Ben Vigoda

Founder & CEO, Product Genius AI · 1 appearance on the record.

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founderexecutivescientistengineerLinkedIn ↗benvigoda.com ↗

Ben Vigoda is an MIT-trained computer scientist who co-founded Lyric Semiconductor and Gamalon to advance Bayesian program synthesis. In addition to co-founding the medical non-profit Design that Matters, he currently leads Product Genius AI, building real-time learning AI models for e-commerce.

8statements → 4claims → 0claims resolved → 4/5average certainty → 2.12/5average debate potential →

1 not yet assessed 3 not checkable as stated how the 4 claims stand · each chip opens the sources

4 assertions · 3 insights · 1 what if · every statement was checked. The predictions and assertions are the 4 claims: statements the public record can support or contradict. 0 are resolved, 1 is not yet assessed, and 3 name no date, number or outcome precise enough to check. Everything else (opinions, insights, what ifs, disclosures) can never be settled by the record, so it carries no assessment.

The record, in short

What the tape says about how Ben argues and how the claims held up. Everything they said, and everything said about them, is in the tabs below.

How they sound: speaking style how? →

267 words/min while actually speaking · 27.6 um and uh per 1k words

No argument clarity score for Ben Vigoda: only 1 usable question→answer exchange on raw tape (a fair score needs 8+). We do not score a sample that small. Roundtable and news formats yield far fewer direct exchanges than interviews.

Measured by listening to the audio itself: 3,944 words across 1 episode of raw-level tape, transcribed verbatim with every um and uh kept, each one attributed only where the alignment onto our timed stream is unambiguous. These are measurements of speaking style. We do not rank them: across this corpus, fluency and argument quality are nearly uncorrelated (ρ≈0.2), and smooth talking does not signal clear thinking. How it's measured →

What they said8 Episodes1

Everything Ben Vigoda said on the MAD Podcast that made the record, most notable first. Filter by type, assessment or year in the ledger →

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.”

Ben Vigoda May 22, 2018 ▶ 7:46 A New Approach to Machine Intelligence // Ben Vigoda, Gamalon (FirstMark's Data Driven)

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.”

Ben Vigoda May 22, 2018 ▶ 8:03 A New Approach to Machine Intelligence // Ben Vigoda, Gamalon (FirstMark's Data Driven)

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.”

Ben Vigoda May 22, 2018 ▶ 19:13 A New Approach to Machine Intelligence // Ben Vigoda, Gamalon (FirstMark's Data Driven)

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.”

Ben Vigoda May 22, 2018 ▶ 4:38 A New Approach to Machine Intelligence // Ben Vigoda, Gamalon (FirstMark's Data Driven)

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.”

Ben Vigoda May 22, 2018 ▶ 21:46 A New Approach to Machine Intelligence // Ben Vigoda, Gamalon (FirstMark's Data Driven)

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.”

Ben Vigoda May 22, 2018 ▶ 2:16 A New Approach to Machine Intelligence // Ben Vigoda, Gamalon (FirstMark's Data Driven)

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.”

Ben Vigoda May 22, 2018 ▶ 17:18 A New Approach to Machine Intelligence // Ben Vigoda, Gamalon (FirstMark's Data Driven)

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.”

Ben Vigoda May 22, 2018 ▶ 0:39 A New Approach to Machine Intelligence // Ben Vigoda, Gamalon (FirstMark's Data Driven)

Appearances (1)

Episode	Date	Speaking time
A New Approach to Machine Intelligence // Ben Vigoda, Gamalon (FirstMark's Data Driven)	May 22, 2018	18m

Ben Vigoda

The record, in short

How they sound: speaking style how? →

Appearances (1)

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