History 5 (binary conversion :101) of Artificial Intelligence

AI encompasses many different aspects of our world, from virtual assistants to advanced trading algorithms. In essence, artificial intelligence is the ability of machines to simulate the human reasoning and intelligence process. To truly understand the direction of AI in the global economy, it is important to realize the complex history of artificial intelligence and its experience with flourishing outlook as well as dry spells. The progression of AI can be divided into a cycle of summers and winters; summers reflect times of scientific expansion and increasing investments in AI, while winters signify droughts of funding for AI projects and a retraction of progress in AI developments. The roots of AI date back to the 1950s when Alan Turing suggested that computers could reason like humans, but computational technology limited machines from doing so, as computers could only execute and not store commands at the time. However, upon developments regarding the understanding of an artificial, mathematical neuron for computers and psychologist Frank Rosenblatt’s method of improving that artificial neuron model years later and simultaneous growth in technology, strong reasoning, and problem-solving skills were developed within machines. Machine learning improvements helped computers optimize their problem-solving strategy. Growth would however be stifled for some time in the 1970s due to the Lighthill Report, which suggested that expectations and claims regarding AI at the time were exaggerated in nature; as a result, AI funding dried up and advancements slowed for some time. Growth would soon return with the integration of the C programming language and also the development of IBM Deep Blue: an AI-based system that defeated a human champion in the game of chess. Deep Blue’s success illustrated the expanding capabilities of AI(especially in the sphere of interaction with humans), but also the controversial idea that AI could technically outperform humans in certain tasks, raising ethical questions of all sorts. Since the 2000s to this day, AI has expanded in capabilities and precision across a variety of fields, through projects such as Pixl, a high-level x-ray scanning system for space exploration.

The team circled around a few specific ideas in discussing the research update. Many asked about and debated the relationship between AI and consciousness. The first question proposed to get discussions and moderated debates started was: “Given the fact that A.I. has a basis and is rooted in human-based neurological networks (deep learning), will we be able to create A.I with self-awareness i.e consciousness without fully understanding the human consciousness ourselves? The first point brought up was the term “consciousness” and what it means when we say and use it. Which then shifted the focus to where we find consciousness. An interesting answer based on religion and historical data was “everywhere and in everything”. Rather than a debate about consciousness, the scope was refocused on A.I, specifically the “thought process” vs. that of any human. This eventually led to the conclusion that “consciousness-methodology” was our current and best answer to that question. The next discussion question presented was: “Will we one day generate machines that do our work better than we do? If so, which job sectors should we be allowed AI to infiltrate? Which jobs are ethical for AI machines to take?” After this, the ethics and social responsibility of A.I was brought into question, whether or not we know how, where, when, to whom, and to what extent we should apply A.I and substitute machines from currently human-occupied jobs. The end conclusion on how we integrate A.I into our workforce without compromising job availability was to identify the benefits of A.I and to acknowledge when it can do an objectively better job than humans, and use this information to effectively merge human and A.I workforces, rather than competing for jobs. The discussion was then shifted back to consciousness in A.I, with the argument that A.I in certain cases when given the chance have acted in self-interest, making its own judgment based on a given scenario. Which lead to a more cynical discussion on whether human augmentation would be necessary for us to relatively “keep up” with the exponential growth of A.I. Adding to the cynicism in the air, a new perspective on consciousness was brought forth claiming that we humans think too highly of our level of intelligence based on our accumulated successes as a group. Some scholars believe we are incapable of creating “new” ideas, and our minds simply rearrange perceptions and interpretations of already existing knowledge, known as recursive theory. The same goes for A.I, meaning that are also possess a level of recursivity, meaning the only difference between our levels of intelligence is allotted time and different levels of results. One final point mentioned was the dilemma on A.I intimacy, and whether it is “good” to allow robots to fill our not only physical but emotional needs, along with potential implications of substitute humans for A.I intimacy in the short and long term.

Artificial intelligence has become a significant force to consider in terms of global economic forecasting and raises interesting questions of prediction. For example, are led to ask ourselves, “How will the race for AI integration within military defense forces affect trade/economic relations between the US and China? ”The interaction between artificial intelligence and the human workforce is an additional topic that can be further explored, especially in regards to AI’s position and relationship to humans within the workforce. Such considerations also lead to interesting ideas of ethical dilemmas with the field of AI. The are many aspects of A.I that can and need to be explored within the immediate future for us to thrive in a new era of human existence.