high_derivative

While I enjoy reading some of the political reporting on Brexit on the guardian, they are amongst the least balanced newspapers on general societal and cultural issues.

"Money is a motivating factor for most of us, but assuming comparable pay, what is it that makes some companies attract and retain developers while others churn through them like toilet paper?"

My take on that would be that paying more would almost certainly help. Companies who are wondering why they can't seem to find or retain top talent should most likely not wonder about all the soft factors but pay more.

There is this almost comical aspect to companies hand-wringing about how they tried everything, except paying closer to top of the market.

Facebook criminally misleading and deceiving people about how data is used (i.e. email scandal) is a reflection of our own flaws?

My highly cynical take as someone researching in ML is that all these interdisciplinary AI studies, AI policy, etc are mostly grifters trying to get in on the gold-rush.

What has AI policy produced so far, really? Algorithm analysis and safety research is done on the algorithmic side, not on the policy side. Every time I have read an AI policy paper, it has been listings of desirable properties with absolutely no input on how to make any real steps towards them. In the meantime, realities of algorithm driven mass surveillance and censorship is moving ahead at a rapid pace.

Possible conclusion 1: We just need way more AI policy research.

Possible conclusion 2: AI policy is done by the wrong people and deep algorithmic understanding is a pre-requisite.

Possible conclusion 3: Current AI policy is just used as a fig-leaf by tech companies who hire a few policy essay-writers without substance. The lack of progress in that area is a feature, not bug.

Not mutually exclusive, not exhaustive. Feel free to point to valuable policy research.

Might as well directly read Cahill's piece which the above article refers to:

https://quillette.com/2019/03/29/denying-the-neuroscience-of...

Reading about systems theory. I read Niklas Luhmann (e.g. Trust and Power) in undergrad and it made strong impressions on me. Throughout my PhD (sciences), I tried to view every problem I tackled, every collaboration from the lens of understanding the social system of the research scene, the problems tackled, the conversation happening within that community etc.

I believe systems theory can provide a real edge when competing with people who few most things at work as intellectual d*ck measurement contests, including career advancement based on naive notions of being just more clever, or just working harder to advance. Especially in academic settings, focusing on the social dynamics of your research community is key.

N.b.: This is under the consideration that you are already good/constantly improving your main professional skill, be it coding, writing, selling.

I interpreted it as:

There are great opportunities to set yourself up for life, but many of those require a chunk of money to begin with.

The way I understood the article is that the knowledge is not removed. Just the learned anxiety related to the knowledge.

"Highlighting the achievements of a brilliant, enthusiastic scientist does not diminish the contributions of the other 214 people who worked on the project, either. "

That is, in my experience, untrue, if it is highlighted in a way that unjustly lifts one contribution above all others.

Does not tend to make collaborators happy. Since there is no money involved (as in, no big salary increases), academics (being one myself) are incredibly petty about these things, and bitter feuds are fought about small points of recognition.

For example, another female collaborator of hers was actually the one to point out that Katie Bouman's algorithm was not the one used for the image, as pointed out by the NYT:

https://www.nytimes.com/2019/04/11/science/katie-bouman-blac...

Key point: her algorithm was not actually used to create the image.

As a scientist, I think the shitty thing about these media hype stories is that it can create massive resentment in a reseaerch team when one person is suddenly made out to be a "historic figure".

Is that not technically untrue as this is already priced, with options being very expensive due to high implied volatility now?

I encourage downvoters to read the article below for a perspective on how LeCun, Hinton and Bengio may have unfairly excluded a wealth of prior work in their version of deep learning history:

http://people.idsia.ch/~juergen/deep-learning-conspiracy.htm...

Note for people put off by the link: The conspiracy refers to the title of a paper by the Turing award winners, not Schmidhuber's.

Yes seems political to snub him, but then ACM is an American association.

I had a similar (although cynical reading):

Flat hierarchies: Chaotic leadership / implicit and unwritten hierarchies with CEO's favorites.

Values diversity: Politicised office focusing on SJW issues and quotas, not diversity.

Fosters psychological safety: Empowers toxic characters to bring their drama into work.

Empirically, from the limited placements I have seen, the more a workplace outwardly focuses on one of these aspects, the more they are ridden by toxic tribalism/outrage culture.

Missing the most important filter of all: No Leet-code style white-board interviewing.

I have with friends, but with undergraduates this is a bit more difficult.

An undergrad tells me they are going to FB for an internship. I am not their tutor or mentor beyond teaching. They have gone through stress and trouble for their internship, and it's too late to get something else for the year.

The only thing I can do in this instance (where there is no chance at all they would give up their internship to do nothing) is make them feel bad about it. Uncomfortable situation.

Honestly, I think the department should step up and disinvite FB from campus events and not allow them to advertise. It should also hold courses on ethical impact of technology and discuss a few cases of misuse.

This is just an anecdote but being a PhD student at a well-known university in CS I often hear from undergrads (via teaching) what they think about companies/where they go for internships.

Observationally, ethics does not even enter the equation. Especially career-driven students from backgrounds where name-brand prestige is important just want such a name-brand on their CV.

Not that faculty are any better given how many academics effectively sign away their lab to FB through 'collaborations'. All they see is resources to use, name-brand recognition, a big personal pay-check, and publicity for their work.

What I find most mystifying is that this is a literal non-topic. At best someone may forward a blogpost about a security leak and make a snarky comment, but I have never witnessed any political discussion.

Oh I won't turn down the offer, as said, I have no choice and it would be an irrational career move to do so.

The ultra-negative interviewing experience just completely changed the mindset with which I will go into that job. This then got me thinking about the hidden cost of a negative interviewing process.

Having recently gone through some interviewing (for machine learning research), a very cynical but overlooked aspect (in this thread) is the following:

After receiving an offer from a big tech company, the interviewing process has already completely turned me off from the idea of working there.

Now despite this being a dream position for many and me having no alternative but to take it currently, the smug interviewers have already gotten me in the corporate mind-set: No matter, the reputation and salary, treat it like any other job, do not bother being loyal - they will not be.

So the terrible interview process has at least the advantage of reminding future employees what they are signing up for.

I wonder what kind of psychological filtering is at play. Do employees feel loyalty after an interview process that is best described as hazing? Are they projecting the humiliation they experienced when interviewing future employees? That's always been my impression.

I have long thought the term 'digital native' is a sort of filter for people who are not actually technologists but avid consumers of mostly social platforms.

I (as a computer scientist) have never heard people actually working in tech refer to themselves as that.

I think her tone is quite antagonising and I am not sure if it is intentional:

" No one wants to live in a world where the little German lady with her oh-so rational arguments ends up being right. Not even the German lady wants that. Wait, what did I say? I must be crazy."

This is meant to sound self-deprecating (I think) but in the context of the articles comes across as arrogant to me because clearly she thinks she is not the little lady, and others are irrationally ignoring her superior insights. Could be a cultural artefact but it makes her articles uncomfortable to read for me.

I am a PhD student in computer science/machine learning. I do not have a family or a significant other to spend time with.

I work every day from morning to evening with no regular days off. I take a 3-4 day break every 6 months or so.

I work out almost every day, sometimes in the morning, sometimes over a lunch break.

Productivity is managed via detailed to do lists for every project. The day is broken down into morning session, afternoon session, and evening session. I constantly re-organise by moving things between the slots to optimise deep work.

On top of my own research, I receive constant tasks from my adviser who uses me as a kind of assistant for everything they feel like. They do not pay my tuition or have hired me as a TA (I am funded via an unrelated scholarship).

I cannot wait to get out.

Thanks for these insights!

Do experimental (!) particle physicists have any other way of doing research on this than colliders?

As someone in academia, I often encounter (although mostly tangentially via student politics) grievance studies or rather the mindset that goes with it.

I think about it as an asymmetric game. Most researchers have to give their all to get ahead in their field of study (speaking from a hyper-competitive STEM subfield, ymmv). Grievance scholars however have as part of their march through institutions achieved for themselves not to be subjected to similar standards. Consequentially, academic politics from the undergraduate level onwards is dominated by students from fields whose very existence depends on asserting their importance through these politics.

To us, this is academic politics with nothing to win, everything to lose. To them, it is a vicious existential fight. It is no surprise that the rational choice most people take is to try to ignore this.

However, ignoring it is not an option any more, so out of fear, people chose sides. Taking one side means having to make empty statements and then being (for the most) left in peace. Taking the other side means having career-ending events happening to you.

I am not sure how this is supposed to end but I am getting out of academia as fast as I can.

Thank you. Well here is a potential counterpoint:

I think one could also imagine a world where there will be a cannibalization/consolidation on the lower end of the market because of the democratization (which I am not saying is bad or good because I am not sure about the effects ML has on people and democracies, but that is another discussion).

Rather, I observe that nobody today would hire someone to (in essence) build numpy functionality. Yet there are a lot of jobs in ML/AI currently which amount to building things that will be commoditized within years (and be used by everyone like numpy today). So in my opinion learning AI basics will not necessarily lead to more AI jobs or startups because these basic features are commoditized via big tech open source.

Second, on applications:

My research is actually in the applied domain and that's where my argument mostly stems from. Doing novel, valuable applications in AI requires concentrated resources and expertise. For example, if you think about self-driving cars as an extremely high-value application, they require highly specialized domain expertise in various domains and machine learning combined with massive compute resources.

Simple applications (e.g. applying basic machine learning tools to common workflows and processes) are prone to undergo commoditization precisely because they are obvious. So I do not think the gatekeeping argument holds because there is no way around obtaining deep and wide expertise to generate valuable application IP.

So for a country wanting to focus on applications, I still think there is a good argument that concentrating resources is a more successful approach.

1. They are not teaching CS people, but a random 1% slice of the population.

2. Where did I not condone? I made an observation what I think leads to better outcomes. I said both is desirable but culturally only one thing seems to happen.

Before you angrily accuse me of "Delusional gatekeeping", maybe re-read (or maybe for the first time read) both the article and my comment.

Your confrontational tone does not help the discussion and is not in the spirit of hackernews to assume the best intentions.

Controversial opinion as a machine learning researcher:

Well roundedness (or teaching basic elements to many) will not pay off as much, it would likely yield better results for the country to invest heavily at the top.

Observation based on success of machine learning research in highly concentrated organisations (certain universities in the US, DeepMind, etc) versus countries with scattered expertise and resources (e.g. Germany).

If you can afford to, do both, but observationally, this does not seem to happen. Not for a lack of resources but rather the mindset and a society's perspective.

Whenever I see these updates, I am reminded of the three body problem trilogy and its contemplations on space exploration (the parts I enjoyed the most).

Then I get sad because things just take so incredibly long on a stellar scale, and a human life is so short.