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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.
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In this episode Gemma chats to Adrian Daub about his latest book What Tech Calls Thinking: An Inquiry into the Intellectual Bedrock of Silicon Valley.
Adrian Daub is a professor of comparative literature and German studies at Stanford University, and the director of Stanford’s Program in Feminist, Gender, and Sexuality Studies.
The podcast and artwork embedded on this page are from Radical Science, 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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Mays Imad discusses trauma-informed teaching and learning on episode 335 of the Teaching in Higher Ed podcast.
Quotes from the episode
I am able to recognize when they are triggered and when they feel disengaged. I also am able to recognize it in myself. We can’t give what we don’t have.
-Mays Imad
I want to acknowledge that we can heal from trauma.
-Mays Imad
My goal is to engage students and help them feel empowered and liberated.
-Mays Imad
We are not just thinking machines; we are feeling machines capable of thinking.
-Mays Imad
Julie Gould asks six higher education experts if it’s now time to go back to the drawing board and redesign graduate programmes from scratch.
Suzanne Ortega, president of the US Council of Graduate Schools, says programmes now include elements to accommodate some of the skills now being demanded by employers, including project and data management expertise. “We can’t expect to prepare doctoral researchers in a timely fashion by simply adding more and more separate activities,” she tells Gould. “We need to redesign the curricula and the capstone project,” referring to the PhD as a long-term investigative project that culminates in a final product.
Jonathan Jansen, professor of education at Stellenbosch University, South Africa, calls for more flexible and modular programmes and describes as an example how MBA programmes have evolved from a full-time one year course to include part-time online only programmes and a “blended” combination of the two approaches. “It’s about trying to figure out in terms of your own lifestyle what kind of progarmme design works for you,” he says. “One size does not fit all.”
But Jansen’s colleague Liezel Frick, director of the university’s centre for higher and adult education, says it’s important to remember the ultimate goal of a PhD. She tells Gould: “I get the point around flexibility but it’s still a research focused degree. You still have to make an original contribution to your field of knowledge. Otherwise it becomes a continuing professional development programme where you can do odds and ends but never get to the core of it, which is a substantive research contribution.”
David Bogle, a doctoral school pro-vice-provost at UCL, London, says it’s important to remember that graduate students are part of a cohort and community who should be respected and rewarded, not looked down on and treated as second class citizens. “At the moment there’s a certain amount of ‘I’m the supervisor. You should be looking to me as the primary source of inspiration,’ when in fact the inspiration comes from peers, professional communities, training and cross disciplinary activities.”
This is the second episode in a five-part series timed to coincide with Nature’s 2019 PhD survey. Many of the 6,300 graduate students who responded call for more one-to-one support and better career guidance from PhD supervisors.
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