Freddy

The Action Lab - I Broke ChatGPT With This Paradox in Video

frankinstien

Death in complex organisms is deliberate in General Chat

For decades now the issue of aging and eventual death have been topics where some argue death is not something life implements as an adaptation. But looking at Salmon one can see that death in that organism is deliberate as the process is induced after the fish procreates. Well if it's deliberate in Salmon might it also be deliberate in other organisms? The reason this issue has even come up is I have another dog coming to that stage in life where they start to have health issues. Many argue the occurrence of cancer in the aged human population is due to prolonged exposure to carcinogens. If that were the case then why does it happen to dogs and even mice as they get older since they live much shorter lives and therefore are less exposed than humans with respect to time?  

Now I'll introduce the Thymus, it's an organ located in your chest and is responsible for T-cell production, and guess what? It shrinks over time and that shrinkage starts at birth! The less Thymus tissue you have the more vulnerable you are to infectious disease and cancers. The T-cells can detect cancer cells and as the Thymus shrinks it produces fewer T-cells and could get damaged from this shrinkage to produce malformed T-cells.

It would appear that death is deliberate since the Thymus is on an involution path that's genetically controlled. It would appear species have to reproduce frequently enough to cope with their genetic death sentence. So, we can see that life developed this approach as a species adaptation that is reinforced through natural selection. This means the life span of organisms is intrinsic to a species' viability.

Of all the mammals bowhead whales have the longest life span, 200 years! It would be interesting to study the bowhead's Thymus and its genetic differences from other animals including humans. If we can apply genetic tools to the bowhead Thymus, including AI, we might be able to triple human life spans...


https://en.wikipedia.org/wiki/Thymic_involution#:~:text=Under%20certain%20circumstances%2C%20the%20thymus,infections%2Cpregnancy%2C%20and%20malnutrition.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3222953/#:~:text=The%20incidence%20of%20most%20common,immune%20function%2C%20termed%20immune%20senescence.

https://oceanservice.noaa.gov/facts/bowhead.html#:~:text=Scientists%20agree%20that%20the%20bowhead,in%20the%20contemporary%20animal%20kingdom.

MikeB

OpenAI Speech-to-Speech Reasoning Demo in AI News

This is trending #11 on YouTube.

Speech has a solid three second delay, and responses are what you expect from the statistical analysis and middle-average contextual analysis approach.

It would be a whole new layer of interesting if responses included the own robots' survival instinct, like throwing in a few "what about me?" and "have you seen my charger?" responses, but maybe that's a bad look or conclusions could be drawn about the danger of AI in a physical sense.

WriterOfMinds

Project Acuitas in General Project Discussion

Block diagram of Acuitas, the symbolic cognitive architecture:



<<< EDIT >>>
Since this is the post that pops up on the home page every time my thread gets bumped, I'm editing it to drop the most recent block diagram here. The info that was originally in this post, I will move into the second post of the thread.
<<< EDIT >>>

ivan.moony

A rather dark question... in General Chat

Any opinions, maybe?

8pla.net

Garbage - The World Is Not Enough in Video

frankinstien

Say good-bye to GPUs... in AI News

Here's a method to do classifications without any traditional GPU  or CPU, it's done with a single fiber optic cable!

frankinstien

Neuron clustering in General Chat

From this study, it appears that there is a common pattern of connectivity and clustering in mammalian brains. When looking at the layering of mammal brains: paleo, archicortex, and neo cortexes you can get a picture of what other animals may experience. For instance, the temporal immersive experience humans have is due to the hippocampus, it literally takes snaps shots of a wide array of neural activity where the system can differentiate the occurrences of events. That, and the limbic system that processes emotional states to make decisions of an anticipated outcome or experienced event. This means that the experience of being in the now but feeling that flow of events fading into the past and the anticipation of future events isn't just human since the architectural structures that perform those functions are in other animals as well.  This study furthers that assertion by validating that not only is the layered structure of the brains of mammals identical but how they cluster and wire are identical as well.  Imagine not having the symbolic abilities of a human and just feeling the notions that you can feel that represent complex scenarios and relationships. I don't know how many times I've gotten an idea and it's just a feeling that I know I can translate into words, but I don't have to.

This study insinuates that human intelligence is derived from animal intelligence. But this goes even further because avian brains are architecturally different and so are octopi where avians and octopi do have the ability to empathize and avians at least, have a similar brain structure that performs an identical process to the hippocampus in mammalian brains. Because these different brain architectures exhibit identical types of processing there can be logically equivalent processes to affect the same effect. This is subtle and implies that because neurons are state machines the human experience of that temporal immersion could be experienced by an AI!

While the mammalian brain is far more complex than any AI system to date, that doesn't mean optimization could not allow for a similar sophistication. I mean look at fractals in nature, look at how complex and time-consuming the processes are to effect a fractal rendering. Fractals in landscapes can take centuries,  or millennials if not millions of years, with a decent GPU complex fractal structures of landscapes take milliseconds. So the potential to find optimized solutions to what biology has mastered is possible...

ivan.moony

Reasoner.js: a framework for generalized theory synthesis in General Project Discussion

the idea

Experimentation with different custom made programming languages related to theorem proving has been one of my interests for a long time. Why theorem proving? Because it seemed like a logical step towards AI. In a meanwhile, deep learning ANNs (artificial neural networks) took over the world, and it could be a good thing. Similarity between an ANN and a theorem prover is that the starting point and ending result are the same in both cases, but the process in between is different. ANN utilizes pretty obscure weighted graph connecting input and output while theorem proving (in cases I'm considering) utilizes potentially human readable set of constructive theorems that need to be combined to connect input to output.

The theorem proving is still an area of my interest, but with addition of one missing link that ANNs seem to do pretty well (or at least in some extent). ANNs use the nowdays-all-present deep learning techniques to automatically form the connections between input and output. Analogously, the link I'm trying to explore is an alternative to deep learning in a form of automated semi-axiom construction. Here, I'm making a distinction between theorem proving and semi-axiom construction. In theorem proving, axioms need to be manually fed into the system to be used in further automated theorem construction. In semi-axiom construction, I'm seeking for a way to automatically construct semi-axioms, with semi-axioms being both axioms or theorems.

Googling around the web about this process (in 2022) gives somewhat rare and specialized results, so the space for a progress may be opened. I believe that appropriate term for the process I'm trying to explore could be "generalized theory synthesis". There are already some achievements on this process in a form of algorithm synthesis, and that is exactly direction I'm trying to head at, only applied also to theorems.

To finally explore the blurry cloud of theory synthesis and to see it in more focused features, the plan would be to fuse a selection of my existing languages, and present them in gradual form under the same language named Tricosm. The language should be applicable to theory synthesis, as well as to algorithm synthesis. In a simple example, from provided data:

input -> output
---------------
    2 -> 4
    4 -> 8
    8 -> 16
   16 -> 32

one of the generated theories (given that we already know how to handle integers and multiplication) would be:

input -> output
---------------
    x -> 2 * x

MikeB

Pattern based NLP & ASR in General Project Discussion

This is a project I've been working on for a few years. In 2019, I tested it on Pandora Bots. This year I'm converting it to C/C++.

The main goal is to be small, fast, and white-box, instead of algorithms, knowledge, self-learning.

Broadly it works as a word and sentence compressor.

Words are matched to a pre-defined list of words in a lookup table. Matches return one 8-bit character symbol for sentence matching, and two other 8-bit symbols for context and uniqueness. [March 2023] There are 49 total word groups for the first symbol, and up to 256 for the second and third individually.

Sentences made of 4-10 one character symbols, where each symbol is 1 of 49 options, each containing hundreds to thousands of words. This means each sentence can detect hundreds of millions of sentences which contain similar meaning. These sentences are grouped and stored in a pre-defined list which compress the sentence to an intention symbol.

For a chatbot, the developer may use the one-character intentions, with multiple one-character word context in pre-defined lists to cover practically all possible spoken interactions, with a good level of attentiveness to the original sentence and a deliberate white-box response. Output can be further modified using another lookup table to output randomness in text/audio response. A good number of literal responses to cover a broad range of sentence intentions is 50-100. This makes changing chatbot personality very easy.

Size and Response times:

In 2019 in pandora bots (1000 words, 200 sentences) size is ~500kb, speed ~1 second response.
In 2020 in C on an Arm Cortex M4 @ 120mhz , total size is ~159kb, speed 15-100 ms / sentence.
In 2021 in C on a PC @ 2.6ghz with Binary Searching (set up time of 70ms), speed ~1ms / sentence.

Other chatbot features:

Total size is < 500kb including word databases.
One sentence generally takes less than 1 ms to process.
Limited chatbot responses make it easy to record an actors voice and change personality.
Private information is stripped during word compression (words that aren't in the pre-defined list are lost and non-recoverable).

Fine differentiation of intentions, eg between: Wondering, Questions, and Directions - "can you speak english" "do you speak english" "speak english".

Can count occurance of emotional words, logical words, burning-analyser words, light-sense words to reply in kind better.

For the problem of chatbots in experiences/games and/or cpu restricted platforms, it solves
Too much data or processing power required.
Cannot change the personality/no personality.
Cannot change the language/only one language.
Chatbot escaping the topic due to bad intention reading.
Chatbot returning bad views / knowledge calculation (only pre-determined responses).
Chatbot terrible voice synthesis (a voice actor can record all lines including random alternates).