Opinion


Meet the Netflix of Big Data & Data Science

@machinelearnbot

KDnuggets has the 7 Steps and Key Terms, Explained series (among others), as well as numerous in-house one-off tutorials and opinion pieces covering a wide variety of machine learning, data science, Big Data and AI topics, such as 10 Free Must-Read Books for Machine Learning and Data Science and Machine Learning overtaking Big Data? Netflix finds quality content from elsewhere and runs it as guest programming in order to increase reach, just as KDnuggets does with republished guest posts. Netflix syndicates quality content originally found elsewhere, which has allowed so very many British shows to make their way to North America for mass consumption, as but one example of this. KDnuggets also runs quality tutorials, overviews, and opinion pieces from other blogs and sites around the web, in order to increase their exposure.


Awesome Deep Learning: Most Cited Deep Learning Papers

@machinelearnbot

We believe that there exist classic deep learning papers which are worth reading regardless of their application domain. Rather than providing overwhelming amount of papers, We would like to provide a curated list of the awesome deep learning papers which are considered as must-reads in certain research domains.


Awesome Deep Learning: Most Cited Deep Learning Papers

@machinelearnbot

We believe that there exist classic deep learning papers which are worth reading regardless of their application domain. Rather than providing overwhelming amount of papers, We would like to provide a curated list of the awesome deep learning papers which are considered as must-reads in certain research domains.


Hard numbers: The mathematical architectures of Artificial Intelligence

#artificialintelligence

Above statistics we have data mining, which is the process of using generalised algorithms to find patterns in data. Wherever you set the bar, I guarantee that you will find that the system you are calling AI is heavily dependent on machine learning, which only works if we have data mining, which relies heavily on statistics, which is fundamentally founded on maths. We have a good understanding of machine learning so any system that simply learns a set of rules from raw data and then applies them is not AI; it is machine learning. From the evidence I have seen I don't believe that Fukoku Mutual Life Insurance has an AI system; it sounds like machine learning to me.


Hard numbers: The mathematical architectures of Artificial Intelligence

#artificialintelligence

Above statistics we have data mining, which is the process of using generalised algorithms to find patterns in data. Wherever you set the bar, I guarantee that you will find that the system you are calling AI is heavily dependent on machine learning, which only works if we have data mining, which relies heavily on statistics, which is fundamentally founded on maths. We have a good understanding of machine learning so any system that simply learns a set of rules from raw data and then applies them is not AI; it is machine learning. From the evidence I have seen I don't believe that Fukoku Mutual Life Insurance has an AI system; it sounds like machine learning to me.


10 Data Science, Machine Learning and IoT Predictions for 2017

#artificialintelligence

Data science and machine learning will become more mainstream, especially in the following industries: energy, finance (banking, insurance), agriculture (precision farming), transportation, urban planning, healthcare (customized treatments), even government. With the rise of IoT, more processes will be automated (piloting, medical diagnosis and treatment) using machine-to-machine or device-to-device communications powered by algorithms relying on artificial intelligence (AI), deep learning, and automated data science. Data science and machine learning will become more mainstream, especially in the following industries: energy, finance (banking, insurance), agriculture (precision farming), transportation, urban planning, healthcare (customized treatments), even government. With the rise of IoT, more processes will be automated (piloting, medical diagnosis and treatment) using machine-to-machine or device-to-device communications powered by algorithms relying on artificial intelligence (AI), deep learning, and automated data science.


10 Data Science, Machine Learning and IoT Predictions for 2017

@machinelearnbot

Data science and machine learning will become more mainstream, especially in the following industries: energy, finance (banking, insurance), agriculture (precision farming), transportation, urban planning, healthcare (customized treatments), even government. With the rise of IoT, more processes will be automated (piloting, medical diagnosis and treatment) using machine-to-machine or device-to-device communications powered by algorithms relying on artificial intelligence (AI), deep learning, and automated data science. Data science and machine learning will become more mainstream, especially in the following industries: energy, finance (banking, insurance), agriculture (precision farming), transportation, urban planning, healthcare (customized treatments), even government. With the rise of IoT, more processes will be automated (piloting, medical diagnosis and treatment) using machine-to-machine or device-to-device communications powered by algorithms relying on artificial intelligence (AI), deep learning, and automated data science.


Three Original Math and Proba Challenges, with Tutorial

@machinelearnbot

While having myself a strong mathematical background, I have developed an entire data science and machine learning framework (mostly for data science automation) that is almost free of mathematics, and known as deep data science. You will see that you can learn serious statistical concepts (including limit theorems) without knowing mathematics, much less probabilities or random variables. Anyway, for algorithms processing large volume of data in nearly real-time, computational complexity is still very important: read my article about how bad so many modern algorithms are and could benefit from some lifting, with faster processing time allowing to take into account more metrics, more data, and more complicated metrics, to provide better results. New algorithms are regularly invented, for instance automated tagging to perform very fast clustering on large unstructured data sets, and old ones may require changes to adapt to new environments (Hadoop, HPC, quantum computers and so on.)


Three Original Math and Proba Challenges, with Tutorial

@machinelearnbot

While having myself a strong mathematical background, I have developed an entire data science and machine learning framework (mostly for data science automation) that is almost free of mathematics, and known as deep data science. You will see that you can learn serious statistical concepts (including limit theorems) without knowing mathematics, much less probabilities or random variables. Anyway, for algorithms processing large volume of data in nearly real-time, computational complexity is still very important: read my article about how bad so many modern algorithms are and could benefit from some lifting, with faster processing time allowing to take into account more metrics, more data, and more complicated metrics, to provide better results. New algorithms are regularly invented, for instance automated tagging to perform very fast clustering on large unstructured data sets, and old ones may require changes to adapt to new environments (Hadoop, HPC, quantum computers and so on.)


Raja Mandala: Artificial intelligence, real politics

#artificialintelligence

Media reports say an artificial intelligence (AI) system called MogIA, developed by Sanjiv Rai, an innovator based in Mumbai, has predicted that Donald Trump will win Tuesday's presidential elections in the United States. In any case, new technologies like artificial intelligence, on which MogIA is based, are already being used to influence social media interactions. According to researchers from Oxford University, nearly one-third of tweets favouring Trump and one-fifth promoting Clinton between the first and second election debates came from handles run by robots -- together, they produced more than a million tweets. As advanced societies debate AI, Delhi seems strangely passive.