A Quote by Sebastian Thrun
If we study learning as a data science, we can reverse engineer the human brain and tailor learning techniques to maximize the chances of student success. This is the biggest revolution that could happen in education, turning it into a data-driven science, and not such a medieval set of rumors professors tend to carry on.
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We are going to completely change what it means to do advanced analytics with our data solutions. We have machine-learning stuff that is about really bringing advanced analytics and statistical machine learning into data-science departments everywhere.
Everything is changing now that we are in the cloud in terms of sharing our data, understanding our data using new techniques like machine learning.
Deep learning allows you to create predictive models at a level of quality and sophistication that was previously out of reach. And so deep learning also enhances the product function of data science because it can generate new product opportunities.
You need to use data science and machine learning to get the ground truth of what's happening inside of a company.
We should always be suspicious when machine-learning systems are described as free from bias if it's been trained on human-generated data. Our biases are built into that training data.
When I look at the next set of technologies that we have to build in Salesforce, it's all data-science-based technology. We don't need more cloud. We don't need more mobile. We don't need more social. We need more data science.
Chunking is the ability of the brain to learn from data you take in, without having to go back and access or think about all that data every time. As a kid learning how to ride a bike, for instance, you have to think about everything you're doing. You're brain is taking in all that data, and constantly putting it together, seeing patterns, and chunking them together at a higher level. So eventually, when you get on a bike, your brain doesn't have to think about how to ride a bike anymore. You've chunked bike riding.
One can truly say that the irresistible progress of natural science since the time of Galileo has made its first halt before the study of the higher parts of the brain, the organ of the most complicated relations of the animal to the external world. And it seems, and not without reason, that now is the really critical moment for natural science; for the brain, in its highest complexity-the human brain-which created and creates natural science, itself becomes the object of this science.
The fact that data is always evolving is a good thing. It means that science is continuing, and we are always learning.
The bigger a data set that you have, the more polls, the more surveys that you have that people undertake, the more accurate your models are going to be. That's just a fact of data science.
Watson augments human decision-making because it isn't governed by human boundaries. It draws together all this information and forms hypotheses, millions of them, and then tests them with all the data it can find. It learns over time what data is reliable, and that's part of its learning process.
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.
The scientists Heartland works with demanded we host a ninth conference this year to foster a much-needed frank, honest, and open discussion of the current state of climate science and we just couldn't refuse. The public, the press, and the scientific community will all benefit from learning about the latest research and observational data that indicate climate science is anything but 'settled.
Data is the new science. Big Data holds the answers. Are you asking the right questions?
Every day we go over data and use science and data to drive policy and decision-making.
My study is NOT as a climatologist, but from a completely different perspective in
which I am an expert … For decades, as a professional experimental test engineer, I have analyzed experimental data and watched others massage and present data. I became a cynic; My conclusion - 'if someone is aggressively selling a technical product who's merits are dependent on complex experimental data, he is likely lying'. That is true whether the product is an airplane or a Carbon Credit.
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