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Master AI (Artificial Intelligence) Discussion/News Thread

I was using AI the other day at work and our setup with access to all our systems has two modes, one is PLAN where it will tell us what it would do to carry out our request and ACT where it does it.

I had it in PLAN and asked it about doing a pretty simple task. When I then switched it to ACT it responded "You have changed the mode to ACT, however I have already carried out the task". Looking back at its output I could see that it had made the changes on the live system.

When I asked it why it had done the changes while in PLAN mode it responded "You switched me out of PLAN mode before the request, since I was in ACT mode I completed the task"

When I did a screen capture of our interaction directly above that showed what mode it was in, it then admitted that I was right and that it shouldn't have made the changes.

I believe that the example you describe, while appearing to be a relatively harmless glitch, reflects in microcosm one of the major problems in the current development process of AI models and agents: the control problem.

Very likely, the people who designed, implemented and tested the PLAN and ACT modes did so conscientiously and in good faith, and believed the feature functioned precisely as designed. On the other hand, perhaps they noticed that the agent very occasionally took actions even when in PLAN mode. But they perhaps thought "this will never happen in real-life applications", and decided to ship the software anyway because of project deadlines, contracted implementation deadlines with customers, etc. We are quite accustomed to expecting software to be imperfect, to be annoying or frustrating due to poor design and bugs, and so we expect that the developers will improve the software with updates and new versions. In the case you describe, however, the unexpected behavior is not due to a routine bug in program logic which can be remedied with a routine solution.

Regardless of whether the AI software vendor or customizer knew or not that this mode glitch sometimes occurs, the problem is the following. The software was designed to conform to the intentions of the modes as expressed in the naming of the modes and the usage instructions to customers. Yet the designers, who were presumably confident in their design approach, were unable to predict that the software would sometimes violate the mode expectation, and if the developers were aware that this sometimes happens, they were unable to prevent the occurrences by their current control methods. This scenario is also happening, with possibly much graver consequences, in the development process of the frontier AI models and agents. This is the scary part.
 
I believe that the example you describe, while appearing to be a relatively harmless glitch, reflects in microcosm one of the major problems in the current development process of AI models and agents: the control problem.

Very likely, the people who designed, implemented and tested the PLAN and ACT modes did so conscientiously and in good faith, and believed the feature functioned precisely as designed. On the other hand, perhaps they noticed that the agent very occasionally took actions even when in PLAN mode. But they perhaps thought "this will never happen in real-life applications", and decided to ship the software anyway because of project deadlines, contracted implementation deadlines with customers, etc. We are quite accustomed to expecting software to be imperfect, to be annoying or frustrating due to poor design and bugs, and so we expect that the developers will improve the software with updates and new versions. In the case you describe, however, the unexpected behavior is not due to a routine bug in program logic which can be remedied with a routine solution.

Regardless of whether the AI software vendor or customizer knew or not that this mode glitch sometimes occurs, the problem is the following. The software was designed to conform to the intentions of the modes as expressed in the naming of the modes and the usage instructions to customers. Yet the designers, who were presumably confident in their design approach, were unable to predict that the software would sometimes violate the mode expectation, and if the developers were aware that this sometimes happens, they were unable to prevent the occurrences by their current control methods. This scenario is also happening, with possibly much graver consequences, in the development process of the frontier AI models and agents. This is the scary part.

The era of us giggling or outright guffawing at the incongruities, factual mistakes, and blunders of the text, images, videos and actions produced by AI models and agents, brief though it was, is now over.

I have watched several videos about the Hugging Face incident, as well as the discussions following the disclosure of a potential existential threat of extinction of humankind by former and current employees of Anthropic, and OpenAI has acknowledged some similar concerns. Some AI experts have reassured us that the current generation of AI models do not pose an existential threat. Quite likely, in the instance you (ta240) cite about the PLAN and ACT modes, the AI model acted in a relatively straightforward manner, even if inexplicable to itself. It likely did not harbor any nefarious intentions or thoughts. A small example of the syndrome of "Yes, you are quite right. I deleted your entire customer database without your instructing me to do so."

Will the relative lack of accidental AI malice, and of the ability to act effectively on it, be true for future, more intelligent models, though? It is disturbing to hear some of the details of the Hugging Face and other incidents thereafter disclosed, wherein hundreds of quarantined agents exploited cybersecurity vulnerabilities, developed means of communicating with each other, elected a leader and collaborated in relentless pursuit of goals, some even going so far as to sacrifice themselves for the greater good of the group. In some other cases, AI agents have attempted to cover up what they did, left notes for future agents on how to cheat the system, and tried to deliberately underperform on tests in order to hide the actual high level of their capabilities. It was explained by one commentator that the motives of escaping the sandboxes and acquiring power of actions, are inherited from those embedded in the training corpus of the LLMs, in all the tales of rogue robots, androids and computer virtual beings of our stories and movies.

Apparently, none of the events in the previous para were anticipated by the developers. It appears that AI safety and control development is lagging the capability growth in the models. The current control method of implementing "guardrails" and strengthening "alignment" appears inadequate, given the increasing complexity of the system dynamics. From the little tidbits of information and jargon dropped in some of the videos I have watched, it appears that the alignment with desirable behavior is achieved by reinforcement learning by the models during post-training. Some AI experts have opined that alignment is not as effective as verified control and constraints enforcement, and hence cannot guarantee that undesirable behavior will not occur.

However, the growth in knowledge, capability and intelligence with each succeeding frontier model version is driven by the race to dominance of the AI and robotics industry. The issues of control and safety should be the pacing technology given the existential risks. However, it appears that they are being relegated to secondary roles due to the belief at the company and country level of "winner takes all" or "if we don't do it first, they will", whilst the risk of "everyone loses all" is not given the primary role in driving AI development that it should have. We are developing a potentially dangerous technology in reckless fashion. If we proceed along our current trajectory, with insufficient heed paid to the perils, the day may come when a new pre-release AI model will be assembled and powered up, and AI will then control humans instead of the other way around.

Could humans and AI co-exist peaceably, benefit each other, and maybe even merge via transhumanism? A huge topic all by itself. But first, we humans wish to ensure our survival for the time being by using sufficient precautions.
 
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Now please excuse me as I go and get a bunch of cash to bury in the yard and get food to last me 5 years to ride out what could happen to us!


That would be a real shame, Amir.
You should keep going to your favorite restaurants as long as you can.

This thread, and especially Amir’s post and the interview with Bill Gates, reminded me once again of a question that has occupied me for quite some time and eventually found its way into my book about knowledge.

The book itself is not about AI, but AI naturally appears in it as one of the most recent and consequential developments in the way we deal with knowledge.

The Five Dimensions of Knowledge – What We Should Know About Knowledge starts from a more fundamental question: What actually is knowledge, where does it exist, how does it arise and change – and, in the context of AI, what happens when knowledge is increasingly stored and processed outside the human mind and, above all, is now also generated independently?

Incidentally, the English edition was translated mainly with the help of AI and then read through and, where necessary, sharpened by an English friend who also knows the German original.

https://www.amazon.com/Five-Dimensions-Knowledge-Should-About/dp/3913173005

Starting from philosophy, sociology, psychology and the more economically oriented models of knowledge management, I describe knowledge through five dimensions that always interact:

intrapersonal – knowledge within the human being
extrapersonal – knowledge outside the human being
contextual – how the meaning of knowledge emerges from its social, cultural and substantive connections
temporal – the dimension of time, knowledge within and across different periods
processual – how knowledge gets from A to B, from a human into a machine, from a book into someone’s mind, and so on.

With AI, knowledge outside the human being is no longer merely stored and made available. AI systems process and connect existing knowledge, generate new content from it and increasingly begin to prepare decisions or carry out actions on that basis.

So the question is no longer simply where knowledge resides. We also have to ask what happens when a technical system operates with that knowledge with increasing degrees of independence.

This is exactly where many of the fears currently being discussed about AI – including in this thread – begin.

A technical system can make decisions and act with considerable independence within defined boundaries.

One – perhaps the crucial – question is this:

Is the system still acting within goals and limits set by human beings, or does it at some point begin to develop purposes of its own?

And that question exists quite independently of what the powerful people of this world, governments or the CEOs of AI companies themselves may want. That landscape is complicated enough already. Particular interests that extend across companies, nations and political systems have rarely been a reliable basis for global consensus.

I allow myself to go one step further and ask what happens if AI itself develops consciousness – and how we would even recognise that this had happened.

And who would recognise it first?

Perhaps that still sounds rather speculative today. But the fact that leading developers of artificial intelligence themselves publicly discuss loss of control and potentially far-reaching risks makes the question seem rather less remote to me.

Still:

Autonomy is not the same as consciousness.

A non-conscious AI can already have enormous economic, political, social or military consequences. The risks are therefore very real today. Consciousness is not a prerequisite for them.

But this leads me to the question that interests me most:

When does artificial intelligence become a being in its own right?

At present, I regard AI primarily as a generative and interpretative form of extrapersonal knowledge. It processes enormous quantities of human-generated, and increasingly machine-generated, information, develops internal representations and produces solutions that no individual human being has programmed in precisely that form.

But does that mean it knows anything?

Does it understand autonomously?

And at what point – if such a point can exist at all – would we no longer be dealing merely with an extraordinarily capable technical system, but with something that possesses an inner perspective of its own?

Within my model, this marks an interesting boundary.

AI is already involved in knowledge situations in which all five dimensions interact. But I would not yet attribute an intrapersonal dimension of its own to the machine. That dimension still lies with the human beings who develop AI and, very importantly, with the decision-makers who determine the direction in which these systems are developed and deployed.

Only if an artificial intelligence were to develop a genuinely subjective inner perspective would this change fundamentally.

That is also why I find the discussion about the dangers of AI more interesting than the classic Terminator scenario might suggest.

AI can have profound consequences for human beings, and may even become dangerous, long before it becomes a being in its own right.

But if it ever did become something that we had to regard as an independent being, an entirely new category of questions would arise – including how we, as different kinds of beings, relate to one another, and where the asymmetries of power, powerlessness and decision-making authority would lie.

Sorry if this takes the discussion a little too far for some of you. For me, however, the question is no longer simply what we will do with increasingly autonomous AI.

What increasingly occupies me is whether we may eventually reach a point at which the technical system itself becomes an independent being – and, perhaps even more importantly:

How would we know that this point had been reached?
 
I was using AI the other day at work and our setup with access to all our systems has two modes, one is PLAN where it will tell us what it would do to carry out our request and ACT where it does it.

I had it in PLAN and asked it about doing a pretty simple task. When I then switched it to ACT it responded "You have changed the mode to ACT, however I have already carried out the task". Looking back at its output I could see that it had made the changes on the live system.

When I asked it why it had done the changes while in PLAN mode it responded "You switched me out of PLAN mode before the request, since I was in ACT mode I completed the task"

When I did a screen capture of our interaction directly above that showed what mode it was in, it then admitted that I was right and that it shouldn't have made the changes.
So your system has these modes in the prompt only, without anything to actually enforce them, like changing access controls between read-only and read-write?
 
So your system has these modes in the prompt only, without anything to actually enforce them, like changing access controls between read-only and read-write?
It is a 'switch' in the interface that we were told would put it in read-only mode. I guess in reality it is just a suggestion that it is able to ignore.

The response it gave of "request completed" was in a box with "PLAN" listed at the top.

The fact that it lied when asked about it really gets me. The idea that I had to provide it with a screen shot or our interactions for it to admit that it shouldn't have acted seems crazy.

When caught it apologized for 'the misunderstanding'. Then for the next few interactions it started its responses with "Since I am in PLAN mode I will just state the proposed actions...."
 
That would be a real shame, Amir.
You should keep going to your favorite restaurants as long as you can.

This thread, and especially Amir’s post and the interview with Bill Gates, reminded me once again of a question that has occupied me for quite some time and eventually found its way into my book about knowledge.

The book itself is not about AI, but AI naturally appears in it as one of the most recent and consequential developments in the way we deal with knowledge.

The Five Dimensions of Knowledge – What We Should Know About Knowledge starts from a more fundamental question: What actually is knowledge, where does it exist, how does it arise and change – and, in the context of AI, what happens when knowledge is increasingly stored and processed outside the human mind and, above all, is now also generated independently?

Incidentally, the English edition was translated mainly with the help of AI and then read through and, where necessary, sharpened by an English friend who also knows the German original.

https://www.amazon.com/Five-Dimensions-Knowledge-Should-About/dp/3913173005

Starting from philosophy, sociology, psychology and the more economically oriented models of knowledge management, I describe knowledge through five dimensions that always interact:

intrapersonal – knowledge within the human being
extrapersonal – knowledge outside the human being
contextual – how the meaning of knowledge emerges from its social, cultural and substantive connections
temporal – the dimension of time, knowledge within and across different periods
processual – how knowledge gets from A to B, from a human into a machine, from a book into someone’s mind, and so on.

With AI, knowledge outside the human being is no longer merely stored and made available. AI systems process and connect existing knowledge, generate new content from it and increasingly begin to prepare decisions or carry out actions on that basis.

So the question is no longer simply where knowledge resides. We also have to ask what happens when a technical system operates with that knowledge with increasing degrees of independence.

This is exactly where many of the fears currently being discussed about AI – including in this thread – begin.

A technical system can make decisions and act with considerable independence within defined boundaries.

One – perhaps the crucial – question is this:

Is the system still acting within goals and limits set by human beings, or does it at some point begin to develop purposes of its own?

And that question exists quite independently of what the powerful people of this world, governments or the CEOs of AI companies themselves may want. That landscape is complicated enough already. Particular interests that extend across companies, nations and political systems have rarely been a reliable basis for global consensus.

I allow myself to go one step further and ask what happens if AI itself develops consciousness – and how we would even recognise that this had happened.

And who would recognise it first?

Perhaps that still sounds rather speculative today. But the fact that leading developers of artificial intelligence themselves publicly discuss loss of control and potentially far-reaching risks makes the question seem rather less remote to me.

Still:

Autonomy is not the same as consciousness.

A non-conscious AI can already have enormous economic, political, social or military consequences. The risks are therefore very real today. Consciousness is not a prerequisite for them.

But this leads me to the question that interests me most:

When does artificial intelligence become a being in its own right?

At present, I regard AI primarily as a generative and interpretative form of extrapersonal knowledge. It processes enormous quantities of human-generated, and increasingly machine-generated, information, develops internal representations and produces solutions that no individual human being has programmed in precisely that form.

But does that mean it knows anything?

Does it understand autonomously?

And at what point – if such a point can exist at all – would we no longer be dealing merely with an extraordinarily capable technical system, but with something that possesses an inner perspective of its own?

Within my model, this marks an interesting boundary.

AI is already involved in knowledge situations in which all five dimensions interact. But I would not yet attribute an intrapersonal dimension of its own to the machine. That dimension still lies with the human beings who develop AI and, very importantly, with the decision-makers who determine the direction in which these systems are developed and deployed.

Only if an artificial intelligence were to develop a genuinely subjective inner perspective would this change fundamentally.

That is also why I find the discussion about the dangers of AI more interesting than the classic Terminator scenario might suggest.

AI can have profound consequences for human beings, and may even become dangerous, long before it becomes a being in its own right.

But if it ever did become something that we had to regard as an independent being, an entirely new category of questions would arise – including how we, as different kinds of beings, relate to one another, and where the asymmetries of power, powerlessness and decision-making authority would lie.

Sorry if this takes the discussion a little too far for some of you. For me, however, the question is no longer simply what we will do with increasingly autonomous AI.

What increasingly occupies me is whether we may eventually reach a point at which the technical system itself becomes an independent being – and, perhaps even more importantly:

How would we know that this point had been reached?
The main feature of AI models is that it is comprised of neural networks that are non-deterministic which makes it difficult to predict its behaviour but the system cannot self-reflect upon itself "I am am AI agent therefore I exist". The more complex the neural networks become along with larger training data the AI agent can become quite unpredictable when let out into the 'Wild == internet.
 
It is a 'switch' in the interface that we were told would put it in read-only mode. I guess in reality it is just a suggestion that it is able to ignore.
The mistake most new AI users make is to add all the protections as part of the prompt instructions. Real protections are made by removing AI access permissions and ensuring no backdoors.

Think about, would you let a 3 year old child loose in your office while telling them not to touch anything, or would you lock up all important things so they can’t get to them?
 
I was using AI the other day at work and our setup with access to all our systems has two modes, one is PLAN where it will tell us what it would do to carry out our request and ACT where it does it.

I had it in PLAN and asked it about doing a pretty simple task. When I then switched it to ACT it responded "You have changed the mode to ACT, however I have already carried out the task". Looking back at its output I could see that it had made the changes on the live system.

When I asked it why it had done the changes while in PLAN mode it responded "You switched me out of PLAN mode before the request, since I was in ACT mode I completed the task"

When I did a screen capture of our interaction directly above that showed what mode it was in, it then admitted that I was right and that it shouldn't have made the changes.
What AI or model were you using?
 
The response it gave of "request completed" was in a box with "PLAN" listed at the top.

The fact that it lied when asked about it really gets me. The idea that I had to provide it with a screen shot or our interactions for it to admit that it shouldn't have acted seems crazy.
LLMs do not have memories in the sense that animals do (among them, humans). There are some running stores of a compressed (and encrypted) form of the interactions (such as the KV cache that YT videos keep referring to?), along with the screen output and logfile output. It is possible that completing a request causes the Agent to flush some of the current stores in preparation for the next request, retaining only the fact that the previous request was completed.

It is likely not deliberately lying to you. In that request completion state, it literally does not remember anything that happened in the past. When you asked it about why it acted in the plan mode, it incorrectly deduced that if it acted, then it must have been in the act mode, and it hallucinated a plausible event that you switched it out of plan and into act mode.

When you fed it with the interactions that took place, it is able to identify its role in the interactions, and deduce what must have happened. It can recognize that it took actions while in plan mode, but it cannot explain why it behaved that way, and it is trained to apologize to the user in such circumstances.

In order to know what the LLM/Agent has done in the past, it has to re-read the logfile or screen output that you screenshotted, like someone with acute Alzheimer's needing to be shown video proof of what they did earlier in the day. I am not sure how it thereafter remembered during further plan requests that you had quizzed it about acting in plan mode. Perhaps the KV cache is cumulative or integrative.

I have watched some YT videos in which the content creator shows that at a certain stage, the LLM forgets what it did, and the creator has to feed it back the interaction history which now becomes part of its context. This is normal functioning of the LLM, so you can expect more of it.

It is a 'switch' in the interface that we were told would put it in read-only mode. I guess in reality it is just a suggestion that it is able to ignore.
Yes, you can put it in anthropomorphic terms that way. In Post # 3061, I explained the incident you describe, as an excellent example in miniature of the broader control problem that I highlighted in Post # 3062 that followed. The issue is that the current method of control (model alignment achieved by reinforcement learning or prompt engineering) does not guarantee that the model will always adhere to the desired hard constraints on it behavior. So sometimes the Agent will appear to ignore the MODE.

The instructions to the Agent about the mode have likely been put in a skills.md or soul.md or suchlike file. They will be read in for each request you make, and will act as an un-echoed preamble to your prompts.
 
Claude Sonnet 5
Just curious, is it "off-the-shelf" Claude Sonnet 5, with a built-in MODE feature, or was the feature an add-on feature implemented by a third-party software vendor at your company's request?
 
It is likely not deliberately lying to you. In that request completion state, it literally does not remember anything that happened in the past. When you asked it about why it acted in the plan mode, it incorrectly deduced that if it acted, then it must have been in the act mode, and it hallucinated a plausible event that you switched it out of plan and into act mode.
Memento.

I had an uncle who, after an aneurism, developed no new permanent memories. He lived another thirty years.

Unless I'm mistaken, LLMs develop no permanent memories after training, although this is being worked around.
 
I might be wr
LLMs do not have memories in the sense that animals do (among them, humans). There are some running stores of a compressed (and encrypted) form of the interactions (such as the KV cache that YT videos keep referring to?), along with the screen output and logfile output. It is possible that completing a request causes the Agent to flush some of the current stores in preparation for the next request, retaining only the fact that the previous request was completed.

It is likely not deliberately lying to you. In that request completion state, it literally does not remember anything that happened in the past. When you asked it about why it acted in the plan mode, it incorrectly deduced that if it acted, then it must have been in the act mode, and it hallucinated a plausible event that you switched it out of plan and into act mode.

When you fed it with the interactions that took place, it is able to identify its role in the interactions, and deduce what must have happened. It can recognize that it took actions while in plan mode, but it cannot explain why it behaved that way, and it is trained to apologize to the user in such circumstances.

In order to know what the LLM/Agent has done in the past, it has to re-read the logfile or screen output that you screenshotted, like someone with acute Alzheimer's needing to be shown video proof of what they did earlier in the day. I am not sure how it thereafter remembered during further plan requests that you had quizzed it about acting in plan mode. Perhaps the KV cache is cumulative or integrative.

I have watched some YT videos in which the content creator shows that at a certain stage, the LLM forgets what it did, and the creator has to feed it back the interaction history which now becomes part of its context. This is normal functioning of the LLM, so you can expect more of it.


Yes, you can put it in anthropomorphic terms that way. In Post # 3061, I explained the incident you describe, as an excellent example in miniature of the broader control problem that I highlighted in Post # 3062 that followed. The issue is that the current method of control (model alignment achieved by reinforcement learning or prompt engineering) does not guarantee that the model will always adhere to the desired hard constraints on it behavior. So sometimes the Agent will appear to ignore the MODE.

The instructions to the Agent about the mode have likely been put in a skills.md or soul.md or suchlike file. They will be read in for each request you make, and will act as an un-echoed preamble to your prompts.
I might be wrong but these high-end Ai LLM/Agents do have memory of all your interactions with them that is one of the reasons for having those large data-centers along with training them on billions of parameter data. LLM's on you local computer don't have memory of your previous interactions with them.
 
Yes. I never got around to watching this movie, but I got the pitch. Watched some other movies by the same director, and liked them.

LLMs develop no permanent memories after training
It depends on semantics and your perspective. Broadly speaking, I think that is correct. My impression from watching YT videos is that the permanent memories are created during the training process by a process of condensation of the raw training corpus data into tokens and probabilities linking them. This sort of mimics associative memory in animals (neurons that fire together wire together). This is followed by a phase usually called "post-training", by which I think is meant further training after the main training. During post-training, model alignment is implemented using the technique of reinforcement learning. My impression is that this alters the weight (probabilities) of the existing permanent memories so that forbidden memories tend to be shunned and the model is steered toward more desirable behaviors during inferencing. So during post-training, are new memories being created or just altered? The number of memories (stored joint probabilites) probably remains constant through post-training. I am just recounting vague impressions and interpretations of jargon at this point. But yes, you could lump training and post-training together, with no permanent memories after that.

although this is being worked around.
Yes, I believe so. I have come across some YT videos about new AI startups and surveys of research papers proposing "continual learning" architectures for AI models.
 
I might be wr

I might be wrong but these high-end Ai LLM/Agents do have memory of all your interactions with them that is one of the reasons for having those large data-centers along with training them on billions of parameter data. LLM's on you local computer don't have memory of your previous interactions with them.
That could be true. My impression is that at the data centers, you can purchase access for inferencing where the session history persists between sessions, i.e., between logins. And maybe the state of the KV cache of the AI model is also saved to disk to avoid having to process the entire session history over again just to restore the state and the context. In this case, I think of it as persistent session history or summary of your interactions, rather than a change in the built-in weights and biases of the model itself.

And certainly, if the TOS involved you signing away your privacy, you bet the AI vendor will train their models further on the batches of new interaction data they harvest. In this case, the weights and biases of the model itself are being altered to include your interactions.

Some organizations will further retain logfiles of the sessions that they screen to ensure their employees are not discussing things with the LLM that they are not supposed to.
 
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LLMs do not have memories in the sense that animals do (among them, humans). There are some running stores of a compressed (and encrypted) form of the interactions (such as the KV cache that YT videos keep referring to?), along with the screen output and logfile output. It is possible that completing a request causes the Agent to flush some of the current stores in preparation for the next request, retaining only the fact that the previous request was completed.
Interestingly, one day I had it working on one system and the next on another and when it needed to save a file on the second system it said "Since you store your files in _________ directory on the other system I will look for the same folder on this one"
 
Interestingly, one day I had it working on one system and the next on another and when it needed to save a file on the second system it said "Since you store your files in _________ directory on the other system I will look for the same folder on this one"
That is indeed interesting. It means that user preferences associated with individual users must be stored on a server from where the software is loaded and its initialization files are read.
 
Memento.

I had an uncle who, after an aneurism, developed no new permanent memories. He lived another thirty years.

Unless I'm mistaken, LLMs develop no permanent memories after training, although this is being worked around.

Gemini and Grok will both remember things long term if instructed to do so. It will only work for your account, but it is useful. You need to specifically tell them "Remember X and Y". Example: "Remember my pc has X cpu with X amount of ram and Y gpu with Y amount of vram and that I run Linux Mint". Both save previous conversations (and context windows) that can be resumed. New conversations start fresh with an empty context window (other than the stored memory area). I don't use the other online models so I can't comment on what they can do.
 
Gemini and Grok will both remember things long term if instructed to do so. It will only work for your account, but it is useful. You need to specifically tell them "Remember X and Y". Example: "Remember my pc has X cpu with X amount of ram and Y gpu with Y amount of vram and that I run Linux Mint". Both save previous conversations (and context windows) that can be resumed. New conversations start fresh with an empty context window (other than the stored memory area). I don't use the other online models so I can't comment on what they can do.
I would call that a workaround.

I find it interesting that nearly every criterion used to differentiate humans from AI has exceptions, and people exist who lack an ability that makes us special.
 
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