In a thrilling science talk, Kenneth Cukier looks at what's next for machine learning -- and human knowledge. Kenneth Cukier is the Data Editor of The Economist. From 2007 to 2012 he was the Tokyo correspondent, and before that, the paper's technology correspondent in London, where his work focused on innovation, intellectual property and Internet governance. Kenneth is also the co-author of Big Data: A Revolution That Will Transform How We Live, Work, and Think with Viktor Mayer-Schönberger in 2013, which was a New York Times Bestseller and translated into 16 languages. Kenneth Cukier is the Data Editor of The Economist.

The man who saved the world from the hack that took down the NHS didn't pass his IT GCSE. Marcus Hutchins, who accidentally discovered a kill switch that helped shut down the WannaCry virus as it spread around the world, doesn't have the most basic IT qualification. And it's all because his teachers thought he was a hacker. The accidental hero's problems began when he was hauled into the head teacher's office at school and told to explain why the network was down. He couldn't and so was blamed for having hacked into the network – something that despite his claims not to have done anything, led to him being suspended.

About this course: Case Study - Predicting Housing Prices In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). This is just one of the many places where regression can be applied. Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high-performance computing, to analyzing which regulators are important for gene expression. In this course, you will explore regularized linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity.

Right now, Machine Learning and Data Science are two hot topics, the subject of many courses being offered at universities today. Above, you can watch a playlist of 18 lectures from a course called Learning From Data: A Machine Learning Course, taught by Caltech's Feynman Prize-winning professor Yaser Abu-Mostafa. This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. Learning From Data will be permanently added to our list of Free Online Computer Science Courses, part of our ever-growing collection, 1200 Free Online Courses from Top Universities.

A few weeks ago, I wrote about how and why I was learning Machine Learning, mainly through Andrew Ng's Coursera course. Machine Learning is built on prerequisites, so much so that learning by first principles seems overwhelming. Do you really need to spend a month learning linear algebra? You'll be okay if you have some math and programming experience. You really just have to be familiar with Sigma notation and be able to express it in a for loop. Sure, your assignments will take longer to complete and the first few times you see those giant equations your head will spin, but you can do this! Calculus is not even required.

A third to half the jobs that we are currently employed in would disappear in the next 15 years; and yet your child is being prepared in school for those very same jobs that won't exist by the time they graduate. Our curriculum prepares us for a lifetime career, but a child today can expect to change jobs at least seven times over the course of their lives – and five of those jobs don't exist yet. The coming days would see us pursuing careers that we cannot even imagine today. For instance your child could be an expert licensed drone pilot, or a cyber warrior in the army, a data analyst making sense of the peta bytes of data generated through our social interactions and trying to forecast our behavior. The other big challenge facing students today is that the velocity of technology changes has gained incredible speed; this is making knowledge obsolete faster than before.

The current study examined the degree to which the quality and characteristics of students’ essays could be modeled through dynamic natural language processing analyses. Undergraduate students (n = 131) wrote timed, persuasive essays in response to an argumentative writing prompt. Recurrent patterns of the words in the essays were then analyzed using recurrence quantification analysis (RQA). Results of correlation and regression analyses revealed that the RQA indices were significantly related to the quality of students’ essays, at both holistic and sub-scale levels (e.g., organization, cohesion). Additionally, these indices were able to account for between 11% and 43% of the variance in students’ holistic and sub-scale essay scores. Overall, our results suggest that dynamic techniques can be used to improve natural language processing assessments of student essays.

Assessment in intelligent tutoring system (ITS) on concept maps (CM) matches an expert CM to a learner CM. Feedbacks are provided to the learner as semantic comments andvisual corrections. In this paper, quality of feedbacks is improved by using an ontological semantic for matching, formalized as a correlation feedback. Matchings are selected based on an overall assignment solution providing a suboptimal set of correlation feedbacks to the learner.

With the inexorable expansion of the semantic layer on the Web and its ecosystem of connected applications, the global citizens expect more and more data expositions coming from public activities. The recent developments in knowledge representation and reasoning push public structures to deploy their data warehouses in parallel of classical websites exhibitions. This article presents an infrastructure to spread the descriptions of scholarships. After introducing the major contributions concerning the semantical annotation of materials occurring in recruitment processes, we describe our case study about the strategy of the University of Sassari concerning the expositions of academical grants. Supported by a core and aligned ontology of the domain we present our prototypical architecture to support and gather the spread of scholarships.

At the core of an intelligent tutoring system is the ability to estimate a student’s level of skill proficiency. However, making accurate skill estimates can require asking the student relatively many questions. We address this challenge by using “transfer learning,” a field of machine learning which uses data from related, but different, “source” domains to aid in learning in a poorly labeled “target” domain. Thus, to predict the skill of a student who hasn't answered many “target” skill questions, we use estimates of well tested “source” skills. We explore settings where the student has answered no questions related to the target skill (the cold start setting) and those where she has answered a few (the warm start setting). We focus on the challenging situation where the domain expert has not identified the relationship between the skills. We find that the Ridge estimator is useful for transferring knowledge from source to target skills, outperforming nonparametric regression methods and a baseline which only uses student performance on target skill questions.