Robin Sloan is a programmer and novelist whose books like Sourdough and Mr Penumbra's 24-Hour Bookstore are rich and evocative blends of self-aware nerdy playfulness and magical speculation. Read the rest
Pete Warden (previously) writes persuasively that machine learning companies could make a ton of money by turning to data-compression: for example, ML systems could convert your speech to text, then back into speech using a high-fidelity facsimile of your voice at the other end, saving enormous amounts of bandwidth in between. Read the rest
Sarah Gailey's micro-short-story STET is a beautiful piece of innovative storytelling that perfectly blends the three ingredients for a perfect piece of science fiction: sharply observed technological speculation that reflects on our present moment; a narrative arc for characters we sympathize with; and a sting in the tail that will stay with you long after the story's been read. Read the rest
Shitty math kills: shitty math "proved" that being selfish produced optimal outcomes and torched the planet; shitty math rains hellfire missiles down on innocents; in the 1960s, shitty math drove the "hamlet pacification program," producing 90,000 pages a month in data about the endless hail of bombs the US dropped on Vietnam. Read the rest
Some parts of machine learning are incredibly esoteric and hard to grasp, surprising even seasoned computer science pros; other parts of it are just the same problems that programmers have contended with since the earliest days of computation. The problem Amazon had with its machine-learning-based system for screening job applicants was the latter. Read the rest
A (somewhat dubious) survey of 850 business executives for firms of 500 or more employees "with involvement in the decision making process regarding customer experience in their organization" and 4,500 consumers "who have contacted a brand during the last six months with an enquiry or issue to be resolved" found a vast gap between how satisfied the executives believed their customers were and how the customers felt about their interactions. Read the rest
In All You Need is “Love”: Evading Hate Speech Detection, a Finnish-Italian computer science research team describe their research on evading hate-speech detection algorithms; their work will be presented next month in Toronto at the ACM Workshop on Artificial Intelligence and Security. Read the rest
Machine learning image classifiers use context clues to help understand the contents of a room, for example, if they manage to identify a dining-room table with a high degree of confidence, that can help resolve ambiguity about other objects nearby, identifying them as chairs. Read the rest
The NYPD's secretive Lower Manhattan Security Coordination Center uses software from IBM in its video analytics system, which allows cops to automatically scan surveillance footage for machine-generated labels that identify clothing and other identifying classifiers. Read the rest
A research team wrote about how they trained a machine-learning AI to generate images from text descriptions. When fed birds as its dataset, it got very good at painting birds...
... But the more you feed it, the crazier it gets.
More collected here, and you can try it yourself thanks to Chris Valenzuela's online implementation.
Here are my efforts.
James Mickens (previously) has a well-deserved reputation for being the information security world's funniest speaker, and if that were all he did, he would still be worth listening to. Read the rest
Quicksilver is a machine-learning tool from AI startup Primer: it used 30,000 Wikipedia entries to create a model that allowed it to identify the characteristics that make a scientist noteworthy enough for encyclopedic inclusion; then it mined the academic search-engine Semantic Scholar to identify the 200,000 scholars in a variety of fields; now it is systematically composing draft Wikipedia entries for scholars on its list who are missing from the encyclopedia. Read the rest
One of the holy grails of computer science is unsupervised machine learning, where you tell an algorithm what goal you want it to attain, and give it some data to practice on, and the algorithm uses statistics to invent surprising ways of solving your problem. Read the rest
The theme of this year's Burning Man is I, Robot, which focuses "on the many forms of artificial intelligence that permeate our lives..." So, naturally, someone trained a neural network to come up with some camp names.
It spit out believable names like Spankles, Astro Sparkin, and Space Rock Screamin Camp, as well as weirder names like Corn Viral Hammers, Wiq Renames Spaghette, and Hellball Lounge. Then it went with some truly bizarre ones like Cohnie Stacefur Ass Chaos, Sir Liberains the Wreck Middle, and Awes Orpoop.
The woman behind the experiment, research scientist Janelle Shane, writes:
Thanks to an anonymous burner, I had a list of 1593 past Burning Man camps to feed to a neural network. A neural network is a kind of machine learning algorithm that learns to imitate the data it sees. My starting point was a textgen-rnn neural net that had been previously trained on metal bands and roller derby names, so it had a few ideas of its own to bring to the table. It did not disappoint.
There's a bunch more of these machine-learned camp names over at Shane's site.
Let's hope life imitates art and some Burners out there actually create one (or more) of these camps this year on the playa!
Image via simon of the playa
Thanks, Dan S.! Read the rest
Last week, I sent an SMS to our babysitter that said, "Hey, are you free to sit on," and rather than offering autocomplete suggestions like "Saturday" or "Friday," the default Android keyboard suggested "on my face and." Read the rest
Yandex is Russia's answer to Weibo, an everything-under-one-(semi-state-controlled)-roof online service, and its answer to Alexa is Alisa. Read the rest
Research into the shittiness of voice assistants zeroed in on a problem that many people were all-too-aware of: the inability of these devices to recognize "accented" speech ("accented" in quotes because there is no one formally correct English, and the most widely spoken English variants, such as Indian English, fall into this "accented" category). Read the rest
