Learner Posts and Discussions

Into the Unknown delves into the profound implications that the discovery of extraterrestrial life could have on human society, particularly in areas like religion, economics, and science. Johnson emphasizes that such a discovery would not only challenge our understanding of the universe but also co

In his interview, Ben Shneiderman boldly predicted that future generations would find our tendency to anthropomorphize AI bizarre. He really emphasizes that computers are just tools, they're not partners and they're also not collaborators He insisted computers are merely tools, not partners or colla

Ben Shneiderman’s 2018 conversation on artificial intelligence was an interesting portal into the past, especially in its skepticism of AI hype and its focus on responsible design. I think his insistence that we view AI not as partners but as tools is still a powerful counter to the language often u

In the Ben Shneiderman interview of 2018 he states that we should aim for innovations and inventions to be tools rather than partners or mimics of human behavior. He also mentions that in the early stages of innovation the new technology usually tries to mimic human behavior, which is exactly what w

Ben Shneiderman's explanation detailing the purpose of machines, categorizing them as tools rather than partners, reminded me of a story that my grandmother used to tell me growing up about her experience of first being taught to drive a car. Now, as Shneiderman and my great-grandfather would agree,

Ben Shneiderman mentioned that what goes on behind the scenes in machine learning should be transparent and monitored through diagnostics. I think this idea was on point because the AI models nowadays often clearly state their thought process when generating responses. For example, before ChatGPT ou

In the interview with Ben Shneiderman, one of the prognostications was a warning that: "Machine learning, neural networks have great opportunities, but the mindless acceptance of their potency is extremely dangerous." I think this warning has very much come true and will continue to be very importan

Back in 2018, Ben Shneiderman sat down with Alyssa and made a sharp prediction: that AI would quickly become the go-to “catch-all” for fixing the internet’s biggest problems—especially things like algorithmic bias and the way content is given to users. He pointed to Mark Zuckerberg’s congressional t

I was drawn to the conversation with Professor Katherine Blundell of Oxford. As someone deeply interested in how we build scientific understanding, I found Blundell’s reflections on the Padua Rainbow especially compelling—particularly her insight that the framework omits a crucial, invisible force b

In his 2018 interview, Shneiderman predicted that AI would mimic and support human functions rather than replace them outright, comparing AI to tools like cars or music players that enhance, not substitute, human abilities. However, with the rise of generative AI like ChatGPT, this view seems outdat

https://www.labxchange.org/library/items/lb:HarvardX:68789c56:lx_simulation:1 https://www.youtube.com/watch?v=pKWkbWUn71s What new question does the reading and interviews raise for you about the ways in which the discovery of extraterrestrial life could affect human economies, religions, and scie

In the interview, Ben makes his stance on the popularized idea in the media and marketing that AI systems will be partners to humans. Instead, Ben believes that they are not partners, but like any other innovation before they will just be tools that enhance existing human ability. I agree with this.

In his 2018 chat with Alyssa Goodman, Ben Shneiderman warned that “algorithmic hubris” would make opaque models dangerous — and predicted that within a few years engineers, regulators, and insurers would insist on flight-data-recorder style logging, modular design, and human-comprehensible explanati

Prompt: Comment on your interest in choosing this video, and let us (and PredictionX forum readers) know what they might find most surprising in it.  Explain why. Firstly, I chose Gina McCarthy for several reasons. I believe that climate change is one of the most (if not the most) important predict

David Wallom: Energy Future and Climate Mitigation This interview stood out to me for its discussion on energy and climate mitigation, and I am interested in learning about the transition to more renewable energy. I am also interested in how energy connects to almost every part of our lives. Profes

In his 2018 interview, Shneiderman draws on Lewis Mumford’s 1934 work Techniques and Civilization to describe what Mumford called the “obstacle of animism”- our reflex to imbue new technologies with human or animal characteristics rather than treating them as tools in their own right. It reminds me

We’ve always built tools to make life easier as Sheridan notes. For most of history, that meant offloading physical work or boring, repetitive tasks like the bulldozer example. These tools gave us extra muscle or saved time on tedious chores, but we still had to do the thinking part. AI is differe

https://www.labxchange.org//library/pathway/lx-pathway:825945a0-367c-45dc-82b7-3d160c6e6f7a/items/lb:HarvardX:fe7fbba2:lx_simulation:1?source=%2Flibrary%2Fclusters%2Flx-cluster%3AModernPrediction&t=5ccc6b18-826a-4de0-911d-66a4f0f2140c I chose this video because the combination of the topics of healt

Ben Shneiderman's caution about the "pixie dust" is, for me, one of the most insightful moments of the interview. Back then, he was already warning about the tendency that humans would treat machine learning as a magical solution, capable of driving cars, diagnosing disease, or making predictions wi

Ben Shneiderman uses the Google Flu example to describe his fear that humans will overestimate AI's capabilities and then overlook its flaws, especially when it gives us incorrect data that we end up believing. This risk is extremely prevalent today. Because AI is improving so rapidly, we often get

https://openai.com/index/sycophancy-in-gpt-4o/ Last week, Open AI released -- and then rolled back -- a controversial new Chat GPT model. Users began to notice that the chatbot, which already inclines towards cheery validation in its tone, had become excessively, obsessively obsequious and bro

I chose this video because it offers a rare and nuanced conversation between scientists deeply engaged in the challenge of climate prediction, something that is both scientifically fascinating and socially urgent. What surprised me most—and what I think will surprise other forum readers—is the idea

In his interview, Professor Ben Shneiderman offered a much more nuanced perspective on artificial intelligence and what he referred to as the 'magic pixie dust' illusions commonly assoicated with AI and machine learning, and what they offer researchers and casual users alike. The idea of algorithmic

In the interview with Ben Shneiderman from 2018, he had several prognostications about AI. However, one that stuck with me was his worry about AI's integration into manufacturing systems, financial systems, loans, mortgages, and medicine. He believed that AI should be studied and improved, but thes

"The word AI is sprinkled over everything," says Ben Shneiderman in the below interview from 2018. This line, perhaps not intentionally, serves as a prognostication - or foretelling - of artificial intelligence's presence that has never held more true. I hardly remember its inclusion in syllabi duri

One prognostication that stood out to me from Ben Shneiderman's 2018 interview was the point that "the mindless acceptance of [machine learning and neural networks'] potency is extremely dangerous because that leads to deadly outcomes." This is especially important in the modern day because with the

Early in the interview, Schneiderman talks about "Google Flu Trends" - the idea that Google can predict the way illnesses break out in different parts of the world based on the queried put into google. He goes on to say this often helped predict flu outbreaks before data from hospitals suggested the

In the 2018 conversation, Ben Shneiderman strongly emphasized the importance of explainability in AI systems, particularly in high-risk domains like healthcare, transportation, and finance. He argued that if an AI system becomes so complex that "you don’t understand what it’s doing, throw it out or

I watched the Ben Shneiderman interview, recorded in 2018. You can access the video here: Shneiderman interview. More than a clairvoyant of the future of AI, Ben also exemplified the relationship between humans and machines, called HCI (Human-Computer Interaction). Over time, Ben raised the preoccup

In his 2018 PredictionX interview at Harvard, computer scientist Ben Shneiderman made a bold claim that I find particularly thought-provoking in our current AI landscape. He predicted that future generations would look back and find our belief in intelligent, human-like machines as "quaint" - assert