The OSSU curriculum is a complete education in computer science using online materials. It's for those who want a proper, well-rounded grounding in concepts fundamental to all computing disciplines, and for those who have the discipline, will, and (most importantly!) good habits to obtain this education largely on their own, but with support from a worldwide community of fellow learners. It is designed according to the degree requirements of undergraduate computer science majors, minus general education (non-CS) requirements, as it is assumed most of the people following this curriculum are already educated outside the field of CS. The courses themselves are among the very best in the world, often coming from Harvard, Princeton, MIT, etc., but specifically chosen to meet the following criteria. When no course meets the above criteria, the coursework is supplemented with a book.

Azure Machine Learning (AML) is a cloud-based machine learning service for data scientists and ML engineers. You can use AML to manage the machine learning lifecycle--train, develop, and test models, but also run MLOps processes with speed, efficiency, and quality. For organizations that want to scale ML operations and unlock the potential of AI, tools like AML are important. Creating machine learning solutions that drive business growth becomes much easier. But what if you don't need a comprehensive MLOps solution like AML? Maybe you want to build your own stack, and need specific tools for tasks like tracking, deployment, or for managing other key parts of MLOps? Experiment tracking documents every piece of information that you care about during your ML experiments. Machine learning is an iterative process, so this is really important. Azure ML provides experimental tracking for all metrics in the machine learning environment.

Last year, Charlene Xia '17, SM '20 found herself at a crossroads. She was finishing up her master's degree in media arts and sciences from the MIT Media Lab and had just submitted applications to doctoral degree programs. All Xia could do was sit and wait. In the meantime, she narrowed down her career options, regardless of whether she was accepted to any program. "I had two thoughts: I'm either going to get a PhD to work on a project that protects our planet, or I'm going to start a restaurant," recalls Xia.

At a large private university in Northern California, a business professor uses an avatar to lecture on a virtual stage. Meanwhile, at a Southern university, graduate students in an artificial intelligence course discover that one of their nine teaching assistants is a virtual avatar, Jill Watson, also known as Watson, IBM's question-answering computer system. Of the 10,000 messages posted to an online message board in one semester, Jill participated in student conversations and responded to all inquiries with 97% accuracy. At a private college on the East Coast, students interact with an AI chat agent in a virtual restaurant set in China to learn the Mandarin language. These examples provide a glimpse into the future of teaching and learning in college.

New technology and gadgets will be able to record and track speech, visual, and biological data as they grow more complex and sophisticated. Artificial intelligence (AI)will subsequently be able to judge complicated abilities using these technologies. Artificial intelligence (AI) will be able to track eye contact during group talks, for example. What impact does this have on educators? It enables them to assess their students' comprehension and attention.

Often, advanced sensor technologies can assess highway bridge infrastructure. TRB will host a webinar on Monday, February 22, 2021 from 2:00-3:30 PM Eastern to explore how artificial intelligence (AI) and deep learning (DL) may be used to predict the deterioration of bridges. Presenters will discuss recent case studies related to the application of AI in integrating highway data to better explain and predict system performance. They will also identify how AI and DL may improve sensor signal data, and explain how these technologies can provide support design, operations, and management of highway systems. This webinar was organized by the TRB Standing Committee on Testing and Evaluation of Transportation Structures.

This paper presents a novel keypoints-based attention mechanism for visual recognition in still images. Deep Convolutional Neural Networks (CNNs) for recognizing images with distinctive classes have shown great success, but their performance in discriminating fine-grained changes is not at the same level. We address this by proposing an end-to-end CNN model, which learns meaningful features linking fine-grained changes using our novel attention mechanism. It captures the spatial structures in images by identifying semantic regions (SRs) and their spatial distributions, and is proved to be the key to modelling subtle changes in images. We automatically identify these SRs by grouping the detected keypoints in a given image. The ``usefulness'' of these SRs for image recognition is measured using our innovative attentional mechanism focusing on parts of the image that are most relevant to a given task. This framework applies to traditional and fine-grained image recognition tasks and does not require manually annotated regions (e.g. bounding-box of body parts, objects, etc.) for learning and prediction. Moreover, the proposed keypoints-driven attention mechanism can be easily integrated into the existing CNN models. The framework is evaluated on six diverse benchmark datasets. The model outperforms the state-of-the-art approaches by a considerable margin using Distracted Driver V1 (Acc: 3.39%), Distracted Driver V2 (Acc: 6.58%), Stanford-40 Actions (mAP: 2.15%), People Playing Musical Instruments (mAP: 16.05%), Food-101 (Acc: 6.30%) and Caltech-256 (Acc: 2.59%) datasets.

AI is widely thought to be poised to transform business, yet current perceptions of the scope of this transformation may be myopic. Recent progress in natural language processing involving transformer language models (TLMs) offers a potential avenue for AI-driven business and societal transformation that is beyond the scope of what most currently foresee. We review this recent progress as well as recent literature utilizing text mining in top IS journals to develop an outline for how future IS research can benefit from these new techniques. Our review of existing IS literature reveals that suboptimal text mining techniques are prevalent and that the more advanced TLMs could be applied to enhance and increase IS research involving text data, and to enable new IS research topics, thus creating more value for the research community. This is possible because these techniques make it easier to develop very powerful custom systems and their performance is superior to existing methods for a wide range of tasks and applications. Further, multilingual language models make possible higher quality text analytics for research in multiple languages. We also identify new avenues for IS research, like language user interfaces, that may offer even greater potential for future IS research.

Artificial intelligence adoption is increasing in higher education for both academic and research purposes. Too often, though, universities lack the IT infrastructure needed to sustainably power these systems. "To do AI at scale, you need data, but you also need compute power, networking, storage and software," says Cheryl Martin, director of global business development for higher education and research at NVIDIA. "Universities need a platform to bring all those things together." Modern AI requires purpose-built infrastructure that can handle its massively parallel computational demands.

Last year, Charlene Xia '17, SM '20 found herself at a crossroads. She was finishing up her master's degree in media arts and sciences from the MIT Media Lab and had just submitted applications to doctoral degree programs. All Xia could do was sit and wait. In the meantime, she narrowed down her career options, regardless of whether she was accepted to any program. "I had two thoughts: I'm either going to get a PhD to work on a project that protects our planet, or I'm going to start a restaurant," recalls Xia.