Teaching via Zoom has had some unexpected benefits, college professor says, though robotics class is still a challenge. Her real passion is inspiring young women and girls to go into computer science.
TechRepublic’s Karen Roby spoke with Dr. Pauline Mosley, assistant chair of Information Technology at the Seidenberg School of Computer Science and Information Systems at Pace University, about getting girls interested in STEM careers and the challenges associated with teaching robotics via Zoom. The following is an edited transcript of their conversation.
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Pauline Mosley: Transitioning from face-to-face modality into a virtual modality has been challenging. However, it has been very, very rewarding. For example, this past semester, I taught a course called Web Design for Nonprofit Organizations, which was comprised of students from China, Russia, and Saudi Arabia. It was amazing how we were able to,
The news: When Twitter banned, and then unbanned, links to a questionably sourced New York Post article about Joe Biden’s son Hunter, its stated intention was to prevent people from spreading harmful false material as America heads into the final stretch of the election campaign. But thanks to the cycle of misinformation—and claims from conservatives that social-media platforms are deliberately censoring their views—Twitter managed to do the opposite of what it intended.
According to Zignal Labs, a media intelligence firm, shares of the Post article “nearly doubled” after Twitter started suppressing it. The poorly-thought-through ban triggered the so-called Streisand Effect and helped turn a sketchy article into a must-share blockbuster. And then on Friday, the Republican National Committee filed a Federal Election Commission complaint against Twitter, claiming that the ban “amounts to an illegal corporate in-kind political contribution to the Biden campaign.”
The ban: Twitter blocked shares of the story
Commentary: While you may not be able to run like Google, there is one important way others can emulate its engineering success.
The problem with enterprise IT consistency (i.e., with implementing a single software stack across the organization that will bring order to chaos) is that enterprise IT isn’t static. Because technology isn’t static. As Google’s Kelsey Hightower put it in an interview with Comcast’s chief software architect Jon Moore, “Most organizations learn over time. So whatever you build today as the [default stack] is going to branch out based on new learnings.”
So how should an enterprise standardize, thereby reaping cost savings and productivity gains? You don’t, said Hightower. At least, not once and for all: “Standardize where you can, but allow things to grow apart and then re-standardize those things as you go and just follow the path of evolution.”
But how to standardize?
If your company has gotten behind in its big data management, there are ways to get current using automation.
Not all companies have the resources to develop their own big data management systems, hire the staff to manage them, and glean all the information they can from it. With large amounts of data coming in, this can lead to massive data management challenges.
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“There are companies like Netflix and Twitter that immediately understood the value of big data and that had the resources needed to develop big data staffs and applications at the onset of the big data movement,” said Monte Zweben, CEO of Splice Machine, a company that provides a SQL-database platform that’s specifically tailored for accelerated big data modeling and deployment. “Unfortunately, most of the Fortune 2000 companies couldn’t compete with these efforts, so
Machine learning typically requires tons of examples. To get an AI model to recognize a horse, you need to show it thousands of images of horses. This is what makes the technology computationally expensive—and very different from human learning. A child often needs to see just a few examples of an object, or even only one, before being able to recognize it for life.
In fact, children sometimes don’t need any examples to identify something. Shown photos of a horse and a rhino, and told a unicorn is something in between, they can recognize the mythical creature in a picture book the first time they see it.
Now a new paper from the University of Waterloo in Ontario suggests that AI models should also be able to do this—a process the researchers call “less than one”-shot, or LO-shot, learning. In other words, an