Opinion


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.


Ashby: Artificial intelligence already displaying the flaws of its inventors

#artificialintelligence

It's a question posed by the "partnership on AI" formed by major American technology firms. The goal of the partnership, which includes Google, IBM, Microsoft and Facebook, is to "conduct research, recommend best practices, and publish research under an open license (sic) in areas such as ethics, fairness and inclusivity; transparency, privacy, and interoperability; collaboration between people and AI systems; and the trustworthiness, reliability and robustness of the technology." That's the type of artificial intelligence that technology companies investing in machine learning have focused on. As Microsoft researcher and MIT professor Kate Crawford pointed out recently in The New York Times, "Sexism, racism and other forms of discrimination are being built into the machine-learning algorithms that underlie the technology behind many'intelligent' systems that shape how we are categorized and advertised to," thanks to the overwhelmingly homogenous hiring patterns in major technology firms.