Artificial Intelligence: Tool or Threat?

English 09

14 May 2018

Artificial Intelligence: Tool or Threat?

As seemingly every day brings more news about how artificial intelligence (AI) will affect the way humans live, an intense debate has broken out over whether society should embrace it or not. AI as a whole is the assumption and construction of advantageous computer systems capable of performing tasks that typically require human comprehensions, such as speech recognition, critical thinking, visual perception, speech recognition, automated decision-making, and language translation. However, media often paints a different picture, suggesting that these computers will eventually outsmart us and make decisions based on their own desires. The growth of AI presents society with a difficult decision to trust the promise of valuable machines or regulate the pace at which technology changes. Humans must embrace the use of artificial intelligence.

Society must encourage the uninterrupted development and application of artificial intelligence (AI) technology. As the computer industry continues to experience a change of ever-increasing pace, enabling well-established data companies to continue research and development of AI will allow computational power to further its beneficial impact on the future. Increased implementations of basic types of AI will lead reactive machines “to become mainstream … [and] impact many aspects of our lives in truly ubiquitous and meaningful ways” (Haupter, Head of Microsoft Asia). These types of reactive AI systems let a computer be purely reactive without the programmed ability to make memories or inform decisions based on past experiences. IBM’s chess-playing supercomputer and Google’s undefeated AlphaGo have shown that reactive machines have pattern analysis capabilities comparable to humans. IBM has spoken about their plans to implement their technology into pharmaceuticals, wealth management, and rapid file sorting. Similarly, Google has been taking its research to improve search engine results, voice assistance predictability, and conversational replies. These machines will only evolve to become faster at recognizing patterns in specific situations, but it’s imperative that businesses utilize the power these computers offer to improve workflow speeds. As connections between automation machines and humans grow, one may be able to realize how the more complex AI systems can have a greater impact on how humans live. Another, more complex type of artificial intelligence includes machines that have the ability to retain memory from previous situations; unlike reactive machines, these machines factor in patterns and previous experiences from data training to make decisions. As scientists begin to understand the intricacies of abstract machine learning, the “computer industries [are] ... becoming progressively reliant on the promise of breakthroughs and discoveries in artificial intelligence” (Rayland). In fact, AI networks are beginning to stir up discussion in healthcare, finance, cyber security, automotive, and agricultural industries. AI in these fields—while currently limited in superintelligent capability—have been tested on small-scale applications with diagnosing patients in hospitals, wealth management and prediction, anticipating possible cyber attacks, and directing new farming. In comparison to reactive machines, these more complex AI systems give scientists more computational power to work in equally complicated work fields. As one steps back to look at the bigger picture, these small-scale AI tests will soon grow in size to become integral parts of many industries. It won’t be long until more industries like entertainment, manufacturing, and retail will turn to AI to keep up with the pace of technological change. Interlinking more people with more effective technologies clearly shows the potential that artificial intelligence can have on society. With the constant work, time, and resources being put into the endeavour of creating such a system that embraces the possibilities of these industrious machines capable of learning, one must support the real world implementations of AI.

AI Technology will give us an opportunity to better understand ourselves as humans. As scientists push through the early stages of building artificial intelligence, researchers are compelled to look at the general structure of the human brain itself; the challenge lies between simulating the power of the brain and programming it to work in a similar, artificial sense through a computer. For humans to exist in the automation revolution, "it's important to understand that artificial intelligence is almost a humanities discipline. It's really an attempt to understand human intelligence and human cognition” (Thrun). In the same way that humans learn, computers need to be trained based on a set of data to begin recognizing situations around them. The difference is that computers will catch onto learning much faster than humans. And, so, working in a philosophical sense, researchers are looking into how computers connect one variable to another so quickly; this research is being done in the hopes that the human brain can be aided to learn faster the way that computers do. Again, this is where one must truly trust in the promise of AI and embrace the research that is being done. As scientists' understanding of human nature expands, they may be able to apply same functionality to machines. While the implications of such a situation are years into the future, "it's up to us to create our future, and our future will be an exaggerated version of ourselves today. This process, however, will teach us a great deal more about ourselves and our own humanity" (Benedict). The ultimate goal of artificial technology is for evolution to go full circle: humans obtain a greater understanding of AI, which then leads to AI having a greater understanding of humans, and so on. The pace of evolutionary change has the probability of growing ever-exponentially, which can lead to a continually expanding comprehension of the universe. While mostly theoretical, many analysts believe that