Artificial Intelligence

The text below is an in-depth write up, for a summary of this paper in video format, please visit this link or watch below. Predictive Text Predictive text is a feature so embedded in daily communication that we rarely pause to consider how it works—or what its successes and failures reveal about

The sad thing is that Ben Schneiderman could be right, and AI was but a "tool" all along. However, the fact that we believe that AGI is possible, or the fact that current AI approximates AGI well enough, is enough to disprove his neat separation of the "human" and the "machine." To pass a Turing tes

• Explain (In a paragraph) how the interview relates to your chosen final project topic. • The interview with Ben Shneiderman relates to my final project of using NFL Combine results to predict future NFL player success in many ways. One way is through machine learning methods versus statistica

Personally, I will remember a year from now the metaphors Schneiderman made and their implications in reference to our desire to create better tools (improve AI) for humans rather than our desire to create tools that mimic humans. In my future and that of society, I think it will be important to

One piece of this interview that I will remember a year from now is the way in which we use language to describe AI. Words like "partnership", "learn" and "know", are damaging in that they paint machines as being human-like. The idea that AI is a tool rather than human-like is really important for

A year from now, the most important thing that I will remember from this interview is that we have to view AI technology not as replacements or mimics of human actions/abilities, but rather as enhancers. In other words, our focus must be placed on how to develop AI technology applications as tools t

A year from now, the thing I will remember the most from this interview is the characterization of AI and other pieces of technology as tools, not partners. This is important now more than ever, with language models that mimic human speech and attempts to build AI systems that mimic humans entirely.

I will remember how it is insufficient to merely rely on one datapoint when using it to develop a view, especially for policy responses as we saw with the flawed Google Flu Trends. This stood out to me because looking at search engine trends is a very common practice in investing to get clues of whe

https://www.labxchange.org/library/pathway/lx-pathway:b5121779-9f49-49db-93d9-80d5d67dadb3/items/lx-pb:b5121779-9f49-49db-93d9-80d5d67dadb3:lx_simulation:f10b9110?source=%2Flibrary%2Fclusters%2Flx-cluster%3AModernPrediction There are a couple questions that I’d be curious to discuss with Professor

https://www.labxchange.org/library/pathway/lx-pathway:b5121779-9f49-49db-93d9-80d5d67dadb3/items/lx-pb:b5121779-9f49-49db-93d9-80d5d67dadb3:lx_simulation:f10b9110?source=%2Flibrary%2Fclusters%2Flx-cluster%3AModernPrediction&fullscreen=true Apropos this idea of skipping the “rule” and “theory” step

Ben Shneiderman said "I do not think machines are our partners." While it's understandable to view machines as mere tools rather than partners, I think this perspective is increasingly outdated in light of the rise of agentic AI. These advanced systems go beyond passive data processing; they activ

Knowing a little bit about Shneiderman's work, even in 2018 he was already a strong advocate for so-called human-centered AI, and talked about the need for systems that are transparent, accountable, and augment human abilities rather than replace them. At that time, many AI models—particularly deep

My chosen topic for this class is using ML to predict stock market prices. Thus, understanding machine learning when it’s applied to large datasets is absolutely critical. I’m also writing a piece (for a different class) that defends the idea that AI will not take over at least most, if not the en

1. I'll probably remember most the simple statement that AI and machine learning are essentially statistical models. A lot of people look at AI as this magical new technology that we have never had before, but we've always been modeling things based on trends; the difference now is that (as they sai

Hello All, I have decided to comment on Ben Shneiderman's interview with Professor Alyssa Goodman about Artificial intelligence (AI). The most memorable part of this interview was about the Google Flu Tracker. Google would see searches of people in given areas and try to predict if the flu would b

I watched the AI interview with Ben Shneiderman and the two undergraduates (https://www.labxchange.org/library/pathway/lx-pathway:53ffe9d1-bc3b-4730-abb3-d95f5ab5f954/items/lx-pb:53ffe9d1-bc3b-4730-abb3-d95f5ab5f954:lx_simulation:997b23d6?source=%2Flibrary%2Fclusters%2Flx-cluster%3AModernPrediction&

I found it very interesting how Ned Hall tried to draw a distinction between what he sees as the two definitions of a prediction. Namely, he contends that the basic definition of prediction is merely a forecast of what will happen in the future and that a second definition is where we try to base in

One thing that I was surprised by from his interview with Professor Goodman was his apathy towards the survival of our species against machines and AI. If you are unconcerned with the continuation of our biological species, why do you seem to care about the survival of our species against climate ch

I was surprised by David Laibson's belief that the majority of policymaking in the future will be done through machine learning instead of the traditional 20th century scientific method. I am surprised by his belief considering machine learning skips straight from data to prediction, unlike the scie

David Laibson, in his interview on Behavioral Economics, said he would never expect to see the result of understanding physics to the point of creating an accurate prediction for anything and everything. We can never know everything due to an Uncertainty Principle and a degree of randomness in the u

In this interview, Ben Shneiderman mentions FAT ML—Fair, Accountable, and Transparent Machine Learning. With the years that have passed since the interview was conducted in 2018, I believe we have moved farther from this ideal. The rapid release of large and complex models, especially in generative

During this interview, I was fascinated by the difference in machine learning trends and statistical trends, especially the comparison with AWS. However, the discussion around machines as tools rather than partners was interesting. They discussed how machines would be able to mimic many actions that

Essentially, my chosen group's topic would have been a discussion on the theory-driven and data-driven approaches in artificial intelligence (AI) is crucial for understanding its potential and limitations. The example of Google's attempt to predict flu outbreaks through analyzing search trends ill

1. Something I will probably carry with me for a longtime from this interview is the concept of expectation and how that can stunt our progress in any field. For example, I like how Alyssa talked about the fact that people stuck with Aristotle's idea of the solar system because it is what they were

The part of this interview that I believe will stick with me in a year pertains to the differences between how the traditional scientific method moves from data to prediction contrasts with how machine learning/AI accomplishes the same task. Humans partake in intermediate steps -- data to rules, rul

https://www.labxchange.org/library/pathway/lx-pathway:53ffe9d1-bc3b-4730-abb3-d95f5ab5f954/items/lx-pb:53ffe9d1-bc3b-4730-abb3-d95f5ab5f954:lx_simulation:997b23d6?source=%2Flibrary%2Fclusters%2Flx-cluster%3AModernPrediction&fullscreen=true I think what I will remember the most from the interview is

What do you, personally, think you will remember most about this interview a year from now? • I think I will remember the framework of thinking about machines as tools as opposed to partners -- this will really help public perception of AI and make it seem less daunting. Machines will likely only a

https://www.labxchange.org/library/pathway/lx-pathway:b5121779-9f49-49db-93d9-80d5d67dadb3/items/lx-pb:b5121779-9f49-49db-93d9-80d5d67dadb3:lx_simulation:f10b9110?source=%2Flibrary%2Fclusters%2Flx-cluster%3AModernPrediction&fullscreen=true The most surprising thing i learned is that we'll never tru

My question about biases in machine learning is why do they matter? My understanding of bias is that it is something that leads to a distorted perception of reality. However, assuming we are able to build machine learning algorithms that make accurate predictions, why would we need to bother worryin

While there are many well established and mostly accurate predictive systems, many people do not act in accordance to the predictions as laid out. A possible explanation for this is illuminated from corroborating The Divination Tent and Kleinman which argue that humans sometimes don’t heed predictio