Maybe its only my gig and my area of the world, but going through the rounds with companies can be quite a grueling process and many recruiters can't explain the exact technical details or give any concrete salary information.
It's hard to just do it to test the waters when you have a technical project and multiple interviewing rounds (which also include live coding, which for me is very stressful).
I'm by no means an expert in the field but I do find it exceptionally interesting so i try and keep tabs on some of the research done by people who originated from the same group as Kenneth Stanley.
Seems like many from this group now pursue open-endedness in AI and view evolution as a way towards this goal (or lack thereof).
A very interesting evolution (ha!) of these ideas was presented in POET[0] towards evolution of agents in evolving environments.
There is also an interesting paper about accelerating neural architecture search when generating fake training data in generative teacher networks[1].
Lastly, a paper that i find very very interesting but might not be as relevant but still is 'First return, then explore'[2]
I completely agree. I used to live in Liege and would travel to NL, Brussels and Hasselt regularly.
The juggle between different public transport subscription was difficult on top of the constant outages.
The extensive bushfires and droughts are definitely big factors.
I worked briefly for a forestry in rural Victoria, where we would encounter koalas somewhat frequently where we would plant trees, These are lots that would be planted completely and years later cut and burned.
Wonder what would be also the effects of this on them and wildlife in general.When I have seen Koalas in the wild they seemed very apathetic- saw one stay at the very top of a blue-gum (which famously can shed huge pieces without much warning) even during a storm.
I have used the MLP classifier[1] before. It's very simple to use (like most of sklearn's models). Worked well for standard and reasonably small classification model, but lacks some features for it to be a flexible way of using NNs:
- No saving checkpoints (can be crucial for large models who need alot of compute and time)
- No way to assign different activation functions to different layers
- No complex nodes like LSTM, GRU
- No way to implement complex architectures like transformers, encoders etc
I also do not know if its even possible to use CUDA or any GPU with it.
from the article: 'Warning sign (of a critical moment in AGI development) will be, when systems become capable of self-improvement.'
Is the meaning of self improvement here means that the model will actively optimize itself towards improving on its mistakes outside of training? Because under my understanding for this to happen we would need the model to be in a different form than current ML.
While many units generally consist of smaller teams, the teams can often be changed and switched around as people get specialized or re-assigned at different points, this generally doesn't happen for the very elite units.
Small teams are kept together at all times since very early in the training. Doing the same exercises many many times with the same exact people leads to mastery that makes them just seem faster and more fluid.
The lifestyle point is also true, its easier to keep your edge when there aren't constant mindless tasks to be done (gate duty) for many hours a day.
- I'm saying this from experience with armed forces, but can't claim its true for all elite units everywhere.
This is from my own experience working at a TP call center, for a major tech client and not in Albania or Colombia.
External and internal audits are known for weeks in advance such that they could put a facade on in time. During audits you suddenly don't have a limit on bathroom break time (generally an alert would come at 15mins per day) and there would be pizza at the office every other day. Only 'loyal' employees would be chosen to any interviews/meetings about culture etc.
Yes, its hard to tell the exact algorithmic underpinnings of production models that Google uses but you have to assume that although they have some done some impressive strides in fields that isn't immediately profitable (AlphaGo, AlphaFold...) they also continuously push new research in things that are obviously of interest for Google and Alphabet- especially in text-to-speech, speech-to-text, information-retrieval etc.
Yes! The subtitles can be completely different than the dubbing in some cases which makes it difficult for me to watch with both.
Also, they sometime use a bit odd translations. I saw a Belgian show and they translated smoking a cigarette into smoking a fag. Which I guess is technically correct (based on the cambridge dictionary), just an odd choice for general EU viewership
Yes! The subtitles can be completely different than the dubbing in some cases which makes it difficult for me to watch with both.
I suspect in many cases the subbing is done translating off the original script, while the dubbing is done using a completely different translation designed to flow better when spoken.
This seems to be the case. Watching the Spanish dubbed version of Community on Hulu, the dubs have the study group taking an English class, while the subs have them in a Spanish class as in the original.
Side note: in the dubbed version, it's hilarious when they switch to speaking bad English where they would have switched to bad Spanish in the original.
> Yes! The subtitles can be completely different than the dubbing in some cases which makes it difficult for me to watch with both.
Yeah I've tried Czech dubbing with Czech subtitles, but have a lot of trouble following it for this reason. I'm sure once I get better at Czech it'll make more sense, but for the moment it seems like I need to stick with Czech audio and English subtitles or vice versa. Otherwise it's just too confusing.
If you can access the videos on česká televize (presumably you're based here if you are learning Czech) I've found them to be quite useful as they generally provide an option to turn on subtitles that match the audio. Like this kids show I used to watch with my teacher in our lessons: https://decko.ceskatelevize.cz/chaloupka-na-vrsku :D
I don't know if I'd call it "amazing", but yes it is pretty difficult for me. :)
I'm generally pretty good at languages, but all languages I speak and have experience in are either Germanic or Romance. Learning Czech is cool because it is a little related (it is an Indo-European language and you can definitely see that if you pay attention), but different enough that it is really pretty difficult. I'm having a lot of fun with it. Hopefully I'll be able to spend a longer period of time there so I can be more immersed in it.
It's hard to just do it to test the waters when you have a technical project and multiple interviewing rounds (which also include live coding, which for me is very stressful).