Exploring Artificial Intelligence Myths
“Everything will be automated by artificial intelligence, which will eliminate jobs for people.”
“AI is a technology from science fiction.”
“The planet will be overrun by robots.”
In the news media, at board meetings, and throughout enterprises, the euphoria around AI has given rise to numerous falsehoods. Some people fear that an “almighty” AI would take over the world, while others believe that AI is just a trendy term. The middle ground is where the truth lies.
According to Saniye Alaybeyi, Senior Director Analyst at Gartner, “The majority of enterprises have been sustaining or even growing their investments in artificial intelligence during the COVID-19 issue.” However, only 50 percent of these initiatives are ever produced.
AI is enhancing more than just routine tasks. IT executives must provide real business benefits, such as cost savings and improved processes, and by offering useful uses of the technology.
Six AI Myths and Misunderstandings
Myth #1: In the Era of the COVID-19 Epidemic, AI Is an Unneeded Luxury
During the COVID-19 crisis, AI is proving to be a key facilitator for cost optimization and business continuity. Contrary to popular belief, AI is bringing in money even in a time when businesses are having trouble with cash flow and unpredictable economic conditions. It is enhancing consumer interactions, speeding up data analysis, producing early alerts regarding impending disruptions, and automating decision-making.
Myth #2: AI and Machine Learning (ML) Are the Same and Interchangeable
Artificial intelligence is a subset of machine learning. A well-planned training and data collection approach is necessary for ML. Contrarily, artificial intelligence is a catch-all phrase covering a wide range of computer engineering approaches, including machine learning (ML), rule-based systems, optimization methods, and natural language processing.
Myth #3: Intelligent Devices Are Self-Learning
When ML software is done, it appears as though it can learn on its own. To enable the incorporation of fresh information and data into the subsequent learning cycle, experienced human data scientists, however, frame the problem, prepare the data, choose the appropriate datasets, eliminate potential bias in the training data, and most importantly, constantly update the software.
Myth #4: AI Can Be 100% Objective
Every AI system is built on information, guidelines, and other input provided by human professionals. Because every person has an innate prejudice in some manner, the AI also has biases. Even more prone to unintended bias or malicious impacts are systems routinely retrained, such as using fresh data from social media.
Even if your current AI approach is “no AI,” you should make this choice consciously after much thought and study. “At the moment, there is no way to completely banish bias; however, we have to try our best to reduce it to a minimum,” says Alexander Linden, VP Analyst, Gartner. “In addition to technological solutions, such as diverse datasets, it is crucial to also ensure diversity in the teams working with AI and have team members review each other’s work. This simple process can significantly reduce selection and confirmation bias.”
Myth #5: AI Will Mainly Eliminate Routine Tasks
Through forecasts, classifications, and groupings, AI helps organizations make decisions that are more accurate. These capabilities have allowed AI-based solutions to penetrate deeply into workplaces, replacing not just routine jobs but also enhancing more complicated ones.
Consider the use of AI to imaging in the medical field. AI-based applications for chest X-rays can identify ailments more quickly than radiologists. Robo Advisors are employed in the financial and insurance sectors for fraud prevention and asset management. Human involvement in those jobs isn’t eliminated by these skills, but it will eventually be restricted to noticing and handling exceptional occurrences. Be sure to change job descriptions, capacity planning and provide current employees retraining choices.
Myth #6: My Company Doesn’t Require an AI Strategy
Every company should think about how AI could affect its strategy and look at how it might solve some of the company’s business issues. Circumventing AI is like skipping the next stage of automation in many ways, which might put businesses at a competitive disadvantage.
Alaybeyi advises firms to periodically reevaluate their choice not to deploy AI, even if it is not an immediate solution to a problem. Organizations must identify suitable use cases that will take advantage of AI’s capacity to support human labor, choices, and interactions as well as additional potential for functional innovation.
Approximately 70% of what a manager presently performs today will be automated during the next four years. Enterprises must assess their options for integrating AI into their strategies and preparing for impending disruptions in such a disruptive environment.