Like a majority of people, and especially as an English major, I find the increasing use of AI technology to be scary and impractical. However, over the past three weeks I have been learning about the different things that AI has impacted positively, or things in our everyday lives that we didn’t know were AI but evidently were AI all along. There are a few things that are promising in the future of AI: education, medicine, and crime. These are broad terms on purpose, since there are a variety of things that AI does to help each of them function already.
AI has a promising future for education. Already we have things like spell-check and grammarly, which are helping students fine-tune and check their papers daily, but what could the future have in store for education? The Office of Online Programs at University of Illinois writes in their article, “AI in Schools: Pros and Cons,” that AI provides teachers with unlimited access to many resources, “Just a few examples are Canva Magic Write… Curipod… Eduaide… and Quizzizz.” (AI in Schools: Pros and Cons.) These applications that are considered AI are helpful on many levels for educators- they are able to plan more effective and interactive lessons efficiently due to the use of these kinds of AI. In addition, the Office of Online Programs also writes that the use of AI can make lessons more inclusive. “Tools that offer text-to-speech, visual recognition, speech recognition, and more can help teachers adapt resources so that all students have an equal learning opportunity.” (AI in Schools: Pros and Cons) This is especially true in cases where there are students with special needs, as accommodation is made much easier by making material much more accessible for them. While I agree that these are wonderful pros enabled by AI, it is important to note that these tools are best used for polishing and refining, and I do not think that allowing AI to write entire lesson plans would be beneficial until the technology is much more advanced.
The use of AI in medicine is not a new thing; it’s used to diagnose patients, make drugs, and transcribe medical documents, among other things. I took a deep dive into the National Library of Medicine and came across “Artificial Intelligence: How is it Changing Medical Sciences and Its Future?” Basu et al. writes “The most recent application of AI in global healthcare is the prediction of emerging hotspots using contact tracing, and flight traveler data to fight off the novel coronavirus pandemic.” (Basu et al.) Although the article was published in 2020, before the public release of ChatGPT and the widespread use of generative AI, it still generates a sense of hope for the future. We all suffered through COVID-19, and I’ll admit that knowing we have the technology to track hotspots and the spread of a disease also provides a sense of relief. Outbreak risk software and applications similar ensure that each pandemic is handled more effectively and efficiently than the last. One of the concerns with AI, especially in medicine, is the concern that folks will lose their jobs to AI applications. The article states this as a myth, and writes “the fact is that physicians who understand the role of AI in healthcare will likely have an advantage in their career.” (Basu et al.) They go on to show a Radiologist job advertisement, where one of the requirements for the job is “must be … excited about a future where radiologists are supported by world-class AI and machine learning.” (Basu et al.) One of my biggest concerns for AI is the possible loss of jobs for real humans, so if it is true that this is a myth, I am hopeful that in the future AI will continue to transform medicine and make jobs easier for the people who do them.
One last aspect of AI that we discussed briefly in class was facial recognition, specifically its use in law enforcement. After we covered the risk of bias being transferred into AI, I wanted to know what was being done to overcome this obstacle and make the use of AI facial recognition accurate and efficient. Paolo Vilbon published an article last year, “A.I. And Its Impact on Facial Recognition Software” and wrote “One of the issues identified with artificial intelligence and facial detection is that AI face recognition tools ‘rely on machine learning algorithms that are trained with labeled data.’” (Paolo) Unfortunately Paolo’s article didn’t have any information on the progress being made on facial recognition software, but I think that a fix to the bias problem could be in our near future. If the issue with biased facial recognition software is because the AI is trained with biased data, then training AI with unbiased data would solve the problem. Obviously it is not as easy as it sounds, many countries were built on slavery and even in America racism and discrimination are still an issue today. If there was a way to filter out biased data from the unbiased data, that could be fundamental in transforming facial recognition software.
I would be lying if I said I was willing to accept that AI is the future, but regardless of whether any of us accept it or not: it’s a fact. As time goes on the issues and obstacles that prevent AI from being successful and accurate will come closer and closer to being overcome. I look forward to a day where all AI is trained with unbiased data, as far in the future as it may be. As a student, the promising future of education with AI tools is hard to not be inspired and excited for, especially when it comes to diversity, equity, and inclusion. Additionally, with the future of AI in medical science, we can help diagnose and treat patients more efficiently than ever. I can only hope that programmers and coders are doing their part to ensure the end of biased AI systems, which would transform the way AI can be used in many instances, not just education, medicine, and crime.