Title: Why Artificial Useful Intelligence (AUI) Matters More Than AGI Resource URL: https://podcasts.apple.com/us/podcast/ep-521-why-artificial-useful-intelligence-aui-matters/id1683401861?i=1000706839419 Publication Date: 2025-05-08 Format Type: Podcast Reading Time: 37 minutes Contributors: Ruchir Puri;Jordan Wilson; Source: Everyday AI Podcast (YouTube) Keywords: [Artificial Intelligence, Enterprise Technology, Artificial Useful Intelligence (AUI), Agentic AI, IBM Granite Model] Job Profiles: Academic and Research Collaborations Unit;Artificial Intelligence Engineer;Software Engineer;Data Analyst;Chief Technology Officer (CTO); Synopsis: In this podcast episode, AI strategy consultant Jordan Wilson speaks with IBM Research's Ruchir Puri about the importance of prioritizing artificial useful intelligence (AUI) over artificial general intelligence (AGI) for tangible, real-world benefits. Takeaways: [The definition of AGI remains fluid and speculative, while usefulness can be concretely measured by impact., Intelligence comprises intelligence quotient (IQ), emotional quotient (EQ), and relational quotient (RQ), but AI efforts often focus narrowly on IQ., Tools like AI software engineering agents that automate bug fixes exemplify AUI in action., Feedback systems, or “agents” with reasoning and tool-use capabilities, represent the next major shift in AI utility., Business leaders should prioritize hands-on AI understanding, strategic planning for disruption, and upskilling teams.] Summary: Artificial intelligence discourse is saturated with hype around AGI, yet a more pressing need lies in building Artificial Useful Intelligence (AUI)—systems that provide immediate, tangible value in daily human and enterprise workflows. Ruchir Puri frames intelligence not just in terms of IQ but as a balanced combination of IQ, emotional intelligence (EQ), and relational intelligence (RQ). He argues that technological progress should be judged by usefulness, not abstract benchmarks of AGI. The episode emphasizes historical milestones such as IBM’s Deep Blue and Watson, noting that these achievements didn't signal general intelligence but demonstrated domain-specific utility. The rise of large language models (LLMs) introduces a new phase where AI can handle language across domains—from code to spoken word—changing how humans interact with information and tools. Puri introduces the concept of software engineering agents that can autonomously identify, reason about, and fix bugs, which he views as a true representation of AUI. He further highlights the transition from “feedforward” AI systems to “feedback” systems (agents), capable of self-correction and autonomous task execution using toolsets. This shift aligns AI more closely with intelligent behavior by continuously adjusting outputs to meet goals, similar to how rockets adjust trajectory mid-flight. Finally, Puri advises business leaders to focus on three priorities: firsthand understanding of AI capabilities, strategic planning for AI-driven transformation, and workforce upskilling to adapt to new roles. These human-centric skills—particularly emotional and relational intelligence—will become increasingly valuable in managing AI tools and ensuring accountable outcomes. Content: ## Introduction ### Overview of the Episode This episode of *Everyday AI* explores a pragmatic alternative to the widely discussed pursuit of artificial general intelligence (AGI). The host introduces the concept of artificial useful intelligence (AUI)—AI tailored to practical, day-to-day applications that deliver measurable value to businesses and individuals alike. ### Episode Focus: Artificial Useful Intelligence Rather than chasing AGI or debating its definition, the episode contends that decision makers should concentrate on technologies that demonstrably improve workflows, boost productivity, and reduce risk. The guest speaker, a leading AI researcher and chief scientist at a global research organization, outlines why AUI deserves greater attention than grand visions of superintelligent machines. --- ## Revisiting AI Milestones ### Deep Blue and the 1997 Chess Match Thirty years ago, an IBM-developed system dubbed Deep Blue defeated world chess champion Garry Kasparov. Though heralded at the time as proof of machine superintelligence, the victory did not fulfill expectations of a universally intelligent entity. ### Watson’s 2011 Jeopardy Triumph A decade later, the Watson system bested the reigning *Jeopardy!* champions on live television. Despite widespread fanfare, this milestone again fell short of establishing a clear benchmark for general intelligence in machines. ### The Fluidity of AGI Definitions Over the past two decades, definitions of AGI have shifted repeatedly—sometimes narrowing to revenue targets, other times broadening to encompass human-level reasoning. The guest argues that such fluid definitions rarely translate into actionable business goals. --- ## Defining Intelligence True human intelligence often blends three components: ### Intelligence Quotient (IQ) The analytical and logical reasoning abilities traditionally measured by standardized tests. ### Emotional Quotient (EQ) The capacity to recognize, interpret, and manage one’s own emotions and those of others. ### Relationship Quotient (RQ) The skill of building, maintaining, and leveraging interpersonal connections. **Key Insight:** Most AI research focuses narrowly on IQ-like capabilities. By contrast, AUI emphasizes systems that address specific tasks and deliver tangible outcomes. --- ## The Changing Nature of Work ### From Industrial Revolution to Knowledge Economy Just as mechanization reshaped manufacturing, AI is transforming today’s knowledge work. Automation that once replaced manual labor now automates information-processing tasks once thought uniquely human. ### The Rise of Language Automation Modern large-scale models can analyze, generate, and translate code or spoken language, enabling a leap in productivity analogous to the transition from stone hammers to mechanized tools. ### Student Use Case: On-Demand Tutoring At a university campus, students previously waited for office hours to clarify concepts. Today, they can query an AI model with screenshots of problem statements, receive step-by-step explanations, and prepare more effectively for discussions with instructors. --- ## Agentic AI: From Feedforward to Feedback Systems ### Limitations of Prompt Engineering Current AI interactions often operate in a feedforward fashion: users supply a prompt, receive an output, then refine the prompt manually if the result is unsatisfactory. ### Characteristics of AI Agents An AI agent automatically: • Breaks down complex tasks into subtasks • Selects and invokes specialized tools (e.g., calculators, databases) • Evaluates intermediate outputs against the original intent • Iterates until it fulfills the specified objective ### Analogy: Rocket Navigation Just as a spacecraft uses continuous feedback to correct its trajectory toward the Moon, AI agents refine their actions through cycles of self-assessment and adjustment—ensuring greater precision and reliability. --- ## Measuring Artificial Useful Intelligence ### Use Case: Software Development Software engineers face an unending stream of bug reports and feature requests. AUI-driven agents can: 1. Locate the root cause of an issue within thousands of lines of code 2. Propose and implement a fix, accompanied by an explanation 3. Automate repetitive patches, freeing engineers to focus on design and strategy ### Use Case: Cybersecurity AUI tools can continuously monitor codebases and network events, detect vulnerabilities in real time, and recommend or apply patches—reducing enterprise risk in a landscape of escalating cyberthreats. ### Key Performance Indicators Organizations should evaluate AUI by measuring: • Reduction in manual hours spent on routine tasks • Decrease in error rates and security incidents • Improved time-to-market for software releases --- ## Preparing for an AI-Driven Future 1. **Hands-On Exploration** Leaders should personally experiment with AI platforms to understand their potential and limitations. 2. **Strategic Planning** Just as businesses prepare for power outages with backup generators, they must develop road maps for how AI will disrupt—and then rebuild—their core operations. 3. **Upskilling and Cultural Integration** Ensure employees acquire the necessary technical and interpersonal skills (EQ and RQ) to collaborate effectively with AI agents and to sustain accountability for outcomes. --- ## Conclusion Human oversight remains paramount. As AI transitions from feedforward models to fully agentic systems, decision makers must govern these tools, retain accountability, and harness AUI to drive productivity, innovation, and risk mitigation across every industry.