Collaborating Authors


A Retrospective on Mutual Bootstrapping

AI Magazine

When we were invited to write a retrospective article about our AAAI-99 paper on mutual bootstrapping (Riloff and Jones 1999), our first reaction was hesitation because, well, that algorithm seems old and clunky now. But upon reflection, it shaped a great deal of subsequent work on bootstrapped learning for natural language processing, both by ourselves and others. So our second reaction was enthusiasm, for the opportunity to think about the path from 1999 to 2017 and to share the lessons that we learned about bootstrapped learning along the way. This article begins with a brief history of related research that preceded and inspired the mutual bootstrapping work, to position it with respect to that period of time. We then describe the general ideas and approach behind the mutual bootstrapping algorithm.

Amazon facial recognition falsely matches more than 100 politicians to arrested criminals

The Independent - Tech

Amazon's controversial facial recognition technology has incorrectly matched more than 100 photos of politicians in the UK and US to police mugshots, new tests have revealed. Amazon Rekognition uses artificial intelligence software to identify individuals from their facial structure. Customers include law enforcement and US government agencies like Immigration and Custome Enforcement (ICE). It is not the first time the software's accuracy has been called into question. In July 2018, the American Civil Liberties Union (ACLU) found 28 false matches between US Congress members and pictures of people arrested for a crime.

Your Ultimate Data Science Statistics & Mathematics Cheat Sheet


Classifier metrics are metrics used to evaluate the performance of machine learning classifiers -- models that put each training example into one of several discrete categories. Confusion Matrix is a matrix used to indicate a classifier's predictions on labels. It contains four cells, each corresponding to one combination of a predicted true or false and an actual true or false. Many classifier metrics are based on the confusion matrix, so it's helpful to keep an image of it stored in your mind. Sensitivity/Recall is the number of positives that were accurately predicted.

Using AI to predict retinal disease progression


However, we know there's still a lot to do – this work does not yet represent a product that could be implemented in routine clinical practice. While our model can make better predictions than clinical experts, there are many other factors to consider for such systems to be impactful in a clinical setting. While the model was trained and evaluated on a population representative of the largest eye hospital in Europe, additional work would be needed to evaluate performance in the context of very different demographics. A recent study examining the use of a different AI system in a clinical setting highlighted just some of the sociotechnical issues for such systems in practice. Another difficult point to contend with is that any prediction system will have a certain rate of false positives: that is, when a patient is found to have a condition, or predicted to develop one, that they don't actually have.

Everything You Wanted to Know About Machine Learning but Were Too Afraid to Ask


Machine Learning, AI, Deep Learning are buzz words being heard daily on TV, in workplaces, at gatherings, etc. Maybe you're a little bit embarrassed to ask what's Machine Learning or AI, or maybe you have the wrong understanding of Machine Learning. Either way that's okay because this article serves as an introduction to Machine Learning, I wrote it in a Q&A format so it becomes easy to follow and understand. Machine Learning is a subset of Artificial Intelligence (AI) and it's about writing software codes to enables computers (or machines in general) to get better at a given task on their own without human intervention. Some people argue that Machine Learning is a fancy way to say "Statistical Analysis" which is the science of collecting data and uncovering patterns and trends.

How Might AI and Chest Imaging Help Unravel COVID-19's Mysteries?


Artificial intelligence (AI) has the potential to expand the role of chest imaging in COVID-19 beyond diagnosis to enable risk stratification, treatment monitoring, and discovery of novel therapeutic targets. AI's power to generate models from large volumes of information – fusing molecular, clinical, epidemiological, and imaging data – may accelerate solutions to detect, contain, and treat COVID-19. Two healthcare workers fell ill in Wuhan, China, where the first Coronavirus Disease 2019 (COVID-19) case was reported. Both were 29 years old and were hospitalized after contracting the virus. One survived, the other died. In a global pandemic that has suddenly pushed doctors, scientists, and healthcare workers to the frontlines, why some patients are falling critically ill while others have minimal or no symptoms is one of the most mysterious aspects of the disease caused by Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2).

Confusion Matrix and it's 25 offspring: or the link between machine learning and epidemiology Dr. Yury Zablotski


For instance, an LR of 3 suggests that for every false positive, there are 3 true positives. The greater the value of the LR for a particular test, the more likely a positive test result is a true positive. On the other hand, an LR 1 would imply that an individual with a positive test result is more likely to be non-diseased than diseased. The rationale for the diagnostic odds ratio is that it is a single indicator of test performance (like accuracy and Youden's J index, explained below) which is independent of prevalence (unlike accuracy) and is presented as an odds ratio, which is familiar to epidemiologists. Similarly to a usual odds ratio, the diagnostic odds ratio ranges from zero to infinity, where DOR greater then one is already good, and the higher DOR goes, the better the test performs.

FDA investigates COVID-19 test with false negatives

PBS NewsHour

Food and Drug Administration Commissioner Steve Hahn says it will be up to the White House to determine whether it continues to use a coronavirus test that has falsely cleared patients of infection. Hahn Told CBS on Friday the FDA will keep "providing guidance to the White House regarding this test" but whether to keep using the test "will be a White House decision." The test is used daily at the White House to test President Donald Trump and key members of his staff, including the coronavirus task force. The FDA said late Thursday it was investigating preliminary data suggesting Abbott Laboratories' 15-minute test can miss COVID-19 cases, producing false negatives. Hahn told CBS the test is on the market and the FDA continues to "recommend its use or to have it available for use."

The Latest: FDA Investigating Test With False Negatives

U.S. News

Under head of state and ruling Communist Party leader Xi Jinping's leadership, China has been able to "put the outbreak under control through arduous efforts and has been gradually resuming economic and social life while undertaking prevention and control measures on a regular basis," Wang was quoted as saying in a phone call Thursday with the foreign ministers of Hungary, Estonia and Bosnia and Herzegovina.

How accurate are the results from self-testing for covid-19 at home?

New Scientist

IN THE UK, essential workers are now among those being sent home testing kits for coronavirus. This involves swabbing the inside of your own nose and the back of your throat, but how useful are the results? Studies from early in the outbreak in China have suggested that swabs taken by healthcare professionals may give a 30 per cent "false negative" rate, where infected people are told they don't have the virus (NEJM,; medRxiv, This has prompted claims that self-testing will give even more false negatives and could raise the risk of infected people spreading the virus. No test is perfect – swabbing technique and analysis errors can lead to inaccurate results.