{"id":23921,"date":"2025-05-02T06:43:21","date_gmt":"2025-05-02T06:43:21","guid":{"rendered":"https:\/\/www.frost.com\/events\/innovation\/?p=23921"},"modified":"2025-05-09T05:47:27","modified_gmt":"2025-05-09T05:47:27","slug":"ai-computation-and-the-human-touch-thinking-sensing-and-making","status":"publish","type":"post","link":"https:\/\/www.frost.com\/events\/innovation\/ai-computation-and-the-human-touch-thinking-sensing-and-making\/","title":{"rendered":"AI, Computation and the Human Touch (Thinking, Sensing, and Making)"},"content":{"rendered":"

As I keep joining discussions around generative AI and artificial intelligence technologies in general, I observe people swing between unsettling feelings and excitement. My overall suggestion in every instance is to slow down, think about the ways in which we -the humans- work, and then start working on replacing ambivalent feelings with grounded thoughts, applications, and strategies.<\/p>\n

I was recently a panelist at a Digital Innovation event and ended up articulating three key points that flowed from my mind during the discussion. I used my jetlag to transform those thoughts into better-organized paragraphs.<\/p>\n

Highlight 1: Are Computation and Data Pure?<\/strong><\/p>\n

Computation implies converting ideas from tangible reality into numeric entities. In this process, we translate qualities we understand into quantities\u2014numeric values that work only for aspects of reality that can be directly quantified.<\/p>\n

But much of meaning and value remains incomputable in that sense.<\/p>\n

So, while computation suggests a sense of absoluteness and perfection, it inherently involves a kind of reduction. The important aspects of reality and meaning are left out as we use computation to realize designs. Although this might seem like a problem, having this awareness actually helps refine our approach. This realization allows us to use computation in a much more informed way.<\/p>\n

Highlight 2: AI and Its Inherent Connection to the Past<\/strong><\/p>\n

AI is inherently rooted in the past because it operates on data sets composed of things that have already happened and have been recorded in digital form. When we train AI, we work with statistically distributed data points, which we often represent using mean distributions or bell curves. Essentially, we are searching within the central region of these curves, meaning we are reinforcing patterns from the past.<\/p>\n

But if we\u2019re aiming for creativity, innovation, or novel ideas, our search ideally should happen at the extremes of the bell curve. And if we\u2019re looking for a true leap, it needs to take place outside the curve altogether.<\/p>\n

This raises an interesting question: if AI is fundamentally shaped by historical data, can it truly be creative?<\/p>\n

That leads to the next point\u2014how we instinctively anthropomorphize AI, which we\u2019ll cover in the third note.<\/p>\n

Highlight 3: The Immediate Anthropomorphization of AI<\/strong><\/p>\n

AI has been around for a long time, conceptually dating back to the 1930s, with early implementations emerging in the 1950s. Despite decades of research, mainstream awareness of AI only surged in recent years due to increased accessibility.<\/p>\n

As a result, many now equate AI solely with generative AI\u2014a subcategory of the broader artificial intelligence field. This has led to a widespread perception that AI “thinks,” “writes,” and “creates” in ways similar to humans, which is simply not the case.<\/p>\n

At its core, AI is a powerful pattern analyzer and generator. The text and images it produces are fundamentally dictated by statistical patterns rather than human-like cognition.<\/p>\n

Large language models use tokens and statistical probabilities to predict and generate sequences of words, while image-generation models, such as diffusion models, rely on noise manipulation and pattern reconstruction to create visuals based on labeled training data.<\/p>\n

Both systems operate in ways that are fundamentally different from human thought and creativity, yet the tendency to anthropomorphize AI remains strong.<\/p>\n

Main Takeaway: Understanding AI Beyond the Hype<\/strong><\/p>\n

If we step back from the initial awe of AI\u2014whether it\u2019s the speed of generating images and text or the predictive capabilities of synthetic insights\u2014we can develop a clearer, more analytical understanding of these tools.<\/p>\n

At their core, AI systems are large-scale machines that process vast amounts of data, but they do not operate autonomously in a meaningful way. The role of interpretation, direction, and insight-building remains firmly in human hands.<\/p>\n

While deep learning and neural networks function in ways that might resemble aspects of human cognition, they are not direct replications of the human brain.<\/p>\n

AI does not “think” as we do; rather, it operates as a model of the mind, abstracting certain processes but lacking the full biological and experiential complexity of human intelligence.<\/p>\n

This is why intuition, experience, and knowledge remain essential. Humans are responsible for curating and refining the instructions given to AI, interpreting its outputs, and using its results in a meaningful and informed way. AI can generate, but we must make sense of it.<\/p>\n

With a master\u2019s and Ph.D. from MIT, Onur Y\u00fcce G\u00fcn is a computational design leader, researcher, and teacher who works with bleeding-edge technologies by exposing their inherent limitations and developing critical knowledge around them. He challenges the hype around GenAI, while demonstrating its meaningful, context-rich, human-centered integration. <\/em><\/p>\n

Currently Director of Computational Design at New Balance, Onur brings over 20 years of experience in intelligent systems, large-scale architecture, advanced manufacturing, and AI-driven design.\u00a0 <\/em><\/p>\n

Follow him on <\/em><\/strong>LinkedIn<\/em><\/strong><\/a> and <\/em><\/strong>YouTube<\/em><\/strong><\/a> for an in-depth understanding of human and artificial intelligence, and their implications for the future of life and work.<\/em><\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"

As I keep joining discussions around generative AI and artificial intelligence technologies in general, I observe people swing between unsettling feelings and excitement. My overall suggestion in every instance is to slow down, think about the ways in which we -the humans- work, and then start working on replacing ambivalent feelings with grounded thoughts, applications, […]<\/p>\n","protected":false},"author":160,"featured_media":23931,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[27],"tags":[],"coauthors":[139],"class_list":["post-23921","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-pdslog"],"yoast_head":"\nAI, Computation and the Human Touch (Thinking, Sensing, and Making)<\/title>\n<meta name=\"description\" content=\"I recently sat in the closing panel of the Stride 2025 event entitled Creating Synergy Between Traditional Craftsmanship and Digital Innovation\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, 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