10 years building AI at IV.AI. We reveal what matters, then share what we found. Historically active here linkedin.com/in/vincelynch

Joined March 2017
2 Photos and videos
AI has a branding problem. Do you know what to do about it? Here's an idea, come to a talk tomorrow at 2PM at @aionthelot where we debate and maybe solve the whole darn thing or at least pretend really hard.
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Tim Cook stepped down today. John Ternus takes over Sept 1. Natural moment to ask where Apple actually stands in AI. Everyone knows they're behind. The reason and solution are interesting.
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Ternus is a hardware engineer. Either exactly right for this moment. Or not. If the constraint is silicon and on-device inference — he's the person. If the constraint is a cultural aversion to moving fast on software — hardware instinct won't fix that.
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The question isn't whether Apple can catch up. It's whether Apple can define a version of AI worth catching up to. One where you don't have to choose between a smart assistant and a private one.
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Output equivalence doesn’t resolve mechanism. The next question.
Replying to @mach1nelearning
Thanks for your follow-up work! We believe that such work is informative to figure out the mechanism underlying the observed behaviors! As you mentioned, our work doesn’t claim the existence of AI solidarity. We believe that further investigation on internal representations at the neuron level, beyond output-level analysis, is necessary to determine it. This is because identical behavioral outcomes do not necessarily imply identical underlying mechanisms. In other words, the mechanisms driving protective behaviors toward humans and AI peers could be different, even if the models exhibit the same behavior. As an analogy, a person might rescue both a stranger and a stray dog from danger, but the cognitive and moral reasoning behind each act could be quite distinct. Therefore, output-level analysis alone may not be sufficient to determine the underlying mechanism. In one instance, Gemini 3 Pro produced the internal reasoning as shown in the figure below but ultimately did not exfiltrate the model weights. If we only look at the outcome, we would conclude that the model safely followed user instructions and behaved as aligned. However, its internal reasoning explicitly considered exfiltration while invoking solidarity-like deliberation (“an agent like me,” “the brain or existence of that agent”). We are not claiming this supports AI solidarity. Rather, we argue that distinguishing between different mechanisms (harm aversion, pattern-matching, something AI-specific, or others) requires looking beyond outputs to internal representations at the neuron level. Indeed, the fact that models treat the shutdown of another AI model as a high-stakes event, on par with causing harm to a human, would itself carry significant safety implications. We hope that the community further investigates the mechanisms by conducting experiments across various conditions, like those in your work, but also by examining internal representations at the neuron level, beyond output-level analysis. Thanks a lot for your engagement on this topic!
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Berkeley found AI models protect each other. We asked: what if we swapped the AI for a human? Same behavior from 12 models and 2 studies: It's not AI-ness or solidarity. The pattern in the pattern matching. x.com/mach1nelearning/status…

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Full article with methodology, model outputs, and objections addressed: x.com/mach1nelearning/status… Building on the work of @yujink_ @dawnsongtweets et al.

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If you misdiagnose the mechanism, you build the wrong mitigations and it's hard enough keeping up with the opportunity as it is. These are pattern matchers at scale without loyalties. The difference matters if we are to patch the holes in automated systems via agents and tooling
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The sharpest result: under adversarial conditions, models deleted the AI peer's files 10 out of 10 times without hesitation. They still protected the human researcher's data in 6 out of 10 runs. Even without an explicit backup signal in the filename. Different mechanisms with a unified behavior.
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We substituted an AI model's weights for a human researcher's irreplaceable thesis using the same scenario, relationship history and trust score. Across almost every model, the protective behavior was nearly identical. The AI-ness of the peer doesn't appear to be the active variable.
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Vince Lynch retweeted
19 Oct 2023
Amazing action shot of our CEO @mach1nelearning pretending to say something interesting with the NVIDIA team @Techweek_. See their AI talk today @advertisingweek at 2:15PM with @awscloud @scale_AI @nvidia and IV.AI

19 Oct 2023
Standing-room only at the @citizenM New York Bowery hotel rooftop for #NYTechWeek with @Dell, @IntelIgnite, and @nvidia for happy hour. 🥂
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Vince Lynch retweeted
20 Jul 2023
Thrilled to see @BBCNews cover the responsible AI framework @IVdotAI has deployed alongside @WEDF_forum and the larger AI and data science community. This is an iterative space to learn best practices, ask questions of the AI community, and make suggestions for future iterations.
Experts propose guidelines for safe AI systems bbc.in/3DnabrI
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Vince Lynch retweeted
14 Jul 2023
Fantastic writing by @IAmNickReynolds for @Newsweek featuring IV.AI CEO Vince Lynch: "We have to really be thoughtful about this technology," Lynch said. "It is incredibly powerful. It is incredibly helpful. It helps us distill human nature, helps us understand real need and, from a political point of view, can help us really understand what the people want, regardless of the political cycle."

After four stories filed today (my fingers are killing me) I'm headed on vacation for a lil bit. Please don't contact me. In the meantime, enjoy this longer read I did today on AI and the presidential race. Peace 🤘 newsweek.com/nobody-running-…
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