Thoughts
I have a short article out in gnovis, my MA program's interdisciplinary academic journal! It's a very quick pass through research I did for an independent study in fall 2017. Head on over to gnovis's site to read about how language can stump computers, and what that tells us about the limits of our knowledge about the world and ourselves.
UPDATE: I presented about the research on which that snippet was based at the 18th annual STGlobal Conference.
But how do you really feel?
I have a lot of topics I want to look into when it comes to the amorphous idea of AI. One I'm probably not going to get to before I wrap up my MA program is the issue of public opinion, but it's going to be a continuing interest.
A number of people have written about the "mental models" that the public has (Ryan Calo is one). These are the images that mention of "AI" conjures up in people's minds. The visions are often shaped by movies, television, and books, and not so much by the flood of information coming out about the many exciting but more practical ways these computing advances can be used in daily life. (That's part of the reason I'm working on getting Everyday AI off the ground.)
A few info points jumped out at me recently as warranting deeper investigation when it comes to public opinion.
MIT Tech Review started tracking all of the estimates of job losses and creation from automation, and the numbers are all over the map (they vary by tens of millions of jobs).
Meanwhile, NPR/Maris recently came out with a poll in which 94% of full-time workers said it was not very likely/not likely at all that they'd lose their job due to automation. But a slightly older Pew poll indicated that 72% of people are worried about a future in which computers can do a lot of human jobs.
I'd love to have the time to dive into this more, statistically and anecdotally. It's common to see polls and stories in which people expect a certain thing to happen, but not to happen to them. But what factors are influencing these polling outcomes? Lack of exposure to these topics? Psychological factors? Socioeconomics? Type of information consumed about the topics?
If you have any stories to share or you're aware of anyone that has done good digging into the factors that influence opinion on automation or AI in general, I'd appreciate tips!
My brain works better when I get it all out.
I'm doing research this semester on machine learning and fairness. But before I can really dig into that, I need a firmer grasp of machine learning terminology. So I've been working my way through a paper called "A Few Useful Things to Know about Machine Learning" by Pedro Domingos.
Sounds sweet and gentle, right? It's not (for little ol' me at least).
Part of the problem is that I'm a bit of an island on this one. This research is for an independent study that covers a lot more than the details of machine learning algorithms. It's about the process of regulating these tools, and involves public policy, law, ethical frameworks, corporations, nongovernmental regulatory bodies, individual psychology, education, and more.
That means I'm making the most out of my interdisciplinary program. But it also means that I have to save up my big computer science questions and try to find a nice computer scientist to talk them out with me, because I often learn best when I can ask questions and interrogate responses. Until then, I'm left to my own devices. And that often involves writing out ideas to try to get them straight in my head.
Last night, I tackled this sentence by Googling things, searching the trusty Artificial Intelligence: A Modern Approach, and asking questions of a kind software engineer:
"A linear learner has high bias, because when the frontier between two classes is not a hyperplane the learner is unable to induce it."
This is describing an area in which data scientists need to be particularly careful when designing machine learning algorithms. Here's my translation:
"A computer model that is designed to find a way to explain patterns in example data by drawing a clear-cut, straight boundary between Things of Type 1 and Things of Type 2 can be way off in some cases. For one, the patterns in the data might be messier than can be represented by a straight boundary. It might be impossible to clearly say, 'Hey, everyone on that side of the line is this thing, and every on this side of the line is that thing.' Things that fall into the same category may be more mixed together.
Applying a computer model that tries to find a straight boundary to that kind of mixed-up data will give you some sort of model that works. It will find some sort of boundary. But it won't draw the right conclusions from the examples it is fed, and it won't tell you about the way the real world works. This is one reason it is important to make sure you understand your data and are looking for the right kinds of patterns."
And here's how I got to that... (If you're a machine learning person and happen to be reading this, please let me know if I got anything wrong so I can learn!)
Turtles can be teachers.
UPDATE: Project complete! Check it out here.
At Georgetown, I'm exploring a number of things that could be lumped under the heading of educational technologies. I'm trying to get both a broad and deep view of what's out there and what could be integrated into non-educational educational environments to help people develop problem-solving and critical-thinking skills. These things range from scalable and inexpensive tech like video games and simulations that don't require serious processing power to the less accessible world of VR headsets to the principles of human-computer interaction, user interface design, and more.
I'm particularly excited that I get to take a closer look at Seymour Papert's work in education this semester. Papert, among other things, is famous for creating the Logo educational programming language and accompanying floor turtles, which children could use to learn by doing. Much of his work was focused on children, but his ideas have been widely applied and have influenced many, including Alan Kay's Dynabook.
For the next couple of months, I'll be working with a group to dig into Papert's constructionist theories and see how they're being applied in educational robotics today. If you're curious, I've included my project proposal below.
That time Becky got really interested in stuff that happened at Xerox PARC half a century ago.
I took a class last semester with an intimidating title: Semiotics and Cognitive Technologies. It ended up being a revelation.
Bear with me for just a moment while I spew some words that might not be familiar. We covered a lot of ground, moving from humans' first use of tools through extended cognition and Engelbart to embodied technologies and artificial intelligence. Along the way, we applied the theories of a really smart, pretty eccentric guy named C. S. Peirce. He operated in the field of semiotics, along with many others, and was set on figuring out a system of signs and symbolic logic that could be applied all forms of human behavior.
All that basically means we looked at how humans make sense of the world around them. More specifically, we looked at how humans use things that they have created to learn, share memories, and build up the communal store of knowledge for current and future generations. That includes stone axes and beads as well as early computers and virtual reality technology.
The whole class shifted the way I think about the world. But I found myself especially interested in the application of these principles to interface design, particularly the work of Alan Kay at Xerox PARC and his influences. They looked at computing devices not just as tools humans could use to do work, but as partners of a sort in a symbiotic relationship. That was the beginning of personal computing and the drive to create devices with which humans could interact. Machines that could be integrated more seamlessly into normal human life than, say, room-sized computers that performed a series of calculations using punched cards.
More on all this later. For now, though, feel free to take a look at my final project for the class: "Big Ideas and Small Revolutions." It's just a first draft. I plan to work and rework this base as I move through my studies at Georgetown.
