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I'm looking for an article showing that LLMs don't know how they work internally

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  • I found the aeticle in a post on the fediverse, and I can't find it anymore.

    The reaserchers asked a simple mathematical question to an LLM ( like 7+4) and then could see how internally it worked by finding similar paths, but nothing like performing mathematical reasoning, even if the final answer was correct.

    Then they asked the LLM to explain how it found the result, what was it's internal reasoning. The answer was detailed step by step mathematical logic, like a human explaining how to perform an addition.

    This showed 2 things:

    • LLM don't "know" how they work

    • the second answer was a rephrasing of original text used for training that explain how math works, so LLM just used that as an explanation

    I think it was a very interesting an meaningful analysis

    Can anyone help me find this?

    EDIT: thanks to @theunknownmuncher
    @lemmy.world
    https://www.anthropic.com/research/tracing-thoughts-language-model its this one

    EDIT2: I'm aware LLM dont "know" anything and don't reason, and it's exactly why I wanted to find the article. Some more details here: https://feddit.it/post/18191686/13815095

  • I found the aeticle in a post on the fediverse, and I can't find it anymore.

    The reaserchers asked a simple mathematical question to an LLM ( like 7+4) and then could see how internally it worked by finding similar paths, but nothing like performing mathematical reasoning, even if the final answer was correct.

    Then they asked the LLM to explain how it found the result, what was it's internal reasoning. The answer was detailed step by step mathematical logic, like a human explaining how to perform an addition.

    This showed 2 things:

    • LLM don't "know" how they work

    • the second answer was a rephrasing of original text used for training that explain how math works, so LLM just used that as an explanation

    I think it was a very interesting an meaningful analysis

    Can anyone help me find this?

    EDIT: thanks to @theunknownmuncher
    @lemmy.world
    https://www.anthropic.com/research/tracing-thoughts-language-model its this one

    EDIT2: I'm aware LLM dont "know" anything and don't reason, and it's exactly why I wanted to find the article. Some more details here: https://feddit.it/post/18191686/13815095

  • I found the aeticle in a post on the fediverse, and I can't find it anymore.

    The reaserchers asked a simple mathematical question to an LLM ( like 7+4) and then could see how internally it worked by finding similar paths, but nothing like performing mathematical reasoning, even if the final answer was correct.

    Then they asked the LLM to explain how it found the result, what was it's internal reasoning. The answer was detailed step by step mathematical logic, like a human explaining how to perform an addition.

    This showed 2 things:

    • LLM don't "know" how they work

    • the second answer was a rephrasing of original text used for training that explain how math works, so LLM just used that as an explanation

    I think it was a very interesting an meaningful analysis

    Can anyone help me find this?

    EDIT: thanks to @theunknownmuncher
    @lemmy.world
    https://www.anthropic.com/research/tracing-thoughts-language-model its this one

    EDIT2: I'm aware LLM dont "know" anything and don't reason, and it's exactly why I wanted to find the article. Some more details here: https://feddit.it/post/18191686/13815095

    By design, they don't know how they work. It's interesting to see this experimentally proven, but it was already known. In the same way the predictive text function on your phone keyboard doesn't know how it works.

  • By design, they don't know how they work. It's interesting to see this experimentally proven, but it was already known. In the same way the predictive text function on your phone keyboard doesn't know how it works.

    I'm aware of this and agree but:

    • I see that asking how an LLM got to their answers as a "proof" of sound reasoning has become common

    • this new trend of "reasoning" models, where an internal conversation is shown in all its steps, seems to be based on this assumption of trustable train of thoughts. And given the simple experiment I mentioned, it is extremely dangerous and misleading

    • take a look at this video: https://youtube.com/watch?v=Xx4Tpsk_fnM : everything is based on observing and directing this internal reasoning, and these guys are computer scientists. How can they trust this?

    So having a good written article at hand is a good idea imho

  • I found the aeticle in a post on the fediverse, and I can't find it anymore.

    The reaserchers asked a simple mathematical question to an LLM ( like 7+4) and then could see how internally it worked by finding similar paths, but nothing like performing mathematical reasoning, even if the final answer was correct.

    Then they asked the LLM to explain how it found the result, what was it's internal reasoning. The answer was detailed step by step mathematical logic, like a human explaining how to perform an addition.

    This showed 2 things:

    • LLM don't "know" how they work

    • the second answer was a rephrasing of original text used for training that explain how math works, so LLM just used that as an explanation

    I think it was a very interesting an meaningful analysis

    Can anyone help me find this?

    EDIT: thanks to @theunknownmuncher
    @lemmy.world
    https://www.anthropic.com/research/tracing-thoughts-language-model its this one

    EDIT2: I'm aware LLM dont "know" anything and don't reason, and it's exactly why I wanted to find the article. Some more details here: https://feddit.it/post/18191686/13815095

    Define "know".

    • An LLM can have text describing how it works and be trained on that text and respond with an answer incorporating that.

    • LLMs have no intrinsic ability to "sense" what's going on inside them, nor even a sense of time. It's just not an input to their state. You can build neural-net-based systems that do have such an input, but ChatGPT or whatever isn't that.

    • LLMs lack a lot of the mechanisms that I would call essential to be able to solve problems in a generalized way. While I think Dijkstra had a valid point:

      The question of whether a computer can think is no more interesting than the question of whether a submarine can swim.

      ...and we shouldn't let our prejudices about how a mind "should" function internally cloud how we treat artificial intelligence...it's also true that we can look at an LLM and say that it just fundamentally doesn't have the ability to do a lot of things that a human-like mind can. An LLM is, at best, something like a small part of our mind. While extracting it and playing with it in isolation can produce some interesting results, there's a lot that it can't do on its own: it won't, say, engage in goal-oriented behavior. Asking a chatbot questions that require introspection and insight on its part won't yield interesting result, because it can't really engage in introspection or insight to any meaningful degree. It has very little mutable state, unlike your mind.

  • Oh wow thank you! That's it!

    I didn't even remember now good this article was and how many experiments it collected

  • I found the aeticle in a post on the fediverse, and I can't find it anymore.

    The reaserchers asked a simple mathematical question to an LLM ( like 7+4) and then could see how internally it worked by finding similar paths, but nothing like performing mathematical reasoning, even if the final answer was correct.

    Then they asked the LLM to explain how it found the result, what was it's internal reasoning. The answer was detailed step by step mathematical logic, like a human explaining how to perform an addition.

    This showed 2 things:

    • LLM don't "know" how they work

    • the second answer was a rephrasing of original text used for training that explain how math works, so LLM just used that as an explanation

    I think it was a very interesting an meaningful analysis

    Can anyone help me find this?

    EDIT: thanks to @theunknownmuncher
    @lemmy.world
    https://www.anthropic.com/research/tracing-thoughts-language-model its this one

    EDIT2: I'm aware LLM dont "know" anything and don't reason, and it's exactly why I wanted to find the article. Some more details here: https://feddit.it/post/18191686/13815095

    Can’t help but here’s a rant on people asking LLMs to “explain their reasoning” which is impossible because they can never reason (not meant to be attacking OP, just attacking the “LLMs think and reason” people and companies that spout it):

    LLMs are just matrix math to complete the most likely next word. They don’t know anything and can’t reason.

    Anything you read or hear about LLMs or “AI” getting “asked questions” or “explain its reasoning” or talking about how they’re “thinking” is just AI propaganda to make you think they’re doing something LLMs literally can’t do but people sure wish they could.

    In this case it sounds like people who don’t understand how LLMs work eating that propaganda up and approaching LLMs like there’s something to talk to or discern from.

    If you waste egregiously high amounts of gigawatts to put everything that’s ever been typed into matrices you can operate on, you get a facsimile of the human knowledge that went into typing all of that stuff.

    It’d be impressive if the environmental toll making the matrices and using them wasn’t critically bad.

    TLDR; LLMs can never think or reason, anyone talking about them thinking or reasoning is bullshitting, they utilize almost everything that’s ever been typed to give (occasionally) reasonably useful outputs that are the most basic bitch shit because that’s the most likely next word at the cost of environmental disaster

  • Can’t help but here’s a rant on people asking LLMs to “explain their reasoning” which is impossible because they can never reason (not meant to be attacking OP, just attacking the “LLMs think and reason” people and companies that spout it):

    LLMs are just matrix math to complete the most likely next word. They don’t know anything and can’t reason.

    Anything you read or hear about LLMs or “AI” getting “asked questions” or “explain its reasoning” or talking about how they’re “thinking” is just AI propaganda to make you think they’re doing something LLMs literally can’t do but people sure wish they could.

    In this case it sounds like people who don’t understand how LLMs work eating that propaganda up and approaching LLMs like there’s something to talk to or discern from.

    If you waste egregiously high amounts of gigawatts to put everything that’s ever been typed into matrices you can operate on, you get a facsimile of the human knowledge that went into typing all of that stuff.

    It’d be impressive if the environmental toll making the matrices and using them wasn’t critically bad.

    TLDR; LLMs can never think or reason, anyone talking about them thinking or reasoning is bullshitting, they utilize almost everything that’s ever been typed to give (occasionally) reasonably useful outputs that are the most basic bitch shit because that’s the most likely next word at the cost of environmental disaster

    It's a developer option that isn't generally available on consumer-facing products. It's literally just a debug log that outputs the steps to arrive at a response, nothing more.

    It's not about novel ideation or reasoning (programmatic neural networks don't do that), but just an output of statistical data that says "Step was 90% certain, Step 2 was 89% certain...etc"

  • Can’t help but here’s a rant on people asking LLMs to “explain their reasoning” which is impossible because they can never reason (not meant to be attacking OP, just attacking the “LLMs think and reason” people and companies that spout it):

    LLMs are just matrix math to complete the most likely next word. They don’t know anything and can’t reason.

    Anything you read or hear about LLMs or “AI” getting “asked questions” or “explain its reasoning” or talking about how they’re “thinking” is just AI propaganda to make you think they’re doing something LLMs literally can’t do but people sure wish they could.

    In this case it sounds like people who don’t understand how LLMs work eating that propaganda up and approaching LLMs like there’s something to talk to or discern from.

    If you waste egregiously high amounts of gigawatts to put everything that’s ever been typed into matrices you can operate on, you get a facsimile of the human knowledge that went into typing all of that stuff.

    It’d be impressive if the environmental toll making the matrices and using them wasn’t critically bad.

    TLDR; LLMs can never think or reason, anyone talking about them thinking or reasoning is bullshitting, they utilize almost everything that’s ever been typed to give (occasionally) reasonably useful outputs that are the most basic bitch shit because that’s the most likely next word at the cost of environmental disaster

    It's true that LLMs aren't "aware" of what internal steps they are taking, so asking an LLM how they reasoned out an answer will just output text that statistically sounds right based on its training set, but to say something like "they can never reason" is provably false.

    Its obvious that you have a bias and desperately want reality to confirm it, but there's been significant research and progress in tracing internals of LLMs, that show logic, planning, and reasoning.

    EDIT: lol you can downvote me but it doesn't change evidence based research

    It’d be impressive if the environmental toll making the matrices and using them wasn’t critically bad.

    Developing a AAA video game has a higher carbon footprint than training an LLM, and running inference uses significantly less power than playing that same video game.

  • I found the aeticle in a post on the fediverse, and I can't find it anymore.

    The reaserchers asked a simple mathematical question to an LLM ( like 7+4) and then could see how internally it worked by finding similar paths, but nothing like performing mathematical reasoning, even if the final answer was correct.

    Then they asked the LLM to explain how it found the result, what was it's internal reasoning. The answer was detailed step by step mathematical logic, like a human explaining how to perform an addition.

    This showed 2 things:

    • LLM don't "know" how they work

    • the second answer was a rephrasing of original text used for training that explain how math works, so LLM just used that as an explanation

    I think it was a very interesting an meaningful analysis

    Can anyone help me find this?

    EDIT: thanks to @theunknownmuncher
    @lemmy.world
    https://www.anthropic.com/research/tracing-thoughts-language-model its this one

    EDIT2: I'm aware LLM dont "know" anything and don't reason, and it's exactly why I wanted to find the article. Some more details here: https://feddit.it/post/18191686/13815095

    There was a study by Anthropic, the company behind Claude, that developed another AI that they used as a sort of "brain scanner" for the LLM, in the sense that allowed them to see sort of a model of how the LLM "internal process" worked

  • Can’t help but here’s a rant on people asking LLMs to “explain their reasoning” which is impossible because they can never reason (not meant to be attacking OP, just attacking the “LLMs think and reason” people and companies that spout it):

    LLMs are just matrix math to complete the most likely next word. They don’t know anything and can’t reason.

    Anything you read or hear about LLMs or “AI” getting “asked questions” or “explain its reasoning” or talking about how they’re “thinking” is just AI propaganda to make you think they’re doing something LLMs literally can’t do but people sure wish they could.

    In this case it sounds like people who don’t understand how LLMs work eating that propaganda up and approaching LLMs like there’s something to talk to or discern from.

    If you waste egregiously high amounts of gigawatts to put everything that’s ever been typed into matrices you can operate on, you get a facsimile of the human knowledge that went into typing all of that stuff.

    It’d be impressive if the environmental toll making the matrices and using them wasn’t critically bad.

    TLDR; LLMs can never think or reason, anyone talking about them thinking or reasoning is bullshitting, they utilize almost everything that’s ever been typed to give (occasionally) reasonably useful outputs that are the most basic bitch shit because that’s the most likely next word at the cost of environmental disaster

    I've read that article. They used something they called an "MRI for AIs", and checked e.g. how an AI handled math questions, and then asked the AI how it came to that answer, and the pathways actually differed. While the AI talked about using a textbook answer, it actually did a different approach. That's what I remember of that article.

    But yes, it exists, and it is science, not TicTok

  • I'm aware of this and agree but:

    • I see that asking how an LLM got to their answers as a "proof" of sound reasoning has become common

    • this new trend of "reasoning" models, where an internal conversation is shown in all its steps, seems to be based on this assumption of trustable train of thoughts. And given the simple experiment I mentioned, it is extremely dangerous and misleading

    • take a look at this video: https://youtube.com/watch?v=Xx4Tpsk_fnM : everything is based on observing and directing this internal reasoning, and these guys are computer scientists. How can they trust this?

    So having a good written article at hand is a good idea imho

    I only follow some YouTubers like Digital Spaceport but there has been a lot of progress from years ago when LLM's were only predictive. They now have an inductive engine attached to the LLM to provide logic guard rails.

  • Can’t help but here’s a rant on people asking LLMs to “explain their reasoning” which is impossible because they can never reason (not meant to be attacking OP, just attacking the “LLMs think and reason” people and companies that spout it):

    LLMs are just matrix math to complete the most likely next word. They don’t know anything and can’t reason.

    Anything you read or hear about LLMs or “AI” getting “asked questions” or “explain its reasoning” or talking about how they’re “thinking” is just AI propaganda to make you think they’re doing something LLMs literally can’t do but people sure wish they could.

    In this case it sounds like people who don’t understand how LLMs work eating that propaganda up and approaching LLMs like there’s something to talk to or discern from.

    If you waste egregiously high amounts of gigawatts to put everything that’s ever been typed into matrices you can operate on, you get a facsimile of the human knowledge that went into typing all of that stuff.

    It’d be impressive if the environmental toll making the matrices and using them wasn’t critically bad.

    TLDR; LLMs can never think or reason, anyone talking about them thinking or reasoning is bullshitting, they utilize almost everything that’s ever been typed to give (occasionally) reasonably useful outputs that are the most basic bitch shit because that’s the most likely next word at the cost of environmental disaster

    People don't understand what "model" means. That's the unfortunate reality.

  • It's true that LLMs aren't "aware" of what internal steps they are taking, so asking an LLM how they reasoned out an answer will just output text that statistically sounds right based on its training set, but to say something like "they can never reason" is provably false.

    Its obvious that you have a bias and desperately want reality to confirm it, but there's been significant research and progress in tracing internals of LLMs, that show logic, planning, and reasoning.

    EDIT: lol you can downvote me but it doesn't change evidence based research

    It’d be impressive if the environmental toll making the matrices and using them wasn’t critically bad.

    Developing a AAA video game has a higher carbon footprint than training an LLM, and running inference uses significantly less power than playing that same video game.

    Too deep on the AI propaganda there, it’s completing the next word. You can give the LLM base umpteen layers to make complicated connections, still ain’t thinking.

    The LLM corpos trying to get nuclear plants to power their gigantic data centers while AAA devs aren’t trying to buy nuclear plants says that’s a straw man and you simultaneously also are wrong.

    Using a pre-trained and memory-crushed LLM that can run on a small device won’t take up too much power. But that’s not what you’re thinking of. You’re thinking of the LLM only accessible via ChatGPT’s api that has a yuge context length and massive matrices that needs hilariously large amounts of RAM and compute power to execute. And it’s still a facsimile of thought.

    It’s okay they suck and have very niche actual use cases - maybe it’ll get us to something better. But they ain’t gold, they ain't smart, and they ain’t worth destroying the planet.

  • Too deep on the AI propaganda there, it’s completing the next word. You can give the LLM base umpteen layers to make complicated connections, still ain’t thinking.

    The LLM corpos trying to get nuclear plants to power their gigantic data centers while AAA devs aren’t trying to buy nuclear plants says that’s a straw man and you simultaneously also are wrong.

    Using a pre-trained and memory-crushed LLM that can run on a small device won’t take up too much power. But that’s not what you’re thinking of. You’re thinking of the LLM only accessible via ChatGPT’s api that has a yuge context length and massive matrices that needs hilariously large amounts of RAM and compute power to execute. And it’s still a facsimile of thought.

    It’s okay they suck and have very niche actual use cases - maybe it’ll get us to something better. But they ain’t gold, they ain't smart, and they ain’t worth destroying the planet.

    it's completing the next word.

    Facts disagree, but you've decided to live in a reality that matches your biases despite real evidence, so whatever 👍

  • Can’t help but here’s a rant on people asking LLMs to “explain their reasoning” which is impossible because they can never reason (not meant to be attacking OP, just attacking the “LLMs think and reason” people and companies that spout it):

    LLMs are just matrix math to complete the most likely next word. They don’t know anything and can’t reason.

    Anything you read or hear about LLMs or “AI” getting “asked questions” or “explain its reasoning” or talking about how they’re “thinking” is just AI propaganda to make you think they’re doing something LLMs literally can’t do but people sure wish they could.

    In this case it sounds like people who don’t understand how LLMs work eating that propaganda up and approaching LLMs like there’s something to talk to or discern from.

    If you waste egregiously high amounts of gigawatts to put everything that’s ever been typed into matrices you can operate on, you get a facsimile of the human knowledge that went into typing all of that stuff.

    It’d be impressive if the environmental toll making the matrices and using them wasn’t critically bad.

    TLDR; LLMs can never think or reason, anyone talking about them thinking or reasoning is bullshitting, they utilize almost everything that’s ever been typed to give (occasionally) reasonably useful outputs that are the most basic bitch shit because that’s the most likely next word at the cost of environmental disaster

    How would you prove that someone or something is capable of reasoning or thinking?

  • Can’t help but here’s a rant on people asking LLMs to “explain their reasoning” which is impossible because they can never reason (not meant to be attacking OP, just attacking the “LLMs think and reason” people and companies that spout it):

    LLMs are just matrix math to complete the most likely next word. They don’t know anything and can’t reason.

    Anything you read or hear about LLMs or “AI” getting “asked questions” or “explain its reasoning” or talking about how they’re “thinking” is just AI propaganda to make you think they’re doing something LLMs literally can’t do but people sure wish they could.

    In this case it sounds like people who don’t understand how LLMs work eating that propaganda up and approaching LLMs like there’s something to talk to or discern from.

    If you waste egregiously high amounts of gigawatts to put everything that’s ever been typed into matrices you can operate on, you get a facsimile of the human knowledge that went into typing all of that stuff.

    It’d be impressive if the environmental toll making the matrices and using them wasn’t critically bad.

    TLDR; LLMs can never think or reason, anyone talking about them thinking or reasoning is bullshitting, they utilize almost everything that’s ever been typed to give (occasionally) reasonably useful outputs that are the most basic bitch shit because that’s the most likely next word at the cost of environmental disaster

    Who has claimed that LLMs have the capacity to reason?

  • Who has claimed that LLMs have the capacity to reason?

    The study being referenced explains in detail why they can’t. So I’d say it’s Anthropic who stated LLMs don’t have the capacity to reason, and that’s what we’re discussing.

    The popular media tends to go on and on about conflating AI with AGI and synthetic reasoning.

  • People don't understand what "model" means. That's the unfortunate reality.

    They walk down runways and pose for magazines. Do they reason? Sometimes.

  • It's true that LLMs aren't "aware" of what internal steps they are taking, so asking an LLM how they reasoned out an answer will just output text that statistically sounds right based on its training set, but to say something like "they can never reason" is provably false.

    Its obvious that you have a bias and desperately want reality to confirm it, but there's been significant research and progress in tracing internals of LLMs, that show logic, planning, and reasoning.

    EDIT: lol you can downvote me but it doesn't change evidence based research

    It’d be impressive if the environmental toll making the matrices and using them wasn’t critically bad.

    Developing a AAA video game has a higher carbon footprint than training an LLM, and running inference uses significantly less power than playing that same video game.

    but there's been significant research and progress in tracing internals of LLMs, that show logic, planning, and reasoning.

    would there be a source for such research?

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    We all know how well not regulating social media has gone, why the fuck not let's just double down.
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    If you use LLMs like they should be, i.e. as autocomplete, they're helpful. Classic autocomplete can't see me type "import" and correctly guess that I want to import a file that I just created, but Copilot can. You shouldn't expect it to understand code, but it can type more quickly than you and plug the right things in more often than not.
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    That's good to know, thanks.
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    This is also a thing in Denmark. It's required by law to even build a data center.
  • Why doesn't Nvidia have more competition?

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    It’s funny how the article asks the question, but completely fails to answer it. About 15 years ago, Nvidia discovered there was a demand for compute in datacenters that could be met with powerful GPU’s, and they were quick to respond to it, and they had the resources to focus on it strongly, because of their huge success and high profitability in the GPU market. AMD also saw the market, and wanted to pursue it, but just over a decade ago where it began to clearly show the high potential for profitability, AMD was near bankrupt, and was very hard pressed to finance developments on GPU and compute in datacenters. AMD really tried the best they could, and was moderately successful from a technology perspective, but Nvidia already had a head start, and the proprietary development system CUDA was already an established standard that was very hard to penetrate. Intel simply fumbled the ball from start to finish. After a decade of trying to push ARM down from having the mobile crown by far, investing billions or actually the equivalent of ARM’s total revenue. They never managed to catch up to ARM despite they had the better production process at the time. This was the main focus of Intel, and Intel believed that GPU would never be more than a niche product. So when intel tried to compete on compute for datacenters, they tried to do it with X86 chips, One of their most bold efforts was to build a monstrosity of a cluster of Celeron chips, which of course performed laughably bad compared to Nvidia! Because as it turns out, the way forward at least for now, is indeed the massively parralel compute capability of a GPU, which Nvidia has refined for decades, only with (inferior) competition from AMD. But despite the lack of competition, Nvidia did not slow down, in fact with increased profits, they only grew bolder in their efforts. Making it even harder to catch up. Now AMD has had more money to compete for a while, and they do have some decent compute units, but Nvidia remains ahead and the CUDA problem is still there, so for AMD to really compete with Nvidia, they have to be better to attract customers. That’s a very tall order against Nvidia that simply seems to never stop progressing. So the only other option for AMD is to sell a bit cheaper. Which I suppose they have to. AMD and Intel were the obvious competitors, everybody else is coming from even further behind. But if I had to make a bet, it would be on Huawei. Huawei has some crazy good developers, and Trump is basically forcing them to figure it out themselves, because he is blocking Huawei and China in general from using both AMD and Nvidia AI chips. And the chips will probably be made by Chinese SMIC, because they are also prevented from using advanced production in the west, most notably TSMC. China will prevail, because it’s become a national project, of both prestige and necessity, and they have a massive talent mass and resources, so nothing can stop it now. IMO USA would clearly have been better off allowing China to use American chips. Now China will soon compete directly on both production and design too.
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    My ports are on the front of the router. No backdoors for me, checkmate Atheists.