1,613 karma · joined October 28, 2011
I also discuss how bioweapons cannot be avoided via restricting open weight models as originally suggested in Dan's paper. Rather we need to heavily invest in bioweapon defense, and in particular use AI for wastewater monitoring and accelerating metagenomics (detangling mixed DNA).
For local models I highly recommend https://github.com/crizCraig/open-webui
They do the side by side thing that Chorus does and you can serve it to anywhere including your phone.
The upgrade/logout issue you're facing is likely due to not setting WEBUI_SECRET_KEY outside of your docker container. This causes all previous cookies to be unreadable as a new key will get generated by start.sh and won't decrypt the old cookies.
edit: I should add, I'm making only $30/mo in subs, but I just launched so we'll see!
[1] https://drive.google.com/file/d/1rdG5QCTqSXNaJZrYMxO9x2ChsPB...
However, civil wars usually occur in poorer countries [1][2]. Also the proportion of the world in civil war is 1% and falling exponentially [2].
Finally, Metaculus gives a 5% chance of US civil war before 2031, where a recent July bump is discussed in the comments (Roe, Jan 6) [3].
[0] https://www.nature.com/articles/463608a
[1] https://www.buzzfeednews.com/article/peteraldhous/political-...
[2] https://www.nber.org/reporter/2011number3/economic-shocks-we...
[3] https://www.metaculus.com/questions/6179/second-us-civil-war...
Notes: The Harvard December 2021 youth poll participants gave as 35% chance in their lifetime: https://iop.harvard.edu/youth-poll/fall-2021-harvard-youth-p...
Rasmussen 2018 poll: 33% likely, 10% very likely before 2023 https://www.newsweek.com/second-civil-war-likely-one-third-a...
Does anyone have a graph of this probabilty over time from a consistent source like rasmussen? https://www.rasmussenreports.com/public_content/politics/que...
In [1]: import numpy as np
...: import getpy as gp
In [2]: key_type = np.dtype('u8')
...: value_type = np.dtype('u8')
In [3]: keys = np.random.randint(1, 1000, size=10**2, dtype=key_type)
...: values = np.random.randint(1, 1000, size=10**2, dtype=value_type)
...:
...: gp_dict = gp.Dict(key_type, value_type, default_value=42)
...: gp_dict[keys] = values
...:
...: random_keys = np.random.randint(1, 1000, size=500, dtype=key_type)
In [4]: %timeit random_values = gp_dict[random_keys]
2.19 µs ± 11.6 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)
In [7]: %timeit [gp_dict[k] for k in random_keys]
491 µs ± 3.51 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)
[0] https://github.com/atom-moyer/getpyMake sure to disable JavaScript.
https://gist.github.com/crizCraig/f42bc250754bed764ada5f95d1...
raw: https://gist.githubusercontent.com/crizCraig/f42bc250754bed7...
Vision: 10Mb/s => 100Mb/s
Hearing: 30Mb/s
Touch: 135Mb/s
Smell: 100k neurons
Taste: 100kb/s
Proprioception: ??
Balance: ??
Total: ~10Mb/s=>~1Gb/s
Internal brain bandwidth is also worth mentioning as this is the last remaining wetware advantage over hardware due to the three dimensionally fully connected heterogeneous cortical substrate. I can't seem to find a figure on that though.https://www.quora.com/How-much-bandwidth-does-each-human-sen...