There's a reason AI has been in the headlines. The possibilities for the field are endless and there are widespread applications for the technology. Below are just a few examples of how AI can be beneficial.
Efficiency: AI can enhance efficiency and productivity through the automation of traditionally time-consuming tasks like classification and computation. Automation of tasks like computation can often cut down on human error and free up time for individuals to focus on more complex questions and tasks.
Creativity and Innovation: Because of AI’s ability to process large amounts of information, AI is uniquely equipped to find patterns and offer predictions on a range of topics. AI has been effectively leveraged to predict medical and natural disaster risks.
Problem Solving: As with any new technology, AI provides us with an opportunity to adapt and innovate. LLMs in particular can be a great resource for brainstorming ideas and workshopping essays or stories.
In addition to the general improvements AI can bring in efficiency and problem solving, below are a few examples of how AI is being used in ways that benefit society and the pressing challenges we face today.
While AI can afford us many new opportunities, the technology also has many potential drawbacks that we, as conscientious users, need to be aware of.
Reliability: LLMs such as ChatGPT can run into issues with the reliability of their responses. Because there is little transparency on what data AI models are trained on, users can’t assess if the information generated comes from a reputable source. Additionally, LLMs have been known to “hallucinate” information such as citing a book that does not exist. When using these tools, you should always check any facts, laws, or sources referenced in a response.
Transparency: AI can reflect and the biases of the data it is trained on. This problem is exacerbated by the lack of transparency around the content being used to train models. Content generated by AI that is presented as purely factual can contain political or cultural biases. It is always best practice to reflect on LLM responses to determine if there may have been bias introduced.
Bias: AI tools have the capacity to excacerbate existing issues of bias and discrimination, especially when tools are used in decision-making. Notable examples of algorithmic bias have come up in healthcare, hiring, and policing.
Privacy Concerns: Many AI tools are created by for-profit organizations that have a vested interest in the data you feed the tools. For this reason, there are concerns around privacy about the information you feed generative AI.
Copyright and Intellection Property: At the moment, AI generated content is not covered under copyright law; however, this is a developing landscape and the line between AI generated content and content made by people with help from generative AI tools is blurry. There are concerns from creators across a wide range of fields about AI generated content being used to avoid paying artists. Additionally, there are concerns about copyrighted material being used to train AI.
Environmental Impact: Training AI Models is associated with massive energy expenditure as well as water consumption.
Ethical Considerations: With any new technology, there comes a risk of its abuse. AI tools can be used to spread disinformation, surveille vulnerable communities, and exacerbate already existing structural inequalities.