A Quote by Chamath Palihapitiya
You need to use data science and machine learning to get the ground truth of what's happening inside of a company.
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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.
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.
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.
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.
In the US, you even lose legal rights if you store your data in a company's machines instead of your own. The police need to present you with a search warrant to get your data from you; but if they are stored in a company's server, the police can get it without showing you anything.
In the U.S., you even lose legal rights if you store your data in a company's machines instead of your own. The police need to present you with a search warrant to get your data from you; but if they are stored in a company's server, the police can get it without showing you anything. They may not even have to give the company a search warrant.
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.
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.
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.
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.
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.
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.
A lot of the progress in machine learning - and this is an unpopular opinion in academia - is driven by an increase in both computing power and data. An analogy is to building a space rocket: You need a huge rocket engine, and you need a lot of fuel.
You no longer can say that inside a company everything is trusted because you could bring in a contaminated device. And it's the same thing with data, because so much is no longer stored inside a company.
Every company has messy data, and even the best of AI companies are not fully satisfied with their data. If you have data, it is probably a good idea to get an AI team to have a look at it and give feedback. This can develop into a positive feedback loop for both the IT and AI teams in any company.
Science can explain what's happening down inside atoms and what's happening at the edge of the universe, but it cannot explain consciousness. It's a paradox--withou t consciousness there would be no science, but science doesn't know what to do, at all, with consciousness.
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