This is why frontier labs place AI inside their own control architecture. Bound the states the AI can reach. Reduce the variety you are required to absorb. AI in the loop only amplifies. On its own it becomes an arms race you will lose.

This is an old argument dressed up in AI clothing. They weren't designed for AI. They were designed for humans. And there’s the rub. You need to start using AI more. You don't want to be left behind. And if AI isn't working — don't blame the technology. The problem is with you. AI needs to become ready for use — and that’s on the AI provider.

In this article we will explore sustainability and how it relates to AI technologies. AI. Is AI Sustainable? AI technology such as ChatGPT should be designed to be safe. AI Sustainability is perhaps what drives the need for AI safety, security, quality, legal, and ethical In addition to asking, is AI Safe, perhaps we should be asking is AI Sustainable?

When we use AI in its most common form, we come to realize something about it. This is not a backdrop to AI development. It is its organizing goal. AI adoption versus applied AI AI adoption is the use of the general intelligence as it is delivered. You can adopt AI and let your knowledge feed a general intelligence that serves mostly the AI provider , or you can apply AI and build an intelligence that is your own.

This is starting to look like how AI behaves inside our organizations. It over-constructs. Most AI deployments have no equivalent: no clear conditions under which a model is retired, rolled back AI without this isn't intelligence. It's uncontrolled mimicry wearing the face of intelligence.

When it comes to compliance, labelling everything as AI might be a bad idea. statistical analysis, models, and prediction, and this practice should continue without any confusion with AI However, AI does have unique characteristics that, if not understood, could pose significant risks to Nevertheless, labelling all of this as AI might unnecessarily create regulatory uncertainty and complexity The need for defining AI is indeed crucial, not only to separate the boundaries of where new risks not

When Prediction Becomes the Plan AI agents are being built to act on the predictions of AI models. The same trap applies to us when it comes to AI itself. Some say AI may end the world. We change it through how we govern AI. So when I look at AI agents that act on predictions, I find myself asking three questions. We should keep that decision with people, including the decision about how the AI story ends.

About a year ago, I heard an AI expert suggest that we might need AI to control AI. The real question isn't "should AI control AI instead of humans?" This is where AI has to regulate AI because humans lack the requisite variety. So yes, we need AI to regulate AI where speed and scale matter. But I'm not skeptical anymore about needing AI to regulate AI.

The AI will sort it out. The end by any means. This sounds like progress. Most organizations deploying AI agents have not done this. Not all AI agents require the same governance. This applies equally to subordinates who use AI agents as tools. This is what happens when organizations compress strata with AI.

Is the collateral damage from AI worth it, and who should decide? When it comes to AI, we appear to be “hell-bent“ towards developing Artificial General Intelligence ( AGI) so as to consume all available energy, conduct uncontrolled AI experiments in the wild at scale, If we did, we would keep AI in the lab until we studied it carefully. Time will tell if the decisions surrounding AI will prove to be reckless, foolish, or wise.

AI will need to be held accountable. We will need different categories to distinguish between each AI capability: AI Machines - AI systems AI Agents - AI Machines with agency but without moral capacity and limited culpability AI Ethical Agents - AI Agents with moral capacity and full culpability AI Machines can still have agency (self-referencing Perhaps, this is what is meant by autonomous AI.

The same premise applies to AI systems. conversation about AI safety skips the first step. only be defined against an intended state, and few organizations have defined that state for their AI AI systems also produce many unforeseen interactions. Models are probabilistic. AI safety begins with defining what it means to be safe for each system.