My colleague Carly Stambaugh has created an amazingly poetic neural network. Here’s your chance to check out the latest in post-modernist poetry and learn some more about LSTMs.
A.I. and Big Data Could Power a New War on Poverty is the title of on op-ed in today’s New York Times by Elisabeth Mason. I fear that AI and Big Data is more likely to fuel a new War on the Poor unless a radical rethinking occurs. In fact this algorithmic War on the Poor seems to have been going on for quite some time and the Poor are not winning.
Mason posits that AI and Big Data provide three paths forward from the trap of inequality: 1. The ability to match people to available jobs; 2. the ability to deliver customized training that enables people to perform those jobs; and 3. the ability to algorithmically deliver social welfare programs in a more efficient manner.
The first objective seems within the realm of Indeed.com and LinkedIn’s recommendation algorithms and second — personalized training — has a long history in AI systems development. The problem is access: how do you get one of the “good middle-class jobs” in San Francisco when you live in Atlanta and attend a high school that lacks the coursework to prepare you for Stanford? How do you get access to an immersive 3D training environment when your family can’t afford to put down 100 a month for high speed internet and your school lacks the equipment also?
The third part of Mason’s strategy is the most problematic. We’ve seen AI (meaning machine learning and decision making algorithms) used to enforce biased sentencing practices; seen how skewed training data can lead to racial bias in facial recognition; and the use of data-driven methods in predatory lending has also been documented. These examples constitute the tip of a deep problem and still largely un-addressed problem in AI. In short, if the algorithms on which our hopes for transformation are pinned learn from data that reifies the structural racism at the root of social inequity, then we’re simply finding a more optimal route to oppression.
Before we hand over the lives and futures of the most vulnerable members of society to algorithms that we are still trying to fathom, we should strive first for accountability and transparency in algorithms. The efforts underway in New York City to insure algorithmic ethical accountability is one start.
But if machine learning and AI are the new tools of our age, we should empower all people to put the computational tools and conceptual frameworks of data science to work for them. Black Lives Matter activists took the social networking tools to organize protests and share video that has changed and empowered. What could a coming generation do with additional visualization and analytical tools?
It was the prospect of using AI to empower education that first attracted me to the field. I think that the emerging technology has some good to do. But the process must necessarily be participatory. When artists, educators, poets, activists, grocery store owners, gardeners — everyone — can be given access to the tools then I’ll bet on the human capacity to find new paths to expression and opportunity.
The Black in AI workshop at this year’s NIPS conference could be among the most important events in artificial intelligence this year.
Further, the societal threat posed by systems capable of intentionally exploiting racial fears and prejudice should be evident by now.
Developing AI that is free of the gender, racial and other prejudices that continue to mar our society is an immense task and as many have pointed out, there is no one algorithm or tool that will get us there. One part of the solution is opening up the field to scientists, developers, and thinkers from all backgrounds so that norms of oppression and exclusion are questioned and ultimately ushered into the museum.
In that sense, the Black in AI workshop is an important contribution. The stated goal of the workshop is to provide a forum to nurture and develop researchers who are Black — thus promoting the inclusivity of a field that shamefully homogenous.
Does this mean that the inclusion of Black people in AI will spell the end of racist AI? Probably not, but clearly the near exclusion of Black folk has given biased systems a free pass. Perhaps a Black AI researcher might be more inclined to raise concerns about algorithmic bias in facial training sets , or even design commercial facial recognition systems with ethnic diversity baked in. But more importantly, a Black blogger, professor, lecturer, developer might foster important shifts in the way their readers, students, and customers view the world. That has to be a step forward for humanity.
We journey to Lexington, South Carolina to see witness an eclipse.
My wife talks to a couple — two White women from Charlottesville, perhaps in their 60’s — they lament the terror that descended upon their peaceful, thoughtful town. A Jewish family lies on the grass next to us, the father discovering the beauty and mystery of childhood in the waning sun.
Telescopes in the parking lot near the nature center entrance track the sun. My daughter looks in wonder at red and orange orbs, under the gentle guidance of a woman astronomer. A nine-year old African American boy looks at sun spots in awe, peppering the astronomer with questions. She answers him thoughtfully. His mother says that he wants to be a scientist.
In this small pinpoint in the west of South Carolina — a small nature preserve called Saluda Shoals Park — people are speaking in Hindi, Spanish, and Italian, and Mandarin. Grandmothers the color of the 2pm night encourage their grandchildren to look up.
Young Brothers accentuate their designer athletic apparel with solar shades. The other astronomer gives an impromptu hands on talk about meteorites, looking on as the dark remnant of a star passes between white, tan, black, and brown hands. He gives talks to the schools around the state he says, for free, as a service because of his love of the universe.
As sky goes dark, the cicadas awake. My son is giddy with the excitement of being present in a singular communion with the eternal, absorbing the sublime colors of the solar halo and noting the presence of planets.
As the sun waxes, a father with his ‘fro and mother with dark straight hair and skin the color of sand walk past smiling taking their sleeping child home.
In that moment labels — the inadequate one-dimensional badges affixed to us used to define, oppress and limit — become like the shadows in the eclipsing sun. We become to each other human — familiar brothers, sisters — seeing each other in awe, gratitude, and wonder.
If you would like to add to it, just send me an email.
Why am I doing this? Last month in AI and the Souls of Black Folk, I tried to make a case that people from all walks of life — particularly those from historically oppressed groups — have a part to play in shaping how the technology evolves. I think that HBCUs can be a catalyst for making AI an inclusive and responsive undertaking.
The list is a starting point.
A summary of recent NLP work to enhance support happiness.
Our excellent support is a big part of what makes WordPress.com a compelling platform for so many. Each month, we respond to 60,000 support requests on topics ranging from plugins to mapping existing domains to WordPress.com. Some questions arise several times daily, while others require novel solutions from our Happiness Engineers who have a deep understanding of our products, a skill for asking the right questions, a commitment to support, inventiveness, and creativity.
Recently, in looking at how machine learning and natural language processing could be useful to Happiness Engineers (HEs) in responding to support questions, we discovered two places where these technologies offer value.
First, our Happiness Engineers often rely on tried and true responses to some of the more straightforward questions. We call these pre-defined responses predefs. Many HEs develop personal favorites, and some predefs eventually become part of our best practices based on their ability to succinctly express…
View original post 2,072 more words