The Cornell Daily Sun (https://cornellsun.com/tag/machine-learning/)
The SciFi Lab at Cornell has made a breakthrough by creating a miniature wristband camera, BodyTrak, which could play a role in changing the way self-tracking devices such as smartwatches are built in the future.
A recent breakthrough has been made in bio-acoustic deep learning techniques — a method for automated detection of animal sounds — at Cornell’s K. Lisa Yang Center for Conservation Bioacoustics. Dr. Shyam Madhusudhana, a postdoctoral researcher in the Lab of Ornithology, built a toolkit enabling bio-acousticians to create complex audio recognition models with just a few lines of code.
A Cornell research group led by Prof. Peter McMahon, applied and engineering physics,has successfully trained various physical systems to perform machine learning computations in the same way as a computer. The researchers have achieved this by turning physical systems, such as an electrical circuit or a Bluetooth speaker, into a physical neural network — a series of algorithms similar to the human brain, allowing computers to recognize patterns in artificial intelligence. Machine learning is at the forefront of scientific endeavors today. It is used for a host of real-life applications, from Siri to search optimization to Google translate. However, chip energy consumption constitutes a major issue in this field, since the execution of neural networks, forming the basis of machine learning, uses an immense amount of energy.
When typing a simple Google search on the Internet, a vast array of systems have the power to decide which links or topics occupy the coveted top results page. However, automated scoring systems may contain unconscious bias due to a variety of factors — system designers may bring their personal bias when designing algorithms or the data sets used for machine learning may already contain bias. The proliferation of rating or ranking systems in everyday life often leads to complaints of online misrepresentation. Cornell’s Due Process Clinic, an undergraduate “clinical” course designed to understand automated scoring systems such as credit scores and search engine rankings, started sending student researchers to collect qualitative data and build their own case studies on these systems.
Clinic director, Prof. Malte Ziewitz, science and technology studies, founded the clinic because he wanted to use legal frameworks to research non-legal situations. The clinic tries to assess how someone who would not have access to a public relations expert could deal with online backlash or poor reviews, hoping to understand the consequences of those who have been misrepresented or ranked improperly.
Medicine and artificial intelligence are ever-evolving fields at the forefront of scientific discovery. A new Cornell research group — Machine Learning in Medicine — aims to coalesce the two, with the goal of improving methods for disease detection and diagnosis. This endeavor is a collaboration between faculty at Cornell Tech and Weill Cornell Medicine, bringing together “researchers with common interests and complementary expertise.” MLIM’s work is primarily an interdisciplinary dialogue, bridging campuses and research fields.
“The idea was to link people with a machine learning background in Ithaca to [people working with] clinical data and hypotheses at Weill,” said Prof. Amy Kuceyeski, mathematics and radiology, one of the organizing members of the group. While Kuceyeski’s background is in mathematics, she started learning methods for modeling biological systems as a postdoctoral researcher at Weill. Seeing this as an area for innovation, Kuceyeski helped establish MLIM in 2018.
If games can just predict player input and operate on their own, are they truly interactive?
Very few of the students were experts in both microbiome biology and data science — many came from one specialty and learned through their teammates and the event mentors about the others.