“I want AI to do my laundry and dishes so that I can do art and writing, not for AI to do my art and writing so that I can do my laundry and dishes.” – Joanna Maciejewska
That was the promise of AI: take care of our mundane tasks allowing us time to do what we love. But what we’ve found is that AI is actually quite good at writing captivating poetry, composing soul-stirring music, and creating stunning artwork—all by learning patterns from enormous amounts of data. The promise of generative AI may be different than what many of us had imagined, but it’s not less powerful of an ally.
We now live in a world where chatbots not only answer your questions but tailor their responses specifically to you. These systems can do something as extraordinary as speeding up drug discovery by predicting the most effective molecular structures, to something as mundane as crafting our latest email. The applications are nearly limitless.
Yet, to truly grasp the strengths of generative AI, you must understand its weaknesses. Think of it less as a calculator and more as an eager junior employee. It excels at generating creative ideas, recognizing patterns, and handling tasks that thrive on ambiguity and flair—much like a new hire who brings fresh perspectives and adaptability to your team. However, just like an enthusiastic novice, generative AI might struggle with tasks requiring precision. Counting objects or performing exact arithmetic, where strict adherence to predefined rules is essential, can create problems.
Generative AI runs on a probabilistic foundation, meaning it makes predictions based on likelihoods rather than certainties. This probabilistic nature allows generative AI to shine in creative tasks but also explains why it can stumble when exactness is required.
Navigating the Landscape of AI Possibilities
Instead of following strict rules and precise calculations, generative AI considers a myriad of potential outcomes. When generating text, for instance, it predicts the next word based on the likelihood of its occurrence given the preceding words. This flexibility enables the creation of creative and diverse outputs while also introducing the possibility of being flat-out wrong.
If you’re writing a story or a poem, generative AI is perfect because there’s no single correct outcome; the goal is to produce something novel and engaging. The model uses its training data to weave together sentences that make sense in context, often with impressive results.
On the other hand, tasks requiring deterministic precision highlight the limitations of generative AI. These tasks require a single correct answer, and the probabilistic nature of the model can lead to errors. For example, asking generative AI to calculate the exact amount of leave an employee has accrued might result in a wrong answer because it generates outputs based on likelihood rather than certainty. Similarly, when a customer is looking for information about a return policy, generative AI might produce inconsistent results, as it generates responses based on patterns rather than rules.
Knowing where generative AI excels and where it may stumble is important. By blending probabilistic AI with deterministic programming, you can create systems that harness the strengths of both approaches. A hybrid system might use generative AI to produce creative content while employing deterministic algorithms to verify facts or perform precise calculations – walking that tightrope between creativity and accuracy.
What This Looks Like in the Real-World
Businesses are already using generative AI’s strengths while mitigating its weaknesses through innovative integrations with deterministic programming. Here are some real-world examples that show off the true potential of this approach:
Enhancing Customer Experience
Retail and e-commerce companies are using AI to revolutionize customer interactions. Chatbots powered by generative AI can engage customers in natural, human-like conversations, providing personalized recommendations based on user preferences and browsing history. However, when a customer asks about a specific return policy or order status, deterministic algorithms ensure that the information provided is accurate and consistent. This hybrid approach not only enhances the customer experience but also improves operational efficiency by automating routine inquiries while maintaining precision in critical information delivery.
Advancing Healthcare Solutions
In the healthcare sector, generative AI is making huge strides in personalized medicine. AI systems can analyze enormous datasets of medical records and research papers to find potential treatment options and diagnostic insights. Merative, formerly IBM Watson Health, is currently using generative AI to help oncologists in developing personalized cancer treatment plans. These AI-generated recommendations are cross-verified with deterministic algorithms to ensure they align with established medical guidelines and individual patient histories. This integration enables healthcare providers to offer innovative, personalized care while keeping the highest standards of medical accuracy.
Driving Financial Innovations
The financial industry is another area where the blend of generative AI and deterministic programming is showing impressive results. Investment firms are employing AI to analyze market trends and come up with new trading strategies. These insights can uncover unique investment opportunities and allow it to rapidly adapt to changing market conditions. However, the actual execution of trades, compliance checks, and risk management are handled by deterministic systems that ensure adherence to regulations. This combination allows financial institutions to innovate and stay competitive while still managing risk.
Transforming Creative Content Generation
Generative AI is also transforming the creative industries by assisting in content creation. Media companies use AI to generate engaging articles, video scripts, and even music compositions. The Washington Post has employed an AI system named Heliograf to automatically generate news articles for routine reports like sports scores and financial summaries. While AI handles the creative drafting, deterministic algorithms review and edit the content to ensure factual accuracy and coherence. This collaboration enhances productivity and allows human writers to focus on complex and nuanced stories.
The Path Forward
Understanding the balance between generative AI’s creativity and deterministic programming’s precision is important for anyone looking to integrate AI into their businesses. By strategically combining probabilistic and deterministic methods, you can maximize the benefits of AI while mitigating potential risks, ensuring that AI is applied in areas where it can provide the most value.
As we’ve shared, this blended approach is already transforming industries – from customer service and healthcare to finance and creative content generation. Hopefully, these real-world examples show you the immense potential for driving innovation, enhancing productivity, and unlocking new possibilities for your organization.
The future of AI lies in the seamless integration of creativity and precision. By leveraging the strengths of each, we can build systems that are not only innovative and adaptable but also robust and reliable. Balance will be the key to unlocking the full potential of AI and driving transformative changes across industries.
As we move forward, it is critical to create a culture of continuous learning and experimentation. Embracing generative AI while building systems grounded in the principles of deterministic programming enable us to navigate the complexities of this rapidly evolving landscape. By doing so, we can shape a future where machines and humans work together in harmony.
Some Key Points to Remember
- Generative AI excels in creativity and pattern recognition.
- Generative AI struggles with precise, deterministic tasks.
- Combining generative AI with deterministic programming ensures accuracy and reliability.
- Industries like customer service, healthcare, human resources, and finance are already benefiting from this blend.
- Combining probabilistic and deterministic methods maximizes AI’s potential.
- Your AI success depends on this type of integration.
Ready to dive in with a blended approach to generative AI tools, but don’t know where to start? Let the experts at Doyon Technology Group be your guide. Reach out to us at connect@doyontechgroup.com today to get started.
––––––
About the Author
Greg Starling serves as the Head of Emerging Technology for Doyon Technology Group. He has been a thought leader for the past twenty years, focusing on technology trends, and has contributed to published articles in Forbes, Wired, Inc., Mashable, and Entrepreneur magazines. He holds multiple patents and has been twice named as Innovator of the Year by the Journal Record. Greg also runs one of the largest AI information communities worldwide.
Doyon Technology Group (DTG), a subsidiary of Doyon, Limited, was established in 2023 in Anchorage, Alaska to manage the Doyon portfolio of technology companies: Arctic Information Technology (Arctic IT®), Arctic IT Government Solutions, and designDATA. DTG companies offer a variety of technology services including managed services, cybersecurity, and professional software implementations and support for cloud business applications.