Ghideon

Yes, on a daily basis. Some ideas: I find myself spending considerate amount of time on data preparation to ensure the data is accurate, consistent, and ready for analysis. This is an area that is complicated and I see room for improvement. Also, in relation to data governance, Data provenance (documentation of the origins, custody, and transformations that a dataset has undergone) is another area that may be improved. A naive & practical example; assume a manager makes a decision based on numbers and diagrams in a spread sheet. Performing reviews of the decision processes and validating the data used in the decision could benefit from better functionality in the software used in the analytics. I am sure all the building blocks for the improvements already exists and that integrating them is a challenge.

Update: I wanted to share that I didn't (yet) get a chance to use the insights on the history of mathematics we discussed in this thread. An external AI expert covered the background and history of AI in their speech, so I shifted my focus to the current risks, opportunities, and guidelines related to Generative AI. I believe my presentation was well-received, as I've been asked to speak again. Hopefully I can include my view on the history of AI. A big thank you to everyone who contributed!

If there were "grooves" in the gravitational field as you propose, then the trajectory of a spacecraft traveling from Earth to the Moon (or any other celestial body in the solar system) would need to pass "through" or "over" these "grooves", right? The presence of these "grooves" would logically imply a variation in the gravitational field. This variation would, in turn, affect the spacecraft's trajectory, potentially causing deviations in speed and/or direction from the path predicted by established theories like Newton's law of universal gravitation or Einstein's theory of general relativity. However, no such deviations have been observed in any space mission to date, as far as I'm aware.

Sure!

I tried using a language model; Input prompt: "Can you help me make sense of the following post I found on a science forum. I want you to reformat the text so I can analyse it" (followed by OP). An extract from my first attempt: Maybe you should try one of these tools @Capiert to see of the output better communicates your ideas?

I think you missed the point completely. Do you know what message and meaning that appear from your analysis of the painting if it is performed using Swedish?

Where? Maybe this link to apple support: https://support.apple.com/en-us/102598, it explains the reason. (Highlighted below)

I agree. Especially since OP split the string "Tennessee" into English words to make it fit some perceived meaning.

That is because you chose to use some random words from contemporary English. Interesting how these historic "symbols" that keeps occurring in various forms from various individuals only makes sense in the individuals' native language.

Thanks, this is useful for further discussion, it rules out categories of counter arguments that one could think of. Do we agree on the following statement? "Under the assumption that we agree that Artificial Consciousness is a logical contradiction given the definitions in your article then any introduction of counter arguments from the natural sciences is pointless; such arguments do not apply."

I agree. An one addition; I don't think the brain has a "pause button" or a "global clock" that allows for a well defined state even if history wasn't an issue. Here the computer analogy may work as an illustration? A single computer typically allows a snapshot to be persisted and the state can be recreated on a different machine. In a large network with many computers and many concurrent network connections it gets tricky. Assuming constant network traffic and concurrent changes to local computers it may not be simple to take a global snapshot that correctly represents the complete state including signals in transit between computers.

That is correct. (it is a vertical drop*, the information in the question is enough to find images and videos of the ride on the net @DODOma ) * "drop tower" or "big drop" https://en.wikipedia.org/wiki/Drop_tower

Thank you for the clarification. Based on our discussion and the article: 1: Any counterargument rooted in empirical or natural science can be dismissed by referencing the foundational definitions and claims of the article. 2: The article's definitions and interpretations coherently rule out the possibility of artificial consciousness. Thus, any philosophical or logical counterargument can be dismissed, provided the article's foundational premises are accepted as correct. (I trust this sheds light on the relevance of the analogies I introduced earlier.) Side note: I haven't formally studied philosophy and seldom post in this section of the forum, so I appreciate your patience if my argumentation seems methodical.

I have looked at the article and other sources and got curious about science behind why it artificial consciousness is impossible. Your answer I'm trying to understand if that is a matter of definitions and logic. And, if so is the case, ruling out that there is any physical law making artificial consciousness impossible.

Thanks for your input. Can you provide a scientific basis or reference for the idea that artificial consciousness is impossible?

No, I have a serious interest in the topic and the root cause in science that makes artificial consciousness impossible. I'm looking for further information about the science, the basic foundations. Your answer implies that "Artificial Consciousness" is a definitional contradiction and I tried an analogy to illustrate (lets drop the analogy if you find it irrelevant). If possible I want to distinguish between philosophical interpretation and a direct scientific refutation of the possibility of artificial consciousness. Note that I am not arguing against or in favour of your claims, I am engaging in the discussion to get knowledge and insights.

Please explain the connection between the analogy I used and fart jokes.

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.