## How to translate automaton (Turing machine) into the program of high level programming language?

Every program in high level (“industrial”) programming language can be expresses as some Turing machine. I guess, that there exists universal algorithm for doing that (e.g. one can take the Cartesian multiplication of the domains of all the variables and the resulting space can be the state space of the Turing machine, though the handling of computer-representable floats can be a tricky – is there such general algorithm or system that does that? https://github.com/Meyermagic/Turing-Machine-Compiler is example for the programming language for Turing machine and for the transpiler that translates C programs into the language of Turing machines or see https://web.stanford.edu/class/archive/cs/cs103/cs103.1132/lectures/19/Small19.pdf for some kind of Turing assembler language). But what about the other direction – can Turing machine be rewritten in conscise program in high level programming language that uses functions, compositionality of functions and higher-order functions?

Of course, there can be infinite results to that conversion – staring from the naming of the variables and functions and ending with the data structures, content of functions, etc. But there are metrics for the quality of the software code and maximizing such metrics can result ir more or less unique answer to this stated problem.

Such conversion is very actual in the current context of reward macines for the reinforcement learning (e.g. https://arxiv.org/pdf/1909.05912.pdf) – symbolic representation of the reward function (as opposite to tabular or deep neural represenation). Such symbolic representation greatly facilitates skill transfer among different tasks and it introduces the inference during the learning process and in such way reward machines reduces the need for data and need for learning time.

One can say that the extraction of first order and higher-order functions is quite hard tasks, but this task is being tackled by the higher order meta-interpretative learning, e.g. https://www.ijcai.org/Proceedings/13/Papers/231.pdf.

So – are there research trends, works, results, frameworks, ideas, algorithms about conversion of the Turing machine into program in high level programming language (and possibly back). I am interested into any answer – be it about the functional, logic or imperative programming.

## Decidability Turing Machine

If I have the code of a TM as $$\omega$$ with $$\Sigma=\{0,1\}$$, is it decidable that $$\omega$$ is the code of an TM?

I would say yes because there is a finite number of codes of all TMs and i can create a TM that checks if the code is $$\omega$$ or it is not.

Is this the right approach?

## different types of machine learning, what is the difference?

I’m currently working as computer science developer. I created few projects that are using neural networks, but I just can’t get sense of terminology in this science.

Can somebody please help me understand the basic difference between machine learning and deep machine learning or artificial neural networks and deep neural network. I have read definition, but still I have trouble understanding difference.

For example, random forest alogrithm is part of machine learning, or deep machine learning?

And neural network with 7 linear layers, 2 convolutional layers, and dropout is a deep or artificial neural network?

Or what exacly term “artificial inteligence” refers to? etc.

## Is my data safe if I run an android app (a game) on emulator inside a virtual machine

I want to play a very popular game made for mobile platform, but, my father doesn’t allow me to touch any smartphone.
I tried bluestacks to play it on my pc. But, while installing the game, its privacy policy says that it might collect some personal data. So, I decided to use a virtual machine over which I would use an emulator to play that game.
Could that game still collect any information from the host machine?

## Correct Turing machine representation for Rice Theorem proof

Consider the language L1. From Rice Theorem I know L1 is not decidable (i.e. undecidable).

L1 = { R(M) | R(M) is a TM and 1011 ∈ L(M)}

For example if I want to represent by diagram a TM $$M_1$$ that accepts the string 1011 and a TM $$M_2$$ that doesn’t accept the string 1011 (e.g., $$M_2$$ accepts only the empty string), following the Rice Theorem not-trivial property, I need to use a (1) acceptance by final states or (2) acceptance by halting or (3) I can use both because I know (by theorem) they are equivalent?

## Can I include a Metamorphosis Machine in my HQ/Installation to get a supply of superpowered minions?

In the Powers chapter of the sourcebook Gadget Guide, it lists the Metamorphosis Machine as a device that can empower people to superhuman states, using the following power:

Metamorphosis Machine: Summon Empowered Version, General Type, Limited to Available Subjects • 2 points per rank

If I use the Effect option for my Headquarters/Installation, I get to spend up to 20 power points (in a PL 10 campaign) to design a power that can be used inside my base. If I select the Metamorphosis Machine as such an Effect, would I be able to get an arbitrary number of previously-transformed minions for the cost of a single Equipment Point? Would I need to take a different feature to get untransformed minions first, and if I do, would the transformed minions be restricted the same way the original minions were (e.g. if they were originally my Headquarters’ Personnel, would they still be restricted to only being able to act inside the base), or would they be considered a completely unrelated entity within the rules of the game?

## What are the basics of CS i should know,before I start my journey into machine learning

I am myself a non-cs graduate and would love to be a machine learning engineer.

I have learned to code and know the basics of Machine learning as well. Now I would like to know what “basics of CS” I should learn to be completely job ready.

I sometimes have difficulties reading CS documentations and don’t know how programs and computers work in background, I am also naiver on topics like memory management, operating systems, networking, electronics stuff like microprocessor, compiler design etc. Are these all necessary for my transition to AI? If they are, would you please recommend me a short learning path or books or videos. I hope I wouldn’t need to go deep in these areas. Thanks

## universal turing machine encoding

i am trying to learn universal turing machines. and i am stuck at encoding tm’s. is there a specific rule that “there can not be two ‘m’s in one sub-rule encoding.”

E.g. δ (q0, a, λ) = (q1, λ) —-> D1010m0110m

is this encoding illegal?

thanks for every one

## Can I surf the internet with a Virtual Machine not trying to malware test but may run into some

For this project I want to access software using a VM that may contain malware but I won’t be trying to malware test. I understand that malware can breach but are there any precautions to use the internet and not have the malware come back into my host machine and host network?

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