#job guarantee data science course

[juliebowie]

Join our data science course with a job guarantee and start your career with confidence. Learn from experts, gain practical experience, and secure your dream job in data science.

[datasciencelearningplatform]

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[mentorkart]

MentorKart is a Data Science Job Guarantee Program for young students of science and engineering colleges, who aspire to become data scientist.

[humanbyweight]

you’re a wasp researcher!!! that’s super cool, do you have any… advice, i guess, for a recent grad (bsc in bio) to try and get into that same path? i love wasps and insects and invertebrates of all kinds and i would love to be more involved in working with them, but a lot of opportunities outside of citizen science observation seem to be locked behind getting a master’s degree.

anything you know that might be helpful in this?

Congratulations on your recent degree! It's awesome that you're interested in studying Hymenoptera. There is so much important and impactful work to be done in this field!

My professional journey is... well... let's say unconventional. I am a 28-year-old queer woman. I have no formal training in entomology whatsoever. I took a single introductory biology course in college (since my degree is in Mechanical Engineering). And yet, through extremely hard work* and determination, I am now internationally recognized as an expert in certain groups of Hymenoptera and Hemiptera, and I am regularly invited to give lectures at universities. I am an award-winning author of several scientific texts about insects, which provides me with a modest income. I am, without question, a successful entomologist.

*networking with established experts, reading all available literature, spending over a thousand hours identifying and analyzing community science data, volunteering with museum collections, using engineering jobs to fund my early entomology work, etc.

I am proof that you do not need a master's degree to excel in this field (though I admit full-time entomologists without master's degrees are uncommon). Nonetheless, I recommend the following:

Don't be afraid to take on non-entomology jobs to pay the bills while you're just starting out. There is no shame in that.

Anyone can make important scientific discoveries with any level of education. People who say otherwise are either haughty or insecure, and they can shove their gatekeeping directly up [redacted].

Study insects generally, but pick one small and understudied group (that you love) and learn everything there is to know about it. This will automatically make you a world expert in that group. I guarantee you will make many cool new discoveries along the way.

Hope that helps! Good luck!!

[laxmiree]

[CN] MLQC Lucien's Through Thousands of Mirrors event translation (Day 1 -Thursday)

⚠️ SPOILER ALERT ⚠️

This post contains a HEAVY SPOILER for the event that has not been released in EN yet! Feel free to notify me if there are any mistakes in the translation~

Through Thousands of Mirrors Event | Day 1 (You're here!) | Day 2 | Day 3 | Day 4 | Day 5 | Day 6 | Day 7 | HS/Uni SSR Story: Monochrome Scenery

-

[Tidbits: I don't wanna break the flow so I'll put some information here first 😂. Dr. Lawson is Lucien's post-grad professor. Before, he also appears in UR MQ Distant Similarity. During his post-grad he has three seniors Colt, Elliot, and Caroline.]

-

[Math]

Seeing the particularly puzzled expression on the classmate next to him, Lucien starts to consider whether he should offer some assistance within his capabilities.

For instance, he thinks about telling the classmate that the topic currently being discussed on the blackboard is not from the same chapter as the one in the textbook he's currently reading.

-

[MATH/BIOCHEMISTRY]

After the vending machine devours Lucien's one dollar and twenty-five cents for the third time, and with only three minutes left to get to his next class, he begins to seriously contemplate whether he should try some mysterious repair method—like giving it a good smack or a swift kick.

-

[BIOCHEMISTRY]

Lucien coincidentally runs into Colt by the sports field, just as Colt is about to attend a cricket practice session.

Upon realizing that his senior from the lab is not only managing coursework and a significant project workload but also juggling a 20-hour weekly part-time job and daily school cricket team training, Lucien begins to contemplate whether there is any room for further optimization in his own schedule.

-

[PREVIEW/COMPUTER SCIENCE]

During the brief half-hour period, Lucien typically uses the time to prepare for the upcoming class or visit the library to research and gather information.

In any case, that time should not be spent on arguing and explaining to people, like the enthusiastic campus volunteer in front of him.

"No, thank you. I'm not a high school student attending a summer camp. This is my student ID, and I'm indeed a student here, a graduate student. Yes, I'm not lost, and I need to get to my class. Can you please let me go?"

-

[COMPUTER SCIENCE]

Lucien presses the enter key, intending to ask the teacher if he can leave early once his coursework is done. However, the error message on the screen deters him from that thought. So, he sits back down and begins to examine it again.

But that's okay, he does understand the commonality between computer science and experimental research: it's often hard to know right away if the thing at hand will work, why it's not working, or even why it's even working.

-

[LAB]

Come on, come on, come on. After moving this box, there's another.

And after moving that box, there are three more to go.

The prospects for the future and the shine in one's eyes are often taken away by the God of research in such necessary yet mechanical repetitive work.

-

[LAB]

Lucien goes out to get some water and returns to find a school burger on his desk.

Colt, with dark circles under his eyes, waves at Lucien and saying, "No need to thank me, newcomer. Have some food, we might be staying here today."

Lucien quietly eats the burger, hesitant to tell Colt that he has spent more time in the laboratory than in the dorm.

-

[LAB]

When Dr. Lawson enters the laboratory, what he sees is a scene where his graduate and doctoral students are sleeping and sprawled all over the place.

On the laboratory whiteboard, several words were written in large letters: "Publish immediately! Guaranteed to be published in Nature!!"

Dr. Lawson retrieves small blankets from the cabinet, covering each of these research madmen.

He proceeds to organize the data and take over the finishing work on the project. Of course, when it comes to authorship in the paper, not a single one of these kids' names can be left out.

[annes-room]

Why did you decide to study this [degree/topic/language etc]? Do you regret your decision? Do you have any pictures from when you started your learning journey?✨

I guess I've never really shared my full study journey on here, so here goes! :)

I actually started out wanted to go into acting! I still love it a lot, I did a lot in high school and I was part of a few local theatre things that I still hold very dear to me. I did go to film school for acting a few years ago now and it was one of the most amazing times of my life. I was living away from home for the first time, in a different province, and the people in my class were literally from all over the world 🌎 I still follow them on other socials and it's phenomenal to see what they've all been up to since then <3

I moved back home after film school with the intention of saving up to move to Vancouver permanently to pursue my acting career but the Covid pandemic hit literally a week after I got a job 🙃 over the course of a month I watched all of my acting/entertainment friends be out of a job and theatres shut down all over the world. it hit me in a way I hadn't expected and I reflected on how much stability I wanted in my career. acting is already a very fickle industry. you're never guaranteed anything and it can be so much work to get even the smallest jobs. I realized I needed more stability than that in the face of a global pandemic. I was mulling over my options when NASA launched their Mars Perseverance rover.

I woke up at 5 am to watch the live stream of the launch and I felt a draw to it like I was a kid. space and its exploration have always been fascinating to me, so I began looking into it more. astrobiology was a very up and coming field and I wanted to be part of it so I applied to an astrophysics program (the closest I could get) at the university in my city and got in! 📚

I stayed in that program for a year and in the fall semester of my second year I realized I wasn't having fun with the physics. I didn't understand a lot of it and even though I still passed the courses, I didn't feel like I was learning anything and it wasn't exactly what I wanted to do. the one part I did love was the data analysis we had to do in our physics labs and I enjoyed the one compsci course I had taken as an options class. so I switched programs and am now working towards a BSc majoring in stats and minoring in computer science! 💻📖

university has always been an investment for me. no matter what path I take, I know the work will be worth it for me to have a higher quality of life after I graduate. data analysts are needed for everything so I don't doubt I'll be able to find a decent job after graduating. I'm currently in year 4/5. programs here are normally 4 years if you take 5 courses a semester, but I simply cannot do that, so I'm spreading it out a bit more (plus I had to play catch-up with switching programs).

overall I'm happy where I am. I still miss acting but I'll get back into it as a hobby once I graduate. in summary: acting -> astrophysics -> stats and cs

[shituationist]

it's amazing that so many lesswrongers see "sparks" of "AGI" in large language models because

the bulk of them are neo-hayekians, and their widespread belief in prediction markets attests to this

it's now very well documented that "knowledge" which models haven't been trained on ends up being confabulated when models are queried for it, and what you receive is nonsense that resembles human generated text. even with extensive training, without guardrails like inserting a definite source of truth and instructing the model not to contradict the knowledge therein (the much vaunted "RAG" method, which generates jobs for knowledge maintainers and which is not 100% effective - there is likely no model which has a reading comprehension rate of 100%, no matter how much you scale it or how much text you throw at it, so the possibility of getting the stored, human-curated, details wrong is always there), you're likely to keep generating that kind of nonsense

of course, hayek's whole thing is the knowledge problem. the idea that only a subset of knowledge can be readily retrieved and transmitted for the purpose of planning by "a single mind".

hayek's argument is very similar to the argument against general artificial intelligence produced by hubert dreyfus, and I don't think I'm even the first person to notice this. dan lavoie, probably one of the brightest austrian schoolers, used to recommend dreyfus's book to his students. both hayek and dreyfus argue that all knowledge can't simply be objectivized, that there's context-situated knowledge and even ineffable, unspeakable, knowledge which are the very kinds of knowledge that humans have to make use of daily to survive in the world (or the market).

hayek was talking in a relatively circumscribed context, economics, and was using this argument against the idea of a perfect planned economy. i am an advocate of economic planning, but i don't believe any economy could ever be perfect as such. hayek, if anything, might have even been too positive about the representability of scientific knowledge. on that issue, his interlocutor, otto neurath, has interesting insights regarding incommensurability (and on this issue too my old feyerabend hobbyhorse also becomes helpful, because "scientific truths" are not even guaranteed to be commensurable with one another).

it could be countered here that this is assuming models like GPT-4 are building symbolic "internal models" of knowledge which is a false premise, since these are connectionist models par excellence, and connectionism has some similiarity to austrian-style thinking. in that case, maybe an austrianist could believe that "general AI" could emerge from throwing enough data at a neural net. complexity science gives reasons for this to be disbelieved too however. these systems cannot learn patterns from non-ergodic systems (these simply cannot be predicted mathematically, and attempts to imbue models with strong predictive accuracy for them would likely make learning so computationally expensive that time becomes a real constraint), and the bulk of life, including evolution (and the free market), is non-ergodic. this is one reason why fully autonomous driving predictions have consistently failed, despite improvements: we're taking an ergodic model with no underlying formal understanding of the task and asking it to operate in a non-ergodic environment with a 100% success rate or close enough to it. it's an impossible thing to achieve - we human beings are non-ergodic complex systems and we can't even do it (think about this in relation to stafford beer's idea of the law of requisite variety). autonomous cars are not yet operating fully autonomously in any market, even the ones in which they have been training for years.

hayek did not seem to believe that markets generated optimal outcomes 100% of the time either, but that they were simply the best we can do. markets being out of whack is indeed hayek's central premise relating to entrepreneurship, that there are always imperfections which entrepreneurs are at least incentivized to find and iron out (and, in tow, likely create new imperfections; it's a complex system, after all). i would think hayek would probably see a similar structural matter being a fundamental limitation of "AI".

but the idea of "fundamental limitations" is one which not only the lesswrongers are not fond of, but our whole civilization. the idea that we might reach the limits of progress is frightening and indeed dismal for people who are staking bets as radical as eternal life on machine intelligence. "narrow AI" has its uses though. it will probably improve our lives in a lot of ways we can't foresee, until it hits its limits. understanding the limits, though, are vital for avoiding potentially catastrophic misuses of it. anthropomorphization of these systems - encouraged by the fact that they return contextually-relevant even if confabulated text responses to user queries - doesn't help us there.

we do have "general intelligences" in the world already. they include mammals, birds, cephalopods, and even insects. so far, even we humans are not masters of our world, and every new discovery seems to demonstrate a new limit to our mastery. the assumption that a "superintelligence" would fare better seems to hinge on a bad understanding of intelligence and what the limits of it are.

as a final note, it would be funny if there was a breakthrough which created an "AGI", but that "AGI" depended so much on real world embodiment that it was for all purposes all too human. such an "AGI" would only benefit from access to high-power computing machinery to the extent humans do. and if such a machine could have desires or a will of its own, who's to say it might not be so disturbed by life, or by boredom, that it opts for suicide? we tell ourselves that we're the smartest creatures on earth, but we're also one of the few species that willingly commit suicide. here's some speculation for you: what if that scales with intelligence?

[skillfulldeveloper]

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[screechingtacoblizzard]

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[juliebowie]

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[datasciencelearningplatform]

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[advanto-software]

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[mentorkart]

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