Kiran 8700 math pdf free download

These gradients have been erased in the logic of ImageNet. Everything is flattened out and pinned to a label, like taxidermy butterflies in a display case. The results can be problematic, illogical, and cruel, especially when it comes to labels applied to people. With these highly populated categories, we can already begin to see the outlines of a worldview. ImageNet classifies people into a huge range of types including race, nationality, profession, economic status, behaviour, character, and even morality.

Other people are labeled by their careers or hobbies: there are Boy Scouts, cheerleaders, cognitive neuroscientists, hairdressers, intelligence analysts, mythologists, retailers, retirees, and so on. There are many racist slurs and misogynistic terms. Of course, ImageNet was typically used for object recognition—so the Person category was rarely discussed at technical conferences, nor has it received much public attention. However, this complex architecture of images of real people, tagged with often offensive labels, has been publicly available on the internet for a decade.

ImageNet is an object lesson, if you will, in what happens when people are categorized like objects. And this practice has only become more common in recent years, often inside the big AI companies, where there is no way for outsiders to see how images are being ordered and classified. The ImageNet dataset is typically used for object recognition.

The result of that experiment is ImageNet Roulette. Proper nouns were removed. When a user uploads a picture, the application first runs a face detector to locate any faces. If it finds any, it sends them to the Caffe model for classification. The application then returns the original images with a bounding box showing the detected face and the label the classifier has assigned to the image.

If no faces are detected, the application sends the entire scene to the Caffe model and returns an image with a label in the upper left corner.

As we have shown, ImageNet contains a number of problematic, offensive, and bizarre categories. Hence, the results ImageNet Roulette returns often draw upon those categories. That is by design: we want to shed light on what happens when technical systems are trained using problematic training data.

AI classifications of people are rarely made visible to the people being classified. ImageNet Roulette provides a glimpse into that process—and to show how things can go wrong. Images are laden with potential meanings, irresolvable questions, and contradictions.

One photograph shows a dark-skinned toddler wearing tattered and dirty clothes and clutching a soot-stained doll. The image is completely devoid of context. Who is this child? Where are they? But some labels are just nonsensical.

A woman sleeps in an airplane seat, her right arm protectively curled around her pregnant stomach. At the image layer of the training set, like everywhere else, we find assumptions, politics, and worldviews.

Of course, these sorts of assumptions have their own dark histories and attendant politics. This was done to capture and pathologize what was seen as deviant or criminal behavior, and make such behavior observable in the world. And as we shall see, not only have the underlying assumptions of physiognomy made a comeback with contemporary training sets, but indeed a number of training sets are designed to use algorithms and facial landmarks as latter-day calipers to conduct contemporary versions of craniometry.

For example, the UTKFace dataset produced by a group at the University of Tennessee at Knoxville consists of over 20, images of faces with annotations for age, gender, and race. The annotations for each image include an estimated age for each person, expressed in years from zero to Gender is a binary choice: either zero for male or one for female. The politics here are as obvious as they are troubling. The classificatory schema for race recalls many of the deeply problematic racial classifications of the twentieth century.

For example, the South African apartheid regime sought to classify the entire population into four categories: Black, White, Colored, or Indian. Flickr Creative Commons dataset, assembled specifically to achieve statistical parity among categories of skin tone, facial structure, age, and gender. The dataset itself continued the practice of collecting hundreds of thousands of images of unsuspecting people who had uploaded pictures to sites like Flickr.

The IBM DiF team asks whether age, gender, and skin color are truly sufficient in generating a dataset that can ensure fairness and accuracy, and concludes that even more classifications are needed.

So they move into truly strange territory: including facial symmetry and skull shapes to build a complete picture of the face. The researchers claim that the use of craniofacial features is justified because it captures much more granular information about a person's face than just gender, age, and skin color alone.

The paper accompanying the dataset specifically highlights prior work done to show that skin color is itself a weak predictor of race, but this begs the question of why moving to skull shapes is appropriate.

Craniometry was a leading methodological approach of biological determinism during the nineteenth century. However, here too the technical process of categorizing and classifying people is shown to be a political act.

The dataset also contains subjective annotations for age and gender, which are generated using three independent Amazon Turk workers for each image, similar to the methods used by ImageNet. Ultimately, beyond these deep methodological concerns, the concept and political history of diversity is being drained of its meaning and left to refer merely to expanded biological phenotyping. Diversity in this context just means a wider range of skull shapes and facial symmetries.

And even after all these attempts at expanding the ways people are classified, the Diversity in Faces set still relies on a binary classification for gender: people can only be labelled male or female. What are the assumptions undergirding visual AI systems? Second, it assumes a fixed and universal correspondences between images and concepts, appearances and essences. Moreover, the theory goes, that visual essence is discernible by using statistical methods to look for formal patterns across a collection of labeled images.

Finally, this approach assumes that all concrete nouns are created equally, and that many abstract nouns also express themselves concretely and visually i. The training sets of labeled images that are ubiquitous in contemporary computer vision and AI are built on a foundation of unsubstantiated and unstable epistemological and metaphysical assumptions about the nature of images, labels, categorization, and representation.

Furthermore, those epistemological and metaphysical assumptions hark back to historical approaches where people were visually assessed and classified as a tool of oppression and race science. The whole endeavor of collecting images, categorizing them, and labeling them is itself a form of politics, filled with questions about who gets to decide what images mean and what kinds of social and political work those representations perform. Suddenly, 1.

Gone were the pictures of cheerleaders, scuba divers, welders, altar boys, retirees, and pilots. When you search for these images, the ImageNet website responds with a statement that it is under maintenance, and only the categories used in the ImageNet competition are still included in the search results. The URLs for the original images are still downloadable.

Over the next few months, other image collections used in computer-vision and AI research also began to disappear. It turned out that the authors of the dataset had violated the terms of their Institutional Review Board approval by collecting images from people in public space, and by making their dataset publicly available. It was the largest public facial-recognition dataset in the world, and the people included were not just famous actors and politicians, but also journalists, activists, policy makers, academics, and artists.

On one hand, removing these problematic datasets from the internet may seem like a victory. The most obvious privacy and ethical violations are addressed by making them no longer accessible.

By erasing them completely, not only is a significant part of the history of AI lost, but researchers are unable to see how the assumptions, labels, and classificatory approaches have been replicated in new systems, or trace the provenance of skews and biases exhibited in working systems.

Facial-recognition and emotion-recognition AI systems are already propagating into hiring, education, and healthcare. They are part of security checks at airports and interview protocols at Fortune companies. Not being able to see the basis on which AI systems are trained removes an important forensic method to understand how they work. This has serious consequences. For example, a recent paper led by a PhD student at the University of Cambridge introduced a real-time drone surveillance system to identify violent individuals in public areas.

The team created the Aerial Violent Individual AVI Dataset, which consists of 2, images of people engaged in five activities: punching, stabbing, shooting, kicking, and strangling. In order to train their AI, they asked 25 volunteers between the ages of 18 and 25 to mimic these actions.

Watching the videos is almost comic. The actors stand far apart and perform strangely exaggerated gestures. The lead researcher, Amarjot Singh now at Stanford University , said he plans to test the AI system by flying drones over two major festivals, and potentially at national borders in India. There is clearly a significant difference between staged performances of violence and real-world cases. The researchers are training drones to recognize pantomimes of violence, with all of the misunderstandings that might come with that.

This is the problem of inaccessible or disappearing datasets. If they are, or were, being used in systems that play a role in everyday life, it is important to be able to study and understand the worldview they normalize. For the physiognomists, there was an underlying faith that the relationship between an image of a person and the character of that person was inscribed in the images themselves.

For Magritte, the meaning of images is relational, open to contestation. Struggles for justice have always been, in part, struggles over the meaning of images and representations. The pink triangle became a badge of pride, one of the most iconic symbols of queer-liberation movements.

Examples such as these—of people trying to define the meaning of their own representations—are everywhere in struggles for justice. There is much at stake in the architecture and contents of the training sets used in AI. They can promote or discriminate, approve or reject, render visible or invisible, judge or enforce. And so we need to examine them—because they are already used to examine us—and to have a wider public discussion about their consequences, rather than keeping it within academic corridors.

As training sets are increasingly part of our urban, legal, logistical, and commercial infrastructures, they have an important but underexamined role: the power to shape the world in their own images. The images in this essay and many more are part of the Fondazione Prada Osservatorio Training Humans exhibition, in Milan from September 12, through February 24, ; and at the Barbican Centre in London as part of the exhibition From Apple to Anomaly Pictures and Labels from September 26, through February 16, See Seymour A.

This language was very popular in the s and s, but, as the rules of decision-making and qualification became more complex, the language became less usable. At the same moment, the potential of using large training sets triggered a shift from this conceptual clustering to contemporary machine-learning approaches. Lyons et al. Queen Latifah revealed all to Parade about her struggle with burnout as well. Selena Gomez, just 26, took a career hiatus in to overcome burnout, explaining that she even switched off her cell phone for 90 days.

Finding time to recharge helped Latifah feel better mentally and physically. As I said, we tend to think of burnout as affecting doctors, teachers, office workers.

Now, forced productivity or not feeling a sense of purpose at a day job are just two of the reasons. On the contrary, many people are doing work they consider more important than ever. Many of us have been cut off from the people and activities that gave our life meaning before. But more than a year on, says Torsten Voigt, a sociologist at RWTH Aachen University in Germany who has researched burnout, this initial expenditure of energy may be catching up with us.

People in lower-paid jobs are in fact at particular risk of burnout, precisely because they are given less resources and less support. The world in which burnout was initially conceived was quite different to the one we live and work in today. The gig economy, automation, smartphones, zoom calls have transformed the way many of us work.

Though the World Health Organisation has not defined burnout as an occupational disease, the symptoms of burnout have become medical. Living through the pandemic has been making us sick. Any primary-care doctor will tell you that the physical-health toll of collective trauma — high blood pressure, headaches, herniated discs — have become quite common. And this has been before many people have returned to the office or resumed their pre-pandemic schedules. The mental-health crisis of the pandemic is also very real.

According to research by the Kaiser Family Foundation, a staggering four in 10 adults reported symptoms of anxiety and depression, a quadrupling of the pre-pandemic rate.

More than one in four mothers reported that the pandemic has had a major impact on their mental health. I do not suppose that people in Malta have been spared the crisis, though the percentages may be different.

This may be little comfort to those suffering, but this moment may pose an opportunity to rethink our roles at work and to reconsider our relationship with work — not just on an individual level, but on a societal one. Addressing burnout in a systemic way could mean reducing workloads, redistributing resources, or rethinking workplace hierarchies.

One suggestion, is to give people more autonomy in their roles so that they can play to their individual strengths — fitting the job around the person rather than making a person fit into the job. But it could also mean grappling with broader inequalities, in the workplace and beyond. This could mean improving a toxic company culture, adapting parental leave and childcare policies, or introducing more flexible working. It could be offering more social support to parents and carers. It could mean making sure everyone has decent working rights and a living wage.

Making system changes is difficult. Feeling like a zombie. Frans Camilleri 6 min. Same Author Economy. Frans Camilleri posted yesterday. Notify of. Inline Feedbacks. Most Read. Politics Vying for the centre-ground Jacob Callus posted today. Economy Inflation Premonition Frans Camilleri posted yesterday. Subscribe for updates. We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits.

Manage consent.