Dr. Darryl Williams

Thank you for having me here today. The history of this really goes back to my years when I was in the military. I was an officer in the Air Force. And in the national security arena, we are inundated by global flows of data. And from those global flows of data, it's critical that... in security that there is no strategic surprise.

Thank you for having me here today. The history of this really goes back to my years when I was in the military. I was an officer in the Air Force. And in the national security arena, we are inundated by global flows of data. And from those global flows of data, it's critical that... in security that there is no strategic surprise.

You don't wake up someday and find out that what you should have known actually comes to pass and you didn't know it. And so I was left in the mid-1990s, I was left with the problem of how does one discover in global flows of data information that is deemed to be undiscoverable. And so back then, I created these algorithms that, in essence, deconstruct and map supply chains of everything.

You don't wake up someday and find out that what you should have known actually comes to pass and you didn't know it. And so I was left in the mid-1990s, I was left with the problem of how does one discover in global flows of data information that is deemed to be undiscoverable. And so back then, I created these algorithms that, in essence, deconstruct and map supply chains of everything.

And from that, you're then able to discover things. And so it worked quite well, enabled predictive analytics, worked prior to 9-11, after 9-11. It was used extensively to stop terror attacks, discover terror attacks before they would occur. It discovered scores upon scores of them.

And from that, you're then able to discover things. And so it worked quite well, enabled predictive analytics, worked prior to 9-11, after 9-11. It was used extensively to stop terror attacks, discover terror attacks before they would occur. It discovered scores upon scores of them.

And then when I retired in 2007, I was asked by the government but also the private sector to continue this line of work. but it was very bespoke. The technology was just starting to emerge. And so doing four tasks per month, but then when COVID hit and all the global supply chains unraveled at the same time, we appear to be the only ones left standing.

And then when I retired in 2007, I was asked by the government but also the private sector to continue this line of work. but it was very bespoke. The technology was just starting to emerge. And so doing four tasks per month, but then when COVID hit and all the global supply chains unraveled at the same time, we appear to be the only ones left standing.

So at that point there, I was asked to scale. And one of the first lessons I learned about AI, AI doesn't take bad algorithms and make them better. You have to have great algorithms. And with that, AI then enables you to scale. And so using AI, true AI, not Everybody now says they're AI, even though they can't spell it. It's true AI, true machine learning.

So at that point there, I was asked to scale. And one of the first lessons I learned about AI, AI doesn't take bad algorithms and make them better. You have to have great algorithms. And with that, AI then enables you to scale. And so using AI, true AI, not Everybody now says they're AI, even though they can't spell it. It's true AI, true machine learning.

We are able to go from four tasks per month to thousands per minute, probably per second. And from there, the whole world is open because we're the only ones that can do it with complete accuracy. And so it's a fun ride. I've learned a lot of lessons through the entrepreneurial process, but the horizon looks very good.

We are able to go from four tasks per month to thousands per minute, probably per second. And from there, the whole world is open because we're the only ones that can do it with complete accuracy. And so it's a fun ride. I've learned a lot of lessons through the entrepreneurial process, but the horizon looks very good.

So, you know, we don't have enough time on this podcast, but let me give you at least the bird's eye view is that back when I first started in the computer field, there was that phrase garbage in, garbage out. And the idea was is that you have programming that is accurate so that your results can be accurate.

So, you know, we don't have enough time on this podcast, but let me give you at least the bird's eye view is that back when I first started in the computer field, there was that phrase garbage in, garbage out. And the idea was is that you have programming that is accurate so that your results can be accurate.

And so with all the other generative AI processes out there, they are designed to take data and global flows of data. I mean, think of what Elon Musk is doing in Memphis, and Google is spending hundreds of billions of dollars per year, and their algorithms are fantastic, but ultimately, it is garbage in, garbage out. That's why they are constrained to that 88% accuracy at best.

And so with all the other generative AI processes out there, they are designed to take data and global flows of data. I mean, think of what Elon Musk is doing in Memphis, and Google is spending hundreds of billions of dollars per year, and their algorithms are fantastic, but ultimately, it is garbage in, garbage out. That's why they are constrained to that 88% accuracy at best.

From the very beginning, since I started the national security realm, I was compelled to first create algorithms that that filtered error out of data. So since I was compelled to do that, from the very beginning, the algorithms just don't do what everyone else's do. They first filter error, bias, nuances out of data so that the data that is running through the algorithms is 100% accurate.

From the very beginning, since I started the national security realm, I was compelled to first create algorithms that that filtered error out of data. So since I was compelled to do that, from the very beginning, the algorithms just don't do what everyone else's do. They first filter error, bias, nuances out of data so that the data that is running through the algorithms is 100% accurate.

And that way, instead of having garbage in, garbage out, it is... accuracy in and accuracy out. And so that is where I am different, our company's different from anywhere else, is that we not only have to look at the algorithms, but we also have to consider the data. Everyone else is just looking at, is making the assumption that the data is what it is. We hope for the best.

And that way, instead of having garbage in, garbage out, it is... accuracy in and accuracy out. And so that is where I am different, our company's different from anywhere else, is that we not only have to look at the algorithms, but we also have to consider the data. Everyone else is just looking at, is making the assumption that the data is what it is. We hope for the best.