If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Women are not inherently better at multi-tasking - and that's according to scientists. A study examining the long-asserted myth has proved that men are just as capable of juggling numerous jobs simultaneously. In fact, despite years of claims to the contrary, it transpires that both genders are equally able, or unable, to do more than one task concurrently. A team of researchers led by Dr Patricia Hirsch of Germany's Aachen University reached the conclusion after analysing 48 men and 48 women, with an average age of 24, in letter or number identification tasks. Some participants were asked to pay attention to two tasks at once, known as concurrent multitasking.
In this study, we proposed a convolutional neural network model for gender prediction using English Twitter text as input. Ensemble of proposed model achieved an accuracy at 0.8237 on gender prediction and compared favorably with the state-of-the-art performance in a recent author profiling task. We further leveraged the trained models to predict the gender labels from an HPV vaccine related corpus and identified gender difference in public perceptions regarding HPV vaccine. The findings are largely consistent with previous survey-based studies.
Accurate diagnosis is crucial for preventing the progression of Parkinson's, as well as improving the quality of life with individuals with Parkinson's disease. In this paper, we develop a gender specific and age dependent classification method to diagnose the Parkinson's disease using the handwriting based measurements. The gender specific and age dependent classifier was observed significantly outperforming the generalized classifier. An improved accuracy of 83.75% (SD=1.63) with the female specific classifier, and 79.55% (SD=1.58) with the old age dependent classifier was observed in comparison to 75.76% (SD=1.17) accuracy with the generalized classifier. Finally, combining the age and gender information proved to be encouraging in classification. We performed a rigorous analysis to observe the dominance of gender specific and age dependent features for Parkinson's detection and ranked them using the support vector machine(SVM) ranking method. Distinct set of features were observed to be dominating for higher classification accuracy in different category of classification.
The World Economic Forum (WEF) publishes its report entitled, "Business Life and Gender Differences," every year. The report got a new name for the first time this year; as you can imagine, this title relates to artificial intelligence (AI). The WEF researched the rates of female AI experts in the workforce in 146 countries. Some important points in the report include caring for children at home, robots taking the place of workers at factories and offices being the most significant factors in women losing their jobs. The WEF Center for the New Economy and Society President Saadia Zahidi says that robotic and artificial intelligence technologies take place in fields where women traditionally work, such as management, customer relations and telemarketing.
The relationships are predicted from local polynomial regressions. Shaded areas indicate 95% confidence intervals. Preferences concerning time, risk, and social interactions systematically shape human behavior and contribute to differential economic and social outcomes between women and men. We present a global investigation of gender differences in six fundamental preferences. Our data consist of measures of willingness to take risks, patience, altruism, positive and negative reciprocity, and trust for 80,000 individuals in 76 representative country samples. Gender differences in preferences were positively related to economic development and gender equality. This finding suggests that greater availability of and gender-equal access to material and social resources favor the manifestation of gender-differentiated preferences across countries. Fundamental preferences such as altruism, risk-taking, reciprocity, patience, or trust constitute the foundation of choice theories and govern human behavior.
Wachs, Johannes (Central European University) | Hannak, Aniko (Central European University) | Vörös, András (Swiss Federal Institute of Technology in Zurich) | Daróczy, Bálint (Hungarian Academy of Sciences)
Online platforms are an increasingly popular tool for people to produce, promote or sell their work. However recent studies indicate that social disparities and biases present in the real world might transfer to online platforms and could be exacerbated by seemingly harmless design choices on the site (for example: recommendation systems or publicly visible success measures). In this paper we analyze an exclusive online community of teams of design professionals called Dribbble and investigate apparent differences in outcomes by gender. Overall, we find that men produce more work, and are able to show it to a larger audience thus receiving more likes. Some of this effect can be explained by the fact that women have different skills and design different images. Most importantly however, women and men position themselves differently in the Dribbble community. Our investigation of users' position in the social network shows that women have more clustered and gender homophilous following relations, which leads them to have smaller and more closely knit social networks. Overall, our study demonstrates that looking behind the apparent patterns of gender inequalities in online markets with the help of social networks and product differentiation helps us to better understand gender differences in success and failure.
Women are more modest than men in expressing accomplishments, referred to as the “feminine modesty effect”. Given the importance of highlighting accomplishments and skills for professional advancement, our research revisits the classical question of equal opportunity with a modern dataset to examine how women leverage LinkedIn, a professional social networking site. We first apply propensity score matching methods to identify a subset of similarly qualified female and male U.S. users who recently graduated/will be graduating (2011-2017) from a top-ranked MBA program as indicated on their LinkedIn profile. We then analyze gender differences in online self-promotion choices, an often overlooked aspect of understanding the role of gender in the professional hiring pipeline. Among matched subsets of female and male users, we find that females are less likely relative to males to utilize data fields that require writing in free-form such as the Summary and Job Description fields. However, we find for most universities that females and males are equally likely to include more structured data fields such as Honors and Skills, and for some universities females are more likely to include at least one Skill. This work begins to quantify gender biases in user-provided data and introduces important considerations for how self-presentation choices affect professional opportunity in online hiring platforms.
Computers and the Internet: Listening to Girls' Voices – Dorothy Ellen Wilcox concludes that "instead of socializing adolescent girls toward docility, non-hierarchical technology like the Internet may provide a discourse for development of higher-level cognitive skills and the ability to unmask inequities in power and politics."