Then there’s Eric Chong, a 37-year-old who has a background in dentistry and beforehand cofounded a startup that simplifies medical billing for dentists. He was positioned on the “machine” workforce.
“I’m gonna be honest and say I’m extremely relieved to be on the machine team,” Chong says.
At the hackathon, Chong was constructing software program that makes use of voice and face recognition to detect autism. Of course, my first query was: Wouldn’t there be a wealth of points with this, like biased information resulting in false positives?
“Short answer, yes,” Chong says. “I think that there are some false positives that may come out, but I think that with voice and with facial expression, I think we could actually improve the accuracy of early detection.”
The AGI ‘Tacover’
The coworking area, like many AI-related issues in San Francisco, has ties to efficient altruism.
If you’re not conversant in the motion via the bombshell fraud headlines, it seeks to maximise the great that may be performed utilizing individuals’ time, cash, and sources. The day after this occasion, the occasion area hosted a dialogue about methods to leverage YouTube “to communicate important ideas like why people should eat less meat.”
On the fourth flooring of the constructing, flyers coated the partitions—“AI 2027: Will AGI Tacover” reveals a bulletin for a taco get together that not too long ago handed, one other titled “Pro-Animal Coworking” gives no different context.
A half hour earlier than the submission deadline, coders munched vegan meatball subs from Ike’s and rushed to complete up their tasks. One flooring down, the judges began to reach: Brian Fioca and Shyamal Hitesh Anadkat from OpenAI’s Applied AI workforce, Marius Buleandra from Anthropic’s Applied AI workforce, and Varin Nair, an engineer from the AI startup Factory (which can also be cohosting the occasion).
As the judging kicked off, a member of the METR workforce, Nate Rush, confirmed me an Excel desk that tracked contestant scores, with AI-powered teams coloured inexperienced and human tasks coloured pink. Each group moved up and down the listing because the judges entered their choices. “Do you see it?” he requested me. No, I don’t—the mishmash of colours confirmed no clear winner even half an hour into the judging. That was his level. Much to everybody’s shock, man versus machine was an in depth race.
Show Time
In the tip, the finalists had been evenly break up: three from the “man” facet and three from the “machine.” After every demo, the group was requested to boost their fingers and guess whether or not the workforce had used AI.
First up was ViewSense, a instrument designed to assist visually impaired individuals navigate their environment by transcribing dwell videofeeds into textual content for a display screen reader to learn out loud. Given the quick construct time, it was technically spectacular, and 60 % of the room (by the emcee’s rely) believed it used AI. It didn’t.
Next was a workforce that constructed a platform for designing web sites with pen and paper, utilizing a digital camera to trace sketches in actual time—no AI concerned within the coding course of. The pianist venture superior to the finals with a system that permit customers add piano classes for AI-generated suggestions; it was on the machine facet. Another workforce showcased a instrument that generates warmth maps of code modifications: essential safety points present up in pink, whereas routine edits seem in inexperienced. This one did use AI.
https://www.wired.com/story/san-francisco-hackathon-man-vs-machine/