Executive Summary AI tends to be “whitewashing” in terms of how it will be used. What follows is an example of how AI will be used to disempower workers. Introduction Greed drives the AI bubble in multiple dimensions. This is throughout the financing process, how AI projects are sold, and how AI is intended to…
Executive Summary AI has engaged in the projection of human consciousness onto software since its beginnings. We cover the problems with these projections. Introduction While both AI and Data Science precede Big Data as concepts, it was Big Data that went through its recent hype cycle first (That is, in recent times, excluding the previous…
Executive Summary AI has engaged in the projection of human consciousness onto software since its beginnings. We cover the problems with these projections. Introduction Since the earliest days of AI, the emphasis has been on emulating humans. As we cover in the article Who Was More Accurate, Marvin Minsky or Hubert Dreyfus on AI?, a…
Executive Summary Data science is a term that is frequently used without questioning if the term is accurate. We explain why people should think twice when using the term data science. Introduction The term data science is a recent one, and it is used without most of the listeners questioning the accuracy of what the…
Executive Summary The IT media has helped push the AI bubble to feverish heights by bringing close to no critical thinking to AI coverage. We have assembled several quotations that point out the issue. Introduction These quotes provide insight into the reality of coverage on AI by IT media. Our References for This Article If…
Executive Summary AI, data science, and Big Data are frequently based upon false statistical relationships. These false relationships are only set to increase as the pressure mounts to obtain benefits from massive AI investments. Introduction There are massively false statistical relationships being promoted by AI, Big Data, and data science. This is expressed in one…
Executive Summary Most people that work with or read about AI know of the first and second bubble and winter. The second AI bubble was centered on expert systems. So what happened to them? Introduction The first AI bubble was based upon highly inaccurate projections on what could be accomplished in generalized intelligence or Strong…
Executive Summary Dr. Hubert Dreyfus predicted many issues that AI would face when it tried to emulate human brains. How accurate were his predictions? Introduction Hubert Dreyfus’s book, What Computers Can’t Do, was initially published in 1972 and updated several times throughout the decades. Dr. Dreyfus was not an AI academic, practitioner, or mathematician. Instead,…
Executive Summary Neural networks and deep learning are normally black box systems. This black box nature of neural networks leads to problems that tend to be underemphasized in a rush to promote these systems. Introduction Black boxes are where the activity within the solution is unknown to the users or observers. Generally, people tend to…
Executive Summary Neural networks and deep learning have been the primary methods behind the AI boom. Neural networks and deep learning are also more limited than generally known. Introduction Articles on AI or machine learning very frequently propose that deep learning is where the real “action” has moved. Authors invariably leave out that deep learning…