The Incentive to Deceive

Rob Miles is one of the better explainers of AI on YouTube, he's detailed, he rarely holds back on calling out elephants, and, importantly for broadcast media, he's personable. He's also has a long, in YouTube terms, track record of covering Alignment issues. As a PhD student he's particularly adept at explaining the complexities of Alignment issues. In this video he gives a fine explanation of the reward training in LLM's both implying and stating the issues that ensue from such training approaches, including the policies to please humans, and the utility of such models to deceive.

Two parts near the end of the video caught my attention:

'This is potentially fairly dangerous, there are certain type of goals that are instrumentally valuable for a wide range of different terminal goals, in the sense that, you can't get what you want if you're turned off, you can't get what you want if you're modified, you probably want to gain power and influence."

'Reinforcement Learning From Human Feedback, is a powerful Alignment technique, in a way, but it does not solve the problem...extremely powerful systems trained in this way, I don't think they'd be safe.'

Comments

The Whispers in the Machine: Why Prompt Injection Remains a Persistent Threat to LLMs

Large Language Models (LLMs) are rapidly transforming how we interact with technology, offering incredible potential for tasks ranging from content creation to complex analysis. However, as these powerful tools become more integrated into our lives, so too do the novel security challenges they present. Among these, prompt injection attacks stand out as a particularly persistent and evolving threat. These attacks, as one recent paper (Safety at Scale: A Comprehensive Survey of Large Model Safety https://arxiv.org/abs/2502.05206) highlights, involve subtly manipulating LLMs to deviate from their intended purpose, and the methods are becoming increasingly sophisticated. At its core, a prompt injection attack involves embedding a malicious instruction within an otherwise normal request, tricking the LLM into producing unintended – and potentially harmful – outputs. Think of it as slipping a secret, contradictory instruction into a seemingly harmless conversation. What makes prompt inj...

Can We Build a Safe Superintelligence? Safe Superintelligence Inc. Raises Intriguing Questions

Safe Superintelligence Inc . (SSI) has burst onto the scene with a bold mission: to create the world's first safe superintelligence (SSI). Their (Ilya Sutskever, Daniel Gross, Daniel Levy) ambition is undeniable, but before we all sign up to join their "cracked team," let's delve deeper into the potential issues with their approach. One of the most critical questions is defining "safe" superintelligence. What values would guide this powerful AI? How can we ensure it aligns with the complex and often contradictory desires of humanity? After all, "safe" for one person might mean environmental protection, while another might prioritise economic growth, even if it harms the environment. Finding universal values that a superintelligence could adhere to is a significant hurdle that SSI hasn't fully addressed. Another potential pitfall lies in SSI's desire to rapidly advance capabilities while prioritising safety. Imagine a Formula One car wi...

AI Agents and the Latest Silicon Valley Hype

In what appears to be yet another grandiose proclamation from the tech industry, Google has released a whitepaper extolling the virtues of what they're calling "Generative AI agents". (https://www.aibase.com/news/14498) Whilst the basic premise—distinguishing between AI models and agents—holds water, one must approach these sweeping claims with considerable caution. Let's begin with the fundamentals. Yes, AI models like Large Language Models do indeed process information and generate outputs. That much isn't controversial. However, the leap from these essentially sophisticated pattern-matching systems to autonomous "agents" requires rather more scrutiny than the tech evangelists would have us believe. The whitepaper's architectural approaches—with their rather grandiose names like "ReAct" and "Tree of Thought"—sound remarkably like repackaged versions of long-standing computer science concepts, dressed up in fashionable AI clot...

Charting the emergence of AGI?

Search This Blog