In addition there's a severe "passion tax" for these sorts of jobs, the salary difference for a "Data Scientist, Computational Biology", and "Computational Biologist" is pretty big, and hiring is also brutal.
I know a ton of extremely talented people who have been locked out of employment for a long time now. The high interest environment means that biotech investing has been hit extremely hard, as biotech is even higher risk than most software and AI spending (thanks for the correction, Schlagbohrer). Pharma companiees with big hits, like Lilly with GLP1 agonists, are hiring a bit as they try to move into the modern era of pharma with lots of AI tools, but it's still brutal.
There's quite a lot of people skills involved in surviving as an academic in today's environment. Imagine if you had to teach calculus to 150 random, uninterested teenagers (barely adults) every 12 weeks. There's some serious people skills involved in doing a good job at that (most people do actually try to teach well, I've known multiple people this year refused tenure based on rate-my-teacher ratings).
It's a different set of skills for sure, but being an academic isn't as socially challenged as the zeitgeist appears to believe.
i like academics, don't misunderstand me.
yes, or at least largely overlapping circles
what i am saying is, having people skills are the answers "yes" to all those questions. you can cynically call getting a job nepotism, or you can call it, well people like to work with their friends at the cost of measures of competency. and maybe, the core competency is being pleasant to work with or work for.
another place people struggle with this is executive compensation. if i told every DoD employee they could get a 10x better boss for only $20/y, every single one would, which is $58m in executive compensation. but the DoD CAN'T do that, and its leadership is TERRIBLE, so... do you see?
There is no communication there. No concepts for them to communicate. It is just math.
I'm not sure that models are complex enough to have a consistent internal representation of a concept the same way that organic brains can to communicate. I'm not sure of any quantitative science backing this up though. Models don't know anything across iterations yet.
Can you expand. They have both context and memory?
I'm sure there are research prototypes that work differently from this but I haven't seen any enter the mainstream yet.
Also, diffusion language models have a different evaluation order but I think they also do not really have internal thoughts or feelings because they also do not seem to have any sort of hidden state that encodes anything like that.
I've had worse. Mostly much better, but I've had worse.
You cannot share (effectively) if you cannot communicate in a way that others can understand.
Further the entire ecosystem that academics rely on to get what they need to do for their research (grants, and other funding, resources, and so on) necessitate them to convince people who control those, who do not necessarily understand the purpose of the work
I don't think this reasoning can work. To the extent these things are directly related, the relationship would have to be: returns on investment are at an all-time high --> more investing than usual.
When interest rates are high, capital shifts to yield-generating, interest-bearing investments. They give higher returns with less risk.
So basically the ROI of biotech becomes less competitive compared to alternatives. You have the same number of people/firms chasing a smaller supply of investment dollars.
Suppose the ROI of biotech becomes more competitive compared to alternatives, because there's an ongoing series of technological breakthroughs.
The return on investing goes up (by assumption) and this means interest rates go up (by definition; they are the return on investing).
Is this bad for biotech? Does it shift capital out of biotech? Obviously not.
Everyone else is using it to mean "the Federal Reserve has set the interest rate high right now"
Very different situations.
High fed rates means the ‘tide’ is different. For biotech to get more interest, it has to have a higher roi than comparable other risk investments.
High ROI != high interest rates.
Tech investments don't come with interest payments usually, so if interest rates go up it pulls money into government and corporate bonds which are much lower risk. Why take a gamble on new tech that might lose you everything to get 10% ROI, if you can get 6% "risk free" in bonds?
Why not?
Business moves incredibly fast; academia, not so much.
Instead you’ll just whine until they let you import a billion more Indians
> Why not?
Truth is that the work and complexity is not that divorced. Honestly, the work in academia felt harder, though more fulfilling. Industry work hasn't made me have to really think deeply. If anything, I've heard most of my coworkers (at multiple companies) say something along the lines of "we have to move so fast that there's no time to think." Given that (multiple) managers tell me I'm "too slow" just because I'm not producing tons of lines of code (I'm neck and neck with everyone on milestones), I understand what they're talking about. Industry has a working mode of "do first, think second" while academia often thinks first. The reason is really because it is a lot cheaper to think first.
[0] It works enough for some demo to some person
[1] One example is I beat a company's fancy giant transformer based image detector with a scrappy CNN that took only a few hours to train. They were excited for all of 1 day and then wouldn't let me do the same thing to the transformer model (which would have had a bigger impact). Fun fact, my boss also loved to tell me about how dumb academia is because they never do anything useful and how industry makes all the real advancements.
He's not entirely wrong though. Industry makes the advancements that actually supposed to sell and be profitable on the free market. Academia is all over the place, as not everything being researched there can be used commercially, often it's just to get grant money, push papers and raise their egos amongst their peers.
So weird argument. Academia isn't meant to be "profitable" because no one is measuring the indirect profits. But when you do it's comically large
Depends on the industry. All the researchers I know in academia are just wasting government grant money not delivering anything useful. Their words, not mine.
Some is useful shure, a lot is bullshit though.
There are many areas of research where profit is not a goal, and cannot be one. Understanding how and why climate changes is extremely important and useful, but cannot turn a profit. Researching different education methods, same. Hell, the researchers who won the 2024 Nobel prize in economics, who helped us understand how to build economically successful nations, something incredibly useful, cannot turn a profit with their research.
It's frankly absurd to expect everything useful to be profitable.
That definitely is for profit. They aren't researching climate change for the love of the game, but because agriculture, oil futures, real estate development, insurance policies, all depend on predicting climate developments.
To make an analogy, let's pretend we're a company selling water. We measure profit by how many bottles of water we sell. But people like the gp are complaining that building aqueducts, water purifiers, weather machines, or even improving the bottling process "isn't profitable". It's a weird claim and I'm not sure why it's so prolific. It's incredibly myopic
> Fun fact, my boss also loved to tell me about how dumb academia is because they never do anything useful and how industry makes all the real advancements.
1. "Thinkers", those extraordinarily brilliant people who spend lots of time on a single problem, discovering the optimal and most performance theoretical solution
2. "Hackers", who assemble tools by implementing designs from "Thinkers"
Without Thinkers the Hacker could not possibly solve all required problems well a single tool/product requires
Without Hackers the Thinkers work would languish in the ivory tower it was conceived in
Anecdotally, the tide on this is changing—all the low-hanging fruit has been picked and I see/hear many of my older colleagues regret not having gone the whole way with a PhD. This on top of AI being able to answer many of the engineering questions you might have learned the answers to in a masters/bachelors.
I happily had a job in academia in the US. Probably what most would call “successful” after exiting a startup and getting a PhD I was US engineering faculty for 8 years.
We picked up our keys to our new house in another country a few days ago and I start next month with a faculty promotion. Many of my colleagues are or are looking to follow.
does that get you a new fed administration that isn't idiotically anti-science?