Last week we introduced you to GoGuardian Admin 2.0, launching this June. With Admin 2.0, we took our first iteration of the Admin product and improved every feature to offer a better understanding of student online behavior and make students safer. The final product is a direct reflection of the insights and feedback given to us by you, our 2500 customers, our 200 alpha users, and 15 alpha organizations. Introducing Smart Alerts This week we're going to take a deep dive into perhaps the most game-changing aspect of Admin 2.0 - the new "Smart Alerts" feature. Check out this video for a closer look at Smart Alerts' new and improved features!

I'm a software engineer with 10 years of experience who recently decided to switch my focus to machine learning. I did the coursera course and did CS231n: Convolutional Neural Networks for Visual Recognition, read up on basic theory, did some image processing networks like VGG, Resnets and most recently trying to get Faster-RCNN to work, so my currently knowledge is ML basics and heavily focussed on ML in the Image domain. I recently landed my first ML job at a company that does mostly NLP, so I lack a lot of knowledge in that domain. I'm currently reading the NLTK book, which has been very approachable in introducing basic concepts in a code-focussed way. So I was wondering if anyone could point me to some good mid to advanced level resources (online courses/videos/books) to get up to speed with where the field is at now, to help me understand current research and more advanced concepts?

Young people who are going to be leaving school and starting their tertiary education studies soon might be wondering what career paths they should focus on. And for anyone who is in touch with what is happening in the world and future technology trends, the answer should be obvious. Literally changing the way our world operates, we're all dealing with aspects of this tech daily and likely don't even know it. Youth unemployment globally is reaching higher levels than ever and is set to continue increasing this year. Research by the International Labor Organization shows unemployment is set to increase by half a million this year to reach 71 million – the first such increase in three years.

We describe the SemEval task of extracting keyphrases and relations between them from scientific documents, which is crucial for understanding which publications describe which processes, tasks and materials. Although this was a new task, we had a total of 26 submissions across 3 evaluation scenarios. We expect the task and the findings reported in this paper to be relevant for researchers working on understanding scientific content, as well as the broader knowledge base population and information extraction communities.

Swedish startup Gavagai AB, a language technology company that originated at the Swedish Institute of Computer Science, has mastered 40 human languages with its language analysis software. Now, researchers from the KTH Royal Institute of Technology are teaming up with Gavagai AB to take on the language of dolphins, in a project undoubtedly focused as much on testing and expanding the system's capabilities as deciphering the thoughts of dolphins. The team will monitor bottlenose dolphins in a wildlife park and use Gavagai's artificial intelligence (AI) language analysis technology to decode the sounds and, if all goes according to plan, compile a dictionary of dolphin language. The team is confident that they'll be able to do this, thanks not only to the AI capabilities of the Gavagai AB system, but also to the availability of more dolphin data, larger computational resources, and newer recording methods. "We hope to be able to understand dolphins with the help of artificial intelligence technology," KTH adjunct professor and Gavagai co-founder Jussi Karlgren said in an interview with Bloomberg.

Introduce yourself to the basics of data science and leave armed with practical experience extracting value from big data. This course teaches the basic techniques of data science, including both SQL and NoSQL solutions for massive data management (e.g., MapReduce and contemporaries), algorithms for data mining (e.g., clustering and association rule mining), and basic statistical modelling (e.g., linear and non-linear regression).

Opinions expressed by Forbes Contributors are their own. The author is a Forbes contributor. The opinions expressed are those of the writer. When Timnit Gebru attended a prestigious AI research conference last year, she counted 6 black people in the audience out of an estimated 8,500. As a PhD candidate at Stanford University who has published a number of notable papers in the field of artificial intelligence, Gebru finds the lack of diversity in the industry to be "extremely alarming" and effectively an "international emergency."

When I first started out learning about machine learning algorithms, it turned out to be quite a task to gain an intuition of what the algorithms are doing. Not just because it was difficult to understand all the mathematical theory and notations, but it was also plain boring. When I turned to online tutorials for answers, I could again only see equations or high level explanations without going through the detail in a majority of the cases. It was then that one of my data science colleagues introduced me to the concept of working out an algorithm in an excel sheet. And that worked wonders for me.

Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. It's incredible, but it can also be overwhelming. 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.

Big data in machine learning is the future. But how to deal with data analysis and limited resources: Computational power, data distribution, energy or memory? From September 25th to 28th, 2017 TU Dortmund University, Germany, hosts the 4th summer school on resource-aware machine learning. Topics of the lectures include: Exercises help bringing the contents of the lectures to life. The PhyNode low power computation platform was developed at the collaborative research center SFB 876.