Groups | Search | Server Info | Keyboard shortcuts | Login | Register [http] [https] [nntp] [nntps]

Groups > comp.lang.prolog > #15068 > unrolled thread

What is analog computing nowadays? (Re: An old Busy Beaver ASIC (Application-Specific Integrated Circuit) (Was: Could AlphaEvolve find the sixth busy beaver ?)

Back to article view | Back to comp.lang.prolog

This discussion starts older than the indexed window; earlier articles aren't shown. The article labeled Started by below is the oldest one visible, not the original post.

Contents

  What is analog computing nowadays? (Re: An old Busy Beaver ASIC (Application-Specific Integrated Circuit) (Was: Could AlphaEvolve find the sixth busy beaver ?) Mild Shock <janburse@fastmail.fm> - 2025-12-01 11:25 +0100
    Wake-up call until everybody gets ear-bleeding (Re: What is analog computing nowadays?) Mild Shock <janburse@fastmail.fm> - 2025-12-01 12:01 +0100
      BB(745) is independent of ZFC (Was: Wake-up call until everybody gets ear-bleeding) Mild Shock <janburse@fastmail.fm> - 2025-12-01 12:07 +0100
        Write ZFC formulas on a tape (of a Turing machine) (Re: BB(745) is independent of ZFC ) Mild Shock <janburse@fastmail.fm> - 2025-12-02 17:18 +0100
          Turing machines have neurons (Re: Write ZFC formulas on a tape (of a Turing machine)) Mild Shock <janburse@fastmail.fm> - 2025-12-02 17:19 +0100
            A logical calculus in nervous activity [McCulloch & Pitts 1943] (Re: Turing machines have neurons) Mild Shock <janburse@fastmail.fm> - 2025-12-02 17:20 +0100
              Busy Beaver and Theory Consistency (Was: A logical calculus in nervous activity [McCulloch & Pitts 1943]) Mild Shock <janburse@fastmail.fm> - 2025-12-02 17:39 +0100
              Busy Beaver and Theory Consistency (Was: A logical calculus in nervous activity [McCulloch & Pitts 1943]) Mild Shock <janburse@fastmail.fm> - 2025-12-02 17:43 +0100
                Re: Busy Beaver and Theory Consistency (Was: A logical calculus in nervous activity [McCulloch & Pitts 1943]) Mild Shock <janburse@fastmail.fm> - 2025-12-02 23:18 +0100
    Re: What is analog computing nowadays? (Re: An old Busy Beaver ASIC (Application-Specific Integrated Circuit) (Was: Could AlphaEvolve find the sixth busy beaver ?) Maciej Woźniak <mlwozniak@wp.pl> - 2025-12-01 12:09 +0100
      parallel random-access machine (parallel RAM or PRAM (Was: What is analog computing nowadays?) Mild Shock <janburse@fastmail.fm> - 2025-12-01 12:15 +0100
        Re: parallel random-access machine (parallel RAM or PRAM (Was: What is analog computing nowadays?) Maciej Woźniak <mlwozniak@wp.pl> - 2025-12-01 13:23 +0100
          Nope, you can't, because of the CRCW instuction (Was: parallel random-access machine) Mild Shock <janburse@fastmail.fm> - 2025-12-01 17:12 +0100
            Algorithm introduced in Hogwild! SGD (Niu et al., 2011) (Was: Nope, you can't, because of the CRCW instuction) Mild Shock <janburse@fastmail.fm> - 2025-12-01 17:31 +0100
              PRAMs might be closer to physics: Boltzman machines, etc.. (Was: Algorithm introduced in Hogwild! SGD) Mild Shock <janburse@fastmail.fm> - 2025-12-01 18:02 +0100
            Re: Nope, you can't, because of the CRCW instuction (Was: parallel random-access machine) Maciej Woźniak <mlwozniak@wp.pl> - 2025-12-01 17:59 +0100
              PRAMs might be closer to physics: Boltzman machines, etc.. (Re: Nope, you can't, because of the CRCW instuction) Mild Shock <janburse@fastmail.fm> - 2025-12-01 18:05 +0100
              PRAMs might be closer to physics: Boltzman machines, etc.. (Re: Nope, you can't, because of the CRCW instuction) Mild Shock <janburse@fastmail.fm> - 2025-12-01 18:08 +0100
                Physics more difficult than Rasperry LED cube? (Was: PRAMs might be closer to physics: Boltzman machines, etc..) Mild Shock <janburse@fastmail.fm> - 2025-12-01 18:25 +0100
          Re: parallel random-access machine (parallel RAM or PRAM (Was: What is analog computing nowadays?) Thomas Heger <ttt_heg@web.de> - 2025-12-03 07:17 +0100
            Re: parallel random-access machine (parallel RAM or PRAM (Was: What is  analog computing nowadays?) Python <python@cccp.invalid> - 2025-12-03 06:46 +0000
            Re: parallel random-access machine (parallel RAM or PRAM) Thomas 'PointedEars' Lahn <PointedEars@web.de> - 2025-12-03 08:02 +0100
        Linux kernel's RCU-protected hash tables (Re: Algorithm introduced in Hogwild! SGD (Niu et al., 2011)) Mild Shock <janburse@fastmail.fm> - 2025-12-01 22:26 +0100
          String interning is HashSet and not HashMap (Was: Linux kernel's RCU-protected hash tables) Mild Shock <janburse@fastmail.fm> - 2025-12-01 22:40 +0100
        POINT OF VIEW OF AN ALGORITHM (Re: Algorithm introduced in Hogwild! SGD (Niu et al., 2011)) (Re: parallel random-access machine) Mild Shock <janburse@fastmail.fm> - 2025-12-01 23:12 +0100
          Introduction to AMBA® 4 ACE™ (2011) (Was: POINT OF VIEW OF AN ALGORITHM) Mild Shock <janburse@fastmail.fm> - 2025-12-01 23:37 +0100
            Sputnik Schock: Academia is Disposable [I. J. Good Ultraintelligence] (Was: Introduction to AMBA® 4 ACE™ (2011)) Mild Shock <janburse@fastmail.fm> - 2025-12-01 23:53 +0100

Page 1 of 2  [1] 2  Next page →

#15068 — What is analog computing nowadays? (Re: An old Busy Beaver ASIC (Application-Specific Integrated Circuit) (Was: Could AlphaEvolve find the sixth busy beaver ?)

Hi,

1) Classical computing = Boolean logic + von Neumann architecture

For decades, all mainstream computation was built on:
Boolean algebra
Logic gates
Scalar operations executed sequentially
Memory and compute as separate blocks
Even floating-point arithmetic was implemented on top of Boolean logic.

This shaped how programmers think — algorithms expressed
as symbolic operations, control flow, and discrete steps.

2) AI accelerators break from that model

Modern accelerators — GPUs, TPUs, NPUs, and custom matrix
engines — use a different computational substrate:

Instead of Boolean logic:
→ Bulk linear algebra over vectors/tensors

Instead of instruction-by-instruction control:
→ Dataflow graphs

Instead of sequential compute on registers:
→ Massively parallel fused-multiply-add units

Instead of manually orchestrated loops:
→ High-level declarative specs (XLA, MLIR, TVM)

Have Fun!

Bye

Mild Shock schrieb:
> Hi,
> 
> Wonder why the Coq proof even should be
> different from anything that AI could produce.
> Its not a typical Euclid proof in a few steps,
> 
> it rather uses also enumeration, just like the
> Fly Speck proof, for the Keppler Conjecture. So
> lets see what happens next, could AlphaEvolve
> 
> find the sixth busy beaver?
> 
> Bye
> 
> P.S.: Here picture of an old Busy Beaver ASIC
> (Application-Specific Integrated Circuit)
> 
> Application    Fun
> Technology    1500
> Manufacturer    VLSI Tech
> Type    Semester Thesis
> Package    DIP64
> Dimensions    3200μm x 3200μm
> Gates    2 kGE
> Voltage    5 V
> Clock    20 MHz
> 
> The Busy Beaver Coprocessor has been designed to solve the Busy Beaver 
> Function for 5 states. This function (also known as the Rado's Sigma 
> Function) is an uncomputable problem from information theory. The input 
> argument is a natural number 'n' that represents the complexity of an 
> algorithm described as a Turing Machine.
> http://asic.ethz.ch/cg/1990/Busy_Beaver.html
> 
> Mild Shock schrieb:
>> Hi,
>>
>> What we thought:
>>
>> Prediction 5 . It will never be proved that
>> Σ(5) = 4,098 and S(5) = 47,176,870.
>> -- Allen H. Brady, 1990  .
>>
>> How it started:
>>
>> To investigate AlphaEvolve’s breadth, we applied
>> the system to over 50 open problems in mathematical
>> analysis, geometry, combinatorics and number theory.
>> The system’s flexibility enabled us to set up most
>> experiments in a matter of hours. In roughly 75% of
>> cases, it rediscovered state-of-the-art solutions, to
>> the best of our knowledge.
>> https://deepmind.google/blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/ 
>>
>>
>> How its going:
>>
>> We prove that S(5) = 47, 176, 870 using the Coq proof
>> assistant. The Busy Beaver value S(n) is the maximum
>> number of steps that an n-state 2-symbol Turing machine
>> can perform from the all-zero tape before halting, and
>> S was historically introduced by Tibor Radó in 1962 as
>> one of the simplest examples of an uncomputable function.
>> The proof enumerates 181,385,789 Turing machines with 5
>> states and, for each machine, decides whether it halts or
>> not. Our result marks the first determination of a new
>> Busy Beaver value in over 40 years and the first Busy
>> Beaver value ever to be formally verified, attesting to the
>> effectiveness of massively collaborative online research
>> https://arxiv.org/pdf/2509.12337
>>
>> They claim not having used much AI. But could for
>> example AlphaEvolve do it somehow nevertheless, more or
>> less autonomously, and find the sixth busy beaver?
>>
>> Bye
> 

[toc] | [next] | [standalone]

#15070 — Wake-up call until everybody gets ear-bleeding (Re: What is analog computing nowadays?)

Hi,

I am doing the wake-up call until everybody
gets ear-bleeding. It just too cringe to
see the symbolics computing morons struggle

with connectionism. But given that humans
have a brain with neurons, it should be obvious
that symbolism and connectionism are just two

sides of the same coin.

Good Luck!

Bye

Mild Shock schrieb:
> Hi,
> 
> 1) Classical computing = Boolean logic + von Neumann architecture
> 
> For decades, all mainstream computation was built on:
> Boolean algebra
> Logic gates
> Scalar operations executed sequentially
> Memory and compute as separate blocks
> Even floating-point arithmetic was implemented on top of Boolean logic.
> 
> This shaped how programmers think — algorithms expressed
> as symbolic operations, control flow, and discrete steps.
> 
> 2) AI accelerators break from that model
> 
> Modern accelerators — GPUs, TPUs, NPUs, and custom matrix
> engines — use a different computational substrate:
> 
> Instead of Boolean logic:
> → Bulk linear algebra over vectors/tensors
> 
> Instead of instruction-by-instruction control:
> → Dataflow graphs
> 
> Instead of sequential compute on registers:
> → Massively parallel fused-multiply-add units
> 
> Instead of manually orchestrated loops:
> → High-level declarative specs (XLA, MLIR, TVM)
> 
> Have Fun!
> 
> Bye
> 
> Mild Shock schrieb:
>> Hi,
>>
>> Wonder why the Coq proof even should be
>> different from anything that AI could produce.
>> Its not a typical Euclid proof in a few steps,
>>
>> it rather uses also enumeration, just like the
>> Fly Speck proof, for the Keppler Conjecture. So
>> lets see what happens next, could AlphaEvolve
>>
>> find the sixth busy beaver?
>>
>> Bye
>>
>> P.S.: Here picture of an old Busy Beaver ASIC
>> (Application-Specific Integrated Circuit)
>>
>> Application    Fun
>> Technology    1500
>> Manufacturer    VLSI Tech
>> Type    Semester Thesis
>> Package    DIP64
>> Dimensions    3200μm x 3200μm
>> Gates    2 kGE
>> Voltage    5 V
>> Clock    20 MHz
>>
>> The Busy Beaver Coprocessor has been designed to solve the Busy Beaver 
>> Function for 5 states. This function (also known as the Rado's Sigma 
>> Function) is an uncomputable problem from information theory. The 
>> input argument is a natural number 'n' that represents the complexity 
>> of an algorithm described as a Turing Machine.
>> http://asic.ethz.ch/cg/1990/Busy_Beaver.html
>>
>> Mild Shock schrieb:
>>> Hi,
>>>
>>> What we thought:
>>>
>>> Prediction 5 . It will never be proved that
>>> Σ(5) = 4,098 and S(5) = 47,176,870.
>>> -- Allen H. Brady, 1990  .
>>>
>>> How it started:
>>>
>>> To investigate AlphaEvolve’s breadth, we applied
>>> the system to over 50 open problems in mathematical
>>> analysis, geometry, combinatorics and number theory.
>>> The system’s flexibility enabled us to set up most
>>> experiments in a matter of hours. In roughly 75% of
>>> cases, it rediscovered state-of-the-art solutions, to
>>> the best of our knowledge.
>>> https://deepmind.google/blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/ 
>>>
>>>
>>> How its going:
>>>
>>> We prove that S(5) = 47, 176, 870 using the Coq proof
>>> assistant. The Busy Beaver value S(n) is the maximum
>>> number of steps that an n-state 2-symbol Turing machine
>>> can perform from the all-zero tape before halting, and
>>> S was historically introduced by Tibor Radó in 1962 as
>>> one of the simplest examples of an uncomputable function.
>>> The proof enumerates 181,385,789 Turing machines with 5
>>> states and, for each machine, decides whether it halts or
>>> not. Our result marks the first determination of a new
>>> Busy Beaver value in over 40 years and the first Busy
>>> Beaver value ever to be formally verified, attesting to the
>>> effectiveness of massively collaborative online research
>>> https://arxiv.org/pdf/2509.12337
>>>
>>> They claim not having used much AI. But could for
>>> example AlphaEvolve do it somehow nevertheless, more or
>>> less autonomously, and find the sixth busy beaver?
>>>
>>> Bye
>>
> 

[toc] | [prev] | [next] | [standalone]

#15071 — BB(745) is independent of ZFC (Was: Wake-up call until everybody gets ear-bleeding)

Hi,

Quizz: How much neurons are necessary in the
head of turning machine, to simulate ZFC?

You have possibly to look up some modelling
of the logic of ZFC by Bernays. Don't know the

details but maybe check out:

The Undecidability of BB(748)
Understanding Godels Incompleteness Theorems
Johannes Riebel - March 2023
https://www.ingo-blechschmidt.eu/assets/bachelor-thesis-undecidability-bb748.pdf

Bye

Mild Shock schrieb:
> Hi,
> 
> I am doing the wake-up call until everybody
> gets ear-bleeding. It just too cringe to
> see the symbolics computing morons struggle
> 
> with connectionism. But given that humans
> have a brain with neurons, it should be obvious
> that symbolism and connectionism are just two
> 
> sides of the same coin.
> 
> Good Luck!
> 
> Bye
> 
> Mild Shock schrieb:
>> Hi,
>>
>> 1) Classical computing = Boolean logic + von Neumann architecture
>>
>> For decades, all mainstream computation was built on:
>> Boolean algebra
>> Logic gates
>> Scalar operations executed sequentially
>> Memory and compute as separate blocks
>> Even floating-point arithmetic was implemented on top of Boolean logic.
>>
>> This shaped how programmers think — algorithms expressed
>> as symbolic operations, control flow, and discrete steps.
>>
>> 2) AI accelerators break from that model
>>
>> Modern accelerators — GPUs, TPUs, NPUs, and custom matrix
>> engines — use a different computational substrate:
>>
>> Instead of Boolean logic:
>> → Bulk linear algebra over vectors/tensors
>>
>> Instead of instruction-by-instruction control:
>> → Dataflow graphs
>>
>> Instead of sequential compute on registers:
>> → Massively parallel fused-multiply-add units
>>
>> Instead of manually orchestrated loops:
>> → High-level declarative specs (XLA, MLIR, TVM)
>>
>> Have Fun!
>>
>> Bye
>>
>> Mild Shock schrieb:
>>> Hi,
>>>
>>> Wonder why the Coq proof even should be
>>> different from anything that AI could produce.
>>> Its not a typical Euclid proof in a few steps,
>>>
>>> it rather uses also enumeration, just like the
>>> Fly Speck proof, for the Keppler Conjecture. So
>>> lets see what happens next, could AlphaEvolve
>>>
>>> find the sixth busy beaver?
>>>
>>> Bye
>>>
>>> P.S.: Here picture of an old Busy Beaver ASIC
>>> (Application-Specific Integrated Circuit)
>>>
>>> Application    Fun
>>> Technology    1500
>>> Manufacturer    VLSI Tech
>>> Type    Semester Thesis
>>> Package    DIP64
>>> Dimensions    3200μm x 3200μm
>>> Gates    2 kGE
>>> Voltage    5 V
>>> Clock    20 MHz
>>>
>>> The Busy Beaver Coprocessor has been designed to solve the Busy 
>>> Beaver Function for 5 states. This function (also known as the Rado's 
>>> Sigma Function) is an uncomputable problem from information theory. 
>>> The input argument is a natural number 'n' that represents the 
>>> complexity of an algorithm described as a Turing Machine.
>>> http://asic.ethz.ch/cg/1990/Busy_Beaver.html
>>>
>>> Mild Shock schrieb:
>>>> Hi,
>>>>
>>>> What we thought:
>>>>
>>>> Prediction 5 . It will never be proved that
>>>> Σ(5) = 4,098 and S(5) = 47,176,870.
>>>> -- Allen H. Brady, 1990  .
>>>>
>>>> How it started:
>>>>
>>>> To investigate AlphaEvolve’s breadth, we applied
>>>> the system to over 50 open problems in mathematical
>>>> analysis, geometry, combinatorics and number theory.
>>>> The system’s flexibility enabled us to set up most
>>>> experiments in a matter of hours. In roughly 75% of
>>>> cases, it rediscovered state-of-the-art solutions, to
>>>> the best of our knowledge.
>>>> https://deepmind.google/blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/ 
>>>>
>>>>
>>>> How its going:
>>>>
>>>> We prove that S(5) = 47, 176, 870 using the Coq proof
>>>> assistant. The Busy Beaver value S(n) is the maximum
>>>> number of steps that an n-state 2-symbol Turing machine
>>>> can perform from the all-zero tape before halting, and
>>>> S was historically introduced by Tibor Radó in 1962 as
>>>> one of the simplest examples of an uncomputable function.
>>>> The proof enumerates 181,385,789 Turing machines with 5
>>>> states and, for each machine, decides whether it halts or
>>>> not. Our result marks the first determination of a new
>>>> Busy Beaver value in over 40 years and the first Busy
>>>> Beaver value ever to be formally verified, attesting to the
>>>> effectiveness of massively collaborative online research
>>>> https://arxiv.org/pdf/2509.12337
>>>>
>>>> They claim not having used much AI. But could for
>>>> example AlphaEvolve do it somehow nevertheless, more or
>>>> less autonomously, and find the sixth busy beaver?
>>>>
>>>> Bye
>>>
>>
> 

[toc] | [prev] | [next] | [standalone]

#15095 — Write ZFC formulas on a tape (of a Turing machine) (Re: BB(745) is independent of ZFC )

Hi,

Do not underestimate turing machines. I said neurons
in the "head". But a turing machine has two parts a "head"
and a moving "tape". It can then write ZFC formulas on

a "tape". But I haven't studied the proposals yet,

but its from here:

The Undecidability of BB(748)
Understanding Gödel’s Incompleteness Theorems
Johannes Riebel - March 2023
https://www.ingo-blechschmidt.eu/assets/bachelor-thesis-undecidability-bb748.pdf

The problem was proposed already here:

The Busy Beaver Frontier
Scott Aaronson
https://www.scottaaronson.com/papers/bb.pdf

Bye

Richard Damon schrieb:
 > On 12/1/25 6:08 AM, Mild Shock wrote:
 >> Hi,
 >>
 >> Quizz: How much neurons are necessary in the
 >> head of turning machine, to simulate ZFC?
 >
 > Which is just a category error, as ZFC is a set of definitions, and 
thus not something that can be "simulated"
 >
 > Also, "Turning Machines" (if you mean Turing Machines) don't have 
"neurons".
 >
 >>
 >> You have possibly to look up some modelling
 >> of the logic of ZFC by Bernays. Don't know the
 >>
 >> details but maybe check out:
 >>
 >> The Undecidability of BB(748)
 >> Understanding Godels Incompleteness Theorems
 >> Johannes Riebel - March 2023
 >> 
https://www.ingo-blechschmidt.eu/assets/bachelor-thesis-undecidability- 
bb748.pdf
 >>
 >> Bye
 >
 > But that "Modeling" isn't the sort of thing you "simulate".
 >
 > One problem is we haven't found a way to actually "reason" with 
"neurons".


Mild Shock schrieb:
> Hi,
> 
> Quizz: How much neurons are necessary in the
> head of turning machine, to simulate ZFC?
> 
> You have possibly to look up some modelling
> of the logic of ZFC by Bernays. Don't know the
> 
> details but maybe check out:
> 
> The Undecidability of BB(748)
> Understanding Godels Incompleteness Theorems
> Johannes Riebel - March 2023
> https://www.ingo-blechschmidt.eu/assets/bachelor-thesis-undecidability-bb748.pdf 
> 
> 
> Bye
> 
> Mild Shock schrieb:
>> Hi,
>>
>> I am doing the wake-up call until everybody
>> gets ear-bleeding. It just too cringe to
>> see the symbolics computing morons struggle
>>
>> with connectionism. But given that humans
>> have a brain with neurons, it should be obvious
>> that symbolism and connectionism are just two
>>
>> sides of the same coin.
>>
>> Good Luck!
>>
>> Bye
>>
>> Mild Shock schrieb:
>>> Hi,
>>>
>>> 1) Classical computing = Boolean logic + von Neumann architecture
>>>
>>> For decades, all mainstream computation was built on:
>>> Boolean algebra
>>> Logic gates
>>> Scalar operations executed sequentially
>>> Memory and compute as separate blocks
>>> Even floating-point arithmetic was implemented on top of Boolean logic.
>>>
>>> This shaped how programmers think — algorithms expressed
>>> as symbolic operations, control flow, and discrete steps.
>>>
>>> 2) AI accelerators break from that model
>>>
>>> Modern accelerators — GPUs, TPUs, NPUs, and custom matrix
>>> engines — use a different computational substrate:
>>>
>>> Instead of Boolean logic:
>>> → Bulk linear algebra over vectors/tensors
>>>
>>> Instead of instruction-by-instruction control:
>>> → Dataflow graphs
>>>
>>> Instead of sequential compute on registers:
>>> → Massively parallel fused-multiply-add units
>>>
>>> Instead of manually orchestrated loops:
>>> → High-level declarative specs (XLA, MLIR, TVM)
>>>
>>> Have Fun!
>>>
>>> Bye
>>>
>>> Mild Shock schrieb:
>>>> Hi,
>>>>
>>>> Wonder why the Coq proof even should be
>>>> different from anything that AI could produce.
>>>> Its not a typical Euclid proof in a few steps,
>>>>
>>>> it rather uses also enumeration, just like the
>>>> Fly Speck proof, for the Keppler Conjecture. So
>>>> lets see what happens next, could AlphaEvolve
>>>>
>>>> find the sixth busy beaver?
>>>>
>>>> Bye
>>>>
>>>> P.S.: Here picture of an old Busy Beaver ASIC
>>>> (Application-Specific Integrated Circuit)
>>>>
>>>> Application    Fun
>>>> Technology    1500
>>>> Manufacturer    VLSI Tech
>>>> Type    Semester Thesis
>>>> Package    DIP64
>>>> Dimensions    3200μm x 3200μm
>>>> Gates    2 kGE
>>>> Voltage    5 V
>>>> Clock    20 MHz
>>>>
>>>> The Busy Beaver Coprocessor has been designed to solve the Busy 
>>>> Beaver Function for 5 states. This function (also known as the 
>>>> Rado's Sigma Function) is an uncomputable problem from information 
>>>> theory. The input argument is a natural number 'n' that represents 
>>>> the complexity of an algorithm described as a Turing Machine.
>>>> http://asic.ethz.ch/cg/1990/Busy_Beaver.html
>>>>
>>>> Mild Shock schrieb:
>>>>> Hi,
>>>>>
>>>>> What we thought:
>>>>>
>>>>> Prediction 5 . It will never be proved that
>>>>> Σ(5) = 4,098 and S(5) = 47,176,870.
>>>>> -- Allen H. Brady, 1990  .
>>>>>
>>>>> How it started:
>>>>>
>>>>> To investigate AlphaEvolve’s breadth, we applied
>>>>> the system to over 50 open problems in mathematical
>>>>> analysis, geometry, combinatorics and number theory.
>>>>> The system’s flexibility enabled us to set up most
>>>>> experiments in a matter of hours. In roughly 75% of
>>>>> cases, it rediscovered state-of-the-art solutions, to
>>>>> the best of our knowledge.
>>>>> https://deepmind.google/blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/ 
>>>>>
>>>>>
>>>>> How its going:
>>>>>
>>>>> We prove that S(5) = 47, 176, 870 using the Coq proof
>>>>> assistant. The Busy Beaver value S(n) is the maximum
>>>>> number of steps that an n-state 2-symbol Turing machine
>>>>> can perform from the all-zero tape before halting, and
>>>>> S was historically introduced by Tibor Radó in 1962 as
>>>>> one of the simplest examples of an uncomputable function.
>>>>> The proof enumerates 181,385,789 Turing machines with 5
>>>>> states and, for each machine, decides whether it halts or
>>>>> not. Our result marks the first determination of a new
>>>>> Busy Beaver value in over 40 years and the first Busy
>>>>> Beaver value ever to be formally verified, attesting to the
>>>>> effectiveness of massively collaborative online research
>>>>> https://arxiv.org/pdf/2509.12337
>>>>>
>>>>> They claim not having used much AI. But could for
>>>>> example AlphaEvolve do it somehow nevertheless, more or
>>>>> less autonomously, and find the sixth busy beaver?
>>>>>
>>>>> Bye
>>>>
>>>
>>
> 

[toc] | [prev] | [next] | [standalone]

#15096 — Turing machines have neurons (Re: Write ZFC formulas on a tape (of a Turing machine))

Hi,

The head of a turing machine is usually a finite
state machine. That digests the tape reading, and
creates a new top writing or head movement.

A finite state machines complexity can be measured
in the number of states. Transitions between states
are labeled with tape reading and tap wrinting/

head movement. So the state is not what is writte
on the tape. Its an internal state. Its relatively
easy to turn a finite state machine, into an

artificial neural network. Already ChatGPT does that,
when reads tokens and writes tokens, just like
a turning machine.

"A Turing machine is a mathematical model of
computation describing an abstract machine that
manipulates symbols on a strip of tape according
to a table of rules"
https://en.wikipedia.org/wiki/Turing_machine

Its really funnny how people really need some
ear bleeding to understand the two sides,
symbolism and connectionsim.

Have Fun!

Bye

Mild Shock schrieb:
> Hi,
> 
> Do not underestimate turing machines. I said neurons
> in the "head". But a turing machine has two parts a "head"
> and a moving "tape". It can then write ZFC formulas on
> 
> a "tape". But I haven't studied the proposals yet,
> 
> but its from here:
> 
> The Undecidability of BB(748)
> Understanding Gödel’s Incompleteness Theorems
> Johannes Riebel - March 2023
> https://www.ingo-blechschmidt.eu/assets/bachelor-thesis-undecidability-bb748.pdf 
> 
> 
> The problem was proposed already here:
> 
> The Busy Beaver Frontier
> Scott Aaronson
> https://www.scottaaronson.com/papers/bb.pdf
> 
> Bye
> 
> Richard Damon schrieb:
>  > On 12/1/25 6:08 AM, Mild Shock wrote:
>  >> Hi,
>  >>
>  >> Quizz: How much neurons are necessary in the
>  >> head of turning machine, to simulate ZFC?
>  >
>  > Which is just a category error, as ZFC is a set of definitions, and 
> thus not something that can be "simulated"
>  >
>  > Also, "Turning Machines" (if you mean Turing Machines) don't have 
> "neurons".
>  >
>  >>
>  >> You have possibly to look up some modelling
>  >> of the logic of ZFC by Bernays. Don't know the
>  >>
>  >> details but maybe check out:
>  >>
>  >> The Undecidability of BB(748)
>  >> Understanding Godels Incompleteness Theorems
>  >> Johannes Riebel - March 2023
>  >> 
> https://www.ingo-blechschmidt.eu/assets/bachelor-thesis-undecidability- 
> bb748.pdf
>  >>
>  >> Bye
>  >
>  > But that "Modeling" isn't the sort of thing you "simulate".
>  >
>  > One problem is we haven't found a way to actually "reason" with 
> "neurons".
> 
> 
> Mild Shock schrieb:
>> Hi,
>>
>> Quizz: How much neurons are necessary in the
>> head of turning machine, to simulate ZFC?
>>
>> You have possibly to look up some modelling
>> of the logic of ZFC by Bernays. Don't know the
>>
>> details but maybe check out:
>>
>> The Undecidability of BB(748)
>> Understanding Godels Incompleteness Theorems
>> Johannes Riebel - March 2023
>> https://www.ingo-blechschmidt.eu/assets/bachelor-thesis-undecidability-bb748.pdf 
>>
>>
>> Bye
>>
>> Mild Shock schrieb:
>>> Hi,
>>>
>>> I am doing the wake-up call until everybody
>>> gets ear-bleeding. It just too cringe to
>>> see the symbolics computing morons struggle
>>>
>>> with connectionism. But given that humans
>>> have a brain with neurons, it should be obvious
>>> that symbolism and connectionism are just two
>>>
>>> sides of the same coin.
>>>
>>> Good Luck!
>>>
>>> Bye
>>>
>>> Mild Shock schrieb:
>>>> Hi,
>>>>
>>>> 1) Classical computing = Boolean logic + von Neumann architecture
>>>>
>>>> For decades, all mainstream computation was built on:
>>>> Boolean algebra
>>>> Logic gates
>>>> Scalar operations executed sequentially
>>>> Memory and compute as separate blocks
>>>> Even floating-point arithmetic was implemented on top of Boolean logic.
>>>>
>>>> This shaped how programmers think — algorithms expressed
>>>> as symbolic operations, control flow, and discrete steps.
>>>>
>>>> 2) AI accelerators break from that model
>>>>
>>>> Modern accelerators — GPUs, TPUs, NPUs, and custom matrix
>>>> engines — use a different computational substrate:
>>>>
>>>> Instead of Boolean logic:
>>>> → Bulk linear algebra over vectors/tensors
>>>>
>>>> Instead of instruction-by-instruction control:
>>>> → Dataflow graphs
>>>>
>>>> Instead of sequential compute on registers:
>>>> → Massively parallel fused-multiply-add units
>>>>
>>>> Instead of manually orchestrated loops:
>>>> → High-level declarative specs (XLA, MLIR, TVM)
>>>>
>>>> Have Fun!
>>>>
>>>> Bye
>>>>
>>>> Mild Shock schrieb:
>>>>> Hi,
>>>>>
>>>>> Wonder why the Coq proof even should be
>>>>> different from anything that AI could produce.
>>>>> Its not a typical Euclid proof in a few steps,
>>>>>
>>>>> it rather uses also enumeration, just like the
>>>>> Fly Speck proof, for the Keppler Conjecture. So
>>>>> lets see what happens next, could AlphaEvolve
>>>>>
>>>>> find the sixth busy beaver?
>>>>>
>>>>> Bye
>>>>>
>>>>> P.S.: Here picture of an old Busy Beaver ASIC
>>>>> (Application-Specific Integrated Circuit)
>>>>>
>>>>> Application    Fun
>>>>> Technology    1500
>>>>> Manufacturer    VLSI Tech
>>>>> Type    Semester Thesis
>>>>> Package    DIP64
>>>>> Dimensions    3200μm x 3200μm
>>>>> Gates    2 kGE
>>>>> Voltage    5 V
>>>>> Clock    20 MHz
>>>>>
>>>>> The Busy Beaver Coprocessor has been designed to solve the Busy 
>>>>> Beaver Function for 5 states. This function (also known as the 
>>>>> Rado's Sigma Function) is an uncomputable problem from information 
>>>>> theory. The input argument is a natural number 'n' that represents 
>>>>> the complexity of an algorithm described as a Turing Machine.
>>>>> http://asic.ethz.ch/cg/1990/Busy_Beaver.html
>>>>>
>>>>> Mild Shock schrieb:
>>>>>> Hi,
>>>>>>
>>>>>> What we thought:
>>>>>>
>>>>>> Prediction 5 . It will never be proved that
>>>>>> Σ(5) = 4,098 and S(5) = 47,176,870.
>>>>>> -- Allen H. Brady, 1990  .
>>>>>>
>>>>>> How it started:
>>>>>>
>>>>>> To investigate AlphaEvolve’s breadth, we applied
>>>>>> the system to over 50 open problems in mathematical
>>>>>> analysis, geometry, combinatorics and number theory.
>>>>>> The system’s flexibility enabled us to set up most
>>>>>> experiments in a matter of hours. In roughly 75% of
>>>>>> cases, it rediscovered state-of-the-art solutions, to
>>>>>> the best of our knowledge.
>>>>>> https://deepmind.google/blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/ 
>>>>>>
>>>>>>
>>>>>> How its going:
>>>>>>
>>>>>> We prove that S(5) = 47, 176, 870 using the Coq proof
>>>>>> assistant. The Busy Beaver value S(n) is the maximum
>>>>>> number of steps that an n-state 2-symbol Turing machine
>>>>>> can perform from the all-zero tape before halting, and
>>>>>> S was historically introduced by Tibor Radó in 1962 as
>>>>>> one of the simplest examples of an uncomputable function.
>>>>>> The proof enumerates 181,385,789 Turing machines with 5
>>>>>> states and, for each machine, decides whether it halts or
>>>>>> not. Our result marks the first determination of a new
>>>>>> Busy Beaver value in over 40 years and the first Busy
>>>>>> Beaver value ever to be formally verified, attesting to the
>>>>>> effectiveness of massively collaborative online research
>>>>>> https://arxiv.org/pdf/2509.12337
>>>>>>
>>>>>> They claim not having used much AI. But could for
>>>>>> example AlphaEvolve do it somehow nevertheless, more or
>>>>>> less autonomously, and find the sixth busy beaver?
>>>>>>
>>>>>> Bye
>>>>>
>>>>
>>>
>>
> 

[toc] | [prev] | [next] | [standalone]

#15097 — A logical calculus in nervous activity [McCulloch & Pitts 1943] (Re: Turing machines have neurons)

Hi,

You might also try this here:

McCulloch, Warren S.; Pitts, Walter (1943-12-01).
"A logical calculus of the ideas immanent in
nervous activity". The Bulletin of Mathematical
Biophysics. 5 (4): 115–133.
https://www.cs.cmu.edu/~epxing/Class/10715/reading/McCulloch.and.Pitts.pdf

It has a simple neuron model, and shows
for example in Figure 1. How it can act
in a Boolean algebra way.

If you have Booean algebra, you can also
build finite state machine. You can encode
state as bit vectors.

Bye

Mild Shock schrieb:
> Hi,
> 
> The head of a turing machine is usually a finite
> state machine. That digests the tape reading, and
> creates a new top writing or head movement.
> 
> A finite state machines complexity can be measured
> in the number of states. Transitions between states
> are labeled with tape reading and tap wrinting/
> 
> head movement. So the state is not what is writte
> on the tape. Its an internal state. Its relatively
> easy to turn a finite state machine, into an
> 
> artificial neural network. Already ChatGPT does that,
> when reads tokens and writes tokens, just like
> a turning machine.
> 
> "A Turing machine is a mathematical model of
> computation describing an abstract machine that
> manipulates symbols on a strip of tape according
> to a table of rules"
> https://en.wikipedia.org/wiki/Turing_machine
> 
> Its really funnny how people really need some
> ear bleeding to understand the two sides,
> symbolism and connectionsim.
> 
> Have Fun!
> 
> Bye
> 
> Mild Shock schrieb:
>> Hi,
>>
>> Do not underestimate turing machines. I said neurons
>> in the "head". But a turing machine has two parts a "head"
>> and a moving "tape". It can then write ZFC formulas on
>>
>> a "tape". But I haven't studied the proposals yet,
>>
>> but its from here:
>>
>> The Undecidability of BB(748)
>> Understanding Gödel’s Incompleteness Theorems
>> Johannes Riebel - March 2023
>> https://www.ingo-blechschmidt.eu/assets/bachelor-thesis-undecidability-bb748.pdf 
>>
>>
>> The problem was proposed already here:
>>
>> The Busy Beaver Frontier
>> Scott Aaronson
>> https://www.scottaaronson.com/papers/bb.pdf
>>
>> Bye
>>
>> Richard Damon schrieb:
>>  > On 12/1/25 6:08 AM, Mild Shock wrote:
>>  >> Hi,
>>  >>
>>  >> Quizz: How much neurons are necessary in the
>>  >> head of turning machine, to simulate ZFC?
>>  >
>>  > Which is just a category error, as ZFC is a set of definitions, and 
>> thus not something that can be "simulated"
>>  >
>>  > Also, "Turning Machines" (if you mean Turing Machines) don't have 
>> "neurons".
>>  >
>>  >>
>>  >> You have possibly to look up some modelling
>>  >> of the logic of ZFC by Bernays. Don't know the
>>  >>
>>  >> details but maybe check out:
>>  >>
>>  >> The Undecidability of BB(748)
>>  >> Understanding Godels Incompleteness Theorems
>>  >> Johannes Riebel - March 2023
>>  >> 
>> https://www.ingo-blechschmidt.eu/assets/bachelor-thesis-undecidability- bb748.pdf 
>>
>>  >>
>>  >> Bye
>>  >
>>  > But that "Modeling" isn't the sort of thing you "simulate".
>>  >
>>  > One problem is we haven't found a way to actually "reason" with 
>> "neurons".
>>
>>
>> Mild Shock schrieb:
>>> Hi,
>>>
>>> Quizz: How much neurons are necessary in the
>>> head of turning machine, to simulate ZFC?
>>>
>>> You have possibly to look up some modelling
>>> of the logic of ZFC by Bernays. Don't know the
>>>
>>> details but maybe check out:
>>>
>>> The Undecidability of BB(748)
>>> Understanding Godels Incompleteness Theorems
>>> Johannes Riebel - March 2023
>>> https://www.ingo-blechschmidt.eu/assets/bachelor-thesis-undecidability-bb748.pdf 
>>>
>>>
>>> Bye
>>>
>>> Mild Shock schrieb:
>>>> Hi,
>>>>
>>>> I am doing the wake-up call until everybody
>>>> gets ear-bleeding. It just too cringe to
>>>> see the symbolics computing morons struggle
>>>>
>>>> with connectionism. But given that humans
>>>> have a brain with neurons, it should be obvious
>>>> that symbolism and connectionism are just two
>>>>
>>>> sides of the same coin.
>>>>
>>>> Good Luck!
>>>>
>>>> Bye
>>>>
>>>> Mild Shock schrieb:
>>>>> Hi,
>>>>>
>>>>> 1) Classical computing = Boolean logic + von Neumann architecture
>>>>>
>>>>> For decades, all mainstream computation was built on:
>>>>> Boolean algebra
>>>>> Logic gates
>>>>> Scalar operations executed sequentially
>>>>> Memory and compute as separate blocks
>>>>> Even floating-point arithmetic was implemented on top of Boolean 
>>>>> logic.
>>>>>
>>>>> This shaped how programmers think — algorithms expressed
>>>>> as symbolic operations, control flow, and discrete steps.
>>>>>
>>>>> 2) AI accelerators break from that model
>>>>>
>>>>> Modern accelerators — GPUs, TPUs, NPUs, and custom matrix
>>>>> engines — use a different computational substrate:
>>>>>
>>>>> Instead of Boolean logic:
>>>>> → Bulk linear algebra over vectors/tensors
>>>>>
>>>>> Instead of instruction-by-instruction control:
>>>>> → Dataflow graphs
>>>>>
>>>>> Instead of sequential compute on registers:
>>>>> → Massively parallel fused-multiply-add units
>>>>>
>>>>> Instead of manually orchestrated loops:
>>>>> → High-level declarative specs (XLA, MLIR, TVM)
>>>>>
>>>>> Have Fun!
>>>>>
>>>>> Bye
>>>>>
>>>>> Mild Shock schrieb:
>>>>>> Hi,
>>>>>>
>>>>>> Wonder why the Coq proof even should be
>>>>>> different from anything that AI could produce.
>>>>>> Its not a typical Euclid proof in a few steps,
>>>>>>
>>>>>> it rather uses also enumeration, just like the
>>>>>> Fly Speck proof, for the Keppler Conjecture. So
>>>>>> lets see what happens next, could AlphaEvolve
>>>>>>
>>>>>> find the sixth busy beaver?
>>>>>>
>>>>>> Bye
>>>>>>
>>>>>> P.S.: Here picture of an old Busy Beaver ASIC
>>>>>> (Application-Specific Integrated Circuit)
>>>>>>
>>>>>> Application    Fun
>>>>>> Technology    1500
>>>>>> Manufacturer    VLSI Tech
>>>>>> Type    Semester Thesis
>>>>>> Package    DIP64
>>>>>> Dimensions    3200μm x 3200μm
>>>>>> Gates    2 kGE
>>>>>> Voltage    5 V
>>>>>> Clock    20 MHz
>>>>>>
>>>>>> The Busy Beaver Coprocessor has been designed to solve the Busy 
>>>>>> Beaver Function for 5 states. This function (also known as the 
>>>>>> Rado's Sigma Function) is an uncomputable problem from information 
>>>>>> theory. The input argument is a natural number 'n' that represents 
>>>>>> the complexity of an algorithm described as a Turing Machine.
>>>>>> http://asic.ethz.ch/cg/1990/Busy_Beaver.html
>>>>>>
>>>>>> Mild Shock schrieb:
>>>>>>> Hi,
>>>>>>>
>>>>>>> What we thought:
>>>>>>>
>>>>>>> Prediction 5 . It will never be proved that
>>>>>>> Σ(5) = 4,098 and S(5) = 47,176,870.
>>>>>>> -- Allen H. Brady, 1990  .
>>>>>>>
>>>>>>> How it started:
>>>>>>>
>>>>>>> To investigate AlphaEvolve’s breadth, we applied
>>>>>>> the system to over 50 open problems in mathematical
>>>>>>> analysis, geometry, combinatorics and number theory.
>>>>>>> The system’s flexibility enabled us to set up most
>>>>>>> experiments in a matter of hours. In roughly 75% of
>>>>>>> cases, it rediscovered state-of-the-art solutions, to
>>>>>>> the best of our knowledge.
>>>>>>> https://deepmind.google/blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/ 
>>>>>>>
>>>>>>>
>>>>>>> How its going:
>>>>>>>
>>>>>>> We prove that S(5) = 47, 176, 870 using the Coq proof
>>>>>>> assistant. The Busy Beaver value S(n) is the maximum
>>>>>>> number of steps that an n-state 2-symbol Turing machine
>>>>>>> can perform from the all-zero tape before halting, and
>>>>>>> S was historically introduced by Tibor Radó in 1962 as
>>>>>>> one of the simplest examples of an uncomputable function.
>>>>>>> The proof enumerates 181,385,789 Turing machines with 5
>>>>>>> states and, for each machine, decides whether it halts or
>>>>>>> not. Our result marks the first determination of a new
>>>>>>> Busy Beaver value in over 40 years and the first Busy
>>>>>>> Beaver value ever to be formally verified, attesting to the
>>>>>>> effectiveness of massively collaborative online research
>>>>>>> https://arxiv.org/pdf/2509.12337
>>>>>>>
>>>>>>> They claim not having used much AI. But could for
>>>>>>> example AlphaEvolve do it somehow nevertheless, more or
>>>>>>> less autonomously, and find the sixth busy beaver?
>>>>>>>
>>>>>>> Bye
>>>>>>
>>>>>
>>>>
>>>
>>
> 

[toc] | [prev] | [next] | [standalone]

#15098 — Busy Beaver and Theory Consistency (Was: A logical calculus in nervous activity [McCulloch & Pitts 1943])

Hi,

If you know BB(N), you have a halting decision procedure
for N-turing machines. Since if BB(N) is maximum number
S(N) of steps before halting,

you can just run an arbitrary turing machine, and when
its steps exceeds S(N), you know its not a halting
turing machine.

So knowing BB(N) makes the halting problem decidable.
But the halting problem is not decidable. So there
must be some M maybe where BB(M) has no S(N) , no

maximum. Idea is to construct turing machines that
relate to consistency problems, consistency problems
can be even harder than halting problems, we might

ask for the opposite, does a program never halt.
Since never halt could be interpreted that no
inconsistency is derived. Again knowing BB(N) would

help, since dedidability via S(N) is established both
ways, saying "Yes" to halt, and saying "No" to halt.
So we can show a reducibility from consistency

to busy beaver, I guess.

Bye

[toc] | [prev] | [next] | [standalone]

#15099 — Busy Beaver and Theory Consistency (Was: A logical calculus in nervous activity [McCulloch & Pitts 1943])

Hi,

If you know BB(N), you have a halting decision procedure
for N-turing machines. Since if BB(N) is maximum number
S(N) of steps before halting,

you can just run an arbitrary turing machine, and when
its steps exceeds S(N), you know its not a halting
turing machine.

So knowing BB(N) makes the halting problem decidable.
But the halting problem is not decidable. So there
must be some M maybe where BB(M) has no S(N) , no

maximum. Idea is to construct turing machines that
relate to consistency problems, consistency problems
can be even harder than halting problems, we might

ask for the opposite, does a program never halt.
Since never halt could be interpreted that no
inconsistency is derived. Again knowing BB(N) would

help, since dedidability via S(N) is established both
ways, saying "Yes" to halt, and saying "No" to not halt.
So we can show a reducibility from consistency

to busy beaver, I guess.

Bye

[toc] | [prev] | [next] | [standalone]

#15102 — Re: Busy Beaver and Theory Consistency (Was: A logical calculus in nervous activity [McCulloch & Pitts 1943])

Hi,

I don't have a problem with the notion of computability.
What makes you think citing an interesting research paper,
implies that I have a problem with computability?

Could you explain yourself?

Bye

Richard Damon schrieb:
 > On 12/2/25 11:06 AM, Mild Shock wrote:
 >> Hi,
 >>
 >> Do not underestimate turing machines. I said neurons
 >> in the "head". But a turing machine has to parts a "head"
 >> and a moving "tape". It can then write ZFC formulas on
 >
 > I think your problem is you just don't understand what computing is, 
as used in Computation theory.


Mild Shock schrieb:
> Hi,
> 
> If you know BB(N), you have a halting decision procedure
> for N-turing machines. Since if BB(N) is maximum number
> S(N) of steps before halting,
> 
> you can just run an arbitrary turing machine, and when
> its steps exceeds S(N), you know its not a halting
> turing machine.
> 
> So knowing BB(N) makes the halting problem decidable.
> But the halting problem is not decidable. So there
> must be some M maybe where BB(M) has no S(N) , no
> 
> maximum. Idea is to construct turing machines that
> relate to consistency problems, consistency problems
> can be even harder than halting problems, we might
> 
> ask for the opposite, does a program never halt.
> Since never halt could be interpreted that no
> inconsistency is derived. Again knowing BB(N) would
> 
> help, since dedidability via S(N) is established both
> ways, saying "Yes" to halt, and saying "No" to not halt.
> So we can show a reducibility from consistency
> 
> to busy beaver, I guess.
> 
> Bye

[toc] | [prev] | [next] | [standalone]

#15072

On 12/1/2025 11:25 AM, Mild Shock wrote:
> Hi,
> 
> 1) Classical computing = Boolean logic + von Neumann architecture



> 
> For decades, all mainstream computation was built on:
> Boolean algebra
> Logic gates
> Scalar operations executed sequentially
> Memory and compute as separate blocks
> Even floating-point arithmetic was implemented on top of Boolean logic.
> 
> This shaped how programmers think — algorithms expressed
> as symbolic operations, control flow, and discrete steps.
> 
> 2) AI accelerators break from that model

No, they don't, they just add one (or some)
more layer on top of it.

On the other hand, neural networks were
always outside. So were quantum computers.
It was never the only one and never the
most powerful one.

[toc] | [prev] | [next] | [standalone]

#15073 — parallel random-access machine (parallel RAM or PRAM (Was: What is analog computing nowadays?)

Hi,

You wrote:

 > No, they don't, they just add one (or some)
 > more layer on top of it.

Techically they are not von Neuman architecture.
Unified Memory with Multiple Tensor Cores is
not von Neuman architecture. But the architecture

is possibly toned down by Data Flow, so that
in principle one can run the same thing on a
von Neuman architecture.

But in principle the architecture is rather:

parallel random-access machine (parallel RAM
or PRAM) is a shared-memory abstract machine.
https://en.wikipedia.org/wiki/Parallel_RAM

The above class of machines is not widely know.
But PRAM has been also studied, already in the 80's.

Bye

Maciej Woźniak schrieb:
> On 12/1/2025 11:25 AM, Mild Shock wrote:
>> Hi,
>>
>> 1) Classical computing = Boolean logic + von Neumann architecture
> 
> 
> 
>>
>> For decades, all mainstream computation was built on:
>> Boolean algebra
>> Logic gates
>> Scalar operations executed sequentially
>> Memory and compute as separate blocks
>> Even floating-point arithmetic was implemented on top of Boolean logic.
>>
>> This shaped how programmers think — algorithms expressed
>> as symbolic operations, control flow, and discrete steps.
>>
>> 2) AI accelerators break from that model
> 
> No, they don't, they just add one (or some)
> more layer on top of it.
> 
> On the other hand, neural networks were
> always outside. So were quantum computers.
> It was never the only one and never the
> most powerful one.
> 
> 
> 
> 

[toc] | [prev] | [next] | [standalone]

#15074 — Re: parallel random-access machine (parallel RAM or PRAM (Was: What is analog computing nowadays?)

On 12/1/2025 12:15 PM, Mild Shock wrote:
> Hi,
> 
> You wrote:
> 
>  > No, they don't, they just add one (or some)
>  > more layer on top of it.
> 
> Techically they are not von Neuman architecture.
> Unified Memory with Multiple Tensor Cores is
> not von Neuman architecture. 

We can use von Neumann architecture
to emulate other architectures, but as long as it
is performed by our computers it is technically
von Neumann's.

[toc] | [prev] | [next] | [standalone]

#15075 — Nope, you can't, because of the CRCW instuction (Was: parallel random-access machine)

Hi,

Simulation is not so easy. You would need an
element of non-determinism, or if you want
call it randomness. Because PRAM has this

instructions, ERCW, CRCW, etc..

- Concurrent read concurrent write (CRCW)—
multiple processors can read and write. A
CRCW PRAM is sometimes called a concurrent
random-access machine.
https://en.wikipedia.org/wiki/Parallel_RAM

Modelling via von Neuman what happens there
can be quite challenging. At least it doesn't
allow for a direct modelling.

What a later processor sees, depends extremly
on the timing and which processor "wins" the
write.

Also I don't know what it would buy you
intellectually to simulate a PRAM on a random
von Neuman machine. The random von Neuman

machine could need more steps than the PRAM
in summary, because it has to simulate a PRAM.
But I guess its the intellectual questioning

that needs also a revision when confronted
with the new architecture of unified memory
and tensor processing cores.

Bye

Maciej Woźniak schrieb:
> On 12/1/2025 12:15 PM, Mild Shock wrote:
>> Hi,
>>
>> You wrote:
>>
>>  > No, they don't, they just add one (or some)
>>  > more layer on top of it.
>>
>> Techically they are not von Neuman architecture.
>> Unified Memory with Multiple Tensor Cores is
>> not von Neuman architecture. 
> 
> We can use von Neumann architecture
> to emulate other architectures, but as long as it
> is performed by our computers it is technically
> von Neumann's.
> 

[toc] | [prev] | [next] | [standalone]

#15076 — Algorithm introduced in Hogwild! SGD (Niu et al., 2011) (Was: Nope, you can't, because of the CRCW instuction)

Hi,

PRAM effects are a little bit contrived in AI
accelerators, since they work with matrix tiles,
that are locally cached to the tensor core.

But CRCW is quite cool for machine learning.
When the weights get updated. ChatGPT suggested
me to read this paper:

Hogwild!: A Lock-Free Approach to
Parallelizing Stochastic Gradient Descent
https://arxiv.org/pdf/1106.5730

Didn't read yet...

You might also have read the recent report how
Google trained Gemini. They had to deal with other
issues as well, like failure of a whole

tensore core.

Bye

Mild Shock schrieb:
> Hi,
> 
> Simulation is not so easy. You would need an
> element of non-determinism, or if you want
> call it randomness. Because PRAM has this
> 
> instructions, ERCW, CRCW, etc..
> 
> - Concurrent read concurrent write (CRCW)—
> multiple processors can read and write. A
> CRCW PRAM is sometimes called a concurrent
> random-access machine.
> https://en.wikipedia.org/wiki/Parallel_RAM
> 
> Modelling via von Neuman what happens there
> can be quite challenging. At least it doesn't
> allow for a direct modelling.
> 
> What a later processor sees, depends extremly
> on the timing and which processor "wins" the
> write.
> 
> Also I don't know what it would buy you
> intellectually to simulate a PRAM on a random
> von Neuman machine. The random von Neuman
> 
> machine could need more steps than the PRAM
> in summary, because it has to simulate a PRAM.
> But I guess its the intellectual questioning
> 
> that needs also a revision when confronted
> with the new architecture of unified memory
> and tensor processing cores.
> 
> Bye
> 
> Maciej Woźniak schrieb:
>> On 12/1/2025 12:15 PM, Mild Shock wrote:
>>> Hi,
>>>
>>> You wrote:
>>>
>>>  > No, they don't, they just add one (or some)
>>>  > more layer on top of it.
>>>
>>> Techically they are not von Neuman architecture.
>>> Unified Memory with Multiple Tensor Cores is
>>> not von Neuman architecture. 
>>
>> We can use von Neumann architecture
>> to emulate other architectures, but as long as it
>> is performed by our computers it is technically
>> von Neumann's.
>>
> 

[toc] | [prev] | [next] | [standalone]

#15078 — PRAMs might be closer to physics: Boltzman machines, etc.. (Was: Algorithm introduced in Hogwild! SGD)

Hi,

The bottom line is often, PRAMs might be
closer to physics. Especially for certain
machine learning algorithms or questions

from modelling perception or action. You
might get better results if you model the
problem in terms of Boltzman machines,

or whatever from the arsenal of physics.

Bye

Mild Shock schrieb:
> Hi,
> 
> PRAM effects are a little bit contrived in AI
> accelerators, since they work with matrix tiles,
> that are locally cached to the tensor core.
> 
> But CRCW is quite cool for machine learning.
> When the weights get updated. ChatGPT suggested
> me to read this paper:
> 
> Hogwild!: A Lock-Free Approach to
> Parallelizing Stochastic Gradient Descent
> https://arxiv.org/pdf/1106.5730
> 
> Didn't read yet...
> 
> You might also have read the recent report how
> Google trained Gemini. They had to deal with other
> issues as well, like failure of a whole
> 
> tensore core.
> 
> Bye
> 
> Mild Shock schrieb:
>> Hi,
>>
>> Simulation is not so easy. You would need an
>> element of non-determinism, or if you want
>> call it randomness. Because PRAM has this
>>
>> instructions, ERCW, CRCW, etc..
>>
>> - Concurrent read concurrent write (CRCW)—
>> multiple processors can read and write. A
>> CRCW PRAM is sometimes called a concurrent
>> random-access machine.
>> https://en.wikipedia.org/wiki/Parallel_RAM
>>
>> Modelling via von Neuman what happens there
>> can be quite challenging. At least it doesn't
>> allow for a direct modelling.
>>
>> What a later processor sees, depends extremly
>> on the timing and which processor "wins" the
>> write.
>>
>> Also I don't know what it would buy you
>> intellectually to simulate a PRAM on a random
>> von Neuman machine. The random von Neuman
>>
>> machine could need more steps than the PRAM
>> in summary, because it has to simulate a PRAM.
>> But I guess its the intellectual questioning
>>
>> that needs also a revision when confronted
>> with the new architecture of unified memory
>> and tensor processing cores.
>>
>> Bye
>>
>> Maciej Woźniak schrieb:
>>> On 12/1/2025 12:15 PM, Mild Shock wrote:
>>>> Hi,
>>>>
>>>> You wrote:
>>>>
>>>>  > No, they don't, they just add one (or some)
>>>>  > more layer on top of it.
>>>>
>>>> Techically they are not von Neuman architecture.
>>>> Unified Memory with Multiple Tensor Cores is
>>>> not von Neuman architecture. 
>>>
>>> We can use von Neumann architecture
>>> to emulate other architectures, but as long as it
>>> is performed by our computers it is technically
>>> von Neumann's.
>>>
>>
> 

[toc] | [prev] | [next] | [standalone]

#15077 — Re: Nope, you can't, because of the CRCW instuction (Was: parallel random-access machine)

On 12/1/2025 5:12 PM, Mild Shock wrote:
> Hi,
> 
> Simulation is not so easy. 

I've never said it is easy. Some randomness
or pseudorandomness existed for a long time,
it's not enough for me to speak about a
different architecture.

[toc] | [prev] | [next] | [standalone]

#15079 — PRAMs might be closer to physics: Boltzman machines, etc.. (Re: Nope, you can't, because of the CRCW instuction)

Hi,

The bottom line is often, PRAMs might be
closer to physics. Especially for certain
machine learning algorithms or questions

from modelling perception or action. You
might get better results if you model the
problem in terms of Boltzman machines,

or whatever from the arsenal of physics.

Bye

P.S.: Whats was a little popular for a certain
moment of time, was also the idea of partical
swarm optimization, for machine learning or

for problem solving:

Particle swarm optimization
https://en.wikipedia.org/wiki/Particle_swarm_optimization

Not sure how much of it got supperseeded
by multi sample updates, or some such.

Maciej Woźniak schrieb:
> On 12/1/2025 5:12 PM, Mild Shock wrote:
>> Hi,
>>
>> Simulation is not so easy. 
> 
> I've never said it is easy. Some randomness
> or pseudorandomness existed for a long time,
> it's not enough for me to speak about a
> different architecture.
> 

[toc] | [prev] | [next] | [standalone]

#15080 — PRAMs might be closer to physics: Boltzman machines, etc.. (Re: Nope, you can't, because of the CRCW instuction)

Hi,

The bottom line is often, PRAMs might be
closer to physics. Especially for certain
machine learning algorithms or questions

from modelling perception or action. You
might get better results if you model the
problem in terms of Boltzman machines,

or whatever from the arsenal of physics.

Bye

P.S.: Whats was a little popular for a certain
moment of time, was also the idea of partical
swarm optimization, for machine learning or

for problem solving:

Particle swarm optimization
https://en.wikipedia.org/wiki/Particle_swarm_optimization

Not sure how much of it got supperseeded,
it mostlikey survides in AlphaEvolve by Google,
looks like a genetic algorithm thing, which

is another name for this "physics".

Maciej Woźniak schrieb:
> On 12/1/2025 5:12 PM, Mild Shock wrote:
>> Hi,
>>
>> Simulation is not so easy. 
> 
> I've never said it is easy. Some randomness
> or pseudorandomness existed for a long time,
> it's not enough for me to speak about a
> different architecture.
> 

[toc] | [prev] | [next] | [standalone]

#15081 — Physics more difficult than Rasperry LED cube? (Was: PRAMs might be closer to physics: Boltzman machines, etc..)

Hi,

But the topic of physics could be much more
difficult to discuss, then the topic of von
Neumann machines, like building your obligatory

hobby LED cube with a Rasperry Pi von Neuman
style with one thread. So I basically intend not
to respond anymore, to this silly thread, since

I was flamed for these things being off topic to
physics. Already just few months ago these
AI pioneers got physics nobel prices:

Why did they get Physics Nobel prices?

John J. Hopfield
Geoffrey Hinton
https://www.nobelprize.org/prizes/physics/2024/summary/

They both worked in neural networks:

The Nobel Prize in Physics 2024 was awarded
jointly to John J. Hopfield and Geoffrey Hinton
"for foundational discoveries and inventions
that enable machine learning with artificial
neural networks"

Bye

P.S.: Not to mention from Google DeepMind:

Demis Hassabis
https://www.nobelprize.org/prizes/chemistry/2024/press-release/

Its also a premier of artificial intelligence nobel:

Demis Hassabis and John Jumper have developed an AI
model to solve a 50-year-old problem: predicting
proteins’ complex structures. These discoveries
hold enormous potential.

Mild Shock schrieb:
> Hi,
> 
> The bottom line is often, PRAMs might be
> closer to physics. Especially for certain
> machine learning algorithms or questions
> 
> from modelling perception or action. You
> might get better results if you model the
> problem in terms of Boltzman machines,
> 
> or whatever from the arsenal of physics.
> 
> Bye
> 
> P.S.: Whats was a little popular for a certain
> moment of time, was also the idea of partical
> swarm optimization, for machine learning or
> 
> for problem solving:
> 
> Particle swarm optimization
> https://en.wikipedia.org/wiki/Particle_swarm_optimization
> 
> Not sure how much of it got supperseeded,
> it mostlikey survides in AlphaEvolve by Google,
> looks like a genetic algorithm thing, which
> 
> is another name for this "physics".
> 
> Maciej Woźniak schrieb:
>> On 12/1/2025 5:12 PM, Mild Shock wrote:
>>> Hi,
>>>
>>> Simulation is not so easy. 
>>
>> I've never said it is easy. Some randomness
>> or pseudorandomness existed for a long time,
>> it's not enough for me to speak about a
>> different architecture.
>>
> 

[toc] | [prev] | [next] | [standalone]

#15108 — Re: parallel random-access machine (parallel RAM or PRAM (Was: What is analog computing nowadays?)

Am Montag000001, 01.12.2025 um 13:23 schrieb Maciej Woźniak:
> On 12/1/2025 12:15 PM, Mild Shock wrote:
>> Hi,
>>
>> You wrote:
>>
>>  > No, they don't, they just add one (or some)
>>  > more layer on top of it.
>>
>> Techically they are not von Neuman architecture.
>> Unified Memory with Multiple Tensor Cores is
>> not von Neuman architecture. 
> 
> We can use von Neumann architecture
> to emulate other architectures, but as long as it
> is performed by our computers it is technically
> von Neumann's.
> 
Did you know, that 'von Neuman architecture' was actually invented and 
patented by Konrad Zuse in Germany in the early 1930th?

The liberators stole it from Zuse (like zillions of other patents from 
other German inventors).

TH

[toc] | [prev] | [next] | [standalone]

Page 1 of 2  [1] 2  Next page →

Back to top | Article view | comp.lang.prolog

csiph-web