Tag: research

Algorithms that describe a researcher’s mind

Algorithms that describe a researcher’s mind: (a) Work on the paper “closest to publication”. => downside: can reduce the willingness to solve difficult problems because they are farther from publication (b) Always switch to more interesting topic, when you see one. => downside: you’ll never get anything published (but upside can be that you learn […]

Tips on Data Imputation From Machine Learning Experts

Missing values are a critical issue in statistics and machine learning (which is “advanced statistics”). Data imputation deals with ways to fill those missing values. Andriy Burkov made this statement a few days ago [1]: “The best way to fill a missing value of an attribute is to build a classifier (if the attribute is […]

Social media marketing for researchers: How to promote your publications and reach the right people

Today the Social Computing group at Qatar Computing Research Institute had the pleasure of listening to the presentation of Luis Fernandez Luque about social media marketing for researchers. Luis talked about how to promote your publications and personal brand, as well as how to reach the right people on social media with your research. Luis […]

Startups! Are you using a ‘mean’ or an ‘outlier’ as a reference point?

Introduction This post is about startup thinking. In my dissertation about startup dilemmas [1], I argued that startups can exhibit what I call as ‘reference point bias’. My evidence was emerging from the failure narratives of startup founders, where they reported having experienced this condition. The reference point bias is a false analogy where the […]

Experimenting with IBM Watson Personality Insights: How accurate is it?

Introduction I ran an analysis with IBM Watson Personality Insights. It retrieved my tweets and analyzed their text content to describe me as a person. Doing so is easy – try it here: https://personality-insights-livedemo.mybluemix.net/ I’ll briefly discuss the accuracy of the findings in this post. TL;DR: The accuracy of IBM Watson is a split decision […]

The black sheep problem in machine learning

Just a picture of a black sheep. Introduction. Hal Daumé III wrote an interesting blog post about language bias and the black sheep problem. In the post, he defines the problem as follows: The “black sheep problem” is that if you were to try to guess what color most sheep were by looking and language […]

How to teach machines common sense? Solutions for ambiguity problem in artificial intelligence

Introduction The ambiguity problem illustrated: User: “Siri, call me an ambulance!” Siri: “Okay, I will call you ‘an ambulance’.” You’ll never reach the hospital, and end up bleeding to death. Solutions Two potential solutions: A. machine builds general knowledge (“common sense”) B. machine identifies ambiguity & asks for clarification from humans The whole “common sense” […]

Analyzing sentiment of topical dimensions in social media

Introduction Had an interesting chat with Sami Kuusela from Underhood.co. Based on that, got some inspiration for an analysis framework which I’ll briefly describe here. The model Figure 1 Identifying and analyzing topical text material The description User is interested in a given topic (e.g., Saara Aalto, or #saaraaalto). He enters the relevant keywords. The […]

Defining SMQs: Strategic Marketing Questions

Introduction Too often, marketing is thought of being advertising and nothing more. However, already Levitt (1960) and Kotler (1970) established that marketing is a strategic priority. Many organizations, perhaps due to lack of marketers in their executive boards, have since forgotten this imperative. Another reason for decreased importance of marketing is due to marketing scholars […]