A Quote by Satya Nadella
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.
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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.
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.
This is my favorite part about analytics: Taking boring flat data and bringing it to life through visualization.
You need to use data science and machine learning to get the ground truth of what's happening inside of a company.
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.
Definitely there's growing use of machine learning across Google products, both data-center-based services, but also much more of our stuff is running on device on the phone.
Data science is the combination of analytics and the development of new algorithms.
Previously, we might use machine learning in a few sub-components of a system. Now we actually use machine learning to replace entire sets of systems, rather than trying to make a better machine learning model for each of the pieces.
At Deloitte, our programs for veterans are bringing new approaches to the table. For instance, we're helping veterans' organizations use data analytics to sift through streams of information about veteran needs.
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.
Machine learning and artificial intelligence applications are proving to be especially useful in the ocean, where there is both so much data - big surfaces, deep depths - and not enough data - it is too expensive and not necessarily useful to collect samples of any kind from all over.
You look to Google, you see this incredible world of information, you see the advertising, but you also get Google Analytics. And Google Analytics coupled with Salesforce's sales and service and marketing means that both of our customers are going to have customer insights that they've never had before. That is really exciting.
The company started in the early 90s or late 80s. We were a behavioural science company. We didn't pivot into data analytics till 2012. So, all the data that we collected pre-2012, which was done by the British company SBL group, was collected through quantitive and qualitative research on the ground.
The problem for most self-starting leaders who do not have a mentor begins when they measure the results and find themselves fully aware of the data and the analytics, but are completely unaware of what to do.
I think there are a lot of industries that are collecting a lot of data and have not yet considered the implications of machine learning but will ultimately use it.
Remember that the machine is there to help you, because at the end of the day, you're not playing freestyle chess, advanced chess, human-plus-machine. If you are playing against other humans, it's about winning the game. The machine will not be assisting you, unless you are cheating of course. And since the machine is not there, you have to make sure that everything you learn from the computer will not badly affect the way you play the real game.
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