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The risks of Deepfakes and a proposal to combat them - Edited

Author note. I wrote this article below after seeing the "Sassy Justice" deepfake online in 2021. Since then the matter has become much more serious since the proliferation of online AI tools in the last year. Update : The proposal has been updated to include copyright of one's own voice in the wake of the Stephen Fry incident and another example of cyberstalking with facial recognition.  Update 2: The USA is considering legislation like this.  https://petapixel.com/2023/10/16/no-fakes-act-seeks-to-ban-unauthorized-ai-generated-likenesses   Proposal to United Nations General Assembly (UNGA) Pre-empting the problem of Deepfake videos Background We presently stand on the edge of an abyss in which social media threatens to uproot our world order and cast us into chaos, as we see with the recent attacks (6 January 2021) on the American Capitol by conspiracy theorists incentivised on social media. Deepfake videos are motion pictures that are created to look like they depict
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What is Generative AI?

This is the second of two posts on Generative AI. Late last year (November), OpenAI released ChatGPT to the public. However, it took a few months for the internet at large to catch on and realise the potential of this new tool. The trend started to take off in December, and by February everyone was talking about it. ChatGPT is an example of a "Generative AI". To recapitulate, let's see what this means. For convenience, let's call AIs that are not generative, "regular" AIs. Regular or "Narrow" AI (Artificial Intelligence): Regular AI, often referred to as "narrow" or "specific" AI, focuses on building systems that can perform specific tasks or solve specific problems. These AI systems are designed to operate within predefined boundaries and excel at specialised tasks. For example, a regular AI could be created to classify images, play chess, or process natural language. Another example is the AI that chooses your words in predict
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How to do Proper Online Research

Introduction On this page I list common errors people make in doing online research, that is, gathering data online for the purpose of ultimately writing a research document of some sort, e.g., an academic paper, a factual book, etc. I hope this page is helpful to you. If you think it needs some additional pointers mentioned on it, please let me know. Please note this page contains the author's opinions only and may differ from the Rules and Regulations of the University. Please consult the University Rules, which overrule these. http://www.cracked.com/blog/5-easy-ways-to-spot-b.s.-news-story-internet  Interpreting and Choosing Sources The most important thing is to use reputable, recognised, accurate sources. There is a lot of information on Internet which is of questionable provenance. In particular, you should avoid making the following mistakes:   Do not assume that because a page is online, its content is worth referencing. The content may be completely inaccurate, false, misl
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