By Doug Sanzone, Veterans Business Outreach Specialist Photo Courtesy of VBOC of the Dakotas
About the VBOC
The Veterans Business Outreach Center (VBOC) program is designed to provide entrepreneurial development services such as business training, counseling, and resource partner referrals to transitioning service members, veterans, National Guard and Reserve members, and military spouses interested in starting or growing a small business. U.S. Small Business Administration (SBA) has 22 organizations participating in this cooperative agreement and serving as VBOCs.
Not a day goes by without some mention of artificial intelligence or AI. On one side are those who claim that AI will solve all our problems, leaving humanity with little to do other than maintain our technology. On the other side are those who think that AI will take over the world, creating a dystopian existence of humanity serving superior machines or even deciding to end humanity’s existence itself.
After spending more than 30 years in the technology field, witnessing its ever-forward evolution, I can unequivocally state the answer will lie somewhere in between those extreme points. AI has been and will be a larger part of our technological toolkit as time goes on. Let’s learn some more about AI so we can both embrace the new applications and understand artificial intelligence’s limitations.
It began in 1763 when Richard Price posthumously presented “An Essay Toward Solving a Problem in the Doctrine of Chances” by Thomas Bayes, an English Presbyterian minister, statistician, and philosopher. This essay laid much of the foundational groundwork that would evolve into Bayesian probability theory, which fundamentally altered our approach to uncertainty and predictive analysis.
Bayes’ theorem revolutionized statistical methods by providing a systematic approach to making decisions where both uncertainty and the integration of prior knowledge are important inputs. Bayes heralded a paradigm shift underscoring the significance of prior information in forecasting and the decision-making process. Bayesian statistics are at the core of modern conditional probability estimation approaches, including probabilistic machine learning, sequential estimation, risk assessment, mapping, and information theory. A theory more than 250 years old is one of the backbones of our current AI craze.
The strategic adoption of AI technologies mirrors the principles of Bayesian inference, where existing knowledge and historical data serve as guideposts for future decisions. This approach gains importance as businesses traverse the complexities of integrating AI products, needing to emphasize the continual refinement of strategies based on emerging data. Let’s look at AI as not merely a technology tool but as a strategic asset grounded in principled, data-informed decision-making.
