2013 Artificial Inteligence Big Data 
Acuvate
DECEMBER 27, 2019
This goal seems achievable with massive advancements in automotive technology and big data. Today, one of the biggest use cases of big data and advanced analytics in the automobile and transport industry is to leverage data to improve the safety of vehicles and on the road. Microsoft Azure Data Factory.
Idea to Value
MARCH 11, 2016
In 2013, I wrote a breakthrough article on the nascent examples of computers beginning to generate ideas in a way similar to human creativity. Here I revisit the article with all-new evidence showing how close we are to artificial creativity. Machine Learning. Big data, predictions and instant experimentation.
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mjvinnovation
DECEMBER 13, 2019
Regarding public health, data recorded in the systems allows researchers to access statistics that are entered in real-time. Big Data and Cloud Computing are two technological phenomena that have gradually altered the way the healthcare market has grown and developed. Artificial Intelligence is pointing to the future.
The BMI Lab Blog
DECEMBER 6, 2017
The majority of venture capital investment is in digital technologies such as big data, artificial intelligence (AI) and financial technology companies. Graphic showing the growth of mobile payments users in China between 2009 and 2013, compared to not mobile payment users.
Harvard Business Review
APRIL 14, 2017
Every few months it seems another study warns that a big slice of the workforce is about to lose their jobs because of artificial intelligence. Between 34% and 44% of global companies surveyed are using AI in in their IT departments in these four ways, monitoring huge volumes of machine-to-machine activities.
Harvard Business Review
AUGUST 28, 2017
The buzz over artificial intelligence (AI) has grown loud enough to penetrate the C-suites of organizations around the world, and for good reason. Total investment (internal and external) in AI reached somewhere in the range of $26 billion to $39 billion in 2016, with external investment tripling since 2013.
Harvard Business Review
MAY 22, 2014
Data scientists, supported by the stunning growth in the gathering and processing of so-called big data, can extract patterns from massive stores of browsing and sales data in order to predict our likes and dislikes and tailor marketing experiences to us. Big data flexed its muscles. But there is a problem.
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