One thing that distinguishes what I call the 'Deep Thinkers' versus the 'Mechanics', are people that actually understand philosophy.
Human-centricity: The relentless push for AI development often rewards technical prowess, but there's a growing call for a deeper philosophical understanding of intelligence to truly drive the field forward. While the industry races ahead with an endless stream of Gen AI launches across every conceivable discipline, embracing diverse methodologies and interdisciplinary approaches could illuminate the ethical and philosophical implications that are too often sidelined.
Breadth of expertise: Joseph Byrum is the Chief Technology Officer of Consilience - an AI company that offers solutions to enhance financial decision-making and risk management. Byrum also has extensive experience outside of the tech world in fields like genomics and quantitative genetics.
"One thing that distinguishes what I call the 'Deep Thinkers' versus the 'Mechanics', are people that actually understand philosophy," says Byrum.
Consider this: There's no shortage of companies churning out generalized AI products, GPT wrappers, and clones. That's not to say that there aren't business problems to be solved by painting in broad strokes. But in higher-stakes sectors like finance, medicine, and law, reliability and precision are a must and offers a second wave of opportunity.
- Byrum calls for an interdisciplinary approach to AI innovation, drawing from diverse fields to foster non-linear progress.
- "Innovation is anything but linear," he says, urging the industry to consider the ethical and philosophical implications of AI technologies. "There's an opportunity... to take a step back and a breath and just kind of think."
What you need [to achieve AGI] is millions and millions of little specialized agents that have deep domain and expertise.
Between the lines: You can't speak about philosophy and AI without addressing the concept of AGI. But AGI is really just a sum of its parts. It's about connecting an infinite amount of dots to create a fabric that effectively resembles how a human brain makes decisions. "What you'd need is millions and millions of little specialized agents that have deep domain and expertise," says Byrum.
Fortunately, it's easier and cheaper than ever to stand up small, specialized models, allowing for rapid innovation cycles and cost savings. "I can spin a new model up in two weeks and test it now. And if it's not working or wrong, I don't necessarily care because I'll just redo it." It's that exact methodology, continually optimized, criticized, and philosophized by millions of humans, millions of times that will make some semblance of sentience possible.
