RapidMiner, the company that delivers real data science, fast and simple, today announced the immediate availability of RapidMiner 8.1 and RapidMiner Auto Model, a new addition to RapidMiner Studio that accelerates everything data scientists do when building machine learning models. "Automated machine learning promised data scientists a better, faster way to build models, but the reality never matched the hype," said Dr. Ingo Mierswa, founder and president of RapidMiner. When I looked closely at automated machine learning solutions, I found them to be black boxes. They restricted my ability as a data scientist to understand how the models worked and tune them when necessary. We built Auto Model on top of RapidMiner Studio to improve the productivity of data scientists without hiding the ability to understand how and why a model works. As data scientists need to tune or tweak models, they have the full power of the RapidMiner Studio visual workflow designer at their disposal."

Artificial intelligence is the next strong phase of technology innovation around the world. It has generated momentum in startups and established businesses, as the gold-rush across a number of sectors continues. AI will have a huge impact on a number of industries, such as health, education, transport, energy. It has and will also spawn many new businesses. Apple, Google and Amazon already use AI to provide a much enhanced customer experience.

Predicting University Students' Academic Success and Choice of Major using Random Forests C edric Beaulac Jeffrey S. Rosenthal August 31,2017 Abstract In this paper, a large data set containing every course taken by every undergraduate student in a major university in Canada over 10 years is analyzed. Modern machine learning algorithms can use large data sets to build useful tools for the data provider, in this case, the university. In this article, two classifiers are constructed using random forests. To begin, the first two semesters of courses completed by a student are used to predict if they will obtain an undergraduate degree. Secondly, for the students that completed a program, their major choice is predicted using once again the first few courses they've registered to. A classification tree is an intuitive and powerful classifier and building a random forest of trees lowers the variance of the classifier and also prevents overfitting. Random forests also allow for reliable variable importance measurements. These measures explain what variables are useful to both of the classifiers and can be used to better understand what is statistically related to the students' choices. The results are two accurate classifiers and a variable importance analysis that provides useful information to the university. Keywords: Higher Education, Students' Success and Choice, Machine Learning, Classification Tree, Random Forest, Variable Importance 1 Introduction As the demand for qualified labour increases it becomes more and more important to understand what motivates students to complete their program and how they select their majors. In parallel, universities are continuously trying to improve their programs and attract more students. It would be useful for a university to be able to predict whether or not a student that begins a program will complete it.

In the past decades, intensive efforts have been put to design various loss functions and metric forms for metric learning problem. These improvements have shown promising results when the test data is similar to the training data. However, the trained models often fail to produce reliable distances on the ambiguous test pairs due to the distribution bias between training set and test set. To address this problem, the Adversarial Metric Learning (AML) is proposed in this paper, which automatically generates adversarial pairs to remedy the distribution bias and facilitate robust metric learning. Specifically, AML consists of two adversarial stages, i.e. confusion and distinguishment. In confusion stage, the ambiguous but critical adversarial data pairs are adaptively generated to mislead the learned metric. In distinguishment stage, a metric is exhaustively learned to try its best to distinguish both the adversarial pairs and the original training pairs. Thanks to the challenges posed by the confusion stage in such competing process, the AML model is able to grasp plentiful difficult knowledge that has not been contained by the original training pairs, so the discriminability of AML can be significantly improved. The entire model is formulated into optimization framework, of which the global convergence is theoretically proved. The experimental results on toy data and practical datasets clearly demonstrate the superiority of AML to the representative state-of-the-art metric learning methodologies.

HQ Asia speaks with the Head of Insight & Futures at the CIPD about AI technologies shaping the future of work and the impact these can have on individuals, organisations and society. Wilson Wong, the Head of Insight & Futures at the CIPD, has responsibility for scanning for drivers shaping the future of work. The aim is to provide insights on business models, employment relationships, individual and societal concerns as well as what is valued by business, society and individuals. These insights, in turn, have implications for the various professions and of course human resource management (HRM). Throughout the interview, a constant theme was technology and people.

It is important to properly take care of your body and health. This can be achieved by many ways. But one important way to take care of our health is by gaining healthy food. But nowadays in our busy life because of the tight schedules, people tend to approach fast-food instead of preparing healthy meals at home which leads to severe diseases like obesity and type 2 diabetes. Concerned about these issues and noticing the increased rate of consumers for healthy eating, there are some cooking robots which enable us to cook the food.

As artificial intelligence becomes more sophisticated and its ability to perform human tasks accelerates exponentially, we're finally seeing some attempts to wrestle with what that means, not just for business, but for humanity as a whole. From the first stone ax to the printing press to the latest ERP solution, technology that reduces or even eliminates physical and mental effort is as old as the human race itself. However, that doesn't make each step forward any less uncomfortable for the people whose work is directly affected – and the rise of AI is qualitatively different from past developments. Until now, we developed technology to handle specific routine tasks. A human needed to break down complex processes into their component tasks, determine how to automate each of those tasks, and finally create and refine the automation process.

This powerful quote by William Shakespeare applies well to techniques used in data science & analytics as well. Allow me to prove it using a short story. In May ' 2015, we conducted a Data Hackathon ( a data science competition) in Delhi-NCR, India. We gave participants the challenge to identify Human Activity Recognition Using Smartphones Data Set. The data set had 561 variables for training model used for the identification of Human activity in test data set.

In this post I'll share how I've been studying Deep Learning and using it to solve data science problems. It's an informal post but with interesting content (I hope). I come from physics and computer engineering. I studied both in Venezuela, and then I did a Master in Physics in Mexico. But I consider myself a Data Scientist.

Very good overview of NN, and met all my expectations. What I hoped for was to gain an overall understanding of different NN approaches and understand how to design a NN model architecture. A bonus would be a few exercises on how to use Keras/Tensorflow, as that is what people are mostly expected to use for NN. It shouldn't be so hard to treat comma and space similarly to comma without a space.