Recitations from Tel-Aviv University introductory course to computer science, assembled as IPython notebooks by Yoav Ram. Exploratory Computing with Python, a set of 15 Notebooks that cover exploratory computing, data analysis, and visualization. No prior programming knowledge required. Each Notebook includes a number of exercises (with answers) that should take less than 4 hours to complete. Developed by Mark Bakker for undergraduate engineering students at the Delft University of Technology.
Not many scientists get solicited for photo ops, but for Daphne Koller it's a regular occurrence. "It happens at pretty much any event that has tech people," Koller says when asked about one recent snapshot. It's not like I feel like this is something I deserve." Selfie requests are just one sign of Koller's stardom, earned from more than 20 years bridging computer science, biology and education. She chalked up a string of accolades along the way: getting a master's degree from Jerusalem's Hebrew University at 18; becoming a Stanford University professor focused on machine learning at 26; winning, nearly a decade later, a Mac Arthur "genius grant" for research that combined artificial intelligence and genomics; and cofounding $1 billion (valuation) Coursera, an early platform to let people around the world take university classes for free. The next act for this 51-year-old innovator: Insitro, a firm in South San Francisco that aims to find new drugs by sorting through masses of data. If it succeeds, it will have overturned how drugs get discovered. Lab biologists typically focus on a few specific proteins as drug targets. If those fail, data scientists make suggestions for others to try. Insitro, on the other hand, wants to collect much more data before the biologists go off on their hunt. It will leverage advances in bioengineering (such as Crispr gene editing) and in software that enables computers to see things that escape humans. Koller describes her aha moment this way: "Machine learning is now doing amazing things if you give it enough data.
New research suggests that computer models could help doctors achieve greater accuracy in the diagnosis of cancer and other diseases. A research team from Beth Israel Deaconess Medical Center (BIDMC) and Harvard Medical School (HMS) have developed an artificial intelligence (AI) system which is able to train computers to analyse pathologic image data [PDF]. The scientists hope that the programme could one day aid in diagnosing disease. 'Our AI method is based on deep learning, a machine-learning algorithm used for a range of applications including speech recognition and image recognition,' explained Andrew Beck, director of bioinformatics at the Cancer Research Institute at BIDMC and associate professor at HMS. He added: 'This approach teaches machines to interpret the complex patterns and structure observed in real-life data by building multi-layer artificial neural networks, in a process which is thought to show similarities with the learning process that occurs in layers of neurons in the brain's neocortex, the region where thinking occurs.'
Pathologists have been largely diagnosing disease the same way for the past 100 years, by manually reviewing images under a microscope. But new work suggests that computers can help doctors improve accuracy and significantly change the way cancer and other diseases are diagnosed. A research team from Harvard Medical School and Beth Israel Deaconess Medical Center and recently developed artificial intelligence (AI) methods aimed at training computers to interpret pathology images, with the long-term goal of building AI-powered systems to make pathologic diagnoses more accurate. "Our AI method is based on deep learning, a machine-learning algorithm used for a range of applications including speech recognition and image recognition," explained pathologist Andrew Beck, HMS associate professor of pathology and director of bioinformatics at the Cancer Research Institute at Beth Israel Deaconess. "This approach teaches machines to interpret the complex patterns and structure observed in real-life data by building multi-layer artificial neural networks, in a process which is thought to show similarities with the learning process that occurs in layers of neurons in the brain's neocortex, the region where thinking occurs."
For cognitive scientists, neurobiologists, and even some physicists, consciousness presents a unique and alluring problem. Although we know we are conscious, we know almost nothing about how it arises out of inanimate matter, and from where in the brain it comes from. Now, a team of researchers led by neurologists at Harvard Medical School's Beth Israel Deaconess Medical Center (BIDMC) believe it has discovered the physical foundations of consciousness. In a study published in the latest edition of the journal Neurology, the researchers pinpointed regions of the brain that appear to work together to create consciousness. "For the first time, we have found a connection between the brainstem region involved in arousal and regions involved in awareness, two prerequisites for consciousness," lead researcher Michael Fox from BIDMC, said in a statement.