Could you explain why? Is it because people will only share information worth sharing if they have to pay to do so? If so, then you deserve a [redacted].
389 karma · joined December 6, 2008
In this post, I'm trying to use exercises that I think will be more interesting and engaging than just showing off the language. What do you think? Is analyzing Redditors interesting to you? Do you have any thoughts for other datasets that would be even more interesting for people to work with while learning Scala?
In general, I'd appreciate any critical feedback because I'm looking to get better at developing such educational resources.
Thanks for your time!
Similarly with startups, if someone is only motivated by a huge exit then they won’t last through the years of low-success and failure that precede building a moderately successful business. Individual who succeed in these careers get some satisfaction out of the process and the small successes along the way (e.g. performing before ten people in a bar or having one potential customer get excited about your new product).
Currently such investigations are limited in scope by the large computational resources required to perform a single quantum mechanical calculation on a molecular fragment. With quantum computers, tens of thousands of such calculations could be performed and the results could be used to optimize new molecular force fields through multivariate regression.
Additionally, it’s wonderful to be able to “dive under the hood” if you need to create a special type of visualization: i.e. the internal model is easy to understand and highly customizable. This has enabled me to create several awesome plots that would have been very difficult, if not impossible to create with any other plotting program or library.
The conditioning on Sex/Class seems to be the best way to illustrate the trends in these 4 dimensions.
I ask as a grad student with a background in chemical engineering. I’ve known two professors who’ve done the inverse – industry to academia – and they’ve provided convincing stories for academia. In both cases they found academic research (in engineering and the physical sciences) to be more interesting and personally rewarding than industry.
Is the situation different in software and related industries?
I ask, as I’ve been considering transitioning to such a career myself after grad school (my research is in computational modeling and simulation). My biggest fear is that I’ll find myself in a job that I find boring and will regret my decision. Doubly so, as such a career transition will make it very difficult to reenter my academic field.
The majority of the western population has a fairly rigid definition of job: trade your time for money. Its one thing to change how you trade your time for money: e.g. going from plowing a field a shuffling papers at a desk. It’s a whole another thing to adopt a certain level of self-responsibility (and risk) in determining how you provide value for your employer/customers. This is intrinsic to almost all new-era, tech-enabled jobs.
This is what is different now.
A cultural evolution in the western world is needed to facilitate widespread success in the evolved economy. It’s going to hurt.
Also, the minor difference in slice vs. list() for large lists are likely platform dependent and highly sensitive to the details of branch prediction and cache.
While I rarely comment on HN, this topic has really struck a cord with me as I’ve found many people (both online and in person) are focusing primarily on the low interest rate. While not an expert in corporate finance, I’m strongly convinced Google’s current liquid assets aren’t generating returns in excess of 3.7%. (See my comment in another thread of this discussion where I compare these returns to Tbonds.)
Unless Google has a revolutionary strategy for short term trading of liquid assets that generates returns in excess of 3.7%, in which they are so sure of they’ve committed their entire $37B cash-on-hand into, they’d be better off reallocating some of their existing liquid assets into whatever new investment they have in mind for the $3B.
The only other explanations would be preparations for a massive acquisition (major news) or this was done for some corporate politics reason (not news worth).
If they really have investment opportunities that they are convinced will surpass 3.7% return, why aren’t they just using their existing liquid assets. I agree with other commenters that these funds are either in preparation for a large acquisition or just a dumb decision resulting from irrational and inefficient buracracy common to all large companies.
This only makes sense if all of Google’s current funds are tied up in wealth generating investments and they want even more money to play with. I highly doubt Google’s liquid assets (cash on hand) yield such returns.
(add-hook 'after-save-hook
(lambda ()
(when (string-match "\.coffee$" (buffer-name))
(coffee-compile-file))))Results based funding (and tenure granting) promotes these more conservative and obvious projects. Proposals for more risky, and therefore potentially more rewarding projects aren’t submitted as no one wants to gamble with their own career.
Still, I’d say the best researchers/professors that I’ve met combined a deep understanding of the techniques in their own line of research with a broad understanding of their field and adjacent fields. Personally, I’d fear that removing technical proficiency as a requirement for a PhD would eliminate an essential bullshit detector that filters out certain people; people who do a great job speaking in broad generality but are incapable of formulating a path to accomplish these grand aims.
(As a quick anecdote, I once saw a research presentation from a grad student who made broad claims about his project’s direct role in curing many forms of cancer. As he progressed, we learned he was only exploring strategies to synthesize a potential ligand that some other researchers would consider using in imaging a specific cancer within rat models. To this end he showed very little progress, but continued to speak at great lengths about the imminent eradication of cancer as he concluded his talked.)
To argue for the inclusion of quantum theory in explaining protein dynamics is to assert that proteins undergo classically forbidden conformational transitions. The authors of the paper have provided no evidence for such transitions nor even hypothesized about how such transitions could arise. Their only argument against a classical description of protein folding is the inability of molecular dynamics simulations to model the folding-kinetics of some proteins. Seeing as molecular dynamics force fields only provide a rough and approximate description of the forces between atoms within a protein it should be no surprise these methods can’t accurately model the intricate conformational transitions in protein folding.
There additionally may be other mechanisms by which the coating is rendered less effective within a year, but I highly doubt a significant portion of the coating detaches from the surface within a year.
Given the complexity of chlorophyll and its significant support system in photosynthesis, I would highly doubt such a similarly complex molecular system would evade discovery. Further, due to the weak energy of microwaves, as quantified next, such a microwave harvesting system would have to be located at the surface of our skin (or other periphery tissue like hair) to absorb this energy before other biomolecules absorb it non-specifically. I believe these relatively simple periphery tissues have been exhaustively studied.
As the article mentions, microwave energy is on the order of 0.001 kJ/mole. Just to put that in perspective, the random thermal (kinetic) energy contained in each degree of freedom of a biochemical systems is on the order of 2.5 kJ/mole. Due to their weak energy, microwaves have only been shown to non-trivially interact with molecular systems in the gas phase, where collisions between molecules are suppressed, as each molecular collision occurs with enough energy (thermal energy) to overwhelm the much weaker microwave interactions. In the fluid tissues of the body this energy would only be non-specifically absorbed causing a trivial and temporary rise in temperature (1e-4 deg C) within the tissue.
Therefore, I highly doubt microwaves interact with human biochemistry in any interesting or pathology-relevant way.
Duress only implies impaired judgment of the contract signer as due to immediate fear; it doesn’t imply the other party created the situation. Any lawyer would easily argue that the fire department charged an irrationally high cost for fire fighting that the home owner wouldn’t have agreed to had they had time to fully weight the cost and benefits of fire fighting services. In the worst case, one only need say they planned to demolish the house in the near future and therefore paying to have the fire extinguished was irrational.
A contract for non-insurance, pay-for-use fire fighting would only be enforceable if signed before the fact.
Anton is impressive in that it can provide millisecond long trajectories of protein/solvent systems, but single trajectories are of limited utility. You need many (1000s) of such trajectories for statistical analysis of the molecular system. Further, there already exist several clever methods that leverage chemical statistical mechanics to provide the same analysis without the need for single long trajectories. As an example, see Pande’s work developing Markov models of protein folding using millions of short trajectories between metastable states. This method has already provided a complete statistical analysis of protein folding for proteins that fold on times scales an order of magnitude beyond what Anton can simulate.
As for Desmond, there already exists a plethora of free MD programs (MMTK, LAMMPS, NAMD, CHARMM, Gromacs, and many more). Many of these, especially Gromacs, have already been highly optimized for a range of hardware and I wouldn’t expect Desmond to surpass these free codes by a margin worth dropping dollars.
Personally, I still have high hopes for DE Shaw Research, I just don’t see how their current offerings will turn a significant profit or greatly advance science. I’d love to be proven wrong, and I’m sure they’ll have plenty of additional novel future projects, some of which could be paradigm-shift-changing for chemical physics and molecular biology. My guess is that such advances won’t come from their hardware geniuses, but instead from their math/physics geniuses that will develop new statistical mechanics methods to bend & contract in silico time.
Additionally, all IO code in the standard library and most C-extensions that I've seen release the GIL around blocking IO calls. This allows multi-threading to be used for the standard blocking IO use cases. Further, the computationally intensive routines in numpy and scipy that I’m familiar with also release the GIL to facilitate multithreading at the C-level
The GIL only becomes an issue if you want multiple threads to be executing Python bytecode concurrently. I don’t believe this is a common problem as one rarely runs computationally intensive code at the python level. Such computationally intensive work is generally performed through C extensions or through external C functions (as accessed through ctypes). As long as such C extensions properly release the GIL, multithreading at the C-level is unaffected.