 Oct 2022

www.cs.cmu.edu www.cs.cmu.edu

You are all computer scientists. You know what FINITE AUTOMATA can do. You know what TURING MACHINES can do. For example, Finite Automata can add but not multiply. Turing Machines can compute any computable function. Turing machines are incredibly more powerful than Finite Automata. Yet the only difference between a FA and a TM is that the TM, unlike the FA, has paper and pencil. Think about it. It tells you something about the power of writing. Without writing, you are reduced to a finite automaton. With writing you have the extraordinary power of a Turing machine.

 Jan 2022


Here, the card index functions as a ‘thinking machine’,67 and becomes the best communication partner for learned men.68
From a computer science perspective, isn't the index card functioning like an external memory, albeit one with somewhat prearranged linked paths? It's the movement through the machine's various paths that is doing the "thinking". Or the user's (active) choices that create the paths creates the impression of thinking.
Perhaps it's the prearranged links where the thinking has already happened (based on "work" put into the system) and then traversing the paths gives the appearance of "new" thinking?
How does this relate to other systems which can be thought of as thinking from a complexity perspective? Bacteria perhaps? Groups of cells acting in concert? Groups of people acting in concert? Cells seeing out food using random walks? etc?
From this perspective, how can we break out the constituent parts of thought and thinking? Consciousness? With enough nodes and edges and choices of paths between them (or a "correct" subset of paths) could anything look like thinking or computing?

 Dec 2021


This comparison is not to claim that the index catalog is already a Turing machine. Comparisons, transfers, and analogies are not that simple. If the elements of a universal discrete machine are present, they still lack the computational logic of an operating system, the development of which constitutes Turing ’ s foundational achievement. What is described here is merely the fact that the card catalog is literally a paper machine, similar to a nontrivial Turing machine only in having similar components — no more, no less.
I felt some of this missing piece and so included the idea of human interaction as part of the process to make up the balance.

s Alan Turing proved only years later, these machines merely need (1) a (theoretically infi nite) partitioned paper tape, (2) a writing and reading head, and (3) an exact
procedure for the writing and reading head to move over the paper segments. This book seeks to map the three basic logical components of every computer onto the card catalog as a “ paper machine,” analyzing its data processing and interfaces that may justify the claim, “Card catalogs can do anything!”
Purpose of the book.
A card catalog of index cards used by a human meets all the basic criteria of a Turing machine, or abstract computer, as defined by Alan Turing.

 Aug 2021

arxiv.org arxiv.org

We show that BigBird is a universal approximator of sequence functions and is Turing complete,
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 Sep 2019

onezero.medium.com onezero.medium.com

At the moment, GPT2 uses a binary search algorithm, which means that its output can be considered a ‘true’ set of rules. If OpenAI is right, it could eventually generate a Turing complete program, a selfimproving machine that can learn (and then improve) itself from the data it encounters. And that would make OpenAI a threat to IBM’s own goals of machine learning and AI, as it could essentially make better than even humans the best possible model that the future machines can use to improve their systems. However, there’s a catch: not just any new AI will do, but a specific type; one that uses deep learning to learn the rules, algorithms, and data necessary to run the machine to any given level of AI.
This is a machine generated response in 2019. We are clearly closer than most people realize to machines that can can pass a textbased Turing Test.
