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"OH WATERS, TEEM WITH MEDICINE TO KEEP MY BODY SAFE FROM HARM, SO THAT I MAY LONG SEE THE SUN." - Rig Veda
I frequently get asked in America why India’s caste system, a pre-feudalistic division of labor that assigns one’s line of work at birth, has persisted into the 21st century. I typically answer: the need of the privileged upper castes for cheap labor. But there is an even more tragic explanation, as I discovered during a recent visit to New Delhi while talking to Maya, the dalit or untouchable — the lowest of the four castes — who has serviced my family for 35 years. Maya herself clings to her caste because it still offers her the best possible life in India.
What’s puzzling about the caste system is that it endures without legal force. Unlike slavery, where whites actively relied on authorities to maintain their slave holdings, the caste system is an informal, self-perpetuating institution.
How? Consider Maya’s story.
Maya assigned herself to our house in 1977. We had no choice. If we wanted our trash picked up, bathrooms scrubbed and yards cleaned, Maya was it. Indians find dealing with other people’s refuse not just unpleasant, but polluting. Hence only dalits are willing to do this work, something that both stigmatizes them and gives them a stranglehold on the market. And they have transformed this stranglehold into an ironclad cartel that closes all other options for their customers.
When Maya got married at 16, her father-in-law paid another dalit $20 for her wedding gift: the “rights” to service 10 houses in our neighborhood, including ours. Maya has no formal deed to these “rights,” yet they are more inviolable than holy writ. Maya’s fellow dalits, who own the “rights” to other houses, can’t work in hers, just as she can’t work in theirs.
Doing so, Maya insists, would be tantamount to theft that would invite a well-deserved beating and ostracism by the dalit community. No one would help a “poacher” or attend her family functions like births, weddings or funerals.
This arrangement has guaranteed Maya a monthly income of $100 that, along with her husband’s job as a “gofer” at a government lab, has helped her raise three children and build a modest house with a bathroom, a prized feature among India’s poor. But Maya’s monopoly doesn’t give her just money. It also hands her clout to resist the upper-caste power structure, not always for noble reasons.
None of Maya’s employers dares challenge her work. Maya takes more days off for funerals every year than there are members in her extended family. Complaining, however, is not only pointless but perilous. It would result in stinking piles of garbage outside the complainer’s home for days. Every time my mother gets into spats with Maya over her sketchy scrubbing, my mother loses. One harsh word, and Maya boycotts our house until my mother cajoles her back. Nor is Maya the only sweeper, or jamadarni, with an attitude. All of New Delhi is carved up among Maya-style sweeper cartels and it is a rare house whose jamadarni is not a “big problem.”
But the price for this clout is the loss of inter-caste acceptability. Segregation has loosened considerably among the first three castes. But dalits are allowed to socialize with other castes only if they abandon trash-related work. Otherwise, every interaction involving them becomes subject to an apartheid-like social code.
Some of Maya’s houses, for example, have separate entrances that allow her to access bathrooms without having to enter the main house. Although the families have formed a genuine bond with her and treat her generously, plying her with lavish gifts during festivals, there are limits. They give her breakfast and lunch, but in separate dishes. Sitting at their table, sharing a meal, is forbidden. Not even my mother’s driver, a higher caste, would visit Maya and accept a glass of water, even though he is poorer than she.
Maya is resigned to such discrimination, but not her oldest son, 36. He holds a government job, works as a sales representative for an Amway-style company and dreams big. He is embarrassed by his mother and lies to his customers about her work. He makes enough money to support Maya and wants her to quit, but she will have none of it. She fears destitution and poverty more, she says, than she craves social respectability.
But the choice may not be hers much longer.
Upon retirement, she had planned to either pass her “business” to her children or sell it to another dalit for about $1,000. But about six months ago, municipal authorities started dispatching vans, Western-style, to collect trash from neighborhoods, the one service that protected Maya from obsolescence in an age of sophisticated home-cleaning gadgetry.
Maya and her fellow dalits held demonstrations outside the municipal commissioner’s office to stop the vans. They finally arrived at a compromise that lets Maya and her pals collect trash from individual homes and hand it to the vans for disposal. But Maya realizes that this arrangement won’t last. “I got branded as polluted and became unfit for other jobs, for what?” she wept. “To build a business that has now turned to dust?”
Her son, however, is pleased. He believes that this will finally force his siblings to develop skills for more respectable work instead of joining their mother. But Maya shakes her head.
And she might be right. Post-liberalization, the most dogged and determined dalits are able to escape their caste-assigned destiny and get rich. But for the vast majority, as Maya says, opportunities are better within the caste system than outside it.
When that changes, the system will die, but not until then.
AN ARTIFICIAL brain has taught itself to estimate the number of objects in an image without actually counting them, emulating abilities displayed by some animals including lions and fish, as well as humans.
Because the model was not preprogrammed with numerical capabilities, the feat suggests that this skill emerges due to general learning processes rather than number-specific mechanisms. "It answers the question of how numerosity emerges without teaching anything about numbers in the first place," says Marco Zorzi at the University of Padua in Italy, who led the work.
To investigate ANS, Zorzi and colleague Ivilin Stoianov used a computerised neural network that responds to images and generates new "fantasy" ones based on rules that it deduces from the original images. The software models a retina-like layer of neurons that fire in response to the raw pixels, plus two deeper layers that do more sophisticated processing based on signals from layers above.
The pair fed the network 51,800 images, each containing up to 32 rectangles of varying sizes. In response to each image, the program strengthened or weakened connections between neurons so that its image generation model was refined by the pattern it had just "seen". Zorzi likens it to "learning how to visualise what it has just experienced".
Infants demonstrate ANS without being taught, so the network was not preprogrammed with the concept of "amount". But when Zorzi and Stoianov looked at the network's behaviour, they discovered a subset of neurons in the deepest layer that fired more often as the number of objects in the image decreased. This suggested that the network had learned to estimate the number of objects in each image as part of its rules for generating images. This behaviour was independent of the total surface area of the objects, emphasising that the neurons were detecting number.