Despite the many amazing applications of statistics, machine learning, and visualization in industry, many attempts at doing "data science" are anything but scientific. Specifically, data science processes often lack reproducibility, a key tenet of science in general and a precursor to having true collaboration in a scientific (or engineering) community.
YouTube is consistently one of the most popular linked-to destinations across online platforms, has been blamed for promoting radicalization pathways through its recommendations, and has been used by foreign entities for political manipulation. While recent work has started to investigate YouTube’s role in these issues, open questions remain about YouTube’s ideological distribution, efficacy of its moderation, and its role in propagating disinformation.
The proliferation of social media has given rise to widespread study and speculation about the impact of digital technologies on politics, activism, and social change. Key among these debates is the role social media play in shaping the contemporary public sphere, and by proxy, democracy in the US and around the world. Maligned by some as “slacktivism,” I will argue social computing platforms such as Twitter create unique opportunities for traditionally excluded voices to challenge the terms of public debate.
There are now thousands of online social and commercial platforms available on the web. Yet, many of these face specific challenges causes by malicious actors such as trolls on Slashdot and Twitter, bots on Twitter, vandals on Wikipedia, sockpuppets in online discussion forums, and review fraud on e-commerce sites. In this talk, I will quickly provide some examples of the challenges caused by such malicious actors and then focus on two specific case studies.
The Defense Advanced Research Projects Agency (DARPA) was established in 1958 to prevent strategic surprise from negatively impacting U.S. national security and create strategic surprise for U.S. adversaries by maintaining the technologicalsuperiorityoftheU.S.military. Tofulfillitsmission,theAgencyreliesondiverseperformerstoapply multi-disciplinary approaches to both advance knowledge through basic research and create innovative technologies that address current practical problems through applied research.
The promise of the Human Genome Project continues to be realized with successes such as targeted therapeutics or genomic based medicine yet challenges still remain. The causes of genetic disorders continue to be discovered but for many cures remain elusive. This in part due to the complexity that underlies genome function from epigenetics to functions being uncovered in the noncoding regions of the genome. Examples of genetic variation will be discussed and the role of variation in human health.
In recent years, deep neural networks have improved the benchmarks of learning in areas such as vision and speech recognition. This improvement comes with a big price tag. Deep neural networks are very large supervised models and need huge quantities of labelled data at the time of training. In medical image analysis, labeling data is expensive. In certain imaging modalities, such as MRI and CT, 3D analysis and segmentation are required which increases the size of networks and limit our ability to use transfer learning from 2D models of the mainstream computer vision community.
Big-data Trained algorithms are increasingly used to make big decisions about people’s lives, such as who gets loans, whose resumes are reviewed by humans for possible employment, and even the length of prison terms. Algorithmically moderated platforms are making profound impacts on our personal and public relationships such as how we find a job, how we get our news, even how we find a spouse.