xylogx @ xylogx @lemmy.world

Posts

69

Comments

291

Joined

2 yr. ago

Google was fully owned by the Chinese Government back in 2009. As a result Google pulled out of China.

https://en.wikipedia.org/wiki/Operation_Aurora

One if my coworkers installed this on his computer one morning before I got into the office. When I arrived I saw it in a boot loop, so I helpfully put in a network boot floppy and re-imaged the hard drive. Good times.

I took this with a Sony A7 and a Sony FE 200-600mm zoom lens.

ILCE-7RM4A ƒ/6.3 1/2000. 600mm ISO100

Does the person she is speaking with know where she is?

Loud grunting and farting noises intensify

My experience with a number of bug zappers was that, while they did zap some number of mosquitos, this was not enough to prevent other mosquitos from biting us. On the other hand a good strong fan can act as an area denial defense. Not always easy/possible to have a good strong fan going, so YMMV.

Bug zappers dont work too well. A fan can be more effective in some cases depending on your circumstances.

I noticed this morning for a small amount of my posts with pictures, maybe 5-10%, the pictures were deleted or missing. Not sure if this is related to the incident.

Good communication is key to effective incident response. This is a good example of that. Kudos.

Ebike subsidies.

99% so far. I rarely open Reddit at all aymore.

I have two:

1. Maxing out employer matching retirement plans
2. Investing in college savings accounts from the day my kids were born

I did both decades ago, now I am set to retire early without worrying about paying big college bills for the kids

Canadian Geese. They are super aggressive but cannot hurt you.

I use wefwef.app from my desktop browser which gives me an Apollo like experience.

Have a look at this paper from MS research -> https://www.microsoft.com/en-us/research/publication/orca-progressive-learning-from-complex-explanation-traces-of-gpt-4/

“ Recent research has focused on enhancing the capability of smaller models through imitation learning, drawing on the outputs generated by large foundation models (LFMs). A number of issues impact the quality of these models, ranging from limited imitation signals from shallow LFM outputs; small scale homogeneous training data; and most notably a lack of rigorous evaluation resulting in overestimating the small model’s capability as they tend to learn to imitate the style, but not the reasoning process of LFMs. To address these challenges, we develop Orca, a 13-billion parameter model that learns to imitate the reasoning process of LFMs. Orca learns from rich signals from GPT 4 including explanation traces; step-by-step thought processes; and other complex instructions, guided by teacher assistance from ChatGPT. To promote this progressive learning, we tap into large-scale and diverse imitation data with judicious sampling and selection. Orca surpasses conventional state-of-the-art instruction-tuned models such as Vicuna-13B by more than 100% in complex zero-shot reasoning benchmarks like Big-Bench Hard (BBH) and 42% on AGIEval. Moreover, Orca reaches parity with ChatGPT on the BBH benchmark and shows competitive performance (4 pts gap with optimized system message) in professional and academic examinations like the SAT, LSAT, GRE, and GMAT, both in zero-shot settings without CoT; while trailing behind GPT–4. Our research indicates that learning from step-by-step explanations, whether these are generated by humans or more advanced AI models, is a promising direction to improve model capabilities and skills.”