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.


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

Here I offer a few off-the-beaten-path interesting problems that you won't find in textbooks, data science camps, or in college classes. These problems range from applied maths, to statistics and computer science, and are aimed at getting the novice interested in a few core subjects that most data scientists master. The problems are described in simple English and don't require math / stats / probability knowledge beyond high school level. My goal is to attract people interested in data science, but who are somewhat concerned by the depth and volume of (in my opinion) unnecessary mathematics included in many curricula. I believe that successful data science can be engineered and deployed by scientists coming from other disciplines, who do not necessarily have a deep analytical background yet are familiar with data. 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. It is most and foremost relying on data, metrics and algorithm architecture.


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. Unveiled in 2004, the system apparently got it right in the last three presidential elections. It also predicted that Trump and Hillary Clinton will be the nominees of the Republican and Democratic Parties respectively.



The method to Microsoft's madness with LinkedIn deal

#artificialintelligence

In my opinion, the marriage of the leading professional social network and the world's largest software company demonstrates that we are decidedly at the start of a new era in software, where proprietary data is king, and will start to come bundled together with software. We've seen this rise in the consumer realm, where technology companies are fundamentally aggregating and analyzing user behavior, and providing value back to users (and, of course, advertisers.) There are countless other examples that also demonstrate that consumer technology puts behavioral and user data front and center, in a way that I expect we will start to see from the enterprise as the divide between these two segments starts to collapse. Taken together, this demonstrates that proven machine learning algorithms have both the horsepower and access to granular datasets that are unprecedented.