A Quote by Tan Le
! want to leverage the creativity of researchers across mathematics, statistics, data mining, computer science, biology, medicine, and the public at large.
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The training one receives when one becomes a technician, like a data scientist - we get trained in mathematics or computer science or statistics - is entirely separated from a discussion of ethics.
We will continue to work with agencies across the government to unleash the power of open data and to make government data more accessible and usable for entrepreneurs, companies, researchers, and citizens everywhere - innovators who can leverage these resources to benefit Americans in a rapidly growing array of exciting and powerful ways.
The State of Israel must be at the forefront of global science - in physics, in mathematics, in medicine, in biology.
The term "informatics" was first defined by Saul Gorn of University of Pennsylvania in 1983 (Gorn, 1983) as computer science plus information science used in conjunction with the name of a discipline such as business administration or biology. It denotes an application of computer science and information science to the management and processing of data, information and knowledge in the named discipline.
I think of 'data science' as a flag that was planted at the intersection of several different disciplines that have not always existed in the same place. Statistics, computer science, domain expertise, and what I usually call 'hacking,' though I don't mean the 'evil' kind of hacking.
Medicine is a social science, and politics is nothing else but medicine on a large scale. Medicine, as a social science, as the science of human beings, has the obligation to point out problems and to attempt their theoretical solution: the politician, the practical anthropologist, must find the means for their actual solution. The physicians are the natural attorneys of the poor, and social problems fall to a large extent within their jurisdiction.
Too few people in computer science are aware of some of the informational challenges in biology and their implications for the world. We can store an incredible amount of data very cheaply.
I was interested in data mining, which means analyzing large amounts of data, discovering patterns and trends. At the same time, Larry started downloading the Web, which turns out to be the most interesting data you can possibly mine.
The main motivations were to try to leverage Google's expertise with large computer systems and to try to give something back to science
I do not ... reject the use of statistics in medicine, but I condemn not trying to get beyond them and believing in statistics as the foundation of medical science. ... Statistics ... apply only to cases in which the cause of the facts observed is still [uncertain or] indeterminate. ... There will always be some indeterminism ... in all the sciences, and more in medicine than in any other. But man's intellectual conquest consists in lessening and driving back indeterminism in proportion as he gains ground for determinism by the help of the experimental method.
When you have a large amount of data that is labeled so a computer knows what it means, and you have a large amount of computing power, and you're trying to find patterns in that data, we've found that deep learning is unbeatable.
If everybody has to take biology and chemistry, they can take computer science. Computer science is a more useful skill right now than a lot of other things that people are learning at school.
Mathematics has two faces: it is the rigorous science of Euclid, but it is also something else. Mathematics presented in the Euclidean way appears as a systematic, deductive science; but mathematics in the making appears as an experimental, inductive science. Both aspects are as old as the science of mathematics itself.
I can't be as confident about computer science as I can about biology. Biology easily has 500 years of exciting problems to work on. It's at that level.
My high school, the Illinois Mathematics and Science Academy, showed me that anything is possible and that you're never too young to think big. At 15, I worked as a computer programmer at the Fermi National Accelerator Laboratory, or Fermilab. After graduating, I attended Stanford for a degree in economics and computer science.
Computer science only indicates the retrospective omnipotence of our technologies. In other words, an infinite capacity to process data (but only data -- i.e. the already given) and in no sense a new vision. With that science, we are entering an era of exhaustivity, which is also an era of exhaustion.
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