This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Researchers have developed a new method for teaching self-driving cars how to respond to emergencies. Unlike other approaches, which teach cars to respond according to hard and fast rules, this new method trains onboard computers to react like humans do. That unique ability could make self-driving cars vastly quicker at recognizing and avoiding potential accidents. Human drivers react instinctively to road hazards—whether that’s a car that brakes suddenly or a cyclist who rushes into traffic. It’s an ability that comes from years of experience and one that’s often taken for granted. As AI experts have learned, teaching computers to do the same is notoriously difficult. Rule-based methods provide basic functionality. But they tend to be very time-consuming and can’t account for unforeseen emergencies—two tremendous liabilities for self-driving cars..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

Natural Language Processing using Big Data

Processing information is the hallmark of all modern organizations, which are increasingly digital: absorbing, processing and generating information is a key element of their business.
Being able to interact flexibly and efficiently with the underlying data and software systems is an indispensable skill. Knowledge of the Unix shell and its command-line tools boosts the effectiveness and productivity of software developers, IT professionals, and data analysts.

The Unix tools were designed, written, actively used and refined by the team that defined the modern computing landscape. They allow the performance of almost any imaginable computing task quickly and efficiently by judiciously combining key powerful concepts. The power of Unix tools for exploring, prototyping and implementing big data processing workflows, and software engineering tasks remains unmatched. Unix tools, running on hardware ranging from tiny IoT platforms to supercomputers, uniquely allow an interactive, explorative programming style, which is ideal for the efficient solution of many of the engineering and business analytics problems that we face every day.

Through the use of Unix tools:
- Software developers can quickly explore and modify code, data, and tests.
- IT professionals can scrutinize log files, network traces, performance figures, filesystems and the behavior of processes.
- Data analysts can extract, transform, filter, process, load, and summarize huge data sets.

The course is uniquely based on carefully-selected, interactive walk-through examples that demonstrate how each command operates in practice. The examples that we use involve problems that engineers and analysts face every day.

The course explores the interactions between state and market as instigators of China's urbanization, and its consequences of land, housing, transportation, energy, environment, migration, finance, urban inequality. Themes include the de-synchronization of China's urbanization, potential differences between China's past and future development, and differentiators between China's urbanization and those of other countries. This discussion-based course asks students to participate in the conversation with the course instructor and guest lecturers by drawing upon their experiences and academic or professional backgrounds.

This is the first of two lessons/labs for teaching and learning of computer science and sociology. Either and be used on their own or they can be used in sequence, in which case this should be used first.

Students will develop CS skills and behaviors including but not limited to: learning what an API is, learning how to access and utilize data on an API, and developing their R coding skills and knowledge. Students will also learn basic, but important, sociological principles such as how poverty is related to educational opportunities in America. Although prior knowledge of CS and sociology is helpful, neither is necessary for student (or instructor) success on this two-week project. Three instructional hours per week (total of six hours over two weeks).

This lesson is connected to but can be used independently of "Using Big Data to Identify and Understand Educational Inequality in America (1)"

Students will develop CS skills and behaviors including but not limited to: learning what an API is, learning how to access and utilize data on an API, and developing their R coding skills and knowledge. Students will also learn basic, but important, sociological principles such as how poverty is related to educational opportunities in America (and how this relationship varies between and among states). Although prior knowledge of CS and sociology is helpful, neither is necessary for student (or instructor) success on this project. Three instructional hours.

As technology continues to grow, so does access to data.  Teaching students methods to analyze this data, identify trends, and weed out useful information is a 21st century skill that is lacking in many classrooms. This lesson will help students tackle the world of Big Data through the use of basic commands in Python which allows them to complete a one-variable data analysis determining statistical summaries and  generate box plots, histograms and scatter plots.