LinkedIn: Machine learning is like oxygen, but the human element is not going away anytime soon
How do data and machine-learning powered algorithms work to control newsfeeds and spread stories? How much of that is automated, how much should you be able to understand and control, and where is it all headed? LinkedIn has answers.
Read More →Big Data versus money laundering: Machine learning, applications and regulation in finance
Could financial fraud such as the Laundromat be avoided by applying machine learning to scan through data? And if yes, why is that not happening?
Read More →Disrupting insurance: data-driven customer value
Why hasn't the insurance business been disrupted yet, and how could data be used to do this? Atidot has a go at addressing those questions.
Read More →Artificial intelligence in the real world: What can it actually do?
What are the limits of AI? And how do you go from managing data points to injecting AI in the enterprise?
Read More →Googling prescriptive analytics: YouTube recommendations and the analytics continuum
Understanding prescriptive analytics is complicated, let alone implementation. Would turning to Google help? Seeing how it works for Google raises questions, but may also lead to some answers.
Read More →Big Data, Crystal Balls and Looking Glasses: Reviewing 2016, predicting 2017
End-of-year reviews are boring — and everyone does them. Predictions are boring — and they are hard. Of course, this is different — because big data.
Read More →Predictive analytics and machine learning: A dynamic duo
Predictive analytics and machine learning are seen as the pair of tools to save the day for most organizations currently. We try to de-mystify both, taking a look at what they are, how they work, and what they are good for.
Read More →Birst-ing into mainstream: Machine Learning meets Semantics in a networked world
Birst is one of the poster children of self-service analytics. Convergence and democratization are the key themes underlying Birst's new release out today, as Birst is trying to balance self-service with enterprise requirements, and making a case for some of the industry's defining trends while at it.
Read More →Data to analytics to AI: From descriptive to predictive analytics
Artificial Intelligence (AI) seems to be the buzzword du jour for organizations, but this is not an obvious or straightforward transition even for those building advanced products and platforms. Many organizations are still struggling with digital transformation to become data-driven, so how should they approach this new challenge?
Read More →Samsung’s acquisition of Viv brings up questions about massive-scale AI
The war for mainstream AI dominance is raging. The latest episode was last week's announcement of Samsung acquiring Viv, a hitherto under the radar startup working on building the next Siri. We take the opportunity to ponder on questions related to massive-scale AI.
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