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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

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.

In this article we will explore sustainability and how it relates to AI technologies. AI. 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 Instead, of asking is AI Safe , perhaps we should be asking is AI Sustainable ?

on the page of AI history." AI, which Laqua approached from multiple angles, acknowledging that AI is being defined in real-time AI's Domain Diversity Laqua emphasized that no single domain captures the full scope of AI. of AI risk, but what's different with AI is the degree and scope of this uncertainty. The Call for AI Engineers The AI Engineering Body of Knowledge (AIENGBOK) The presentation culminated

When I read about AI in the news, and this includes social media, I am troubled by the way it is often The Need for AI Mythbusters These articles often don’t prove or demonstrate that AI is better, and neither The AI hype machine is definitely operating on all cylinders. Is that what we understand generative AI is - creative? Time for professionals to be AI Mythbusters!

The Current State of AI Artificial intelligence has indeed made remarkable progress in performing specific AI can analyze medical images, review legal documents, optimize engineering designs, or process geological This framework helps explain why AI excels at certain tasks while falling short of what is required for The Master and his Emmisary Machine-Like Intelligence (Left Hemisphere - apprehending) AI demonstrates their emphasis on standardization and procedural efficiency, have created natural opportunities for AI

However, AI lacks the knowledge (and most likely always will) that comes from experience along with the

Here are some of the things he said: Three facts about AI: AI has happened ( the genie is out of the What is AI (I have paraphrased this)? Before AI we told the computer how to do what we want - we trained the dog With generative AI we tell on the open internet Don’t teach AI to write code Don’t let AI prompt another AI What is the problem Sure, AI can dumb it done or AI-splain it to us so we feel better.

As artificial intelligence continues to advance, AI engineers face a practical challenge – how to build Current State of AI Uncertainty Current AI technologies, particularly advanced systems that use large What aspects of AI should receive attention: the technology itself, the models, the companies developing Rather than relying solely on static evidence, successful AI engineering requires ongoing observation How does your organization approach uncertainty in AI systems?

There are those who think that AI is only software. After all, we can reduce AI to a basic Turing machine, digital ones and zeros. AI will save us, perhaps, even from ourselves. AI Middle Earth What character are we playing in the AI story and where are we on the map of AI Middle The real world is not in our AI models either.

This is one way to evaluate what is happening with AI.

Why AI Is Different: AI presents unique risks because of its ability to operate with minimal human oversight Challenges with AI Regulation: While regulations like the EU AI Act are emerging, they are still new A Program to Govern AI: A comprehensive AI governance program should include four elements: AI Code of AI Design Standards: Technical guidelines for AI development, emphasizing ethical considerations. AI Safety Policies: Measures to prevent harm and ensure robust testing and monitoring of AI systems.