- Various types of generative AI exist, including text generators, image generators, sound and music generators, video generators, and research discovery tools.
- Ethical considerations surrounding generative AI include environmental impacts, accessibility, copyright issues, rights management challenges, privacy concerns, and accuracy challenges.
- Generative AI usage requires careful consideration of its implications on society, including academic integrity, data privacy, and the reliability of generated content.
Also read: Adobe Premiere Pro’s generative AI tools make video editing easier
Dive into the world of generative AI, ranging from text to images. Uncover ethical dilemmas and practical challenges shaping its usage in today’s society.
What are the types of generative AI?
There exist numerous varieties of generative AI capable of producing text, images, sound, video, and more.
1. Text generators
Text-based generative AI tools generate fresh text resembling the data they were trained on. The training process for these AI chatbots involves ingesting substantial amounts of text from sources like webpages, books, and other texts, followed by analyzing the text to identify patterns and relationships in human language.
2. Image generators
This category of AI learns by analyzing datasets of images accompanied by captions or text descriptions. If it understands two distinct concepts, such as a cat and a skateboard, it can combine these concepts when instructed to create an image of a cat on a skateboard.
3. Sound and music generators
AI music generators scrutinise music tracks and associated metadata (artist name, album title, genre, release year, playlists) to recognise patterns and characteristics specific to certain music genres. They may also be trained on song lyrics.
4. Video generators
Producing a video typically necessitates the integration of audio, visual, and textual elements. Some generative AI video programs have utilised existing videos to learn how to create new ones, while others have sourced these three elements to craft videos from audio, visual, and text sources.
5. Research discovery and explanation generators
Certain generative AI tools can automate segments of the research process and simplify the comprehension of lengthy, intricate texts. This type of AI often analyses research papers uploaded by users to extract crucial information or summarise a paper.
Examples of generative AI that can facilitate research discovery and provide explanations include: Elicit and Scite.
Also read: Google.org launches generative AI accelerator for nonprofits
What are some ethical considerations for using generative AI?
Generative AI tools can aid us in our daily routines, professional endeavors, or educational pursuits. Similar to any tool, ethical, evaluative, and appropriate usage is paramount. Below are ethical considerations associated with generative AI for your exploration.
1. Environmental impacts
The construction, training, and operation of generative AI models demand a significant amount of energy and contribute to carbon emissions. It also entails substantial water consumption for cooling purposes. Researchers and companies are exploring methods to render generative AI more sustainable, yet it remains crucial to assess whether the environmental impact of utilizing AI justifies its benefits and to employ generative AI tools as efficiently as possible.
2. Accessibility
While numerous generative AI tools are presently available at no cost, an increasing number are imposing charges for access or premium features. This poses barriers for individuals who cannot afford access. Nonetheless, generative AI tools can also serve as aids for accessibility.
3. Creatorship and academic integrity
University experiences cultivate your knowledge and skills to equip you adequately for employment or further studies. Leveraging generative AI to generate content that you have not expanded upon, modified, or meaningfully
4. Copyright considerations
Several copyright issues arise in the development and use of generative AI tools. The gathering of training data, potential inclusion of copyrighted material, and the necessity of acquiring permission or licenses from rights holders are crucial factors. Employing significant portions of copyrighted works as inputs or outputs with AI tools may have copyright implications. Though Canada lacks a statutory basis for copyright protection of AI-generated outputs, they may still infringe on existing copyrights, posing legal risks for developers and users.
5. Rights management challenges
Generative AI poses intricate challenges for rights management as technology advances rapidly, necessitating regulatory adaptation. Your content contributions carry significant implications for rights management when using generative AI tools. Submission of content to AI platforms grants them rights to reuse and distribute it, potentially leading to copyright or privacy breaches. Exercise caution, especially when sharing information not created by you, with AI platforms.
6. Privacy concerns
Similar to other digital tools, generative AI tools collect and store user data upon signup, allowing companies to tailor their tools and engage users. However, this data may also be sold or shared with third parties for marketing or surveillance purposes. Be cautious when providing sensitive information to AI tools, including personal, confidential, or proprietary data.
7.Accuracy challenges
Generative AI models often lack transparency regarding the data used for training, making it difficult to verify content credibility. They cannot disclose data sources or provide accurate citations, potentially leading to misinformation. AI models may produce incorrect, biased, or outdated information, sometimes referred to as “hallucinations.” To mitigate this, always verify AI-generated content using reliable sources before use to avoid spreading misinformation.
