HN user

tedd4u

1,597 karma
Posts20
Comments451
View on HN
www.youtube.com 1y ago

I built a 1B FPS video camera to watch light move

tedd4u
3pts1
electrek.co 1y ago

Tesla shuts down Cybertruck production for days at critical time for the company

tedd4u
27pts35
www.nature.com 1y ago

Carbon dioxide capture from open air using covalent organic frameworks

tedd4u
2pts0
www.washingtonpost.com 1y ago

Chinese government hackers penetrate U.S. internet providers to spy

tedd4u
4pts3
www.intel.com 4y ago

New Intel RISC V Softcore (Nios V)

tedd4u
2pts0
www.riscosopen.org 6y ago

RISC OS Porting to the New ARM Based Apple MacMini

tedd4u
1pts0
github.com 6y ago

OpenTrace Trial Methodologies (Covid-19 Bluetooth Tracing)

tedd4u
2pts0
www.sfchronicle.com 6y ago

Tesla driver dies after car bursts into flames in Pleasanton crash

tedd4u
2pts0
socketsite.com 6y ago

Asking Rents Slip in San Francisco, Nearing Peak in Oakland

tedd4u
3pts1
www.frbsf.org 6y ago

Why Is Inflation Low Globally?

tedd4u
4pts0
www.sciencemag.org 6y ago

Eyeing organs for human transplant, companies unveil most gene-edited pigs yet

tedd4u
3pts0
www.fieggen.com 6y ago

70+ ways to lace shoes (Ian's Shoelace Site)

tedd4u
2pts0
avc.com 7y ago

Thinking Ahead to 2019

tedd4u
2pts0
medium.com 9y ago

4 part series: How Quizlet uses Apache Airflow to execute data pipelines

tedd4u
15pts0
code.flickr.net 10y ago

Flickr’s experience with iOS 9

tedd4u
1pts0
code.flickr.net 10y ago

Powering Flickr’s Magic view by fusing bulk and real-time compute

tedd4u
1pts0
code.flickr.net 11y ago

GPU Real-Time Resizing of Images on Flickr

tedd4u
2pts0
code.flickr.net 11y ago

The Ins and Outs of the Flickr 100M Creative Commons Dataset

tedd4u
1pts0
code.flickr.net 11y ago

Performance improvements for photo serving

tedd4u
1pts0
code.flickr.net 12y ago

Flickr: Computer vision at scale with Hadoop and Storm

tedd4u
1pts0

The corps ruled the US until Trump. Now it’s moving to an oligarchical / kleptocratic mode like Russia. Sure the corps are still involved, but not on top. Will they remain after the current administration? I think it will be hard to put the genie back in the bottle.

The Ouro looping results are interesting [1] and they are focused more on the improved reasoning from looping middle layers rather than the parameter efficiency aspect. They train 1.4 and 2.6B parameter models with 7T tokens. The training includes learning how many times to loop on any given token (there’s an early exit module). My guess as to why (as far as we know) looping is not in frontier models yet is that, at frontier training run scale, it’s probably going to require a lot of trial and error and at-scale research. While currently they already probably have a list of dozens or hundreds of of promising ideas that don’t complicate things as much. In the other hand, Ouro’s looping technique shows ability to compete well with models with 3x parameters which seems attention-getting to me. If there’s another 3x to be had down that path. It’s order of magnitude opportunity. Btw there is a great related work section in the paper.

[1] https://ouro-llm.github.io/

I hesitate to propose ulterior motives, but given there have been several seemingly obtuse objections to projection from Rivian, perhaps the CEO is concerned that, if Rivian supports projection, it will harm the perception of the value of their software stack? Related, I think they licensed their stack to VW.

I've heard students react this way to seeing problems on exams that are not strictly of the types taught in class or in previously-assigned homework. "It's not fair! The teacher never showed us how to do problems like that!" This kind of thing was expected and assumed when I was in secondary school, that there would be combining of some concepts from the unit into a single problem. Very worried that there's both a cultural change supported by AI tools that will lead to the outsourcing of thought to the AI rather than outsourcing drudgery.

One problem here is that students that use AI to outsource thinking become people who cannot think. These people are not likely to be very useful to employers or even society. We have to figure out how to allow AI to outsource drudgery but not the thinking itself. It should be a better and better bicycle for the mind not a replacement for the brain.

I know some pretty wealthy people. They are very aware of those who are 10x wealthier than them. If Noam has 1B, he is probably pretty aware of those that have 10B. He's met them and seen their properties, scope, and powers. Likewise, they are thinking about those that have 100B, and those are thinking about Elon, who now has "four commas."

You train the model then do a baseline evaluation. Then you evaluate many variants where you have removed or nulled out different layers or chunks of the model. By comparing the performance of those mutated models to the baseline you can learn a lot about the model. What parts don't have much value and can be removed, the location of "functions" or "facts." Etc. Google it.