Thanks for sharing, Sebastian. Some food for thought:
- The differentiation between 'general' and 'generative' is a bit confusing. In this article, you're talking about machine learning algorithms being used to generate, and you're right, this is not 'true' AI in the sense it is not self-aware. The phrase 'generative AI' has started to pop up more and more, but imho this is branding, and over-stating capabilities. I've not previously seen it used to describe non-artistic machine-learning driven functions such as self-driving cars. Would it perhaps be of benefit to use 'generative ML' or 'generative AI' to describe only those items that are being generated--art, text, etc--through the smooshing-together of existing artworks or texts? That makes it easier to understand why artists and writers are outraged--because it is their work that is being used and combined to create 'new' works, hence the call for IP law updates and for payment. That's the kind of thing that could prove a risk for those using generative tools such as chatGPT or DALL-E for campaigns purposes.
- I find it really interesting that gen Z have lower tech skills than gen Y; they're digital natives, but not digitally proficient. I personally wonder how this will influence uptake of generated content, as if the technically understanding of how content is created is not there, what basis for critique? It's certainly something to mull over!
- To targeted communication and campaign slogans, this is something chatGPT does pretty well. I just asked it to "Pretend that a referendum has been announced to decide whether Australia leaves the Commonwealth and becomes an independent country. Generate 5 slogans FOR independence and 5 slogans AGAINST it."
Does it know that "A New Chapter Begins" is super-lame? No. Or that "Australia Unleashed" sounds a bit violent/dramatic? Probably not. But "Our time, our nation" is just the kind of thing that you could imagine a campaigns team throwing into the mix. In the slogans against independence, the diametric opposite of that slogan was "Common goals, common ground"--less compelling, but you can see the focus on commonality in the face of disruption, and this was just one example it gave.
- Perhaps you could explain in further detail the difference between auto-translation we already have and how you see machine learning assisting this for campaigns purposes?
- How do you see AI technologies revolutionising mass participation and engagement in a positive way (fun, meaningful, inclusive), in light of research indicating that it is more likely to create divisive, exclusive, radicalising content?
Thanks for sharing, Sebastian. Some food for thought:
- The differentiation between 'general' and 'generative' is a bit confusing. In this article, you're talking about machine learning algorithms being used to generate, and you're right, this is not 'true' AI in the sense it is not self-aware. The phrase 'generative AI' has started to pop up more and more, but imho this is branding, and over-stating capabilities. I've not previously seen it used to describe non-artistic machine-learning driven functions such as self-driving cars. Would it perhaps be of benefit to use 'generative ML' or 'generative AI' to describe only those items that are being generated--art, text, etc--through the smooshing-together of existing artworks or texts? That makes it easier to understand why artists and writers are outraged--because it is their work that is being used and combined to create 'new' works, hence the call for IP law updates and for payment. That's the kind of thing that could prove a risk for those using generative tools such as chatGPT or DALL-E for campaigns purposes.
- I find it really interesting that gen Z have lower tech skills than gen Y; they're digital natives, but not digitally proficient. I personally wonder how this will influence uptake of generated content, as if the technically understanding of how content is created is not there, what basis for critique? It's certainly something to mull over!
- To targeted communication and campaign slogans, this is something chatGPT does pretty well. I just asked it to "Pretend that a referendum has been announced to decide whether Australia leaves the Commonwealth and becomes an independent country. Generate 5 slogans FOR independence and 5 slogans AGAINST it."
Does it know that "A New Chapter Begins" is super-lame? No. Or that "Australia Unleashed" sounds a bit violent/dramatic? Probably not. But "Our time, our nation" is just the kind of thing that you could imagine a campaigns team throwing into the mix. In the slogans against independence, the diametric opposite of that slogan was "Common goals, common ground"--less compelling, but you can see the focus on commonality in the face of disruption, and this was just one example it gave.
- Perhaps you could explain in further detail the difference between auto-translation we already have and how you see machine learning assisting this for campaigns purposes?
- How do you see AI technologies revolutionising mass participation and engagement in a positive way (fun, meaningful, inclusive), in light of research indicating that it is more likely to create divisive, exclusive, radicalising content?