The starkness and suddenness of the pandemic has forced many of us to stop and reconsider our lifestyles. In this episode, our storytellers will share tales of how their priorities and values have come into focus since lockdown began.
Our first story is from award-winning standup comedian and Story Collider senior producer Gastor Almonte. In his story, Gastor is forced to confront his health issues when he almost dies from undiagnosed diabetes at the start of the pandemic. Find photos and transcripts from all of our stories on our website.
After Gastor’s story, our host speaks with Mati Hlatshwayo Davis, who told a story in our Decisions episode. As you may remember, Mati is an infectious disease doctor who researches the impact of COVID-19 on marginalized communities. In this interview, Mati discusses the ways the pandemic has brought clarity to conversations about structural racism in medicine.
In episode 93, Luke Cuddy from Southwestern College’s philosophy program talks about the video game ‘The Witness,’ which presents players with a multitude of increasingly sophisticated and frustrating puzzles that perhaps result from a theory of knowledge it reflects.
The podcast and artwork embedded on this page are from Parsing Science: The unpublished stories behind the world’s most compelling science, as told by the researchers themselves., which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
In this episode, we are joined by Larry Medsker, a Professor at The George Washington University and the founder/co-editor in chief of the AI and Ethics journal.
Larry talks about the importance of ethics in Machine Learning and Data Science. He outlines the novel idea of “Ethics by Design”, where ethics is considered at the same time the AI system is under construction. Additionally, we discuss the role that government agencies play in industry and whether there should be stricter controls on how AI systems are built, trained, and deployed.
Check out the AI and Ethics Journal HERE [https://www.springer.com/journal/43681]
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Dr. Aleix Martinez is a Professor in the Department of Electrical and Computer Engineering and Director of the Computational Biology and Cognitive Science Laboratory at the Ohio State University. He is also affiliated with the Department of Biomedical Engineering and to the Center for Cognitive and Brain Sciences. The work in Aleix’s lab focuses on cognitive science. They hold the view that the brain operates like a big (very complicated) computer. To understand the brain, they need to understand the algorithms that are encoded in that computer. His lab uses fMRI and computational methods to understand what areas of the brain are activated or work together to solve certain problems. Some of Aleix’s favorite activities are hanging out with his family, reading, and running (he runs 50-60 miles per week!). Aleix received a Master’s degree and PhD in Computer Engineering from the Autonomous University of Barcelona and a PhD in Computer Science from the University of Paris. Afterward, he conducted postdoctoral research at Purdue University, and also spent some time working as a Researcher at the Sony Computer Science Laboratory in Paris before joining the faculty at OSU. Aleix and his research have been widely featured in the media by sources like CNN, The Huffington Post, Time Magazine, CBS News, NPR, and The Guardian. During our interview, Aleix discussed his research, his career, and his life outside of science.
The podcast and artwork embedded on this page are from Dr. Marie McNeely, featuring top scientists speaking about their life and c, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
In this episode of the Voices from DARPA podcast, Bruce Draper, a program manager since 2019 in the agency’s Information Innovation Office, explains how his fascination with the ways people reason, think, and believe what they believe steered him into a lifelong embrace of computer science and artificial intelligence (AI) research. At DARPA, Draper—who says he welcomes working at a place where an academic scientist like himself can influence the direction of entire fields of research—oversees a portfolio of programs that collectively are about making artificial intelligence learn faster, less prone to mistakes and flawed inferences, and less vulnerable to misuse and deception. One of his programs aims to imbue computers with nonverbal communication abilities so that AIs collaborating with people can integrate a human being’s facial and gestural cues with written and oral ones. Another program seeks to make machine-learning algorithms into quicker studies that require simpler data sets to learn how to identify objects, actions, and other categories of phenomena. Two of Draper’s programs fall into the category of “adversarial AI,” in which, for example, those with ill intent might try to deceive an AI with “poisoned data” that could lead to inappropriate inferences and actions. Yet another program, a new one, aims to develop AIs that can serve as competent guides for people in the midst of tasks, say, fixing the brakes on a military aircraft or preparing tiramisu for a dinner party. “It’s sort of the do-it-yourself revolution on steroids,” says Draper. AI holds exciting possibilities, he adds, but it will take close attention to privacy concerns, built-in biases, and other hidden perils for AI to become the technology we want it to be for us all.
The podcast and artwork embedded on this page are from DARPA, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Sarah had achieved her dream. With a PhD in Physics, she had accepted a new position as a Theoretical Physicist.
But as the months wore on, she started to feel overwhelmed and depressed. She’d done well in school and enjoyed her classes – why couldn’t she focus on her work?
Sean graduated with honors from his engineering program. But after six months on the job as a field representative for a machine company, he was fired.
He had been an excellent student, and excelled in class with top grades and praise from his professors. In the field, he had none of that feedback, and his motivation plummeted. He blamed himself for the failure, but he couldn’t understand how all his success had collapsed so quickly.
Passion and Purpose
Sarah and Sean are just two examples of what happens every day in academia. Bright, well trained students graduate to find all of that training led to a career that didn’t live up to their expectations.
Or even more commonly, they may like aspects of the job, but other factors weigh them down. The research is interesting, but they clash with the PI, or lose motivation when the experiments don’t work.
With that data in hand, you’ll have the confidence to choose your next opportunity and maximize your happiness and productivity.
System for Identifying Motivated Abilities (SIMA)
You may have taken a Meyers-Briggs test, or some other psychometric analysis aimed at describing your personality traits or interests that could improve your career.
But, Ms. Hanson points out, those are preference-based tests, and our biases can creep into our choices and we actually select answers that don’t describe us well.
“Our preferences are not clean evidence,” she says. “They’re so impacted by our biases. Their reliability and validity are not very high, and they’re not very effective in making informed career decisions.”
The System for Identifying Motivated Abilities, on the other hand, is an “Evidence Based Assessment.” The process starts when you list achievements from your childhood onward.
You choose eight such examples – things that you enjoyed doing and thought you did well – and describe each event in as much detail as possible.
How did you get involved? What did you actually do step-by-step? What were you proud of after you accomplished this task?
Then, you or your SIMA analyst can go through those stories looking for patterns – evidence of your past successes and how you achieved your goals.
Building a Profile
Those bits of evidence get sorted into five categories that make up your Motivational Profile.
* Motivated Abilities – which of your skills do you frequently use when you’re happily working?* Subject Matter – What topics inspire you? Do you work with numbers or animals or abstract concepts?* Circumstances – How do you get involved in a project? Do you like to be asked or come up with the idea yourself? Do you prefer a deadline or an open ended engagement?
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