The rise of precision medicine is being augmented by greater use of deep learning technologies that provide predictive analytics for earlier diagnosis of a range of debilitating diseases. The latest example comes from researchers at Michigan-based Beaumont Health who used deep learning to analyze genomic DNA. The resulting simple blood test could be used to detect earlier onset of Alzheimer's disease. In a study published this week in the peer-reviewed scientific journal PLOS ONE, the researchers said their analysis discovered 152 "significant" genetic differences among Alzheimer's and healthy patients. Those biomarkers could be used to provide diagnoses before Alzheimer's symptoms develop and a patient's brain is irreversibly damaged.
Would you like to build predictive models using machine learning? That s precisely what you will learn in this course "Decision Trees, Random Forests and Gradient Boosting in R." My name is Carlos Martínez, I have a Ph.D. in Management from the University of St. Gallen in Switzerland. I have presented my research at some of the most prestigious academic conferences and doctoral colloquiums at the University of Tel Aviv, Politecnico di Milano, University of Halmstad, and MIT. Furthermore, I have co-authored more than 25 teaching cases, some of them included in the case bases of Harvard and Michigan. This is a very comprehensive course that includes presentations, tutorials, and assignments. The course has a practical approach based on the learning-by-doing method in which you will learn decision trees and ensemble methods based on decision trees using a real dataset.
How can you tell if an AI technology that's actually part of the AI revolution? What went wrong with artificial intelligence? This transformative technology was supposed to change everything. I've seen first-hand the incredible potential it has--both as a professor of computer science at the University of Michigan and as the founder of Clinc, ZeroShotBot, Myca.ai, a non-profit called ImpactfulAI, and several other AI-focused companies. So, why has it devolved into overhyped solutions, marketing noise, and an endless spin of the same, tired ideas?
Researchers from University of Minnesota, New York University, University of Pennsylvania, BI Norwegian Business School, University of Michigan, National Bureau of Economic Research, and University of North Carolina published a new paper in the Journal of Marketing that examines how advances in machine learning (ML) and blockchain can address inherent frictions in omnichannel marketing and raises many questions for practice and research. The study, forthcoming in the Journal of Marketing, is titled "Informational Challenges in Omnichannel Marketing Remedies and Future Research" and is authored by Koen Pauwels, Haitao (Tony) Cui, Catherine Tucker, Raghu Iyengar, S. Sriram, Anindya Ghose, Sriraman Venkataraman, and Hanna Halaburda. In this new study in the Journal of Marketing, researchers define omnichannel marketing as the "synergistic management of all customer touch points and channels both internal and external to the firm that ensures that the customer experience across channels and firm-side marketing activity, including marketing-mix and marketing communication (owned, paid, and earned), is optimized." Often viewed as the panacea for one-to-one marketing, omnichannel experiences data, marketing attribution, and consumer privacy frictions. The research team demonstrates that advances in machine learning (ML) and blockchain can address these frictions.
"What is fair?" feels like a rhetorical question. But for Michigan State University's Pang-Ning Tan, it's a question that demands an answer as artificial intelligence systems play a growing role in deciding who gets proper health care, a bank loan or a job. With funding from Amazon and the National Science Foundation, Tan has been working for the last year to teach artificial intelligence algorithms how to be more fair and recognize when they're being unfair. "We're trying to design AI systems that aren't just for computer science, but also bring value and benefits to society. So I started thinking about what are the areas that are really challenging to society right now," said Tan, a professor in MSU's Department of Computer Science and Engineering.
If you have a robot in close proximity to a person, and anything that goes wrong, that's a risk to that person," Raibert said. Things have gone wrong, at least on the job. In 2015, a 22-year-old man was killed while helping to set up a stationary robot at a Volkswagen plant in Germany. The robot pushed him against a metal plate and crushed him. In another case that year, a robot's arm malfunctioned, hit and crushed a woman's head in a Michigan auto plant.
A Tesla Model Y traveling on Autopilot crashed into a parked police car in Michigan while officers were investigating an accident involving a deer and another vehicle. The crash took place around 1:12 a.m. Lt. Brian Oleksyk of the Michigan State Police confirmed the Tesla was operating on its driver's assistance system when it crashed into a squad car that was parked partially in the right lane. The name of the driver has not been released. The 22-year-old who was operating the vehicle received citations for having a suspended license and failing to move over.
Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A Tesla driver with a suspended license using his car's Autopilot feature slammed into a Michigan State Police car parked on the side of a highway early Monday morning. The agency tweeted that the trooper was responding to an accident involving a car and a deer on I-96 in Eaton County and had the patrol car's emergency lights illuminated. The Tesla Model Y struck the driver's side rear corner of the police car damaging both vehicles.
In this technical talk, Chad Jenkins from the University of Michigan posed the following question: "who will pay the cost for the likely mistakes and potential misuse of AI systems?" As he states, "we are increasingly seeing how AI is having a pervasing impact on our lives, both for good and for bad. So, how do we ensure equal opportunity in science and technology?" It would be great to talk about the many compelling ideas, innovations, and new questions emerging in robotics research. I am fascinated by the ongoing NeRF Explosion, prospects for declarative robot programming by demonstration, and potential for a reemergence of probabilistic generative inference.
The first challenge that IT pros need to beat when job-hunting is to get past the front door. Those doors, now more than ever, are often virtual: online job boards, networking sites like LinkedIn, corporate application tracking systems (ATS), and so forth. What's more opaque is the behind-the-scenes pre-screening that is applied – often in automated fashion – to a virtual pile of resumes. This typically leads to the culling of multiple potential candidates from consideration before a human hiring manager or recruiter even gives their resumes a passing look. Read also: 6 IT skills to focus on in 2021, from Michigan CIO of the Year winners. For job hunters, it's useful to think about the why behind this process.