Common Misbeliefs about AI and ML and the Truth Behind Them
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Artificial intelligence (AI) and machine learning (ML) have generated a whirlwind of activity and excitement across industries and domains, promising transformative capabilities that sometimes seem too good to be true. Amidst the buzz, it’s imperative to discern fact from fiction and gain a pragmatic understanding of the genuine value of these technologies. Yet the allure of AI and ML often leads to inflated expectations. Headlines and marketing campaigns paint a picture of AI as an all-encompassing solution capable of solving any problem. The reality is far more nuanced.
One of the most common misconceptions when implementing AI or ML solutions is that these technologies rapidly deploy and provide immediate improvement in operations. Developing an effective, scalable strategy that utilizes appropriate models for a company’s goals is complex and requires specialized skills and resources. While transfer learning can significantly reduce the data and time required to develop these models, it is not universally applicable. As such, fine-tuning is typically needed to achieve optimal performance and results.
Not all information is good information. AI and ML models need access to complete, consistent, relevant data to formulate patterns and relationships for accurate predictions. They are only as reliable as the data fed into them, so providing these models with massive, unrefined data often leads to irrelevant predictions. Unfortunately, data can become unintentionally biased, leading to models outputting biased results. In conjunction, people frequently overestimate AI’s capabilities, assuming AI has human-level reasoning and comprehension and cannot make mistakes.
Organizations commonly underestimate the importance of the human element in maximizing the full value of AI and ML because these technologies work best with human interaction, not as replacements for people. AI cannot replicate intelligence or creativity, nor does it possess domain expertise or full contextual understanding. People are still needed to interpret results, understand the implications of AI recommendations, and make nuanced decisions. This is critical because AI and ML models can make mistakes, especially if the data they collect is flawed. Unintentional bias, like dominant cultural prejudices, requires humans to monitor and fine-tune to eliminate these problems.
Implementation and results
The vital first step in implementing AI or ML into a business is to develop a strategy based on the organization’s goals. This step becomes more efficient with a strong team involving data scientists, engineers, and other necessary applicable experts who stay up to date on the latest developments, regulations, and best practices to harness the technology’s full potential. Businesses that evaluate the source of their AI or ML models can ensure they avoid hype and overpromising from the vendor.
Assessing data availability while investing in high quality ensures better, more targeted results, which is crucial. The designated team can increase success by identifying and researching studies and use cases and collaborating with experts. When looking to outside sources for best practices, it is important for organizations to pay strict attention to any applicable regulations to maintain compliance. Once implementation is ready, prototyping and experimentation provide invaluable feedback.
Measuring outcomes is both simple and critical. One common measurement technique is using specified and targeted key performance indicators (KPIs) and return on investment (ROI) analysis, adding them to the existing business plan. It is also important to readdress scalability and resource optimization often, as well as monitor and evaluate model performance. Customer impact cannot be overstated, so surveys and other means of obtaining responses help show areas for improvement. Putting all these things together and incorporating the data into a feedback loop will allow for reliable input into the next stage of development.
The real-world value of AI and ML models is expansive despite the misunderstandings surrounding the technology. Many industries use these models to simplify tasks, increase business, and provide customer support, among countless other benefits.
Healthcare. AI and ML can be used to improve the diagnosis and treatment of diseases, develop new drugs and therapies, and personalize healthcare for individual patients.
Law. The legal industry contains massive amounts of text, requiring more language processing. Automating those tasks increases efficiency.
Application development. AI code generators, like Github’s Copilot, Amazon’s Codewhisperer, Meta’s Code Llama, and similar tools, significantly accelerate development.
AI and ML models are also useful for general organizations by converting legacy code to modern code and running customer support chat features. Another critical business concern is data governance and security. As companies grow, the amount of data they have gathered continues to expand, creating issues with protection and security. AI tools help solve much of this problem through automation.
These technologies have come a long way but are still evolving and will continue to do so for years to come. This is partially due to advancements in research and improvements in algorithms as developers learn new ways to achieve better results, which is also facilitated by increased computation power. As time passes, more data becomes available for consumption, which is often more detailed. The more relevant and accurate the data AI and ML have to work from, the better they become.
Through transfer learning, pre-trained models accelerate the speed with which new models can become up to date, allowing them to learn new information much sooner; model customization and fine-tuning are vital steps in transfer-learning growth and development. While technological improvements lead to AI and ML evolution, a major component in the advancements themselves is competitive pressure. With businesses needing to stay ahead of the competition, the demand for innovation is endless, driving people to push technology further.
Where does tech go from here?
Automation will continue to be a common and critical use for AI and ML models, from enhanced customer support experiences to relieving people of repetitive clerical work. Heavy lifting in the coding and programming landscape leaves increasing opportunities for growth. And entertainment options will expand, bringing more detail and realism to technologies like virtual and augmented realities.
Though AI and ML generate many benefits, the absence of comprehensive regulations has led to debates regarding the need for clear guidelines to ensure ethical and responsible development and use. The proliferation of deepfake technology raises concerns about the spread of false news and identity theft, and the use of AI to amplify disinformation campaigns poses significant challenges. There has even been talk of AI’s role in autonomous weapons systems and the implications of using it for military purposes. The future will bring many changes to these technologies, some good and some bad. Understanding the true capabilities and limitations of AI and ML is critical to their impact on society.
Forbes Advisor reported that 97 percent of business owners think their companies will benefit from using ChatGPT, a popular form of AI. Yet, the same article states that 43 percent of companies have concerns regarding becoming technologically dependent. These numbers indicate the confusion still surrounding this technology; not all businesses need the same level of AI, if at all. With the ever-evolving and expanding utilization of AI and ML in worldwide industries, continuous adaptation and discerning fact from fiction are key to success.
About the Author
Shrivathsan Kumar is a machine learning and platform engineer with over 13 years of experience in IT infrastructure, SRE, DevOps, data science, and advanced AI application architecture. He holds a bachelor’s degree in electronics engineering from SASTRA University and a master’s degree in data science from Liverpool John Mores University. Connect with Shrivathsan on LinkedIn.
Disclaimer: The author is completely responsible for the content of this article. The opinions expressed are their own and do not represent IEEE’s position nor that of the Computer Society nor its Leadership.