How did you come to the conclusion that funding ML research is “pretty messy and unpredictable”? I’ve seen many ML companies funded over the years as straightforwardly as other tech startups, […]
I think it’s important to distinguish here between companies that intend to use existing state-of-the-art ML approaches (where the innovation is in the product side of things) and companies that intend to advance the state-of-the-art in ML. I’m only claiming that research that aims to advance the state-of-the-art in ML is messy and unpredictable.
To illustrate my point: If we use an extreme version of the messy-and-unpredictable view, we can imagine that OpenAI’s research was like repeatedly drawing balls from an urn, where drawing each ball costs $1M and there is a 1% chance (or whatever) to draw a Winning Ball (that is analogous to getting a super impressive ML model). The more funding OpenAI has the more balls they can draw, and thus the more likely they are to draw a Winning Ball. Giving OpenAI $30M increases their chance to draw a Winning Ball; though that increase must be small if they have access to much more funding than $30M (without a super impressive ML model).
I understood what you meant before, but still see it as a bad analogy.
For context I saw many rounds of funding as a board member at Vicarious which was a pure lab for most of its life (and then later attempted robotics but that small revenue actually devalued it in the eyes of investors). There, what it took was someone getting excited about the story and smaller performance milestones along the way.
I think it’s important to distinguish here between companies that intend to use existing state-of-the-art ML approaches (where the innovation is in the product side of things) and companies that intend to advance the state-of-the-art in ML. I’m only claiming that research that aims to advance the state-of-the-art in ML is messy and unpredictable.
To illustrate my point: If we use an extreme version of the messy-and-unpredictable view, we can imagine that OpenAI’s research was like repeatedly drawing balls from an urn, where drawing each ball costs $1M and there is a 1% chance (or whatever) to draw a Winning Ball (that is analogous to getting a super impressive ML model). The more funding OpenAI has the more balls they can draw, and thus the more likely they are to draw a Winning Ball. Giving OpenAI $30M increases their chance to draw a Winning Ball; though that increase must be small if they have access to much more funding than $30M (without a super impressive ML model).
I understood what you meant before, but still see it as a bad analogy.
For context I saw many rounds of funding as a board member at Vicarious which was a pure lab for most of its life (and then later attempted robotics but that small revenue actually devalued it in the eyes of investors). There, what it took was someone getting excited about the story and smaller performance milestones along the way.