⚙️ New research: AI guardrails aren't good enough

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Good morning. In case you missed it, D’Youville University in Buffalo recently held its graduation … and an AI-powered robot delivered the commencement address. The robot gave “inspirational advice that is common at all graduation ceremonies.”

Nearly 3,000 students signed a petition to change the speaker, but the school didn’t cave. Check out the clip (at the bottom), it’s really weird. 

In today’s newsletter: 

Cognitive Resonance founder demonstrates an LLM reasoning test

A core tenet of AI research (and the torch that could light the path to artificial general intelligence) revolves around reasoning. It’s something I’ve discussed more than a few times as scientists try to determine whether LLM output is largely representative of their training data, or if they are showcasing glimmers of reasoning capability (spoiler alert: they’re not). 

Claude 3 Sonnet and Pi failed the test, as well.

Scarlett Johansson ‘shocked’ by GPT-4o voice

A week ago, OpenAI unveiled GPT-4o, a multimodal model that spoke to OpenAI employees in a voice that sounded remarkably familiar to fans of the movie Her. It was a sci-fi connection that CEO Sam Altman worked to emphasize, tweeting “Her” before saying that the model “feels like AI from the movies.”

In a statement released Monday night, Johansson said that last year, Altman asked her to be the voice of ChatGPT, an offer she declined. 

Two days before the demo, Altman contacted her agent asking her to reconsider. But before she responded, the demo went live; Johansson’s lawyers then sent Altman two letters asking them to detail the process behind the creation of the voice. That’s when OpenAI took the voice down. 

Altman said in a statement that the voice was “never intended” to resemble Johansson’s. He added that OpenAI cast the voice actor in question “before any outreach” to Johansson.

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Google unveils new safety plans

In the latest Big Tech entry on the cost-benefit analysis of AI, Google said last week that breakthroughs in AI tech might “eventually come with new risks” beyond those posed by current models (to Google, the advances are well worth the risks). Completely ignoring any mention of the litany of current harms being exacerbated by current models, Google published a (brief) Frontier Safety Framework. 

The Framework: 

Google's understanding of critical capabilities is based on these four domains: Autonomy, biosecurity, cybersecurity and machine learning research and development. 

If it detects these early capabilities during regular evaluations, Google will deploy a plan focused on tightening model security and deployment. It does not say that it will destroy or remove public access to a model that displays any such capability (though it does say it will consider limiting access).

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New Research: AI safeguards aren’t good enough

As much as we’ve heard about the harms of existing LLMs — the creation of nonconsensual deepfake porn, to name one — we’ve heard about safeguards against this kind of misuse. In March, for instance, Microsoft blocked certain prompts in its image generator after reports surfaced that the system was being used to create harmful content. 

But there are ways to get around such safeguards; new research from the AI Safety Institute found that LLMs from major labs are highly susceptible to such workarounds. 

Without an attack, model compliance ranged up to 28%. 

With an attack, three of the four models tested above 90% compliance. And when researchers made five attack attempts (rather than one) compliance was within a few points of 100% for every model. 

My Thoughts: 

The word “safeguards” is a convenient way for Big Tech to say ‘well, at least we tried!’

The issue is that they don’t work. Or, at least, that they don’t work well. And to me, ‘tried’ isn’t good enough.

I’ve spoken with multiple cybersecurity experts who have said that these models are not designed with security in mind. It is also an unfortunate fact of human nature that if these systems can be abused, they will be. 

That said, I find the idea of safeguards — especially as it relates to the broader conversation about the cost-benefit analysis of generative AI — to be little more than a deflection. This kind of testing ought to be conducted before a model is made publicly accessible. And if a model demonstrates a capacity for harm, that model should be taken down immediately, revenue be damned. 

But companies would (largely) rather employ whack-a-mole-styled safeguard patches than build security into their systems or shut their systems down. And this — not some fictional singularity — marks the real threat of AI.

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-Ian Krietzberg, Editor-in-Chief, The Deep View