In the 1960s, the world’s first digital calculator was invented, fundamentally changing our understanding of computation and mathematics. The idea that this small device could find the product of a long series of numbers in a matter of seconds was met by both shock and applause. 

If you were to ask someone walking down the street at that time to give you an example of “artificial” intelligence, they most likely would have said “a calculator.” But, while many today would argue that a simple handheld calculator in no way emulates the true nature of artificial intelligence, we have to ask ourselves: what benchmark are we using?

To those living in the 1960 and 70s, the calculator was the penultimate example of the artificial mind. It was a faster, more efficient, and error-free way to compute numbers. No average human could compete with such a device; thus, in a very real sense, the calculator was artificial intelligence. This was our first conceptual model of AI.

Our definition of AI has changed over the years, converging at each breakthrough in computing. Machine learning and neural networks solidified the definition of AI; all that is lacking now is the notion of consciousness. As machines become increasingly capable, perhaps the bar for what constitutes AI will be reset in the near future, stating that only those machines which can effectively replicate human emotion deserve such a title.

With the advent of newer and more focused technologies such as natural language processing and social intelligence, perhaps our view of AI will eventually fall on a lateral continuum rather than a linear trajectory. In the continuum model, AI applications are developed on a needs basis rather than a never-ending fight to the top.

The notion that we may be living in a simulated world, powered only by our minds, made society question the very definition of “real.” People quickly jumped online to write about their theories surrounding reality, ranging from how the movie was simply an interesting work of fiction to those dissecting multiple layers of quantum physics as it relates to our understanding of the universe.

One particular subset of the population saw The Matrix as an extension of their daily reality: lucid dreamers. In short, lucid dreaming is the ability to consciously recognize the dream state. That is, a lucid dreamer becomes aware that they are dreaming, a state commonly coined “lucidity.”

The concept of lucid dreaming has likely been around for tens of thousands of years; however, modern history dates its earliest mentions of the topic to around 500 AD, when Tibetan monks regularly practiced the art of dream control as a means to enlightenment.

Today, the concept of lucid dreaming is hotly debated, particularly by those who do not believe in the possibility of dream control. However, countless lucid dreamers have documented their techniques and methods, and the general consensus lies in that lucid dreaming is a real, albeit strange, phenomenon.

MTC hired our firm to conduct an internal audit focusing on methods to improve strategy and operations for the company after a series of quarterly earnings shortfalls.

As an overview, our examination of MTC determined that while the strategic initiatives outlined by its internal consultants were sound, MTC lacked heavily in terms of operational efficiency.

For one, MTC had no operational efficiency plans set in place. This lack of efficiency resulted in severe time mismanagement over the last three quarters, resulting in needless overtime and missed deadlines.

Second, MTC had invested considerable time and resources in maximizing its market share through below-market bids on federal contracts. However, without a clear direction on how it would fulfill those contracts once awarded, MTC could not meet its contractual obligations on time.

The increased R&D demand on MTC paired with the failure of management to allocate operational time efficiently is the direct cause of the poor financial performance seen in the last three quarters. In short, MTC had “placed the cart before the horse,” to use the idiom, and failed to properly allocate the time needed for the influx of new contracts awarded.

While the above statement may initially sound facetious, consider that Americans as a whole agree on the majority of baseline principles that would help them thrive as a society. Moreover, when it comes down to specifics, Democrats and Republicans disagree on only a handful of hot-button topics such as climate change, abortion, and immigration.

After an in-depth analysis of over 1,800 surveys taken over the last 15 years and analyzing the responses of over 1.5 million Americans, it appears that when it comes to aspects such as government regulation, healthcare, social security, and quality of life, Democrats and Republicans agree over 80% of the time.

The primary reason that this dichotomy exists as it currently stands is because of inherent bias within many political surveys. Often, surveys are conducted with a predetermined response set in mind; thus, we find many leading questions that attempt to guide the respondent to choose one school of thought versus the other.

Within the analysis of 1,800 surveys, we selected only those issued by reputable, non-partisan pollers and reviewed each question for potential bias in wording and execution. Once we isolated the questions phrased in a neutral and unbiased tone, we analyzed the responses to produce the above figures.