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.
> 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.
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.
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.
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?
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.