3 ms·

those projects were just PR so that c-levels could sell how they were preparing their company for a digital world This is exactly it. The 2017-2019 corporate v

those projects were just PR so that c-levels could sell how they were preparing their company for a digital world This is exactly it. The 2017-2019 corporate version of "invest in AI" meant to build an in-house team to do ML experiments on internal data, and then usually evolved a bit to get some "ml-ops" thrown in so they could "deploy" the models they built. I spent some time with a few companies doing this and it always reminded my of "the cat in the hat comes back" when the cat let all the little cats out of his hat and they went to work on the snow spots... just doing busy work... Anyway it's a symptom of the hype cycle - AI was the next electricity, but there were no actual products and nothing clear to do with it, just hire a bunch of kids to act like they were in a kaggle competition, or worse a bunch of PhDs to be under-utilized building scikit-learn models. Now that there are (potentially) products coming along that at least bypass the low-level layer of ML, having an internal team makes no sense. Maybe the most logical thing that will happen is the pendulum will swing too far, and this bubble will consist more of businessy types using chatGPT without remotely understanding it or realizing it's just a computer program.

visarga 4y ago
You tend to oversimplify the GPT's - they don't just work all the time, you got to test how well they work, then you got to select the best prompt and demonstrations, then you got to update your prompt it as new data comes along. There is plenty of work parsing various inputs into a format it could understand and then parsing its outputs, especially for information extraction.
jazzyjackson 4y ago
> having an internal team makes no sense. Disagree. I was on one of these R&D/prototyping teams running ML experiments and you're right, it was the company wanting to present itself as future-leaning, ready to adapt, and I would say that at this point it was a good move to have employees who understand where the tech is going. Companies with internal teams that are able to implement open source models are in a much better negotiating position for the B2B contracts they're looking at for integrating GPT into their workflow, they won't need GPT as much, if they can fallback on their own models, and they will be better able to sit down with the sales engineers and call bullshit when they're being sold snake oil.
- ShamelessC 4y ago
  They’ve been being sold snake oil for years internally, is I think the point being made.
DebtDeflation 4y ago
>The 2017-2019 corporate version of "invest in AI" meant to build an in-house team to do ML experiments on internal data, and then usually evolved a bit to get some "ml-ops" thrown in so they could "deploy" the models they built. You nailed it, although very few models actually ever got deployed to Prod at Fortune 500 non-tech companies and the few that did delivered little value. I'm a consultant and most internal AI/ML/DS teams that I interacted with were just running experiments on internal data as you said, and the results would get pasted into Powerpoint, a narrative created, and then presented to executives, who did little or nothing with the "insights". Reminded me of the "Big Data" boom a few years earlier where every company created a Big Data Team who then promptly stood up a Hadoop cluster on prem, ingested every log file they could find, and then..................did nothing with it.

visarga 4y ago

You tend to oversimplify the GPT's - they don't just work all the time, you got to test how well they work, then you got to select the best prompt and demonstrations, then you got to update your prompt it as new data comes along. There is plenty of work parsing various inputs into a format it could understand and then parsing its outputs, especially for information extraction.

jazzyjackson 4y ago

> having an internal team makes no sense. Disagree. I was on one of these R&D/prototyping teams running ML experiments and you're right, it was the company wanting to present itself as future-leaning, ready to adapt, and I would say that at this point it was a good move to have employees who understand where the tech is going. Companies with internal teams that are able to implement open source models are in a much better negotiating position for the B2B contracts they're looking at for integrating GPT into their workflow, they won't need GPT as much, if they can fallback on their own models, and they will be better able to sit down with the sales engineers and call bullshit when they're being sold snake oil.

ShamelessC 4y ago
They’ve been being sold snake oil for years internally, is I think the point being made.

DebtDeflation 4y ago

>The 2017-2019 corporate version of "invest in AI" meant to build an in-house team to do ML experiments on internal data, and then usually evolved a bit to get some "ml-ops" thrown in so they could "deploy" the models they built. You nailed it, although very few models actually ever got deployed to Prod at Fortune 500 non-tech companies and the few that did delivered little value. I'm a consultant and most internal AI/ML/DS teams that I interacted with were just running experiments on internal data as you said, and the results would get pasted into Powerpoint, a narrative created, and then presented to executives, who did little or nothing with the "insights". Reminded me of the "Big Data" boom a few years earlier where every company created a Big Data Team who then promptly stood up a Hadoop cluster on prem, ingested every log file they could find, and then..................did nothing with it.