Kevin Mandich

Head of Machine Learning, Sprig · 1 appearance on the record.

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engineerexecutivescientist@kevinmandich ↗LinkedIn ↗sprig.com ↗

Kevin Mandich leads machine learning and AI initiatives at Sprig, developing models to synthesize and derive automated insights from user feedback. He previously worked as a machine learning engineer at Incubit and Agari, following postdoctoral research at UC San Diego.

9statements → 2claims → 0claims resolved → 3.56/5average certainty → 2/5average debate potential → ≈4.5/5argument clarity, estimated →

2 not checkable as stated how the 2 claims stand · each chip opens the sources

1 prediction · 1 assertion · 1 opinion · 6 insights · every statement was checked. The prediction and assertion are the 2 claims: statements the public record can support or contradict. 0 are resolved, and 2 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 Kevin argues and how the claims held up. Everything they said, and everything said about them, is in the tabs below.

How they sound: not measured why? →

We measure speaking style by listening to the audio itself, and a fair number needs at least 2,000 words from one person on tape we have measured. There is too little of Kevin Mandich on measured tape to publish a rate. This says nothing about how they speak.

What they said9 Episodes1

Everything Kevin Mandich said on In Depth that made the record, most notable first. Filter by type, assessment or year in the ledger →

Opinion

Tech is likely near the peak of the current generative AI hype cycle

“I think what we're going through now is we're probably close to the peak of the current hype cycle that was introduced earlier this year with, you know, ChatGPT and everything.”

Kevin Mandich Sep 7, 2023 ▶ 21:21 A guide to building product in a post-LLM world | Ryan Glasgow and Kevin Mandich from Sprig

Insight

Word clouds and topic modeling fail to capture nuanced customer feedback

“You know, people have done word clouds, word counts topic modeling, but, you know, none of those really capture the nuance of what people are saying. And you know, it doesn't account for the fact that people could be saying multiple things per response.”

Kevin Mandich Sep 7, 2023 ▶ 5:28 A guide to building product in a post-LLM world | Ryan Glasgow and Kevin Mandich from Sprig

Insight

Machine learning engineering skills transfer broadly across vision, NLP, and audio domains

“One of the things about machine learning and AI in this industry is that there's a lot of crossover between these different data domains. You know, if you're able to solve a computer vision problem pretty well, a lot of those skills are transferable to natural…”

Kevin Mandich Sep 7, 2023 ▶ 6:27 A guide to building product in a post-LLM world | Ryan Glasgow and Kevin Mandich from Sprig

Insight

Mandich: User research synthesis lacks an objective universal ground truth

“You can take two expert user researchers, give them the same list of, you know, 500 responses, tell them to distill them down into 10 actionable takeaways, and those 10 will be completely different, or even if they are the same 10 takeaways, the responses that…”

Kevin Mandich Sep 7, 2023 ▶ 10:52 A guide to building product in a post-LLM world | Ryan Glasgow and Kevin Mandich from Sprig

Insight

Analyzing machine learning data in batch produces better results than real-time streaming

“From an ML point of view. I think it's advantageous to analyze as much data in batch as possible. You tend to get more information to work with.”

Kevin Mandich Sep 7, 2023 ▶ 12:45 A guide to building product in a post-LLM world | Ryan Glasgow and Kevin Mandich from Sprig

Insight

Complex non-deterministic LLM tasks still require manual human evaluation

“And that's just, that's something that's really hard to evaluate in an automated manner, at least right now, because it's a more complex task, because the output is, you know, non-deterministic and freeform. For now, it really just does require some manual eva…”

Kevin Mandich Sep 7, 2023 ▶ 32:23 A guide to building product in a post-LLM world | Ryan Glasgow and Kevin Mandich from Sprig

Insight

Hosted LLMs shift hiring demand toward full-stack machine learning engineers

“I think given our current switch to hosted LLMs and away from, you know, taking Google's Burt and fine tuning it ourselves, more towards, I guess we call like a full stack ML engineer, somebody who's able to, you know, help integrate this and actually implemen…”

Kevin Mandich Sep 7, 2023 ▶ 57:04 A guide to building product in a post-LLM world | Ryan Glasgow and Kevin Mandich from Sprig

Prediction Not checkable as stated

The next 12 to 18 months of AI will target hallucinations and math

“To your original question, I think issues like those, hallucinations, lack of quant answering, those are probably going to be the major things that get released over the next couple years. I don't know what that looks like. You know, it might be an LLM that's …”

Kevin Mandich Sep 7, 2023 ▶ 1:09:55 A guide to building product in a post-LLM world | Ryan Glasgow and Kevin Mandich from Sprig

Assertion Not checkable as stated

Large language models currently fail at basic math and quantitative reasoning

“Right now a lot of these models fall flat and for an application like ours and for a lot of applications out there, that's kind of a pretty glaring omission. You know, these models are fantastic at summarization. They're really good at generating text based on…”

Kevin Mandich Sep 7, 2023 ▶ 1:06:22 A guide to building product in a post-LLM world | Ryan Glasgow and Kevin Mandich from Sprig

Appearances (1)

Episode	Date	Speaking time
A guide to building product in a post-LLM world \| Ryan Glasgow and Kevin Mandich from Spri	Sep 7, 2023	17m

Kevin Mandich

The record, in short

How they sound: not measured why? →

Appearances (1)

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