Taylor G. Lunt

Software of .

Background

Beaverlabs

2021 — present

Full-stack developer. Turning of marine vessel data into , web dashboards.

React · TypeScript · Elixir/Phoenix · Postgres/SQL · Elasticsearch · ClickHouse

University of Toronto

B.Sc.

how humans and machines .

Psychology · Computer Science

Projects

TrafficMap

A real-time global marine traffic map.

TypeScript · React · Redpanda (Kafka) · ClickHouse · Docker
TrafficMap screenshot

taylor.gl v1

The original version of my personal website, made in 2021.

Elixir · Phoenix · TailwindCSS
taylor.gl v1 screenshot

The Tincture

A video game made for the PirateSoftware Game Jam 15

Construct 3 · Krita
The Tincture screenshot

Writing

LaughBench

Introducing LaughBench . For a long time, I've considered the ability for AI models to tell novel, funny jokes that actually make people laugh to be a robust indicator of real general intelligence (as opposed to, say, coding tasks). I have used this benchmark informally over the months, seeing if any model could generate novel jokes to make me laugh (at a rate above the really low base rate where ...

AI Mistake Seeding

I wonder if AI is being trained to make easy-to-correct mistakes so it can fix them later. That is, it ends up trained to correct its previous message's mistake, then make another mistake, so it can correct it again in the next message. From my understanding of RL, the human/AI judge has to rank several policy model responses. These might be the first response in a conversation, or they might be t...

Why Prediction Markets Aren't Magic

The dream is that prediction markets greatly outperform individual experts, but there's a limit on how much this can actually happen. The reason prediction markets aren't more useful is that you can only profit from a prediction market if gathering information is cheaper than the money you'd make from gathering it. Let's imagine I write down either "horse" or "donkey" on a slip of paper, and put t...

Minimizing Loss ≠ Maximizing Intelligence

Many speculate about the possibility of an AI bubble by talking about past progress, the economy, OpenAI, Nvidia, and so on. But I don't see many people looking under the hood to examine whether the actual technology itself looks like it's going to continue to grow or flatline. Many now realize LLMs may be a dead end, but optimism persists that one clever tweak of the formula might get us to super...

Guys I might be an e/acc

I read If Anyone Builds It, Everyone Dies (IABIED) and nodded along like everyone else, mostly agreeing with the argument but having minor quibbles about the details or the approach. However, I was recently thinking, "how in support of an AI pause am I, actually?" The authors of IABIED were pretty convincing, but I also know I have different estimates of AI timelines and p(doom) than the authors d...

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