Alex Wiltschko

So some of these had never been made before. We sent them to our collaborator at Monell. Professor Mainland was running this. And he trained a panel of people. And this is kind of like what we do now, but just initially it was at a smaller scale. Train people to smell something and say, okay, This smells fruity and mineral, and that's it. So I'll give it a three out of five fruit.

So some of these had never been made before. We sent them to our collaborator at Monell. Professor Mainland was running this. And he trained a panel of people. And this is kind of like what we do now, but just initially it was at a smaller scale. Train people to smell something and say, okay, This smells fruity and mineral, and that's it. So I'll give it a three out of five fruit.

I'll give it a one out of five mineral, the rest zeros. And that's called rate all that apply. It's just like, that's how we label data. And then what we did is we said, okay, we have our predictions. People have their double blind ratings. Where do our predictions fit within the people? Because the best is the average of the panel. That's how you get really high quality data for AI.

I'll give it a one out of five mineral, the rest zeros. And that's called rate all that apply. It's just like, that's how we label data. And then what we did is we said, okay, we have our predictions. People have their double blind ratings. Where do our predictions fit within the people? Because the best is the average of the panel. That's how you get really high quality data for AI.

So, were our predictions worse than the worst person, or were they in the pack somehow? And it turned out that our AI predictions of what these smells were going to be were better than the average panelist.

So, were our predictions worse than the worst person, or were they in the pack somehow? And it turned out that our AI predictions of what these smells were going to be were better than the average panelist.

Meaning, if you were going to add one more person to this panel, you'd actually prefer to ask our software what it smells like that doesn't have access to the physical molecule than to train up another person to physically smell it, which is kind of like passing an odor-turing test.

Meaning, if you were going to add one more person to this panel, you'd actually prefer to ask our software what it smells like that doesn't have access to the physical molecule than to train up another person to physically smell it, which is kind of like passing an odor-turing test.

When that happened, it was very clear that Mother Nature was not going to stand in the way of continuing on this journey of actually digitizing the sense. So if you can solve that one problem, it means you can start to ask, okay, great.

When that happened, it was very clear that Mother Nature was not going to stand in the way of continuing on this journey of actually digitizing the sense. So if you can solve that one problem, it means you can start to ask, okay, great.

Now, what happens if instead of feeding this AI algorithm a pre-digitized molecule, what if I feed it a reading from a sensor, like the data off of a camera, if we were talking about images? And then what if I then ask it to recreate that smell with the ability to mix together different molecules to create a new scent? If you can actually round-trip

Now, what happens if instead of feeding this AI algorithm a pre-digitized molecule, what if I feed it a reading from a sensor, like the data off of a camera, if we were talking about images? And then what if I then ask it to recreate that smell with the ability to mix together different molecules to create a new scent? If you can actually round-trip

a smell, so take a physical smell, put it in one system, and then round trip through the reader this map that we built, this graph neural network-based map, and then write it back out again, and then compare it, and it actually smells like the thing that you put in. It means that you have actually digitized a human sense.

a smell, so take a physical smell, put it in one system, and then round trip through the reader this map that we built, this graph neural network-based map, and then write it back out again, and then compare it, and it actually smells like the thing that you put in. It means that you have actually digitized a human sense.

We hit all of our scientific milestones at Google, and we asked ourselves, what's the right way to scale this idea? And that's where Josh Wolf comes in. So we were thinking internally at Google, maybe this should be a company. And I was working with Krishna Yeshwant at GV, who's a very close friend. We'd worked together for five years.

We hit all of our scientific milestones at Google, and we asked ourselves, what's the right way to scale this idea? And that's where Josh Wolf comes in. So we were thinking internally at Google, maybe this should be a company. And I was working with Krishna Yeshwant at GV, who's a very close friend. We'd worked together for five years.

And someone at GV, another investor named Izzy Rosen, was getting lunch with Josh, who's the founding and managing partner at Lux Capital. And apparently, Josh had this 10-year-long thesis about digitizing Wolf Action, and we had never met. And so Izzy was listening to Josh give this pitch again. He said, hey, have you talked to this guy, Alex? He's kind of in the smell.

And someone at GV, another investor named Izzy Rosen, was getting lunch with Josh, who's the founding and managing partner at Lux Capital. And apparently, Josh had this 10-year-long thesis about digitizing Wolf Action, and we had never met. And so Izzy was listening to Josh give this pitch again. He said, hey, have you talked to this guy, Alex? He's kind of in the smell.

And then Josh and I met and it just was an instant connection. And so Josh was integral in pulling this IP out of Google Brain and building it into a completely new company. So Josh led the round, Krishna at GV co-led, and we build Osmo and we're on our way.

And then Josh and I met and it just was an instant connection. And so Josh was integral in pulling this IP out of Google Brain and building it into a completely new company. So Josh led the round, Krishna at GV co-led, and we build Osmo and we're on our way.