Can AI make your business a little more Human?
Artificial Intelligence (AI) and automation are changing how my software development and website design company creates value for healthcare, banking, ecommerce, churches, food trucks, local government, and other customers. My team and I have been using GitHub Copilot with OpenAI's ChatGPT to automate repetitive coding tasks such as bug resolution and predictive coding (letting AI finish and suggest lines of code) for years. Add AI's testing, quality assurance, and marketing benefits, and the value of AI is hard to overstate -- though I tried by writing about AI and its implications many times this year.
With this, how can a robot make something more human? Artificial Intelligence (AI) and Automation can work in conjunction to create smarter, more customized, and human-centric processes. AI, with its capability for machine learning and data analysis, complements automation's efficiency by enabling systems to learn, adapt, and make decisions based on individual scenarios. With AI, automation becomes capable of intelligently handling complex, varied tasks without compromising speed or accuracy. For instance, AI can learn a user's preferences, behavior patterns, and needs over time, and accordingly customize the automated services offered to them, thereby generating a more tailored and responsive interaction.
Our AI development moved from coding assistant to creating web design elements and development tools such as our newly upgraded AI-enabled AgileSiteLite website development, a tool that uses OpenAI's ChatGPT-4 natural language processing (NLP) to make designing, building, and marketing websites easier for small to medium-sized businesses. Yet, we notice that creation and implementation with AI is often easier and cheaper than maintaining AI systems. Hence, the AI Paradox:
As the machine learning (ML) community continues accumulating experience with live systems, a widespread trend has emerged: developing and deploying ML systems is relatively fast and cheap, but maintaining them over time is difficult and expensive. Hidden Technical Debt in Machine Learning Systems
As we develop AI end-use tools and applications, understanding how real-world business problem-solving promotes the economic growth needed to justify the computing power new digital technologies require makes our breakthroughs produce cost savings instead of incurring "hidden technical debts." Human intelligence is creative and flexible enough to manage the trade-offs our new AI systems bring when we're data-driven and unambiguous in our decision-making. Use links below to dive deeper into this paradox, learning ways to plan, design, and implement architecture to reap AI's benefits while defeating the AI Paradox.