gradascent

Carpenter and park ranger sound gratifying. But truck driver? Can you help me understand the appeal?

I just took the Amtrak from Southern California to Seattle.

Pros:

- space! wide seats and leg room are awesome (I'm 6'5" so this is everything)

- Freedom to move around and explore. Lounge car, dining car, snack bar

- Spectacular views

- Train stations are much more pleasant than airports

- Opportunity to meet people from all over the place. On a plane everyone is going from A->B, people on the train could be starting/ending anywhere along the route, including small towns you've never heard of.

Cons:

- 32 hours of travel

- Pay an extra ~$500 to get a bed, or sleep in your seat

Overall I have no regrets but I'll probably not do this again until I'm retired or extremely bored.

Similar to the author’s story, I crashed on my bike going pretty quickly on a busy road. No serious injuries but I ended up with scrapes and a softball-sized bruise which lasted over a month. But after I fell and got off the road a man sitting on his porch eating dinner asked me if I was ok. I told him what happened and he quickly grabbed some tools to fix my bike and alcohol and bandages for the wound. His roommate came home and assumed we knew each other but nope he was just my guardian angel. I hadn’t thought about this in a while… now I’ll be sure to remember it again.

From the figure in the first paper listed:

Responses to the query “Write a metaphor about time” clustered by applying PCA to reduce sentence embeddings to two dimensions. […] The responses form just two primary clusters: a dominant cluster on the left centered on the metaphor “time is a river,” and a smaller cluster on the right revolving around variations of “time is a weaver.”

I just gave Gemini 3 the same prompt and got something quite different:

Time is a patient wind against the cliff face of memory. It does not strike with a hammer to break us; it simply breathes, grain by grain, until the sharp edges of grief are smoothed into rolling hills, and the names we thought were carved in stone are weathered into soft whispers.

I was thinking the same thing. That one accelerates its growth in the presence of radiation. But it also seeks out human flesh and brains to build its biomass intelligence blob, unfortunately.

Adding to the chorus: I like the older images and the precise year is important! The underground shelter is something that wouldn’t exist just a few years after that photo, or before.

Great deep dive. I've learned a lot already and haven't even finished the introduction

Then why do I never get an “I don’t know” type response when I use Claude, even when the model clearly has no idea what it’s talking about? I wish it did sometimes.

I've found this "book" (series of jupyter notebooks) to be a fantastic course on the Kalman filter from basics to advanced topics. https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Pyt...

They're all names of popular Kendrick Lamar songs. Don't ask me why though

Or maybe the final moment was a sigh of acceptance and gratitude for the live he lived. Nobody knows but him.

On a similar note, I really hope that the AI companies that don't make it, but have invested a lot in curating and annotating high quality datasets, would release them to the public. Autonomous car and robotics companies in particular since that kind of data doesn't exist on the internet as abundantly as, say, natural language text.

If you want to gain familiarity with the kind of terminology you mentioned here, but don't have a background in graduate-level mathematics (or even undergrad really), I highly recommend Andrew Ng's "Deep Learning Specialization" course on Coursera. It was made a few years ago but all of the fundamental concepts are still relevant today.

I didn't find it confusing at all. I think it's totally ok to re-use phrasing made famous by someone else - this is how language evolves after all.

Interesting! I would like to learn more about how AI is being applied to robotics. Do you have any suggestions for how to keep up with developments/ideas in this field?

Very cool. I'm curious - did you find the results from your mixture of experts model to be (qualitatively) better than with the standard approach?

fyi I think you have bias and variance the wrong way around. Over-fitting indicates high variance

Then perhaps a method emerges out of this to make training faster (but not inference) - do early training on highly quantized (even ternary) weights, and then swap out the weights for fp16 or something and fine-tune? Might save $$$ in training large models.

Because it shifts the burden (or at least appearance) of responsibility from those experiencing homelessness to the government orgs tasked with housing them.

This is great, but what is a possible use-case of these massive classifier models? I'm guessing they won't be running at the edge, which precludes them from real-time applications like self-driving cars, smartphones, or military. So then what? Facial recognition for police/governments or targeted advertisement based on your Instagram/Google photos? I'm genuinely curious.

I imagine this is a stage in a larger plan to incorporate an AI assistant to excel? Being able to talk to your spreadsheet in natural language and have it do a bunch of analysis and visualizations would be a huge productivity booster.

That's the question I'm asking myself too. I live in Seattle, where you can't walk two blocks without seeing a new (wooden) residential building going up. I'm sure that newer buildings like these take more precautions than the historic ones in Lahaina, but it still makes me wonder, since Seattle isn't historically prone to wildfires.

If I remember correctly, the algorithm in Alpha Go used a combination of reinforcement learning and searching the space of moves and possible outcomes to determine the next move. So it does indeed use both search and learning.

Regardless, the author's point is that computation is a better way of finding and exploiting patterns/strategies than our own intuitions. The distinction between search and learning is not the important one here.

I imagine that carbon footprint could be reduced substantially by this too, at least on a per-watt of utilized energy basis. Imagine if all computer chips used semiconductor materials - then more of the input electricity is actually put towards computing, and cooling fans are a thing of the past!

I've only tried a few songs but they've mostly been bangers! I did come across a couple examples where the recommended songs just heavily sampled the original but overall very impressed.

I think this line from the article serves as a good summary: > We’ve never seen anything like this in California housing history where a residential building of any height, with any amount of parking, can be placed in the wealthiest communities in the world provided its just 20% affordable and is safe.

In such a safety-critical system, it's important to have backups. You need at least two cameras to accurately estimate depth, and cameras can fail for a variety of reasons (sun glare, low lighting, heavy fog). Radar, at the very least, is a backup for the cameras. Also, with vision, the best you can do is estimate distance, whereas with radar and LIDAR you are explicitly measuring it.

I'm always in awe at how such a simple algorithm (natural selection) can create such beautifully complex systems (organisms or even super-organisms).

Why are you so mean-spirited?

or writing assembly code