Today we are launching a mini-site featuring our collection of short stories inspired by new developments in machine learning. Beginning with our fourth report, we started including a science-fiction story along with the technical and strategic overviews that are the bulk of each report. Using these stories, we can look at the technologies we profile from a different angle and explore their cultural implications.
The SciFi site includes the four stories we have included so far. We will continue to commission and publish a story with each new report, and we are working on plans to open the process up to a wider range of voices.
Below you’ll find some background and interpretation for each of the stories. I can speak authoritatively about the intent for the two that I wrote, but for the others, please note that these are my interpretations.
Mars Terraform Expansion S-217
The first story we published is the least narrative of the bunch. The report focused on summarization as a gateway to making text quantifiable (and therefore computable). In the story, I focused on imagining the kinds of interfaces that capability could enable. The system in the story is able to identify the key points of different articles and synthesize them into a coherent summary. It is also able to identify their political orientation and place them in relation to one another.
I continue to be fascinated by the summarization and mapping possibilities that natural language processing technologies could open up. I’m especially interested by experiments in using deep learning and dimensionality reduction techniques to create visualizations of the relationships between different concepts (see for example, Sepand Ansari’s Encartopedia). If I were rewriting the story today, I think I would include some ambiguity about whether the system’s representations were truly reliable. For a much more comprehensive look at how a similar information processing system could be both extremely useful and also dangerous, check out Nick Harkaway’s mind-bending novel Gnomon.
In “BayesHead 5000”, Liam Sweeney imagines a customer service letter from the future. The report it appears in focused on probabilistic programming, which makes advanced statistical techniques more accessible to a broad programming audience. The story imagines a future where those techniques are made personally available via a brain implant (for a price).
A big part of the fun of the story is piecing together the kind of world it takes place in through the euphemistic sales-speak of the service representative. It’s a good reminder that whatever fantastical technologies we may invent, we’ll still probably relate to one another in the same annoyingly convoluted ways.
The story is inspired by George Saunders’ “I Can Speak”. Saunders is an expert at revealing (often through an unreliable narrator) the absurdity of conventions and systems that we take for granted. Hopefully, stories like these help us remember not to view the systems that develop around new capabilities as inevitable, but rather as things we are all involved in making – things for which we are collectively responsible.
The Definition of Success
The Definition of Success appears in our report on interpretability, which focuses on techniques for making deep learning models more interpretable. The story takes its inspiration very directly from the film Alien. Alien is already the story of uninterpretable AI (the ship computer, Mother). My main additions were imagining an interpretability interface, similar to the prototype we built to accompany the report, that revealed the features underlying the ship’s decisions. Based on this information, the protagonist is able to adjust the AI to provide more survival-oriented advice.
Like “BayesHead 5000”, the story highlights the degree to which larger economic and political systems direct the use of technology. It is not the story of an AI gone rogue. The ship has simply inherited the value system of its owner, Space Exploitation Corp. It is working as designed. Hopefully the story makes the point that interpretability is necessary not just in Matrix-style machine revolt situations, but also to make sure human-controlled systems are not acting contrary to basic decency.
Customers Who Haven’t Read Kafka Also Like
The most recent story, by Kent Szlauderbach, is inspired by Franz Kafka’s “An Imperial Message”. The story appeared in our report on Semantic Recommendations, which looked at building systems that used deep learning to consider the content of an item when making recommendations. Kafka is another writer interested in how we relate to the systems that surround us, making his work an excellent starting point for examining how we relate to algorithmic recommendations.
“The message,” in both the Kafka story and this one, can never be delivered. The story invokes the possibility that we could capture and quantify the true meaning of a story (“Say the most powerful computer in the nation sends a message, in a fatal error, containing the story’s true meaning to you, a modest user”), only to keep withdrawing that message outside our reach. For me, there’s a Zen koan thing happening, where the desire to pin down a fixed meaning is repeatedly denied, and through that I’m forced to reflect on why that denial makes me uncomfortable. The desire to quantify and classify on a large scale is a driving force behind the technology we develop. This story helps me recognize that desire in myself and think about its limits.
A continuing conversation
One thing I really like about the last three stories all having pretty direct influences on which they are based is that it shows how stories continue to be relevant in helping us think through the technology and systems that surround us. They’re part of a conversation that stretches back to (at least) Kafka writing at the beginning of the 20th century. As Annalee Newitz and Charlie Jane Anders discuss on the third episode of their excellent Our Opinions Are Correct podcast, sci-fi books that stand the test of time continue to be relevant not because of the precision of their predictions, but because they meaningfully engage with how we relate to technology as individuals and as a society. These stories are a part of that larger conversation.
More from the Blog
Apr 10 2018
by — Recommenders, generally associated with e-commerce, sift though a huge inventory of available items to find and recommend ones that a user will like. Different from search, recommenders rely on historical data to tease out user preference. How does a recommender accomplish this? In this post we explore building simple recommendation systems in PyTorch using the Movielens 100K data, which has 10...
Jun 26 2018
by — Today’s machines can identify objects in photographs, predict loan repayments or defaults, write short summaries of long articles, or recommend movies you may like. Up until now, machines have achieved mastery through laser-like focus; most machine learning algorithms today train models to master one task, and one task only. We are excited to introduce multi-task learning in our upcoming webina...
Jul 25 2016
These positions have been filled. We are not accepting further applications. For announcements of future opportunities, please subscribe to our mailing list. We’re excited to announce two fall internship opportunities, which are open to current undergraduate and graduate students, as well as recent graduates and career changers seeking an internship position. To apply, send your resume and cov...