Interview with Chandler Wilson, founder of bridge_ci, an end-to-end machine intelligence and alternative data solutions company based in Minnesota.
Hi Chandler. Can you tell us about your background and your company?
I founded bridge_ci in 2020 after fifteen years of creating alternative data, OSINT, and machine intelligence programs at the EU Commission, EU Parliament, Walmart, and HSBC. I saw a gap in the market when it comes to applying machine intelligence and alternative data to strategy and or investments. As a result, I now help private equity funds and corporate strategy teams use these incredible tools to set priorities, surface risks, automate research, create products, or find investment opportunities.
What makes your company different from other tech, open source intelligence and AI companies?
On the AI side of things, there’s probably not as much expertise in how to build an end-to-end transformation with multiple machine learning tools within a large corporation. A lot of the cutting edge AI firms are focused on one aspect of things, like natural language processing, but they won’t be building things in other areas that can be very additive to what they’re doing. They don’t have time to know the product space. I’m very good at understanding the product landscape and putting together the kind of ecosystem that works together and is more power than each one of these tools individually.
On the corporate side of things, I have more actual real-world experience in this field than just about anyone. Other companies just began mentioning alternative data in 2020, but I was looking at it in 2008.
As for open source, that’s almost exclusively dominated by cyber and security and haven’t found anyone applying it in any sort of strategic way. There’s no other firm out there that I know of that operates in open source intelligence and has expertise that’s focused on private equity or geopolitical or corporate strategy type things.
What do you see as some of the greatest challenges you’re facing at bridge_ci when it comes to servicing your customers in open source intelligence, machine intelligence and AI?
When adding technologies, there are three things to consider: your current technological maturity, your talent levels, and the political will to get things done. Too often, organizations discover six months and millions of dollars later that the project will fail because they lack the needed technology infrastructure or talent.
The solution is walking it back and getting the organization's trust to create a scoping project to map out the technology, and a change management strategy is needed before making massive investments. This process also involved a technology landscape analysis - with the use of AI and alternative data, to make sure that whatever infrastructure or technologies are chosen won’t be out of date by the time it is deployed, provide short-term value, and will work with emerging technologies downstream.
Are you planning on releasing any new technologies in the near future?
In addition to driving strategy and insights for our clients, we focus on helping funds and strategy teams build custom proprietary machine intelligence ecosystems that can solve various tasks using multiple off-the-shelf datasets, NLP and ML services, and APIs. Whether or not some of these technologies end up in the public sphere must be determined. Mostly, our clients like to keep them proprietary for competitive advantage. Because of that, we don’t build a general platform because you’ll still need to do a lot of artisan data science.
What are some of the most common misconceptions that customers have when it comes to AI and machine intelligence?
There are two paradoxical things. On one hand, I’ve seen a lot of misconceptions where they think there is a special AI for something. They don’t understand that it is pretty much all the same types of algorithms. Then there is the misconception of AI automatically being able to do everything. For example, I’ll get calls from CEOs who want machines to write content for marketing and then are upset that the result isn’t as good as something produced by a human. It takes time to train AI. Now I think that AI can be trained to do almost anything but it takes resources and time.
Where is AI headed? What does the future look like?
The barrier to entry to build interesting products will be lowered. It’s insane to think of the upside of AI, and I think that it will really change organizations. Building a simple app that will forecast cash flow accurately by simply uploading an Excel spreadsheet will lower sloppy data science.
And I think there are options for creativity. I do a lot of music production. In the past you needed to have a lot of stuff to make a great album and spend hundreds of thousands of dollars to do it. With the advent of sampling and digital audio, people can now make a lot of music with instruments they don’t know how to play. People are going to be able to gain insights into asymmetric opportunities, as well as link up different data sets and look at things and find patterns where you really couldn't have done that before without any sort of significant investment in time and money.
What do you think are the challenges we’ll see with AI and machine intelligence in the next five years?
There has been a lot of focus on AI regulation, inadvertently holding back the potential of these tools in an effort to develop frameworks for “responsible AI” or “explainable AI.” There needs to be a reframing of AI as the only way to solve bias in organizations - not create it. It’s important that business leaders and policy makers understand that even though AI can harbor errors, nine times out of ten, it is far less biased than people, and its bias is consistent and can be adjusted and fixed. This can be done through the design of thoughtful ML devOps infrastructures that have a focus on data quality and vintage, in addition to robust real-time monitoring of our models and how they are considering different variables.
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