We explored the need for automated curriculum alignment in crisis contexts, and the possible role of artificial intelligence (AI) in recognizing curricular mandates and patterns, and recommending pertinent educational content in return. This work is part of a broader collaboration working with refugees and partner organizations to explore utilizing digital education to support learning in these contexts. The Design2Align series has included discussion of contextual display and creation of metadata, teacher-generated content annotations, and the technical considerations in OER for curriculum alignment facilitation. We're delighted to introduce the fourth installment of the blog series by our co-founder and Executive Director, Jamie Alexandre. Jamie discusses how user data from multiple open platforms could be used to train machine learning models, in order to optimize recommendations to teachers of contextually relevant learning pathways for their students. Our table brought together participants from diverse backgrounds and skill sets, spanning government, foundations, the tech industry, education nonprofits, and UN agencies, which made for some lively debates!

We explored the need for automated curriculum alignment in crisis contexts, and the possible role of artificial intelligence (AI) in recognizing curricular mandates and patterns, and recommending pertinent educational content in return. This work is part of a broader collaboration working with refugees and partner organizations to explore utilizing digital education to support learning in these contexts. The experience of engaging our professional communities in such a challenging question was as valuable as the outputs themselves, so we've been sharing the discussions and debates we've had as they may be useful in other's work. Over the past month, the Design2Align blog post series has covered topics such as contextual display and creation of metadata, teacher-generated content annotations, technical considerations in OER for curriculum alignment facilitation, and open models for just-in-time learning pathway recommendations. Today, Learning Equality's UX Design Lead, Jessica Aceret talks about the specific curriculum needs for crisis contexts, and how it requires not only a human touch but also an alignment tool that provides intelligent content recommendations so that the relevant resources can be more easily found. The Design Sprint on Curriculum Alignment in Crisis Contexts, which took place back in March, in Paris, saw many different roles in the education technology space strategically brought together -- curriculum designers, policymakers, technology experts, refugees, and more.

Consider a refugee population coming from country C residing in host country B, with limited or no access to education. The trauma of conflict and displacement, coupled with the difficulty of integration within the host country puts refugee populations at a significant educational disadvantage, so it is worthwhile considering options that could "level the playing field" by providing improved access to education. There is hope that the vast amounts of Open Educational Resources (OER) that are freely available on the internet can play a role in this, in particular in combination with educational platforms like Kolibri. The Kolibri platform aims to provide access to learning opportunities for all and it is particularly suited for the refugee context as the runs-anywhere capabilities of the Kolibri applications allow it to be accessed in computer labs, in the classroom, from phones, and in informal learning centres. Our experience and work with partners like UNHCR have shown that in emergency and crisis contexts, a key bottleneck is the lack of sufficient educational content aligned to the learning goals of the project.

They are engaging on social media and chatbots. Find out what you need to do to reach them effectively. Guest: Mary has been nicknamed the "ChatBotMom" by her Messenger Marketing community, and her innovative development of chatbot copywriting has helped her students and clients sell millions in products, services and online courses. IN 100 words or less, what is your chatbot or AI Voice app and why? Guest – books, Seth Godin's "This is Marketing" and Mark Schaeffer's "Marketing Rebellion" Convert long, story emails into interactive chat conversations to help your bot engage with subscribers and convert.

The advent of cloud-based computing servers, smart sensors that can communicate seamlessly over wireless networks, and advances in the field of data analytics means that artificial intelligence (AI) is more accessible to the mining sector than ever before and miners are beginning to see a return on investment.

Google Colaboratory is a platform built on top of the Jupyter Notebook environment which runs entirely on Google Cloud Platform (GCP). This platform provides GPU which is free of cost and supports Python 2 and 3 versions. With the help of Colab, one can not only improve machine learning coding skills but also learn to develop deep learning applications. You can also learn to work with popular deep learning libraries such as Keras, TensorFlow, OpenCV and others. With Colaboratory you can write and execute code, save and share your analyses, and access powerful computing resources, all for free from your browser.

You don't need to reinvent the wheel, you need to know how to use the wheel to make your car better. The Titanic challenge hosted by Kaggle is a competition in which the goal is to predict the survival or the death of a given passenger based on a set of variables describing him such as his age, his sex, or his passenger class on the boat. I have been playing with the Titanic dataset for a while. As I'm writing this post, I am ranked 113th out of 11002 participants. You must be wondering how did I manage to achieve this.

You don't need to reinvent the wheel, you need to know how to use the wheel to make your car better. The Titanic challenge hosted by Kaggle is a competition in which the goal is to predict the survival or the death of a given passenger based on a set of variables describing him such as his age, his sex, or his passenger class on the boat. I have been playing with the Titanic dataset for a while. As I'm writing this post, I am ranked 113th out of 11002 participants. You must be wondering how did I manage to achieve this.

We can easily use the widely available libraries available in Python and R to build models!" I have lost count of the number of times I've heard this from amateur data scientists. This fallacy is all too common and has created a false expectation among aspiring data science professionals. Let's get this out of the way right now – you need to understand the mathematics behind machine learning algorithms to become a data scientist. There is no way around it. It is an intrinsic part of a data scientist's role and every recruiter and experienced machine learning professional will vouch for this.

After winning 74 consecutive games and earning $3.3 million in prize money, he finally lost to his fiercest opponent -- a newcomer, no less, that went by a single name: Watson. Really, though, it was no contest. With four years of training and a huge research budget, Watson had been born for this moment. If a computer can be born, that is. Created by IBM to answer questions posed in natural language, Watson was initially designed to excel at Jeopardy! but after its win it began tackling other projects: assisting in the treatment of lung cancer patients at New York's Memorial Sloan-Kettering Cancer Center; conversing with kids via smart toys; teaming up with education company Pearson to tutor college students; even helping H&R Block customers file their taxes.