why aren't all 8 resolved? a statement only gets an assessment when the public
record can support or contradict it. opinions and what-ifs never can, and 0 checkable
ones are still open, waiting for their date. predictions held up or didn't;
assertions are supported or contradicted. on every card:
▮▮▮▮▮ certainty ·
▮▮▮▮▮ debate potential. speakers are clickable
Insight
Ben Vigoda: Neurons and synapses are the wrong abstraction for AI
“Neurons and synapses is not the right abstraction. Models should be programs, specifically programs that simulate the system that generated the data.”
Insight
Ben Vigoda: Machine learning variables must include uncertainty and error bars
“Every variable that you're trying to infer in a program should come with an uncertainty. Neural networks today are just, they're just a number in the neuron. It's like an activation level. But you need error bars around those numbers.”
Insight
Ben Vigoda: Deep learning is excellent at instinct but poor at thought
“AI is really good at instinct. I mean deep learning, it's not so great at thought.”
Assertion Not checkable as stated
Changing deep learning target categories requires retraining models from scratch
“But if you want to change your columns, your categories, then you have to redo all your multiple choice tests and then retrain the system. And if you want to change the categories, you have to start over.”
What-if
Cell phones would drop calls 1,000x more often without Bayesian probability bounds
“You would drop calls a thousand times more often if there were, if cell phone receivers didn't found uncertainty in one patient.”
Assertion Not checkable as stated
A major New York bank receives 1.2 billion text messages annually
“One big bank in, in New York City that we've been talking to gets 1.2 billion of these little text messages a year.”
Assertion Not checkable as stated
Gamalon reduced an automaker's text processing costs from $1.25 to 10 cents
“We're able to work with them for a few weeks and generate a model, and they're actually, we're paying a dollar 25 per utterance to get them read. It took months to do it every year, and now it takes 25 milliseconds, and they pay 10 cents.”
Assertion Not publicly verifiable
Ben Vigoda began working on deep learning in 1989 at age 14
“I started doing deep learning in about 1989 with David Rumelhart when I was 14 working at Stanford.”