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Adina Sterling: How will artificial intelligence change hiring?
At a recent live taping of the Stanford School of Engineering podcast “The Future of Everything,” Adina Sterling, an assistant professor of organizational behavior at Stanford Graduate School of Business who studies labor markets, said that roughly three-quarters of the job applications received by major companies will be touched in some way by artificial intelligence.
Because these hiring bots look for very specific criteria, qualified applicants may be screened out early in the process if their resumes don’t contain “the right buzzwords to get through the filters.
Artificial Intelligence That Helps Doctors Predict When Patients Will Die
Advance care planning — which often begins with a simple, structured conversation — can help patients make decisions and settle what will be done ahead of time, relieving some of chaos and confusion that accompanies end-of-life care.
Dr. Stephanie Harman, the clinical chief of palliative care at Stanford Health Care, is leading a pilot program at Stanford Medicine that explores the potential for artificial intelligence (AI) to help doctors guide patients through these decisions.
Though the tool isn't designed to predict a specific time of death — it doesn't give a precise number of months or years — the predictive analytics model identifies patients who have a high probability of dying in three to 12 months.
Would that be something useful, in terms of a decision, a door for clinical care?’ And that was where we said, ‘Wow, yes, that would be useful!’' Taking into account the patient's medical history and millions of other patient records, the AI model uses an algorithm to determine the probability that someone will die within 12 months.
Learning About Machine Learning To assist physicians working with patients nearing the end of their lives, researchers have developed tools for each type of cancer, calibrated for multiple months at a time.
David Hui, a physician in the palliative care department at the University of Texas Anderson Cancer Center in Houston, co-authoreda studythat found a validated prognostic tool called the Palliative Prognostic Index was more accurate than doctors’ estimates when used to determine whether a patient with advanced cancer will diearound 30 days — though notat 100 days.
Stanford Artificial Intelligence Laboratory
Statistical Machine Learning Group
Email: adityag at cs.stanford.edu
My research focusses on various aspects of machine learning, including probabilistic modeling, stochastic optimization, and deep learning.
As a Stanford Teaching Fellow, I recently taught a new class on Deep Generative Models in 2018 with an enrollment of 150+ students.
I am interested in developing algorithms for efficient learning and inference in probabilistic models.
A large part of my research in this direction entails the design and analysis of suitable learning objectives, stochastic optimization algorithms, and representation frameworks for probabilistic reasoning (ICLR 2019, AISTATS 2019a, 2018a,b, AAAI 2018a,b).
These endeavors have often led to algorithms that bridge theory and practice for applications across machine learning, e.g., fair representation learning, constraint satisfaction problems, compressed sensing, and multiagent reinforcement learning (AISTATS 2019b, NeurIPS 2018, ICML 2018a,b).
Full Oral Presentation [acceptance rate: 212/2473 (8.6%)] Modeling Sparse Deviations for Compressed Sensing using Generative Models Manik Dhar, Aditya Grover, Stefano Ermon International
Full Oral Presentation [acceptance rate: 212/2473 (8.6%)] Evaluating Generalization in Multiagent Systems using Agent-Interaction Graphs (short) Aditya Grover, Maruan Al-Shedivat, Jayesh K.
Fei-Fei Li (born 1976), who also publishes under the name Li Fei-Fei (simplified Chinese:
In 2017, she co-founded AI4ALL, a nonprofit organization working to increase diversity and inclusion in the field of artificial intelligence.
Her research expertise includes artificial intelligence (AI), machine learning, deep learning, computer vision and cognitive neuroscience.
She was the leading scientist and principal investigator of ImageNet, a critical dataset and computer vision project that resulted in the recent deep learning revolution.
She joined Stanford in 2009 as an assistant professor, and was promoted to associate professor with tenure in 2012, and then full professor in 2017.
At Google, her team focuses on democratizing AI technology and lowering the barrier for entrance to businesses and developers , including the developments of products like AutoML.
Li is also known for her nonprofit work as the Co-Founder and Chairperson of nonprofit organization AI4ALL, whose mission is to educate the next generation of AI technologists, thinkers and leaders by promoting diversity and inclusion through human-centered AI principles.
SAILORS was an annual summer camp at Stanford dedicated to 9th grade high school girls in AI education and research, established in 2015 till it changed its name to AI4ALL @Stanford in 2017.
Li has lead the team of students and collaborators to organize the international competition on ImageNet recognition tasks called ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) between 2010 to 2017 in the academic community.
Li’s research in computer vision contributed significantly to a line of work called Natural Scene Understanding, or later, Story-telling of images.
- On 24. september 2021
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