It's more straightforward to tax snapchats and facebooks some more and put the money into cancer research.
25 karma · joined May 21, 2015
Note that my advice is rational and completely unironic. It is rational to follow the clique if you want to succeed in tech.
I act like that too.
The compiler is called "genetic engineering" and it's a 30 years old field. In particular genetic engineering allows you to knockout specific genes and observe the phenotype. This technique is commonly used on bacteria, plants and animals. There is an ongoing mouse knockout project, an attempt to study every mouse gene by turning it off. It is 50% complete so far. By the way, the experiments are performed mostly by hand.
>We're very far away from understanding how a particular DNA string relates to phenotypes.
This problem can be solved much faster with a large scale effort. Thousands of automated experiments can run in parallel while recording the phenotype of the animals (including results of behavioral tests) and storing it for later analysis. On this genotype->phenotype dataset one can train an ML model. Actually there is a company that is trying this approach (just ML part, without automated experiments) http://www.deepgenomics.com/
I doubt that one company will be enough, though. With serious funding there could be much more progress in this area. We could have a full knockout map of mouse genome in less than a decade if we really wanted.
Basically read this guy http://www.kalzumeus.com/2011/10/28/dont-call-yourself-a-pro... and follow the clique.
You can make it if you try hard enough.
Also you can play DV lottery. It's a small chance (1.5%) but still larger than zero.
There is fixed and diminishing amount of arable land to grow the wheat and apples. You can make only so many pies.
On the other hand there are HMC and HBM technologies that offer order of magnitude more bandwidth and several times less latency. They are already used in AMD gpus as well as in prototypes of Nvidia's pascal gpu and Intel's Knight's corner 60-core cpu http://www.theplatform.net/2015/03/25/more-knights-landing-x...
I hope HBM comes to consumer CPUs too, but with current lack of competition in the market it can take a long time.
It looks like (science funding) politics is the only reason we are not testing how these genes work in large mammals and primates. The public is scared of genetic engineering.
This is so true. Engineering is undervalued in comparison to science. It's easier to sell multi-billion project to the general public if you label it as science. Scientists are much more publicly visible than engineers; they are Nobel winners, geniuses, brilliant men single-handedly unraveling mysteries of the universe (or so it seems to the layman). Engineers generally work in larger teams, don't seek individual fame, don't receive their Nobel prize.
And yet our lives depend upon the quality of engineers' work on a daily basis. Our largest global problems (climate, energy, pollution) are engineering problems. Shouldn't we praise engineers more?
If you are in the silicon valley or at least in some 1st world country - sure, you have a lot of possibilities and ridiculously high pay. The sad truth is that exactly same skillset can be valued 10x less or 10x more depending on your location.
I wouldn't call it "a dream" in a sense that it's utterly unrealistic; physics says that these systems can work.
The book contains a careful physical analysis of molecular machines. The technical material is unchallenged to the present day.
How far could we go by modifying existing biological molecular machines? Probably quite far.
You see: poor people are accustomed to doing these chores by themselves, while the rich can always use their paper money to hire naturally occurring biological robots.
If this can be scaled to human-like learning remains to be seen, but training a conversation RNN model that remembers some details of past conversations and acts on them should be possible.
To demonstrate the nonlinearity of these models there was an experiment http://www.pnas.org/content/105/9/3593.full where researchers excluded a single spike from the large scale brain simulation. The global state of simulation has diverged after 100 timesteps, compared to the version where this spike was present.
Facebook AI research is probably playing with such models right now.
You, dear reader, probably hope that you will make enough money off your trendy webapp business and will get the best medicine when the time comes, but you probably won't. This man's fate awaits you.