Alex Wiltschko

So it turns out that's like a hundred year old problem nobody had been able to solve. that really stuck in my craw. I'm like, why don't we know how to do this? I ended up leaving academia. I started and sold two AI companies, one in the biotech space, one in the kind of pure ML as a service space. That ML company was bought by Twitter.

So it turns out that's like a hundred year old problem nobody had been able to solve. that really stuck in my craw. I'm like, why don't we know how to do this? I ended up leaving academia. I started and sold two AI companies, one in the biotech space, one in the kind of pure ML as a service space. That ML company was bought by Twitter.

I helped to start their deep learning team with my co-founders and with another company that we were combined with. That's where I really learned like internet scale, artificial intelligence applications. So we... applied AI to their ads platforms and made them a lot of money, and applied AI to their data centers and saved them a lot of money.

I helped to start their deep learning team with my co-founders and with another company that we were combined with. That's where I really learned like internet scale, artificial intelligence applications. So we... applied AI to their ads platforms and made them a lot of money, and applied AI to their data centers and saved them a lot of money.

And then I was recruited away to Google Brain, which is now called Google DeepMind, which is kind of their Xerox PARC or Bell Labs. And I worked on some internal projects for a bit, but after a year or so, I said, you know what? Let's take a crack at this smell problem again.

And then I was recruited away to Google Brain, which is now called Google DeepMind, which is kind of their Xerox PARC or Bell Labs. And I worked on some internal projects for a bit, but after a year or so, I said, you know what? Let's take a crack at this smell problem again.

And it turned out that in the time between when I left academia and got into tech and entrepreneurship, and when I arrived at Google Brain, some breakthroughs happened. And what happened is AI researchers figured out how to make artificial intelligence work on chemistry. And that maybe doesn't sound too crazy, but up until then, AI systems really liked their inputs to be rectangles, right?

And it turned out that in the time between when I left academia and got into tech and entrepreneurship, and when I arrived at Google Brain, some breakthroughs happened. And what happened is AI researchers figured out how to make artificial intelligence work on chemistry. And that maybe doesn't sound too crazy, but up until then, AI systems really liked their inputs to be rectangles, right?

Like images are like grids of pixels and text is like a long thin string of words, but molecules can have any shape. There can be any number of atoms and the bonds can be all rearranged. And there had been a technique that had been really improved and figured out, made to work better, called graph neural networks. And that turned out to be like chocolate and peanut butter for AI and chemistry.

Like images are like grids of pixels and text is like a long thin string of words, but molecules can have any shape. There can be any number of atoms and the bonds can be all rearranged. And there had been a technique that had been really improved and figured out, made to work better, called graph neural networks. And that turned out to be like chocolate and peanut butter for AI and chemistry.

So we didn't figure that out, but a lot of my colleagues, who I was very fortunate to work with at Brain, they figured that out for the world of drug discovery. So the intellectual arbitrage that we did was we said, let's take those techniques and let's apply it to the realm of scent. And I had spent a long time thinking about scent and traveling in that world.

So we didn't figure that out, but a lot of my colleagues, who I was very fortunate to work with at Brain, they figured that out for the world of drug discovery. So the intellectual arbitrage that we did was we said, let's take those techniques and let's apply it to the realm of scent. And I had spent a long time thinking about scent and traveling in that world.

And so I knew where to get the data sets, where to buy them, where to license them, how to treat them. And we fused those two things together. And I was fortunate enough to work with an incredibly talented team of folks at Google Brain. And we made this happen together. And what we're able to do is solve this 100-year-old problem.

And so I knew where to get the data sets, where to buy them, where to license them, how to treat them. And we fused those two things together. And I was fortunate enough to work with an incredibly talented team of folks at Google Brain. And we made this happen together. And what we're able to do is solve this 100-year-old problem.

It sounds so simple, but why does this molecule with this shape smell the way that it does? And we validated it in a really stringent way. We basically did a double-blind trial where we predicted the smell of hundreds of thousands of molecules. We picked 400 that were very different looking from anything we'd seen before. We kept our predictions secret. We bought or made the molecules.

It sounds so simple, but why does this molecule with this shape smell the way that it does? And we validated it in a really stringent way. We basically did a double-blind trial where we predicted the smell of hundreds of thousands of molecules. We picked 400 that were very different looking from anything we'd seen before. We kept our predictions secret. We bought or made the molecules.

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.