lswainemoore
Joined 11 years ago
Karma
94
Submissions
31
Member since
Aug 22
email: [email protected]; site: lincoln.swaine-moore.is
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Comment · 9 months ago
Comment · 1 year ago
That may be true, but if you look at the distribution it puts out for this, it definitely smells funny. It looks like a very steep normal distribution, centered at 0 (ish). Seems like it should have two peaks? But maybe those are just getting compressed into one because of resolution of buckets?
Comment · 1 year ago
Those are not the right population metrics to compare. If you're talking full Bay Area, you might as well talk NYC metro area (MTA claims to serve 15.3 million [1]). Tokyo's even trickier, but I think 36 million [2] seems closer to right.
It's probably not worth arguing about too much, because ultimately I agree with you that there's a lot more to be done to reduce car ridership. But pointing at those places and saying "copy them" misses a lot of structural differences.
[1] https://mta.info/about [2] https://storymaps.arcgis.com/stories/0056b6a98b8b4a48869f822...
Comment · 1 year ago
Why so negative? Given that the served population is conservatively a quarter of either of those areas, doesn't seem like a fair comparison.
More to the point, I've been favorably impressed with the transit options since moving here, and in terms of reliability it's been better than NYC, though obviously there are fewer trains/branches.
I'd love to see BART open later, like NYC, but even Tokyo trains stop at midnight.
Comment · 1 year ago
> I believe this sort of core knowledge is learnable through movement and interaction data in a simulated world and it will not present a very difficult barrier to cross.
Maybe! I suppose time will tell. That said, spatial intelligence (connection/movement included) is the whole game in this evaluation set. I think it's revealing that they can't handle these particular examples, and problematic for claims of AGI.
Comment · 1 year ago
They're in the original post. Also here: https://x.com/fchollet/status/1870172872641261979 / https://x.com/fchollet/status/1870173137234727219
Personally, I think it's fair to call them "very easy". If a person I otherwise thought was intelligent was unable to solve these, I'd be quite surprised.
Comment · 3 years ago
One reason you might not want to run it in production is if it's not a read-only query.
Comment · 3 years ago
Location: US (NYC currently)
Remote: sure
Willing to relocate: yes
Technologies: Python (Flask, SQLAlchemy, Django), JS (React, Next, AngularJS, Typescript), SQL (Postgres, PostGIS), HTML/CSS
Resume/CV: https://lincoln.swaine-moore.is/bragging
Email: [email protected]
About me:
Senior software engineer with experience all over the stack; special interest in sustainable technology/maps/making web applications that are fun to use, but most of all interested in working for you, reader. Currently seeking freelance opportunities, or the right full-time job.
Check out my website: https://lincoln.swaine-moore.is/blue.
Comment · 3 years ago
SEEKING WORK | US | remote or in-person (potentially willing to relocate)
Technologies: Python (Flask, SQLAlchemy, Django), JS (React, Next, AngularJS, Typescript), SQL (Postgres, PostGIS), HTML/CSS
Resume/CV: https://lincoln.swaine-moore.is/bragging
Email: [email protected]
About me:
Senior software engineer with experience all over the stack; special interest in sustainable technology/maps/making web applications that are fun to use, but most of all interested in working for *you*, reader. Currently seeking freelance opportunities, or the right full-time job.
Check out my website: https://lincoln.swaine-moore.is/blue.
Comment · 4 years ago
hmmm
Comment · 4 years ago
/*
When it rains does it pour?
*/
SELECT date, wban, stn, year, mo, da, temp, count_temp, dewp, count_dewp, slp, count_slp, stp, count_stp, visib, count_visib, wdsp, count_wdsp, mxpsd, gust, max, flag_max, min, flag_min, prcp, flag_prcp, sndp, fog, rain_drizzle, snow_ice_pellets, hail, thunder, tornado_funnel_cloud, usaf, name, country, state, call, lat, lon, elev, begin, end, point_gis, fake_date FROM `fh-bigquery.weather_gsod.all_geoclustered` WHERE lat IS NOT NULL AND lon IS NOT NULL AND lat != 0 AND lon != 0 AND lat != 1 AND lon != 1 AND lat != -1 AND lon != -1 AND lat != 2 AND lon != 2 AND lat != -2 AND lon != -2 AND lat != 3 AND lon != 3 AND lat != -3 AND lon != -3 AND lat != 4 AND lon != 4 AND lat != -4 AND lon != -4 AND lat != 5 AND lon != 5 AND lat != -5 AND lon != -5 AND lat != 6 AND lon != 6 AND lat != -6 AND lon != -6 AND lat != 7 AND lon != 7 AND lat != -7 AND lon != -7 AND lat != 8 AND lon != 8 AND lat != -8 AND lon