The Girl Scouts of the USA (GSUSA) announced today a new partnership with Raytheon, an innovator in the cybersecurity space, to further the organization's objective to encourage young women to develop skills in science, technology, engineering, and math, aka STEM. The pair is teaming up to launch the GSUSA's first national computer science program and coding challenge for girls in middle and high school. According to the official release, "the program aims to prepare girls in grades 6-12 to pursue computer science careers in fields such as cybersecurity, artificial intelligence, robotics, and data science." The Girl Scouts are certainly no stranger to the development of STEM skills in young women. The organization partnered with SETI Institute earlier this year to help increase girls' interest in STEM fields.

The Christmas period is typically shrouded in tradition, from the centuries-old folklore of Santa Claus to the classic festive hits that come back year after year. But with technology's grip on society growing ever-stronger, modern gadgetry is bound to change the way we celebrate the holiday season. A new report has looked into how state-of-the-art technology will shape the'Christmas of the future'. The Amazon study, crafted by leading futurists, claims that 3D-printed food and wish lists generated by artificial intelligence will shape the festive period by 2037. A new report has looked into how state-of-the-art technology will shape the'Christmas of the future'.

Chance the Rapper performs in concert on the second day of week two of the Austin City Limits Music Festival at Zilker Park on Oct. 14, 2017 in Austin, Texas. SAN FRANCISCO -- Google is teaming up with Chance the Rapper to bring computer science education to Chicago's public schools. The Internet giant's philanthropic arm Google.org is giving $1 million to Chance the Rapper's SocialWorks organization and $500,000 to the schools. Chicago is the first national school district to mandate computer science education for all students. Chance the Rapper made a surprise appearance at Adam Clayton Powell Jr. Academy on Wednesday where fifth-grade students were working on a coding activity with Google employees as a part of Computer Science Education Week.

In this paper, we propose a new algorithm for learning general latent-variable probabilistic graphical models using the techniques of predictive state representation, instrumental variable regression, and reproducing-kernel Hilbert space embeddings of distributions. Under this new learning framework, we first convert latent-variable graphical models into corresponding latent-variable junction trees, and then reduce the hard parameter learning problem into a pipeline of supervised learning problems, whose results will then be used to perform predictive belief propagation over the latent junction tree during the actual inference procedure. We then give proofs of our algorithm's correctness, and demonstrate its good performance in experiments on one synthetic dataset and two real-world tasks from computational biology and computer vision -- classifying DNA splice junctions and recognizing human actions in videos.

Kernel methods are popular in clustering due to their generality and discriminating power. However, we show that many kernel clustering criteria have density biases theoretically explaining some practically significant artifacts empirically observed in the past. For example, we provide conditions and formally prove the density mode isolation bias in kernel K-means for a common class of kernels. We call it Breiman's bias due to its similarity to the histogram mode isolation previously discovered by Breiman in decision tree learning with Gini impurity. We also extend our analysis to other popular kernel clustering methods, e.g. average/normalized cut or dominant sets, where density biases can take different forms. For example, splitting isolated points by cut-based criteria is essentially the sparsest subset bias, which is the opposite of the density mode bias. Our findings suggest that a principled solution for density biases in kernel clustering should directly address data inhomogeneity. We show that density equalization can be implicitly achieved using either locally adaptive weights or locally adaptive kernels. Moreover, density equalization makes many popular kernel clustering objectives equivalent. Our synthetic and real data experiments illustrate density biases and proposed solutions. We anticipate that theoretical understanding of kernel clustering limitations and their principled solutions will be important for a broad spectrum of data analysis applications across the disciplines.

Biological plastic neural networks are systems of extraordinary computational capabilities shaped by evolution, development, and lifetime learning. The interplay of these elements leads to the emergence of adaptive behavior and intelligence. Inspired by such intricate natural phenomena, Evolved Plastic Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed plastic neural networks with a large variety of dynamics, architectures, and plasticity rules: these artificial systems are composed of inputs, outputs, and plastic components that change in response to experiences in an environment. These systems may autonomously discover novel adaptive algorithms, and lead to hypotheses on the emergence of biological adaptation. EPANNs have seen considerable progress over the last two decades. Current scientific and technological advances in artificial neural networks are now setting the conditions for radically new approaches and results. In particular, the limitations of hand-designed networks could be overcome by more flexible and innovative solutions. This paper brings together a variety of inspiring ideas that define the field of EPANNs. The main methods and results are reviewed. Finally, new opportunities and developments are presented.

As part of the IEEE RAS International Conference on Humanoid Robots in Birmingham, U.K., last month, the awards committee decided to organize a fun photo contest. Participants submitted 39 photos showing off their humanoids in all kinds of poses and places. I was happy to be one of the judges, along with Sabine Hauert from the University of Bristol and Robohub, and with Giorgio Metta, the conference's awards chair, overseeing our selection. All photos were posted on Facebook and Twitter, and users were invited to vote on them. Sabine and I then looked at the photos with the most votes and scored them for originality, creativity, photo structure, and tech or fun factor.

Personalized learning, which tailors educational content to the unique needs of individual students, has become a huge component of K–12 education. A growing number of college educators are embracing the trend, taking advantage of data analytics and artificial intelligence to deliver just-right, just-in-time learning to their students. Data-driven insights are becoming integral to business and financial decision-making by institutional leaders, and educators are quickly finding ways to leverage analytics to increase student retention. Applying data analytics to adaptive learning programs is proving to be another smart application. In adaptive learning, educators collect data on various aspects of student performance -- from engagement with course content to exam performance -- and tailor material to each student's knowledge level and ideal learning style.

Technology is constantly evolving and adapting to boost everyday efficiency and make our lives easier. Modern tools give us the power to connect from around the globe and bridge gaps as soon as they appear. One such advancement is the rise of Machine Learning and Artificial Intelligence. Predictions, algorithms, and analytics come together to create more personalized eLearning experiences. But how exactly will Machine Learning and Artificial intelligence (AI) transform the eLearning landscape in years to come?

Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. That's why we created This Week in Machine Learning! Each week we publish a curated list of Machine Learning stories as a resource to help you keep pace with all these exciting developments. New posts will be published here first, and previous posts are archived on the Udacity blog.