Ghideon

Thanks for your reply. Trying to understand some more from the simple one above; is the following a correct way to express how Artificial Consciousness Is Impossible according to your arguments? "From the definitions of "Artificial" and "Consciousness" it follows that Artificial Consciousness is impossible". An analogy from mathematics would be: From the definitions of "Negative" and "Natural number" it follows that negative natural number is impossible.

Just curious, can you refer to a scientific law or theorem that makes artificial consciousness impossible? Examples, analogies to illustrate my question: 1: Due to Turing's proof, it is an established fact in theoretical computer science that it's absolutely impossible to create a general algorithm that solves the halting problem for all possible program-input pairs. 2: According to the laws of thermodynamics, it's impossible to cool a system to absolute zero or below. In your opinion, is there an equivalent statement regarding the impossibility of artificial consciousness?

You're right! (I intentionally avoided complex numbers. From another thread, I noticed that the OP might benefit from understanding the basics before diving into complex solutions.)

When referencing chatgpt (and similar tools) it is advisable to include version and/or edition to be used. The development is rather quick and there are different capabilities in various editions (free, paid, beta releases...). Open AI has (recently) added python capabilities to ChatGPT. This, in my opinion, allows for possibly better output from an LLM for the type of questions OP asked (given that a reasonable prompt is used as input) since the LLM output can be based on the output from the running python code. (I notice that OP has left this topic to pursue other interests, this response is more of a general observation)

Assuming \(x \in \mathbb{R} \) then \( \sqrt[x]{x}=100 \) has no solution

\[ \frac{a}{b} \] \[ \sqrt[x]{x}=y, x \in \mathbb{R} \] Text \( \sqrt[x]{x}=y, x \in \mathbb{R} \) some more text

Thanks for the list and the book suggestion! That is a good suggestion as well. I'm also thinking of adding "Optimization" (one example: gradient descent). Note: I've not added Turing machine to the list; I see Turing as more foundational to computing in general and not a top candidate in the context of LLMs. But I'm open for suggestions and opinions.

Large Language Models (LLMs) like GPT-4 and its predecessors are, as far as I know, built upon a foundation of mathematical and computational concepts, some of which were established long ago. I've been asked to do a short presentation about LLMs and I'm thinking of including a timeline of mathematical concepts to give some context to the audience. Can you suggest significant discoveries that could be included? There is likely no exact answer and I would value your opinion. For a short list I have these as a starting point: -Probability Theory -Foundations of Calculus -Vectors and Matrices (Linear Algebra) -Neural Networks -Information Theory (entropy) (and maybe some recent things like Word Embeddings and Transformer Architecture ) I'll need to do some research to assign reasonable time stamps to the concepts.

As far as I know absurdity arise when there's a discrepancy between what is expected and what actually occurs. The expectation can arise from context, personal experience, knowledge or preferences. Example: In a scientific discussion on a science forum the opening post in this thread is absurd. Example: Once @Phi for All moved the thread to the lounge and your followups are added the context changes; any attempt at a "formal" or meaningful answer (including this answer) could be considered absurd.

Quick advice @grayson After solving the syntax issues pointed out by @Sensei you might want to take a step back and take a look at the design. It is not likely that the code you have written helps you with the task you presented.

What you have posted is not a database. Given the piece of code you've shared above as a prompt, it's likely that ChatGPT—a machine learning model trained on a large dataset to assist with natural language understanding and generation—would produce an output saying, 'No, this is not a database.'

The GitHub link I gave you above has a licens file; may be the quickest option to check if that licensing suits your needs. Also make sure to check the site where you access the images.

Another note @grayson: large scale processing of someone else's content may be profited unless you have an explicit permission.

google suggests: A list with 10000 words, maybe useful as a starting point: https://www.mit.edu/~ecprice/wordlist.10000 A larger list (466k words): https://github.com/dwyl/english-words Notes: -verify licensing before using -"inappropriate" is for you to define and handle -You need a lot more than just English words (se my note above) to get going with your project

Before digging into technical aspects; don't you need some context to tell what's appropriate and what is the definition? Quick example: nut: usually large hard-shelled seed nut: a small usually square or hexagonal metal block with internal screw thread (yes, there are more homonyms; some of which may be inappropriate depending on context)

Just curious, are you creating something like CLIP interrogator? (The CLIP Interrogator is a tool to optimize text prompts to match a given image) With some more understanding of your goals I may be able to share some tips on this

Here is the solution in my current house, it has a standard closed heating system. There is a tank with a pressurised bladder which allows for expansion: The pressure can be adjusted by adding air through a valve and/or by adding water to the closed system. Valve for adding air: Any overpressure is vented through an emergency valve (see top picture)

In case @tmdarkmatter is interested in common misconceptions and confusions and to get some insight in established models this paper may be helpful: "Expanding Confusion: common misconceptions of cosmological horizons and the superluminal expansion of the Universe" https://arxiv.org/abs/astro-ph/0310808 (Thanks to @joigus comment I remembered the paper and the possible connection to this thread.)

If there were any truth in your claims then modern weapons and warfare would easily include this kind of weather control already. What does this logic reasoning tell you about your idea?

My intuitive answer would be that CHATGPT and similar could be implemented on a Turing Machine. But here are some thoughts; hopefully others may add some insights. Assumptions that as far as I know are sound in this context: 1: CHATGPT and similar systems are build from components that can be realised by Turing machines. 2: The architecture of CHATGPT (and others) is based on distributed components and interacting concurrent computational resources. 3: Frequent changes occur through intentional updates and upgrades, unintentional failures, user interaction etc. There are other computational models than Turing, for instance Hewitt's Actor model of computation. Short description* Question: Is the Actor model of computation more suitable than Turing machine for a large distributed system such as CHATGPT? I don't not know; assume for this discussion that the answer is "yes" and move on to one of Hewitt's claims* This could mean that there may be more to @studiot's initial question than I initially thought (hence my late answer in this thread). Your thoughts? *) https://arxiv.org/pdf/1008.1459.pdf. (I chose to not go into details about the differences between the models and their applicability in this post)

Splunk allows for different approaches such as: Using a Forwarding Agent installed on a server Utilizing HTTP-based Event Collection for data transmission Employing a Logging Driver for Container Platforms Leveraging Add-ons or Serverless Functions to onboard logs More information is required to provide specific help. Once you have gathered the necessary information, it may be better to follow @Phi for All's advice. If that fails, the Splunkbase and Splunk community are quite active, as far as I know. I believe there are a limited number of active members here interested in these matters. (Note: I have integrated Splunk with other service providers in the past but not with AWS; I can only provide some general advice.)

No private meetings needed. The robustness of scientific methods enables independent researchers to reach the same conclusions.

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