Collaborating Authors


#ICML2021 in tweets


Excited to share our new work on measuring and mitigating social biases in pretrained language models, to appear at #ICML2021!



The graph represents a network of 1,490 Twitter users whose tweets in the requested range contained "#iiot", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Tuesday, 13 July 2021 at 20:16 UTC. The requested start date was Tuesday, 13 July 2021 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 1-day, 18-hour, 24-minute period from Sunday, 11 July 2021 at 05:36 UTC to Tuesday, 13 July 2021 at 00:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

Following e-cigarette conversations on Twitter using artificial intelligence


The advertising of nicotine products is highly restricted, but social media allows a way for these products to be marketed to young people. What's more, e-cigarette flavorings make them particularly appealing to teenagers and young adults. A team of researchers have developed machine learning methods to track the conversations on social media about flavored products by one of the most popular e-cigarette brands, JUUL. "An increasing amount of discussions on e-cigarettes is taking place online, in particular in popular social media such as Twitter, Instagram, and Facebook. As the content related to e-cigarettes is often targeted at youth--who are also very active on many social media platforms--it is important to explore these conversations' says Dr. Aqdas Malik, postdoctoral researcher in the Department of Computer Science at Aalto University.

Finding Support for India During its COVID-19 Surge

CMU School of Computer Science

India and Pakistan have fought four wars in the past few decades, but when India faced an oxygen shortage in its hospitals during its recent COVID-19 surge, Pakistan offered to help. Finding these positive tweets, however, was not as easy as simply browsing the supportive hashtags or looking at the most popular posts. And Twitter's algorithm isn't tuned to surface the most positive tweets during a crisis. Ashique KhudaBukhsh of Carnegie Mellon University's Language Technologies Institute led a team of researchers who used machine learning to identify supportive tweets from Pakistan during India's COVID crisis. In the throes of a public health crisis, words of hope can be welcome medicine.

Twitter: On the Move to Improve Its Machine Learning Algorithms


Twitter has started a collaborative effort to improve its Machine Learning algorithms. The company-wide initiative is called Responsible ML. It aims to have responsible, responsive, and community-driven machine learning (ML) systems incorporated into its algorithms. Machine learning is a branch of computer science that makes computers decide on their own. It is a more advanced form of artificial intelligence (AI).

Elon Musk Announces Upcoming Tesla Artificial Intelligence Day


According to Tesla CEO Elon Musk, the company is making plans to have an Artificial Intelligence Day. He tweeted that it will happen in "about a month or so." We all joke about Musk time, so who knows exactly when this event might happen, but it's interesting to learn that it's in the works. Oftentimes, especially of late, many of Musk's most significant tweets are in reply to someone else. This sometimes buries them or at least makes them harder to find. As you're probably aware, most Musk tweets get a massive number of random replies.

STEP-EZ: Syntax Tree guided semantic ExPlanation for Explainable Zero-shot modeling of clinical depression symptoms from text Artificial Intelligence

We focus on exploring various approaches of Zero-Shot Learning (ZSL) and their explainability for a challenging yet important supervised learning task notorious for training data scarcity, i.e. Depression Symptoms Detection (DSD) from text. We start with a comprehensive synthesis of different components of our ZSL modeling and analysis of our ground truth samples and Depression symptom clues curation process with the help of a practicing clinician. We next analyze the accuracy of various state-of-the-art ZSL models and their potential enhancements for our task. Further, we sketch a framework for the use of ZSL for hierarchical text-based explanation mechanism, which we call, Syntax Tree-Guided Semantic Explanation (STEP). Finally, we summarize experiments from which we conclude that we can use ZSL models and achieve reasonable accuracy and explainability, measured by a proposed Explainability Index (EI). This work is, to our knowledge, the first work to exhaustively explore the efficacy of ZSL models for DSD task, both in terms of accuracy and explainability.



The graph represents a network of 4,327 Twitter users whose tweets in the requested range contained "#VR", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Tuesday, 15 June 2021 at 17:45 UTC. The requested start date was Tuesday, 15 June 2021 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 2-day, 5-hour, 6-minute period from Saturday, 12 June 2021 at 16:46 UTC to Monday, 14 June 2021 at 21:52 UTC.

Top 10 Applications of Artificial Intelligence


Here is a quick refresher about artificial intelligence. AI refers to intelligence by machines that simulate human intelligence; basically, it's the ability of a device or a program to think and learn. Now let's take a look at how different domains use AI. Let's have a look at the current state of AI. Have you ever thought about what would happen if Artificial intelligent machines try to create music and art?

Silhouettes and quasi residual plots for neural nets and tree-based classifiers Machine Learning

Classification by neural nets and by tree-based methods are powerful tools of machine learning. There exist interesting visualizations of the inner workings of these and other classifiers. Here we pursue a different goal, which is to visualize the cases being classified, either in training data or in test data. An important aspect is whether a case has been classified to its given class (label) or whether the classifier wants to assign it to different class. This is reflected in the (conditional and posterior) probability of the alternative class (PAC). A high PAC indicates label bias, i.e. the possibility that the case was mislabeled. The PAC is used to construct a silhouette plot which is similar in spirit to the silhouette plot for cluster analysis (Rousseeuw, 1987). The average silhouette width can be used to compare different classifications of the same dataset. We will also draw quasi residual plots of the PAC versus a data feature, which may lead to more insight in the data. One of these data features is how far each case lies from its given class. The graphical displays are illustrated and interpreted on benchmark data sets containing images, mixed features, and tweets.