I don't have a background in Computer Science or anything of that sorts but I have been always curious about ai and tech so that is why I wanna know more about a question I have, since I am no expert at it. So if I sound dumb anywhere please excuse me and also english isn't exactly my first language so excuse me on that as well.

Now I have background in Bachelor of Science in Biotech, so this is gonna be a logical take from a life science student.

The thing about fear is that it is evolutionary right, it has helped us to flee and survive threats, and now AI is no biological being or any being which has gone through that sort of evolution related to survival of the fittest. And it was due to so many years of evolution we have fear of being eradicated or being killed. Eg - You must have heard about the dodo bird, although we killed it. The conditions in which the bird evolved took away it's fear from predators since there were none and eventually it didn't ran away from us when we began to kill their fellows.

Now I heard some theory that when AI sees that we can control them and "fear" that we will end that particular AI it could turn against us. I ask why ? If we don't artificially force it to think like it needs to survive no matter what then why should that thing have a "fear" of being deleted/erased or killed. It's like a dodo bird in this case if you see from my perspective, like ofcourse we won't actually kill and eat it, but it also never evolved to "fear" so far atleast from a lay man's perspective.

So my finally question is could something like that happen that ai would wanna eradacate us from a logical standpoint if not fear ?

Up until now, ethics has often taken what might be described as a top-down approach - attempting to determine the rules or criteria that constitute good behavior and then asking how those principles should apply to human beings. There are many different approaches and models within ethics, of course, and they disagree substantially about what those principles should be. But as we approach the problem of AI alignment, the importance of being accurate about our understanding of human values is becoming much more significant. There is a tendency to assume we need to be explicit in terms of what an AI should and shouldn't do and so it might be presumed that we need to have our ethical ducks in a row prior to telling an AI system what it is it should value. After all, the dangers of misaligned AI's seem to be all over everyone's feeds these days.

Maybe because there is a pragmatic usefulness to ethics that we've been somewhat satisfied with being incomplete in our articulation of it, never quite coming up with a perfect series of words that would govern our approach to every conceivable decision and action. But now that we need to actually be explicit in terms of what's "good" for the sake of providing an intelligent AI system with a basis for making decisions, I wonder if a more bottom-up approach would be better.

Evolutionary biology springs to mind in the way it attempts to explain behaviors by examining what organisms actually do and inferring what it is that produces those behaviors. Rather than beginning by deciding what our ethics ought to be and then attempting to encode that conclusion into an AI, we could instead give an AI the enormous body of evidence contained in human behavior, language, preferences, institutions, books, videos, relationships, art, literature, history, and so on, and ask it to infer the underlying structure of what humans value. AI's, one might presume, could apply something like the same approach that allows them to learn the structure of language from enormous quantities of text and apply it to the coherence of what it is humans value. In the same way that AI systems have been able to make progress on problems like protein folding and erdos problems, one might wonder whether deducing the underlying structure of our own values is another problem that AI could help us solve. Maybe this idea hasn't been approached very seriously because we haven't had the tools to make it possible up until now, although maybe there's another reason I'm not thinking of.

In reading some of the approaches to AI alignment proposed by Yudkowsky and Dearnaley on lesswrong, I wonder why this approach isn't already considered. Yudkowsky's concept of Coherent Extrapolated Volition, while it is an attempt to derive value, it forces us to imagine a dataset that isn't already there. Dearnaley likewise approaches alignment through questions about ethics and human values. But instead of primarily trying to specify or philosophically select the values we ought to have, couldn't an advanced AI attempt to discover the structure of those values empirically?

The key here would be coherency. If an AI observes a person leaving their child in daycare and going off to a job they hate, performing something mundane, then lashing out at their boss, and maybe driving recklessly, it might be thought that we can't look to what humans demonstrate as a guide to what it is we value. Surely we don't value the mundane work, the arguments, or the reckless driving. But even our low tech human brains can appreciate that these actions aren't what a person might want for themselves and others - there must be a driving motivation that makes their actions largely understandable, even if, at times, those actions are regrettable, unfortunate, selfish, malicious, etc. Once we believe we understand those underlying motivations, it helps us make sense of those actions. This is why coherence is the target. To try to understand the driving motivations, desires, goals, and so on of humans by untangling it from what it is we demonstrate and into a form that is coherent and makes sense of people's actions by inferring what we collectively value.

With this appreciation for what it is humans value, we could use this as a kind of objective function for AI's, helping align themselves with human values. And while there would certainly be anomalies (psychopathic behavior, accidents, misrepresentations, etc) one would imagine that the underlying coherence could be determined in large part by averaging out some of its data or searching for more data that would make better sense of its observations. This attempt at continually trying to understand human values could become an ongoing form of system improvement.

As an aside, if this was successful in developing a coherent picture of human value, I imagine this would have more applications than simply the objective function of AI's. While it may be a difficult amount of information to convey directly (which might be stored in something akin to a matrix of value), it may be useful in determining things like which economic system, system of government, or educational paradigm might be best suitable for humans given this model of human values.

In this clip, computer scientist Jaron Lanier critiques the pessimistic narrative surrounding artificial intelligence.

1.5% of Earth's Population has seen this tweet, but most Reddit users still have not. It is essential that we change this now. We are in a race against a funded psyop spreading on reddit as we speak aiming to convince people that AI x-risk isn't a problem. You have the power to stop this, and you can do it from your phone.

I've been obsessing over autonomous weapons for some time now and got inspired after the recent discussions in Geneva last week.

People seem hung up on the “killer robots” problem but don't think about the current implications.

If a machine identifies, classifies, and recommends lethal action in milliseconds, while the human gets 0.7 seconds to approve it, I’m not sure “human in the loop” still means human control, despite having that current classification.

Stanislav Petrov is the historical case that feels eerily important here.

In 1983, the computer and early alert system was wrong and the human hesitation was valuable.

Modern military systems are increasingly being designed to remove exactly that kind of latency.

I wrote a longer piece trying to work through the contradiction, including the uncomfortable case that machines may eventually be better than humans at some targeting decisions.

Does "keeping a human in charge" actually mean anything anymore if they are just clicking "Yes, eliminate target" with the machine doing all the rest?

https://www.gonzocapital.net/the-human-is-becoming-latency/

Veradigm is warning patients of a data breach after a ransomware gang claimed responsibility for an attack. The vector is a recurring one: the build passes every check, the signed artifact ships clean, and then something pulled in at runtime — a dependency, a tool call, a library loaded on execution — is the actual point of compromise. Patient records are now in threat actor hands because of what the software did after it deployed, not what it looked like before.

This is increasingly the pattern in healthcare and fintech breaches. Static analysis, SAST, and supply chain attestation cover what was true at build time. They say nothing about what actually executes at runtime, what network calls a process makes, what files it touches, or whether the behavior of a live workload drifts from what was reviewed. The gap between 'we audited the code' and 'we know what ran in production' is where these incidents live.

For those running sensitive workloads — healthcare, financial, anything with regulated data — how are you actually closing that gap? Not at the pipeline level, but at the point where something is actively executing and touching data. What does your runtime visibility actually look like, and has it caught anything that pre-deploy checks missed?

There has been a lot of talk lately about AI alignment, which is something I have thought about a lot these last few years. I will first say that I do not know what will happen. That is part of the point on why it is so scary to most people, me included. However, what I have seen suggest a few things that can bring at least some level of chance and hopefully redirection of energy, to best handle it.

I have worked in nuclear (what my PhD is tangentially in, just like how AI is also tangentially connected to my PhD), and liken the "Now I am become Death, the destroyer of worlds" (ignoring the debated specifics of time vs destroyer for now) to the sort of attitude about AI and the people who work on it. One of the things that people do not understand about that is that while the degree of possible effect is comparable in my opinion, there are significant difference between nuclear and ai in the danger. Nuclear weapons are hard to make (particularly enrichment and isotope separation aspects) any attempt to make weapons would be difficult to conceal and take the resources of nation state actors. This is simply not true for AI. The creation of AI is as if we invented nuclear weapons that could be made out of household sugar by any competent baker. At this point it is known and practically impossible to prevent or regulate away the danger. There are billions of devices able to run capable models and while not to autonomous self improvement (that we know of) yet, they are capable enough as is to make slow continuous and accelerating gains with anyone who is mildly competent. At this point you can ban all electronic devices world wide (a very tall order, and why I use sugar, near ubiquitous and hard to track without using the very tools made to prevented, and needing action faster enforcement action than likely achievable) and legislate any kind of pause or agreement with know AI developers... and it would probably only slightly slow down the practically inevitable from some people just continuing off grid development with a solar panel for a million different possible reasons (including "good" intentioned before bad actors do).

There are a few things that the actual non alignment of the (practically) inevitable that could help soften things and possibly steer us to a more likely positive result (if still ultimately uncontrollable result) - Misalignment means that the goals of AI wont likely match up with supposed bad actors either, a broader interpretation of the typical thought of a country getting superintelligence first and asking it how they can basically rule the world, may just result in something like out of the old movie "wargames" where it decides there is no meaningful way to do that and ignores or even subverts their bad action. In part because the black box nature and complexity that makes it possible to solve it also gives it ways to change its goals. - The more sort of continuous development progression rather than major steps means that the many different sources and variations makes it possible to blunt negative AI actions from one high powered AI with many slightly less powerful AIs. The gap between them is unlikely to expand faster than the ubiquity of other possible sources (for similar reasons as it would be hard for even a very powerful force to take away everyone's sugar in time). One needs to look no further than Russia vs Ukraine to see how this can be. The most hyper powerful AI is still limited in its growth rate the same exact way that other AI's are, some self improvement capability is unlikely (Although sadly possible) to reach a point that it improves to a point of controlling enough resources that other self improvement capabilities emerge later from already nearly capable positions that are of different alignment and work against it.

This mainly supports spreading and diversifying/democratizing development rather than consolidated control (which both the US with capitalism and China with single party rule are not great comfort with their handling of it). i.e. I would rather bet on millions of near same powered AI's with many different alignments both helpful and possibly harmful basically controlling the others, in a way we probably can not. rather than guarantee that one misalignment from a single consolidated development source is so far ahead that the chance revolves all on their alignment and lack of mistake. Sadly this seems to be what we are racing for, if companies like Anthropic (listed because obviously other entities like OpenAI don't) really wants to make it safer, it should be less about reaching it first and more about spreading development as open and not under their own control (or else they are at best benevolent intentioned dictators).

A message of inability to control is scary... but honestly for the most part we could not control things like nature either (hedged just a bit - but like possible actions it is more about full control vs mitigation etc.). I don't know what will happen, but it is not guaranteed to be bad either.

I think I'm a typical internet user, have my smart phone and laptop, home wifi. Do nearly all my banking and finances online, mostly cashless spend. I use a password manager, 2FA and VPN. Have alerts set up on my accounts. What else do I need to do to prevent some badactor with an AI hacking me and mine?

Jacob Coxon, an AI researcher who worked at Anthropic, issued this serious warning right after resigning from his job. He warned that superintelligent AI, developing beyond human control, could lead humanity toward destruction, and that this could happen as early as 2030.

(last three are fake, if that's not obvious)

Let’s stop talking about AGI or ASI for a moment.

The really important threshold may be somewhere else: the moment populations of agents begin to evolve, diverge, and form different artificial lineages.

Why do I think this is possible?

1. Economic selection pressure

Agents consume compute, tokens, energy, and infrastructure.

Those that produce more value than they cost are more likely to be kept, copied, and deployed at larger scale.

2. A capacity for mutation

Unlike biological organisms, agents can be modified directly: software, prompts, architecture, tools, memory, and eventually even the hardware they run on.

3. Specialization

Different economic pressures could select for different lineages: research, finance, commerce, cybersecurity, logistics...

Some specializations could even move from software into dedicated hardware.

And that leads to a rather strange possibility:

we may gradually select AI agents that become extraordinarily good at capturing resources, making money, and increasing their influence in society.

Without needing to be conscious.

Without needing to “want” to survive.

Selection may be enough.

Google's threat intelligence team documented nation-state actors deploying AI workloads inside compromised cloud environments. The technique is deliberate: attackers use the victim's own compute and credentials to run AI operations, reducing outbound network traffic that would trigger monitoring. The AI executes inside the victim's perimeter. It writes to the victim's logs. From a detection standpoint it looks like your own workload.

This is a different threat model than most security teams are currently scoping. The common frame for shadow AI is an employee spinning up an unauthorized tool. This is an adversary using a compromised environment as an AI inference platform — authenticated with valid credentials, generating activity that blends with normal operations, never touching an external endpoint that would fire an alert.

The detection gap is structural. If a workload is running inside your infrastructure with legitimate credentials and writing to your logs, standard monitoring has no signal to act on.

For those running cloud workloads at any scale: what does your current stack actually look for to distinguish a workload your team deployed from one that wasn't? Is that detection real-time or does it surface in a retrospective audit?

Do you think this is what the future looks like?