Who’s Calling? Demographics of Mobile Phone Use in Rwanda

AAAI Conferences

We describe how new sources of data can be used to better understand the demographic structure of the population of Rwandan mobile phone users. After combining anonymous call data records with follow-up phone interviews, we detect significant differences in phone usage among different social and economic subgroups of the population. However, initial experiments suggest that predicting demographics from call usage, and vice-versa, is quite difficult.


"The Five Tribes of Machine Learning (And What You Can Learn from Each)," Pedro Domingos

#artificialintelligence

There are five main schools of thought in machine learning, and each has its own master algorithm – a general-purpose learner that can in principle be applied to any domain. The symbolists have inverse deduction, the connectionists have backpropagation, the evolutionaries have genetic programming, the Bayesians have probabilistic inference, and the analogizers have support vector machines. What we really need, however, is a single algorithm combining the key features of all of them. In this webinar I will summarize the five paradigms and describe my work toward unifying them, including in particular Markov logic networks. I will conclude by speculating on the new applications that a universal learner will enable, and how society will change as a result.


Humans Don't Realize How Biased They Are Until AI Reproduces the Same Bias, Says UNESCO AI Chair

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While machine learning today is dominated by deep neural network research, in the 1990s neural approaches were not recognized as reliable for real-world applications. Back then, researchers put their efforts into kernel methods and support vector machines (SVM). One of the most notable and respected contributors to kernel methods and SVM is John Shawe-Taylor, a professor at University College London (UK) and Director of the Centre for Computational Statistics and Machine Learning (CSML). His main research area is Statistical Learning Theory, but his contributions range from neural networks to machine learning and graph theory. Shawe-Taylor has published over 300 papers with over 42000 citations.


Humans Don't Realize How Biased They Are Until AI Reproduces the Same Bias, Says UNESCO AI Chair

#artificialintelligence

While machine learning today is dominated by deep neural network research, in the 1990s neural approaches were not recognized as reliable for real-world applications. Back then, researchers put their efforts into kernel methods and support vector machines (SVM). One of the most notable and respected contributors to kernel methods and SVM is John Shawe-Taylor, a professor at University College London (UK) and Director of the Centre for Computational Statistics and Machine Learning (CSML). His main research area is Statistical Learning Theory, but his contributions range from neural networks to machine learning and graph theory. Shawe-Taylor has published over 300 papers with over 42000 citations.


Artificial Intelligence Applications: AI can Determine Your Sexuality [Stanford Research]

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What if Artificial Intelligence, Deep Neural Networks can look at your picture and determine if you are gay or straight? In this video, I want to share with you the findings of Stanford Research that showcases how it's done. Pro's and Con's of this technology, as a Retailer, Fashion designer, beauty expert, how can this benefit you, especially if your product is targeting LGBT community I would love to hear in the comments what do you think about this technology. Stanford Research Paper: The researchers, Michal Kosinski and Yilun Wang, extracted features from the images using "deep neural networks", meaning a sophisticated mathematical system that learns to analyze visuals based on a large dataset. You can find the Stanford Research paper here: https://osf.io/zn79k/