Artificial Intelligence

For good reason, the phrases artificial intelligence (AI) and machine learning (ML) have generated a lot of excitement in the technological industry. They assist firms in streamlining procedures and uncovering data in order to make better business decisions. They are boosting practically every sector by enabling smarter work, and they are quickly becoming crucial technology for firms to maintain a competitive edge. These technologies are responsible for features such as facial recognition on smartphones, customized online shopping experiences, virtual assistants in homes, and even illness diagnosis. The need for these technologies, as well as personnel proficient in them, is increasing. According to a Gartner survey, the average number of AI initiatives in place at a company is likely to more than treble over the next two years.

Organizations face challenges as a result of this exponential expansion. They claim that the most problematic aspects of these technologies are a lack of expertise, difficulties comprehending AI use cases, and worries about data scope or quality. AI and machine learning, which were once the stuff of science fiction, are now becoming ubiquitous in business. While these technologies are closely linked, there are significant distinctions between them.

According to Bethany Edmunds, assistant dean and lead faculty for the Northeastern computer science master’s program, artificial intelligence is a poorly defined word that leads to the misunderstanding between it and machine learning. “Artificial intelligence is simply a machine that appears to be intelligent. That’s not a very good definition, because it’s akin to stating something is ‘healthy.’ “What does that mean?” she asks. “At its most fundamental, artificial intelligence is when a computer appears human-like and can mimic human behavior.” Problem-solving, learning, and planning are examples of these behaviors, which are achieved through studying data and discovering patterns within it in order to repeat such behaviors.

Machine learning, on the other hand, is a subset of artificial intelligence, according to Edmunds. “Whereas artificial intelligence is the overall look of being clever, machine learning is where robots take in data and understand things about the world that humans would find challenging,” she explains. “Machine Learning has the potential to outperform human intellect.” Machine Learning is generally used to handle massive amounts of data fast using algorithms that evolve over time and improve at what they’re supposed to accomplish. A manufacturing plant’s network may collect data from equipment and sensors in volumes much above what any human is capable of processing. Subsequently, Machine Learning is used to detect trends and identify anomalies that may signal an issue that people may then solve. “Machine learning is a technology that enables machines to obtain knowledge that humans cannot,” she explains. “We do not fully understand how our vision or language systems work—tough it’s to express in a simple way.” As a result, we rely on data and give it to computers so they can imitate what we believe we’re doing. That is what machine learning accomplishes.”

AI is predicted to generate considerable economic value and improve worker capacities, according to studies. “By 2021, the increased use of AI in enterprises will generate $2.9 trillion in corporate value and 6.2 billion hours of worker productivity.” AI and machine learning are transforming how organizations communicate with consumers and offer more in less time, from predictive analytics and deep learning to chatbots and picture recognition. AI technology may be employed in a wide range of industries, including healthcare, sales, human resources, operations, manufacturing, marketing, and, of course, technology. AI application cases that are often explored include self-driving cars and other autonomous technologies, the Internet of Things (IoT), medical diagnostics, robotic aid in manufacturing, contactless purchasing, job candidate selection, and many others. The opportunities for business are limitless. However, in order to incorporate AI and machine learning technology into business, we must first have a workforce that is capable of managing the technology.

AI tends to elicit unpleasant emotions in workers. Employees frequently believe that adopting AI entails removing the human aspect from employment. According to Forbes, 34% of employees believe their occupations will be automated by 2023. With so many worries about job security stemming from AI, a large portion of the workforce has lost faith in the technology.

While AI systems may one day destroy employment focused on rote activities (such as assembly line roles), AI really has the ability to create more jobs, not fewer. While Hollywood portrays AI as a super-intelligent technology capable of human dominance, real-world AI is often limited to single jobs, necessitating human interaction to support and deploy technology in the workplace.

Reference:

Burnham, K. (2020, November 10). Artificial Intelligence vs. machine learning: What’s the Difference? Northeastern University Graduate Programs. Retrieved February 22, 2022, from https://www.northeastern.edu/graduate/blog/artificial-intelligence-vs-machine- learning-whats-the-difference/

Watters, A. (2021, June 4). Using AI in business: Examples of artificial intelligence application in business. Default. Retrieved February 22, 2022, from https://connect.comptia.org/blog/using-ai-in-business#:~:text=Artificial%20Intelligence%20and%20Business%20Today&text=Commonly%20discussed%20use%20cases%20for,selection%20and%20so%20much%20more.

West, D. M., & Allen, J. R. (2020, April 28). How artificial intelligence is transforming the world. Brookings. Retrieved February 22, 2022, from https://www.brookings.edu/research/how-artificial-intelligence-is-transforming-the-world/

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