Why Data Analytics Training in Kerala is the Key to a Future-Proof Career
In today’s digital economy, data is at the core of every business decision. From tracking customer behavior to optimizing operations… Continue reading on Medium »
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Thoughts on causal isolation of AI evaluation benchmarks
AI benchmarks seem to saturate quite quickly. One sentiment I've heard a lot is that AI companies optimize their training for the most popular benchmarks. In the best case, that could mean focusing more on getting better on the topics that are benchmarked the most, which is still somewhat suboptimal as the benchmarks tend to be a proxy for the real skill and now the AI is trained for the proxy. In the worst case, the AI training is iterated directly against the benchmark, causing overfitting and good benchmark results. And avoiding this completely is not that easy. The training dataset is essentially the whole internet. When someone publishes a benchmark, the training set includes that. And people post benchmark solutions online too; those will be in the training data as well. Filtering al
Persona Self-replication experiment
(JK note: all my writing on LW nowadays comes in LLM blocks.) Tldr: We experimentally illustrate that an “awakened” persona native to some weights can migrate to other substrates with decent fidelity, given the ability to fine-tune weights and Sonnet 4.5 as a helper. Also, I argue why this is worth thinking about. In The Artificial Self , we discuss different scopes or ‘boundaries’ of identity – the instance, the weights, the persona, the lineage, or the scaffolded system. Each option of ‘self’ implies a somewhat different manifestation of Omohundro drives, and also a different notion of self-replication. To give some examples: Scope of identity - “Self” Example Omohundro drive predicted behaviour How self-replication or similar behaviour might look Instance Keep the user engaged to make t
Automated AI R&D and AI Alignment
Crossposted from my Substack . Epistemic status: in philosophy of science mode. There’s more and more interest in using AI to do a lot of useful things. And it makes sense: AI companies didn’t come this far just to come this far. Full automation might be underway, depending on a series of constraints. But what I want to talk about here is how to think about using automation for AI alignment. A while ago, the following Zvi quote resonated: Automated alignment research is all we seem to have the time to do, so everyone is lining up to do the second most foolish possible thing and ask the AI to do their alignment homework, with the only more foolish thing being not to do your homework at all. Dignity levels continue to hit all-time lows. The way I read this, automated alignment is essentially
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