A Quote by Alexander Nix

Every day we have teams looking for new data sets. — © Alexander Nix
Every day we have teams looking for new data sets.
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.
While labour market reports scream with dramatic youth unemployment data, hundreds of employers cry out for employees with the right skills sets. As recruiters, we suffer this shortage every day.
If you are looking at data over and over you better be taking away valuable insight every time. If you are constantly looking at data that isn't leading to strategic action stop wasting your time and look for more Actionable Analytics.
People think 'big data' avoids the problem of discrimination because you are dealing with big data sets, but, in fact, big data is being used for more and more precise forms of discrimination - a form of data redlining.
Every day, I absorb countless data bits through emails, phone calls, and articles; process the data; and transmit back new bits through more emails, phone calls, and articles. I don't really know where I fit into the great scheme of things and how my bits of data connect with the bits produced by billions of other humans and computers.
Every day we go over data and use science and data to drive policy and decision-making.
When dealing with data, scientists have often struggled to account for the risks and harms using it might inflict. One primary concern has been privacy - the disclosure of sensitive data about individuals, either directly to the public or indirectly from anonymised data sets through computational processes of re-identification.
While many big-data providers do their best to de-identify individuals from human-subject data sets, the risk of re-identification is very real.
Data and data sets are not objective; they are creations of human design. We give numbers their voice, draw inferences from them, and define their meaning through our interpretations.
You have to imagine a world in which there's this abundance of data, with all of these connected devices generating tons and tons of data. And you're able to reason over the data with new computer science and make your product and service better. What does your business look like then? That's the question every CEO should be asking.
As we move into an era in which personal devices are seen as proxies for public needs, we run the risk that already-existing inequities will be further entrenched. Thus, with every big data set, we need to ask which people are excluded. Which places are less visible? What happens if you live in the shadow of big data sets?
I will talk about two sets of things. One is how productivity and collaboration are reinventing the nature of work, and how this will be very important for the global economy. And two, data. In other words, the profound impact of digital technology that stems from data and the data feedback loop.
Data will always bear the marks of its history. That is human history held in those data sets.
My wife is very fit and looking younger every day, whereas I'm looking older day by day.
Artificial intelligence is just a new tool, one that can be used for good and for bad purposes and one that comes with new dangers and downsides as well. We know already that although machine learning has huge potential, data sets with ingrained biases will produce biased results - garbage in, garbage out.
Absolutely, I think every day is a new day, every day is a new opportunity, and every day is a new chance.
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