In "Business as usual at General Motors?"

Dan, Oops, I somehow missed the $NT 1 price. You're right - not much operating capital gained in this deal. Still, we don't know what liabilities they were able to shed by jettisoning the venture. If nothing else, it allows executives to focus on US operations while saving the cost of airfare. For a company worth less than zero that's not insignificant.

While I'm no fan of bailouts (or GW) either, I think what we're seeing is simply the beginning of the 'orderly liquidation' of GM as we know it. I agree that in long term (to the extent that exists) GM needs more ideas like those generated at Yulon. But I think that in the short term they need cash and they need it fast. One could argue that these needs could and should have been foreseen months ago, but that's water under the bridge for now. The Yulon sale gives them a few extra months worth of operating cash to make payroll while they renegotiate contracts and sell off other assets. (Saturn? Saab? Any takers for Hummer? Anyone?) Many of the 'good' ideas from places like Yulon are probably well known by GM management anyway. The problem is that their management seems so inept or bureaucratic that actually executing anything revolutionary isn't a possibility. And even if they could make improvements they'd still be selling cars at a $3k disadvantage due to past labor commitments. So, sadly, the strategic value of Yulon may have been lost on GM anyway. They're probably better off buying time by selling it. Hopefully, this will allow time for the quasi-bankruptcy process to work its magic.

In "Can a thinking, remembering, decision-making, biologically accurate brain be built from a supercomputer?"

Ough, Yeah, of course there are plenty of papers from the wet lab world in Science and Nature. And granted, there are a lot of other fine journals that don't go by these names. I think in general I was simply trying to convey to those outside the field the source of the friction that exists. Sure, you can point to data and reasons the model isn’t relevant. But personally I think that in a lot of ways the criticism simply comes down to emotion. To dig a little deeper on the subject, the situation is this: most topics are not destined to be flashy from the start. This is, of course, by necessity. Not everyone can get a slot in Science ever week. So most folks will toil away on research that will end up somewhere else - no matter how successful. Perhaps JBC, maybe Nucleic Acids Research. (Both are fine journals, by the way.) And of course this is often times no different for the modeling folks. In fact, due to bias that I was originally commenting on, many won’t even see an Impact Factor >5. (If you believe in the Impact Factor as a proxy for quality. But that’s a different post.) But, on the other hand, I do know of some groups (big names in their field to be sure) that seem to churn out the articles in “top” journals (IF>20?). The odds of being one of these groups, however, seems to be a little higher than average. But maybe that’s just an artifact of the newness of bioinformatics as a sexy new field. Eventually every hot new area gets inundated with talent and the easy pickings evaporate. But anyway, even if the models *were* more useful and even if it wasn’t the hot field of the past decade there’d still be frustration from those in the wet lab. The fact that the model makers spend their day drinking coffee in pleasant, dimly lit rooms, while the biochemists spend their days in a damp coldrooms will always create tension. Sour grapes, I know. But that’s life. I throw this out there simply as a way of shining some light on the emotion that is often behind the science. I think this sort of thing is usually lost to the general public. Being human, emotion plays a surprisingly large role in a lot of discoveries. An example: I saw a James Watson lecture some time ago. He basically stated that Rosalind Franklin was left off of the paper announcing the structure of DNA because she wasn’t likable. Note that Watson and Crick are now enshrined by history as two of the most important scientists of all time. While the treatment of Franklin may be more of a statement on Watson as a person it’s still another datapoint on emotion in science. Come to think of it, this is a good analogy on the overall topic too. The person making the data got no recognition and perhaps cancer from work related radiation. The model builders used her data to become rich and famous. (Joking there. Sort of.)

I dunno. As a wet lab molecular biologist, I have to admit that I’m extremely skeptical. I see a lot of this sort of work around my facility from the engineering and CS crowd, and a lot of it is pretty useless for now. In fairness, I readily admit that accurate models of biological processes are the holy grail of research. I just think that we’re a long, long way from anything of value in this area. The problem with the CS folks is that they simply come from a different background and completely miss the complexity of the system they’re dealing with. The CS skillset has its own set of challenges and folks tend to get wrapped up solving these. The result is that they end up with models based on many false assumptions. In other words, the model is highly precise and completely inaccurate. They have no tie to reality as exists in the real world because they simply don’t have enough inputs. So. The output of these complex models usually have so many errors that they’re not useful for anything of consequence. When I talk of ‘complex biological models’ I think of relatively simple tasks like say protein folding. People have been modeling this stuff for decades and the output still isn’t terribly useful. Neuroscience exists on a plane of infinitely greater complexity. Without really understanding the root cause and effect these models are based on correlations. As any good Wall Street quant will tell you of late, correlations tend to break down at inopportune times when they’re based on a third party unknown. When you build a model based on thousands of correlations you end up with something that fails in completely unpredictable ways. I suppose that the retort to this argument is that this logic would lead *all* models starting with the top quark – completely reductionist. This is a valid criticism. That being said, the proof is in the output. I see models every day that are presented by some bright eager assistant professor. They’re made, presented, published, and then forgotten. Why? Because they’re not reliable enough to base any sort of future work on. Anyway, still worth trying I suppose. On the other hand, as one of the struggling wet lab researchers getting dosed daily with harmful chemicals I will admit to some frustration with the publicity that this research often elicits. Shiny young programmers and engineers drop in, throw up fancy 3D models, pull down gobs of funding and praise, and pump out buckets of papers that ultimately produce nothing. Meanwhile their wet lab counterparts slave away in the salt mines for a paper every few years in obscure journals. Not very sexy, but the basis for tangible progress. So as you might imagine there is a good deal of tension between these communities. To sum up: this sort of research has validity. It is important. Hopefully in a few generations it will lead to amazing advances. In the short term, however, I’m not counting on anything of use. This is why when ‘important’ new models are presented the response amongst the wet lab crowd is usually a shrug and an eye roll. “Pretty diagrams. I bet you’ll get lots of press coverage and grant money for that. What can I do with it?”

In "Does smart = liberal?"

While I don’t have a personal axe to grind with the data in this study per se, in general I do have a problem with sociological studies that attempt to deduce a driver for correlated behavior sets. As the cliché goes, correlation does not equal causation. Personally, I’d be cautious about reasoning of the blog post: 1) because the study controlled for a few items, and those items did not explain the differences in data 2) the answer must be biological in nature If you read the original research paper from Deary et al, they do *not* jump to these same conclusions. Simply, the answer must be the result of something that was not controlled for. While it could be a difference in the biology underlying these individuals, it could also be explained by 1,000,000 other things. We just don’t know. In skimming the original paper, one alternate explanation did come to mind. What if people with higher IQs have more options/security in our society? (Seems like a safe bet.) What if, as a result of these extra options, they feel less threatened by change? Obviously the authors attempted to control for this somewhat by looking at educational attainment as well as “occupational social class”. But in looking deeper into their scoring for occupational class it occurred to me that they simply attached a score to each individual’s occupation. From the paper: “Each participant's current social class (professional-managerial, skilled nonmanual, skilled manual, or semi- or unskilled) was derived from his or her own occupation.” To me, this seems like an overly-vague way of capturing occupational opportunity. They’re assigning judgment to whole fields of work, which seems rather presumptive to me. As an example: The most intelligent youth in a rural setting may not aspire to become investment bankers, lawyers, or academics. The most intelligent members of a farming community may become managers of their co-op, the head operator at a milling plant, or the line chief of a manufacturer. These individuals may still have more opportunity than their less-intelligent peers, thus driving down their innate fear of the new. Nonetheless, this study might classify all of them as ‘unskilled manual labor’ – one of the lower scores in their scale of social class. By using a broad scoring system, the study may have missed the correlation between occupational attainment and a ‘liberal’ mentality. Anyway, point being, there are thousands of alternate hypotheses that explain the data without jumping to conclusions about some sort of ‘innate biological difference’. The authors of the original study were careful to avoid this trap, unlike the author of the blog post.

In "Plural the same as Singular"

Another noun that is both singular and plural: data. I believe that there is debate on the correctness of this, but in much of the sciences it is treated as an acceptable singular or plural. *In other words, datum as a singular is considered .)

In "Curious George: The Dwindling..."

I just assumed that folks were moving back over to MeFi now that they are able to register again. After all, MoFi was created in response to the original close in MeFi memberships after the turn of the century. (Is it too early to be using that phrase? If no, then when? 2010?) Anyhow, I still see a place for 'more bananas, less poo.'

In "Curious George the Frosh."

gomichild - I never said that it was *my* measure for success, but I'm simply admitting that my wandering ways are unlikely to get one these toys if that's your goal. (And I will admit that expensive toys are nice, all things else being equal. Which they rarely are, but this is another thread entirely.)

I think an important distinction needs to be made here: grades certainly DO matter for some jobs/careers. Research your chosen path - in law, medicine, anything with a PhD, and certain industries in business your future success will depend on the grades. Another distinction on the advice to 'try any field of study that seems interesting': this advice is fine for some fields but not for others. Understand that some highly competitive careers require a set path that must be followed early in life. For example, I'll use something close to my heart - biological research. If you wish to become a tenured professor at a major university you will have to do 4+ years as an undergrad, ~7 years as a PhD sutdent, 5+ years as a postdoc, and ~5 years to get tenure. While some folks do start the path later in their twenties the vast majority of those that are successful start early. Ditto medicine, and making partner at a major law firm. Want to work for in management consulting? Pedigree matters – the majority of the BA/BS hires for McKinsey come with a 3.5+ GPA from “top 10” universities. That being said, the most interesting people I know have had a less-linear life. Of course, none of us are living in million dollar houses and driving BMWs, so there’s probably something to be said for ambition…

Everything in moderation - note that this includes moderation. There are things you can do in college that would be a felony in the real world. That doesn't mean that you should do them, but it's good to know nonetheless. Your job in college is to get good grades – little else matters work wise. Running for student council may seem great, but it probably won’t land you that grad school slot or first job if your GPA is a 1.8. On the other hand, a good GPA will take you a long way. If you’d like to volunteer to feed the homeless or work at a hospital then by all means do it – it’ll be hugely rewarding – but don’t go into it thinking that you’ll get anything out of it other than personal rewards like wisdom and knowledge. Yes, like every generalization, there are exceptions to this rule – but it applies to more people than one would think. If you’re not being productive at least have fun. Productive in this case pretty much means studying. Fun seems a little more difficult to define for folks these days. Example: playing video games is not fun. In general, if you won’t remember doing it in a few years it isn’t fun. A good case can be made that the mark of a good time is the ability to recall it years or decades later. You’ll have plenty of time to watch soap operas in the future – you’d be wise to spend your free time getting to know that cute boy/girl down the hall, or playing in a band, or exploring the underground tunnels on campus, or volunteering as mentioned above… Studying. If you can read the material before a class, go to said class, then review the material again afterwards you’ll be well on your way to great grades. You’ll probably also spend less total time studying as you’ll have less need to cram come exam time. Truth in advertising: I have never ever been able to do this. But I’ve known people who could – and they generally got better grades than I did while spending more time at the bar.

In ""In Defense of Big Pharma""

Academic research is almost wholly funded by government grants in most countries. The grants usually include a not-insignificant indirect cost that can range up to 90% of the direct. The indirect cost is supposed to cover infrastructure, electricity etc. but in reality has become a huge profit center for universities. The work done there is usually necessary but not sufficient for the next wonder drug. Actually, this seems to understate the case as a few more decades and a few billion dollars are usually required for a medicine to make it to market. In many cases the IP required is from a mix of sources. Placing a license on basic biology - even a GNU/GPL style - implies that many of these thoughts can themselves be owned and licensed. Again, think carefully here – is this really a road you want to go down with even more patentable information? One of the complaints of many of my fellow researchers is that too much information is patented – should a basic biological pathway be owned? Most would favor a shift in the patent law toward placing more information in the public domain – attaching a bio-GPL onto everything would seem to be a step in the opposite direction.

I dunno folks. I'm always amazed by the anger directed at pharma as an industry. What they do is incredibly risky, backbreaking work. The researchers employed by these companies make much less than most people in other industries with comparable education and experience. Yes, the larger corporations among them are decently profitable, but they’re certainly not the highest returns in the marketplace. And anyway, as public corporations these profits flow back to shareholders – most of whom are retirement/pension funds. I’m not trying to shill for pharma either – I AM one of those academic scientists hoping to make the next big medical discovery. I’ll admit that while academic labs do certainly have a big role in discovery, we’re not nearly as big a part of the picture as people tend to think. My point here is that the work is long and difficult and that there is more than enough of it to go around. The folks in pharma certainly add plenty of value, and simply pulling everything under the academic grant system ignores their significant contribution. The argument that corporations exclusively piggyback on the discoveries of academic and government scientists is completely false. Look it up – private industry spends more annually on R&D than does government in the US. The bottom line is this is an industry that has produced the vast, vast majority of drugs that we all know and love today. I’d encourage everyone to think long and hard about any sweeping proposals lest they kill the golden goose. Medicine has made huge advances over the last 50 years under our current system, whatever its shortcomings. As a researcher, I’ll grant that I’d like to see less money being spent on sales and marketing, but equal blame can be assigned to the physicians and patients in this area.

In "Curious George: Monkey on a Board"

The single best items to have IMHO are wrist guards. In learning to snowboard you will fall early and often - much more than in learning to ski in my experience. Another difference with skiing is that it's hard to anticipate when you're going to fall. Anyway, after 10+ hard falls your wrists will be terribly bruised from catching yourself. The wrist guards will make this a lot less painful. As mentioned above, a helmet isn't at all a bad idea either. In the end, if you're going to invest one day in learning to board be sure to stick with it - the worst is already past! For me, the end of the 2nd day is when everything started to click and now I prefer boarding to skiing. Lastly, if you grew up skating you'll pick boarding up MUCH faster - well within the first day. Good luck and have fun!

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