Nestled within a cold containment cage is a supercomputer recognized as one of the fastest and most environmentally friendly in the world, located in a data center in Bluffdale.
While many believe that AI technology is advancing rapidly, I propose that the momentum has stagnated since the debut of ChatGPT. While product innovations have surged, the fundamental technologies have not kept pace.
My journey in AI began over a decade ago, a time when we referred to it mainly as machine learning or statistics. I've had the privilege of building these innovations and teaching others—from my experience as a data scientist in the industry to my current role as a professor at Utah State University.
In light of the recent wave of statements urging caution in AI development and the perceived moral necessity to slow progress, I find myself at odds. It’s not progress we should slow down; rather, we need to be deliberate about where we focus our acceleration.
Regarding some of these recent assertions, I believe that companies creating advanced AI models—like OpenAI and Anthropic—would profit from a pause. A slowdown might mitigate the diminishing returns on their offerings, which, under current circumstances, necessitate increasing resources for enhancement. Moreover, these industry leaders could leverage government regulation to hinder potential competitors from reaching or surpassing their capabilities.
From their perspective, without a slowdown, they feel compelled to continue developing tools that they genuinely believe could pose existential threats. It’s worth noting that it’s unprecedented for any company to admit its product could lead to humanity's end while simultaneously urging for regulatory brakes.
The crux of the matter is this: what do we aim to accomplish by slowing down? Will it rejuvenate the Great Salt Lake or restore dwindling water supplies? Will it address housing affordability or ensure that students we mentor can secure meaningful employment?
The answer is uncertain.
However, I posit that historically, solving significant issues or enhancing quality of life comes from accelerating our efforts. This should not be misinterpreted as a call for unchecked expansion of computational resources. Instead, it is essential to invest more in human creativity and ingenuity.
AI will undoubtedly play a role in this process—perhaps even a fundamental one—but the objective should be reframed to align more closely with our true aspirations.
The justification for slowing AI innovation stems from a belief that these systems are striving to emulate human cognitive abilities—this is the essence of Artificial General Intelligence (AGI). However, without a transformative approach, large language models (LLMs) remain limited by the availability of fresh data and computing power.
Key to progress is centering human experiences in our AI development. If we prioritize increasing human welfare, we may uncover alternative, more effective solutions.
For instance, if I aim to address the adverse effects of a drying Great Salt Lake, creating AGI that uses vast resources to address these issues is one method. A more pragmatic approach could involve bolstering the efforts of dedicated individuals in our state's environmental sectors, using a diverse range of tools to tackle these complex and evolving challenges.
Creative thinkers, equipped to devise innovative solutions with comparatively modest computational resources—our brains operate on roughly 10-20 watts while leading GPUs consume around 600 watts—can significantly contribute while also finding purpose in their work and stimulating the economy.
The potential dangers of any technology often stem from a lack of comprehension by either its creators, users, or both. It's imperative that we cultivate a well-informed society. Calls to eliminate a technology one struggles to define or champion a device that undermines human critical thinking are counterproductive.
The good news is that the best remedy for misgivings about technology has always been education. By deepening our understanding, we can discern the nuances of both the opportunities and risks associated with any technology, enabling us to implement meaningful solutions within our communities. Today, educational resources are more accessible than ever, with teachers, literature, and online tutorials available to aid in this quest for knowledge. I highly recommend the video series on Neural Networks by 3Blue1Brown as a solid starting point.
Encouraging intellectual curiosity is vital, and supporting educators is essential in this endeavor. I believe that the success of the U.S. educational system is less about delivering facts or utilizing new tools effectively and more about fostering curiosity. My greatest concern in this era is the potential loss of that curiosity.
Sharad Jones, Ph.D., serves as a Professional Practice Associate Professor in the Data Analytics and Information Systems Department at Utah State University and is the Executive Director of the Analytics Solutions Center. By teaching courses in Machine Learning, AI, and ethical Data Science, and mentoring within the Analytics Solutions Center, he links students with businesses to tackle real-world challenges. With experience as a research Data Scientist at Carvana, Maxar Technologies, and Striveworks, plus co-founding and serving as CTO of Scribbi, Sharad is committed to developing technologies responsibly and empowering others in a data-driven world.
