MBA 6101 SU 2024 Section A

                 In a first for me, I will utilize ChatGPT to write the entirety of one of my blog posts. It feels less like a skill and more like using a tool designed for an imbecile. It's the difference between actually writing code and using a no-code tool; using a calculator rather than pencil and paper. I am criticizing it as a tool, but I criticize using it as a catch all. There is a reason we make kids learn the fundamental basics of math before giving them a calculator.       Regardless, I asked ChatGPT the following prompt:     I have asked ChatGPT to write this blogpost because if you do not take my criticisms of S urfing the Tsunami's premise and outlook on AI seriously, you might be inclined to take AI's criticism more seriously. Generally, I agree with ChatGPT on the majority of its points and before I digress too much I will simply copy and paste ChatGPT's response.      In "Surfing the Tsunami," Todd Kelsey delves into the profound impact of a

            There is a great quote by B.F. Skinner, " The real problem is not whether machines think but whether men do. The mystery which surrounds a thinking machine already surrounds a thinking man." (Skinner, 2013). This quote on its own reflects many aspects of AI and human relations but what I want to point out is that humans want to be fooled and are often fooled into acceptance of a new status quo.     For example, the Father of LLM's, Joseph Weizenbaum, created the first Chatbot in 1966 called Eliza. It was structured nothing like our current Large Language Models but was based on a script and what he discovered is that some people were "  very hard to convince that Eliza (with its present script) is not human" (Tarnoff, 2023). This was because of a psychological phenomenon called transference, similar to personification or projection, it "refers to our tendency to project feelings about someone from our past on to someone in our present. While it

      The second chapter of Surfing the Tsunami , by Todd Kelsey, covers what AI has been doing for the past few years. Although, with the pace that technology moves it is a bit out of date with what AI is up to "today". In any case, he is priming us for future chapters by trying to explain software and hardware and the link between the two and how they relate to the progress of AI. However, the point of this chapter is to focus on what AI is doing today and why it matters. Although, the chapter is a bit unfocused and uses excessive analogies to its detriment.          Choosing just one small part of the chapter Kelsey focuses on " the automation of virtual processes " and how this will have the biggest impact on the job market (Kelsey, 2018 p. 36). Although, I disagree with his rational for why it will impact the job market he is correct that most routine tasks and most virtual business process will be automated. However, this has always been the case. He uses the

      Marketing is a broad term that seems to refer to the concept of influencing a set of consumers into soliciting a good or service. Marketing, in some form, has probably been around since the beginning of history; we could consider evolutionary marketing where partners of opposite sex "market" their availability and prowess in different forms. For example, in humans we market our availability and prowess or compatibility to a partner verbally through flirting or in the ways we dress. This is just a simple example, but marketing as a field of business has likely also been around since humans have been offering goods or services.      As crude as it may seem, but the world's oldest profession, prostitution, is just an extension of the evolutionary marketing into business marketing in a way (Parsons 2005). Modern marketing has developed to take advantage of our developed evolutionary marketing and while most cannot agree on when or where modern marketing has its origins,

      Going by the picture above it certainly struggles a fair amount with creating words in images. These artifacts or strange portions of an AI's work such as a person having more than five fingers or just not generating the correct image at all are known as hallucinations. In my last post, I pointed out that machine learning models have a theoretical limit to what they can learn. Although I want to be clear that not all AI is machine learning, but all machine learning is AI as machine learning is a subset of AI that uses neural networks to learn.     Recently, Computerphile, a lovely YouTube channel interviewed Dr. Mike Pound at the University of Nottingham about a new paper that came out about whether or not generative AI has already peaked or rather the possibility that we will get further and further diminishing returns.     I will not go in depth over the video as you can watch it for yourself nor in depth over the paper, but I would like to pull a few graphs from the paper

           In chapter one of the book, Surfing the Tsunami , by Todd Kelsey, we are introduced to his background and life and how he came to know artificial intelligence. I think the opening Chapter is a rather superficial and surface level understanding of AI and why we should pay attention to it. This is not to discredit its importance or Todd's story of how he came to know about it but having grown up between the analog and digital world and having friends that work in all types of sectors I think his base assumptions are wrong.      For example, consider the following quote: "It’s probably safe to say that the field of artificial intelligence is in its early stages; ultimately the goal of artificial intelligence is to explore how far computers can be taken to simulate human intelligence, or potentially to exceed human intelligence" (Kelsey Surfing the tsunami p. 10). Artificial intelligence, as a real research field has been around since at least the 1940's when t