A Quote by Oren Etzioni
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.
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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.
I think the first wave of deep learning progress was mainly big companies with a ton of data training very large neural networks, right? So if you want to build a speech recognition system, train it on 100,000 hours of data.
One can think of any given axiom system as being like a computer with a certain limited amount of memory or processing power. One could switch to a computer with even more storage, but no matter how large an amount of storage space the computer has, there will still exist some tasks that are beyond its ability.
There's something that happens with the collection of a large amount of data when it's dumped into an Excel spreadsheet or put into a pie chart. You run the risk of completely missing what it's about.
[The] amount of search is not a measure of the amount of intelligence being exhibited. What makes a problem a problem is not that a large amount of search is required for its solution, but that a large amount would be required if a requisite level of intelligence were not applied.
I am a data hound and so I usually end up working on whatever things I can find good data on. The rise of Internet commerce completely altered the amount of information you could gather on company behavior so I naturally drifted toward it.
Now clearly this advantage is when the data on tape has been found and just needs to be transferred back. You need to add a minute or so of seek time to find the data. On large transfers, though, tape should outpace most disk systems. From an ingest perspective, LTO-6 and other enterprise tape formats may be unrivaled when compared on a single unit basis.
Personalization is based on a bargain. In exchange for the service of filtering, you hand large companies an enormous amount of data about your daily life--much of whic you might not trust your friends with.
Machine learning is looking for patterns in data. If you start with racist data, you will end up with even more racist models. This is a real problem.
MapReduce has become the assembly language for big data processing, and SnapReduce employs sophisticated techniques to compile SnapLogic data integration pipelines into this new big data target language. Applying everything we know about the two worlds of integration and Hadoop, we built our technology to directly fit MapReduce, making the process of connectivity and large scale data integration seamless and simple.
Modern statisticians are familiar with the notion that any finite body of data contains only a limited amount of information on any point under examination; that this limit is set by the nature of the data themselves, and cannot be increased by any amount of ingenuity expended in their statistical examination: that the statistician's task, in fact, is limited to the extraction of the whole of the available information on any particular issue.
There is a rampant tendency in any industry where someone is trying to sell something with a bunch of data, where they cherry pick a little bit... bias a little bit. This becomes quite easy when there is an enormous amount of data to cherry pick from.
Do not be afraid of large patterns, if properly designed they are more restful to the eye than small ones: on the whole, a pattern where the structure is large and the details much broken up is the most useful...very small rooms, as well as very large ones, look better ornamented with large patterns.
No music is going to stop the war. What's going to stop the war is a large amount of body bags, or a large amount of people in the streets, protesting it before it starts.
! want to leverage the creativity of researchers across mathematics, statistics, data mining, computer science, biology, medicine, and the public at large.
Cloud computing means you are doing your computing on somebody else's computer. Looking ahead a little, I firmly believe cloud - previously called grid computing - will become very widespread. It's much cheaper than buying your own computing infrastructure, or maybe you don't have the power to do what you want on your own computer.
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