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) …
Online dating as a lesbian, for the most part, still involves having to deal with men. Many sites continue to surface guys as potential mates, despite setting your preferences otherwise. Up until recently, some of the nation has acted as though lesbianism didn't exist outside of porn and Ellen Degeneres, and acted as if girls only turn to dating women if they had a bad experience with a man. This, of course, is not true. If you're reading this, it's probably because you've experienced the frustration with dating sites and apps that claim to be inclusive to all sexual orientations, only to realize that some closeted quirks make it obvious that the straights are the target. Lesbian tinder is matching with a girl then seeing either "looking for friends:)" or "looking for a 3rd to join me & my man" in their bio. We're here to help you out. Our pick for the best app specifically for lesbians is HER: The fact that it's made by queer women for queer women is a breath of fresh air, and knowing that men showing up is next to impossible is so nice.
Accenture, a professional services company, will soon launch a new tool aimed at helping its customers find unfair bias in AI algorithms. Once such unfair bias is discovered, reps for the company have told the press, they can be removed. As scientists and engineers continue to improve AI technology, more companies are using AI-based tools to conduct business. Using AI applications to process credit applications is becoming routine, for example. But there has a been a concern that such applications might have biases built in, which produce results that could be construed as unfair.
After the fall of the Berlin Wall, East German citizens were offered the chance to read the files kept on them by the Stasi, the much-feared Communist-era secret police service. To date, it is estimated that only 10 percent have taken the opportunity. In 2007, James Watson, the co-discoverer of the structure of DNA, asked that he not be given any information about his APOE gene, one allele of which is a known risk factor for Alzheimer's disease. Most people tell pollsters that, given the choice, they would prefer not to know the date of their own death--or even the future dates of happy events. Each of these is an example of willful ignorance.
Kriti Sharma, vice president of AI and Ethics at Sage, shares what she wishes she knew as a teenager growing up in India and how tech can help underserved communities. You just built your first computer from scratch after reading a few books about them. But first, you need to endure a few more years of high school in India. I know you don't like school. But, I'm asking you to embrace your love for learning.
With trade wars, immigration crackdowns and polarized media, we seem to be in an endless winter of political and societal discontent. Ironically, the primary driver of this discontent, the state of American jobs, has rarely been this good. Unemployment has fallen to 3.8 percent, an 18-year low. For the first time this century, since the Bureau of Labor Statistics has been keeping tabs, there is, today, a job available for every unemployed American worker. This does not mean that everyone can have a dream job or that every job opening has its dream candidate, but it is an important milestone.
A team of researchers from the National Research Nuclear University MEPhI, the National Research Center Kurchatov Institute and the Voronezh State University has developed a new learning algorithm that allows a neural network to identify a writer's gender by the written text on a computer with up to 80 percent accuracy. This is a new development in the field of computational linguistics. The research was funded by a Russian Science Foundation grant. The findings were published in the Procedia Computer Science journal. Many scientific studies show that writing style can reflect certain characteristics of a writer – gender, physiological personality traits, and level of education.
Machine learning predictors are successfully deployed in applications ranging from disease diagnosis, to predicting credit scores, to image recognition. Even when the overall accuracy is high, the predictions often have systematic biases that harm specific subgroups, especially for subgroups that are minorities in the training data. We develop a rigorous framework of multiaccuracy auditing and post-processing to improve predictor accuracies across identifiable subgroups. Our algorithm, MultiaccuracyBoost, works in any setting where we have black-box access to a predictor and a relatively small set of labeled data for auditing. We prove guarantees on the convergence rate of the algorithm and show that it improves overall accuracy at each step. Importantly, if the initial model is accurate on an identifiable subgroup, then the post-processed model will be also. We demonstrate the effectiveness of this approach on diverse applications in image classification, finance, and population health. MultiaccuracyBoost can improve subpopulation accuracy (e.g. for `black women') even when the sensitive features (e.g. `race', `gender') are not known to the algorithm.
Combining complementary information from multiple modalities is intuitively appealing for improving the performance of learning-based approaches. However, it is challenging to fully leverage different modalities due to practical challenges such as varying levels of noise and conflicts between modalities. Existing methods do not adopt a joint approach to capturing synergies between the modalities while simultaneously filtering noise and resolving conflicts on a per sample basis. In this work we propose a novel deep neural network based technique that multiplicatively combines information from different source modalities. Thus the model training process automatically focuses on information from more reliable modalities while reducing emphasis on the less reliable modalities. Furthermore, we propose an extension that multiplicatively combines not only the single-source modalities, but a set of mixtured source modalities to better capture cross-modal signal correlations. We demonstrate the effectiveness of our proposed technique by presenting empirical results on three multimodal classification tasks from different domains. The results show consistent accuracy improvements on all three tasks.
One of the main concerns with AI technologies today is the fear that they will propagate the various biases we already have in society. A recent Stanford study turned things around, however, and highlighted how AI can also turn the mirror onto society and shed light on the biases that exist within it. The study utilized word embeddings to map relationships and associations between words and, through that measure, the changes in gender and ethnic stereotypes over the last century in the United States. The algorithms were fed text from a huge canon of books, newspapers, and other texts, while comparing these with official census demographic data and societal changes, such as the women's movement. The researchers used embedding to single out specific occupations and adjectives that tended to be biased toward women or ethnic groups each decade from 1900 to the present day.
The goal of Author Profiling (AP) is to identify demographic aspects (e.g., age, gender) from a given set of authors by analyzing their written texts. Recently, the AP task has gained interest in many problems related to computer forensics, psychology, marketing, but specially in those related with social media exploitation. As known, social media data is shared through a wide range of modalities (e.g., text, images and audio), representing valuable information to be exploited for extracting valuable insights from users. Nevertheless, most of the current work in AP using social media data has been devoted to analyze textual information only, and there are very few works that have started exploring the gender identification using visual information. Contrastingly, this paper focuses in exploiting the visual modality to perform both age and gender identification in social media, specifically in Twitter. Our goal is to evaluate the pertinence of using visual information in solving the AP task. Accordingly, we have extended the Twitter corpus from PAN 2014, incorporating posted images from all the users, making a distinction between tweeted and retweeted images. Performed experiments provide interesting evidence on the usefulness of visual information in comparison with traditional textual representations for the AP task.