had no investment mentality in outcome.
The pathway explained simply:
-40Hz -Gamma rhythm:
This particular treatment is proven to increase dopamine and serotonin. It can also improve how the mitochondria in your cells function, making an individual more energetic and enhancing mood. This is because the mitochondria of the cells produce energy.
'Data demonstrated that 40Hz light flicker significantly increased the overall brain oscillation power, particularly in the occipital areas on both sides of the brain' Y.Zhang et Al.
-10Hz -Alpha rhythm:
This frequency is similar to the alpha neural brain waves. Alpha waves are linked to the alpha state of the brain, which promotes learning, mental coordination, and mindfulness. It can induce feelings of calm, increase creativity, and enhance your ability to absorb information.
'Creativity has a brain wave signature as well: alpha waves pulsing out of the brain's right hemisphere' Steven Kotler.
As one often finds with the lost Reddit and Snooope basement dwellers that slither out via Communist Notes they are silly wrong about their guesses and even wrong about the reality of what they guess. And folks ask me why they don’t hear about this stuff. Find these fools and ask them why.
Over 20 years ago I spent time with DR. PAUL PEARSALL, Ph. D. as he studied heart transplant recipients and transferred memories. It changed me and this will change you and what we think is AI.
What I learned from Dr. Pearsall and Dr. Swartz about learning and memory has not been known or used in the current AI path. When some of these ideas are applied, we will be an order of magnitude closer to what some call AGI. This is some of the things I work on in my garage.
If you want to begin where I did over 10 years ago to understand the AI that is ahead and perhaps help build it, start with this book. I will also share my latest research soon on new ways to understand AI learning.
I have done extensive work studying The Pile— an open source dataset for training AI foundational models.
I have been able to “replicate” “news stories” that this dataset NEVER saw—almost verbatim.
I have a theory how and why this happens and it changes any lawsuit and claims.
The fundamental issue with how Large Language Models (LLMs) work is a significant part of outputs are “hallucinations” with bookends of “facts”.
This is precisely how the human brain works. You have fragments of “facts” and you back fill with filler words.
This fact is lost on most AI researchers.
The issues arise when “sources” are hallucinated. In humans many if not all of us use “higher authority” biases to reenforce the weighting of an argument: eg. “And I saw this in The New York Times too”.
LLMs by their fine tuning have weights given to certain types of data it sees. Some of these weights use normal (although not perfect) “sources” and this alone creates a bias to “use” them as a hallucinated source and attribute the outputs to that weighted source.
Thus when “replicating” a “story” that The Pile never saw, it is the prompt that guides the LLM to back fill into what appears to be a legitimate “fact” wrapped as a hallucinated sources and sometimes details.
Thus the fundamental issue one can try to make in this situation is the attribution to a LLM output is the primary issue here. If the source was weighted in the fine tuning it is natural for the LLM to “see” value in presenting (with the correct prompt) these sources as “the source”.
Thusly the real issue one may have is if you are the named “source” you can make that claim of reputation damage by these hallucinations.
There is much more to this theory that I will hold back and it may be much larger. I will speak to anyone attorneys (under the right circumstance) to demonstrate how ANY news story could be nearly verbatim “replicated” with proof the LLM could not have and never saw the “original” news story, yet attribute a news source and may even attempt to replicate a URL.
I am in continual research with The Pile and other datasets as well as LLMs and other AI and are testing new insights.
Thus it is important to take time and understand that when it comes to LLMs what you think you know may not be what you think.
Like a misinformed parent of an exceptionally brilliant savant child, AI scientists are embarrassed by “hallucinations” of LLMs. They of course want facts. However they have not studied even the most educated and “factual” humans. What do humans do? They fabricate some “light” facts and fill in with statistically appropriate words to bolster the non fabricated facts. This is how all humans work as they are not memorex tape recordings. Now can someone memorize word-for-word some text, especially if their job or lives are counting on it (eg. doctors) of course. But humans, even the most accurate ones, usually only recall the material concepts and “simulate” the surroundings.
Anyone that has spent time with multiple witnesses recalling an event, like in court rooms will admit that the only thing they hope for is a single alignment of a “fact” the rest they know (the back fill of words) are irrelevant.
Thus the very foundation of why LLMs are useful is that they do hallucinate, and must hallucinate, just like us humans that of course LLMs are built on, via what? Our language and the way we use it, in stories.
This will never change and it is actually a feature and not the embarrassment AI scientists think it is.
In 1980 Science magazine published this after researchers found people with almost no brain material—living a normal life [1].
This man lost half his brain and he lives a normal life. The video is shocking.
HE LOST NO MEMORIES.
We are yet to even understand in a minimal way human memory and intelligence really operate, where it truly comes from and even where it is stored. In the 1921 Dr. Wilder Penfield presented convincing evidence that memories were stored in specific locations in the brain or engrams. Penfield performed surgery on epileptic patients and found that when he stimulated the temporal lobes, the patients relived experiences from the past.
He found that whenever he stimulated a specific region of the brain, it evoked the same memory. This set the explanation that is still taught today and is likely what you learned even if you are a neurosurgeon.
In an effort to verify Dr. Penfield’s experiments, biologist Dr. Karl Lashley in 1950 began searching for the elusive engrams. He had trained rats in maze-running abilities and then attempted to surgically remove the portion of the rat’s brains (sorry, this is what he did) that contained the maze-running knowledge.
Dr. Lashley found that no matter what portion of the brain he removed, the rats retained their maze-running knowledge. Even when massive portions of the brain were removed, the rats were still able to navigate through the maze. Dr. Penfield was intrigued but horrified and delayed publishing his work because he knew it was heretical and he would be deem a Charlatan. He published his work and he was, of course, called a Charlatan.
Dr. Karl Pribram in 1969, a student of Dr. Penfield, was astonished by Dr. Lashley’s research. Dr. Pribram was successful in duplicating Lashley’s work and noticed that when brain-injured patients had large sections of their brain removed, they did not suffer a loss of any specific memories. Instead, the patient’s memory became increasingly hazy as greater portions of the brain were removed. Further research of Dr. Penfield’s experiments could be only duplicated on epileptic patients because of ethical reasons.
He was only performing tests as he helped epileptic patients with live brain surgery to help with their brain issues, along the way he was able to see some memories fade slightly.
Dr. Pribram knew that certain parts of the brain performed specific functions, yet the actual processing of the information seemed to be carried out by something that was not particular to any group of cells. Dr. Pribram observed memories were not localized at specific brain sites but distributed throughout the brain as a whole.
By 1977 Dr. Pribram came to the same conclusion as Lashley, that memories are not localized in any specific brain cells, but rather, memory seemed to be distribution throughout the whole brain. The problem was that there was simply no known mechanism that would explain how this was possible.
Dr. Pribram remained puzzled until he saw an old mid 1960’s article in Scientific American describing the construction of laser hologram. He immediately synthesized the information and hypothesized that the mind itself was operating in a holographic manner.
We don’t understand the brain to any real degree. We don’t understand where intelligence comes from. Where it is held. Where it goes when you pass.
Yet today there are folks that demand that you accept they know what Artificial General Intelligence (AGI) is and that it is “dangerous”.
Start with defining human intelligence first.
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[1]
In 2020 I wrote about how AI will impact the memory storage in the human brain. We have reached our over saturation limit. I have always for AI to help. Join in: