Dr. Darryl Williams
speaker
108 appearances
2 recordings
1 series
first heard Jan 2025
last heard Mar 2025
Dr. Darryl Williams’s voice in public audio — every appearance, attributed to the second.
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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.
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.
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.
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.
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.
I took the area that hope is not a course of action, so let's filter the error out and let's make accuracy a key.
Yeah, good question. So when you have a capability that removes error from global flows of data and then puts it together in context, you can be the equivalent of a peewee soccer team and be wherever the shiny object is. I realized very early on that if this company was going to succeed, I had to focus on the three or four verticals that matter.
And so there are four verticals where you must have 100% accuracy 100% of the time. That's national security, that's fintech or financial technology, banking, legal technology, and health services.
and so for us um we are already in the national security realm um on the 31st of this month we release a tie which is our flagship product which stands for absolute truth ai and that then will be for the the financial technology and also legal tech And then in quarter three, we will start emerging with our product that does health services and health technology.
So those are the four that we're focusing on now.
Sure. So internationally, what we found right now, and Scott, you and I talked about this in one of the podcasts, is that there is a tremendous amount of noise regarding AI in the United States. I mean, everybody's talking about it, and unless you are making billions of dollars a year, you cannot get above that noise threshold.
So what I have found is that by launching overseas, so our commercial headquarters was just stood up in Dublin, Ireland, we're able to get above the noise, and the AI – problems over there are the same here. The only difference is, is their data, they have very strict data laws, GDPR. But us being over there, since we remove error from data, we actually conform to GDPR quite well. And so
Overseas, we're into the family offices, the governments, the legal area. You probably just saw that we just signed a memorandum of understanding with a luxury yacht manufacturer in Dubai. And so it goes across the spectrum right now, but specifically the fintech and the legal tech.
Yes, it was very nice. They are so in need of having visibility on their supply chains that they actually offered me to have use of their yacht for a day as my office. Now, unfortunately, right now I'm in Cleveland, so it's not a luxury yacht, but it was nice for a day.
Yeah, so what we have been asked to do since, once again, we remove the error and we deconstruct supply chains, even quantum computing has a supply chain. And so when we use the system to run against quantum computing, it starts all the way down at the science, which is physical science, which is physics.
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