1,426 posts tagged “ai”
"AI is whatever hasn't been done yet"—Larry Tesler
2021
DALL·E: Creating Images from Text (via) “DALL·E is a 12-billion parameter version of GPT-3 trained to generate images from text descriptions, using a dataset of text–image pairs.”. The examples in this paper are astonishing—“an illustration of a baby daikon radish in a tutu walking a dog” generates exactly that.
2020
How GPT3 Works—Visualizations and Animations. Nice essay full of custom animations illustrating how GPT-3 actually works.
When I was curating my generated tweets, I estimated 30-40% of the tweets were usable comedically, a massive improvement over the 5-10% usability from my GPT-2 tweet generation. However, a 30-40% success rate implies a 60-70% failure rate, which is patently unsuitable for a production application.
Tempering Expectations for GPT-3 and OpenAI’s API. Insightful commentary on GPT-3 (which is producing some ridiculously cool demos at the moment thanks to the invite-only OpenAI API) from Max Woolf.
When data is messy. I love this story: a neural network trained on images was asked what the most significant pixels in pictures of tench (a kind of fish) were: it returned pictures of fingers on a green background, because most of the tench photos it had seen were fisherfolk showing off their catch.
gpt2-headlines.ipynb. My earliest experiment with GPT-2, using gpt-2-simple by Max Woolf to generate new New York Times headlines based on a GPT-2 fine-tuned against headlines from different decades of that newspaper.
2019
I have sometimes wondered how I would fare with a problem where the solution really isn’t in sight. I decided that I should give it a try before I get too old.
I’m going to work on artificial general intelligence (AGI).
I think it is possible, enormously valuable, and that I have a non-negligible chance of making a difference there, so by a Pascal’s Mugging sort of logic, I should be working on it.
2018
Without deep understanding of the basic tools needed to build and train new algorithms, he says, researchers creating AIs resort to hearsay, like medieval alchemists. "People gravitate around cargo-cult practices," relying on "folklore and magic spells," adds François Chollet, a computer scientist at Google in Mountain View, California.
Relational databases are a commodity now, but they power a much larger fraction of the world’s economy that AI ever will. And no company has a “relational database strategy”.
Text to Image (via) Ridiculously entertaining demo by Cris Valenzuela that feeds any text you type to a neural network that then attempts to generate an image for your text.
Half of the time when companies say they need "AI" what they really need is a SELECT clause with GROUP BY.
The synthetic voice of synthetic intelligence should sound synthetic. Successful spoofing of any kind destroys trust. When trust is gone, what remains becomes vicious fast.
Text Embedding Models Contain Bias. Here’s Why That Matters (via) Excellent discussion from the Google AI team of the enormous challenge of building machine learning models without accidentally encoding harmful bias in a way that cannot be easily detected.
Suppose a runaway success novel/tv/film franchise has "Bob" as the evil bad guy. Reams of fanfictions are written with "Bob" doing horrible things. People endlessly talk about how bad "Bob" is on twitter. Even the New York times writes about Bob latest depredations, when he plays off current events.
Your name is Bob. Suddenly all the AIs in the world associate your name with evil, death, killing, lying, stealing, fraud, and incest. AIs silently, slightly ding your essays, loan applications, uber driver applications, and everything you write online. And no one believes it's really happening. Or the powers that be think it's just a little accidental damage because the AI overall is still, overall doing a great job of sentiment analysis and fraud detection.
2010
Originally, however, speech recognition was going to lead to artificial intelligence. Computing pioneer Alan Turing suggested in 1950 that we “provide the machine with the best sense organs that money can buy, and then teach it to understand and speak English.” Over half a century later, artificial intelligence has become prerequisite to understanding speech. We have neither the chicken nor the egg.
2006
Solving Every Sudoku Puzzle. 100 lines of Python illustrating some key concepts in AI.