Since its formal launch, ChatGPT has been receiving a lot of press and has also been the topic of – heated – discussions in the recent past.
I had played with generative AI some time back and also shared the result in one of my earlier posts.
Post ChatGPT, the investments in AI – or more specifically generative AI tech – based companies has seen a sharp rise.
There is also a general sense of fear – rising from uncertainty and the dread of the possibility of such technologies taking away specialized jobs and roles has been noticed across industries.
I was talking to an architect a few days ago and she said that in their community, the awe and fear of AI tech is unprecedented.
With just a few words, some of the sketches generated by tools like Dall-E, Craiyon, Stable diffusion etc are apparently so realistic and logical.. for example, when the query was to have the porch door opening out into the garden with a path to the main gate.. the image was generated in less than a couple of minutes..
With all the promise of creating new content quickly, many questions have also come up, without clear answers.
The first – also a topic of interest on aithougts.org – is that of ethics.
Whether it is deep fakes – btw, I had experimented with a technology that could have been used for this – when I was looking for tools to simplify podcast editing – on a platform called Descript – where I could train the model with my voice.. I had to read a predefined text for about 30 minutes – and then, based on written text, it could synthesize that text in my voice.. At that time, the technology was not yet as mature as today and so, I did not pursue.
I digress..
Getting back to the debate on generative AI, ethics of originality [I believe that there are now tools emerging that can check if content was generated by ChatGPT!] that could influence how students create their assignment papers.. or generate more marketing content, all based on content that is already available on the net – and ingested by the ChatGPT transformer.
Another aspect is the explainability of the generated content. The bias in the generated content or when there is a need for an expert opinion to also be factored in, would not be possible unless the source is known. The inherent bias in the training data is difficult to overcome as much of this is historical and if balanced data has not been captured or recorded in the past, would be very difficult to fix, or at least adjust the relevance.
The third aspect is about the ‘originality’ or ‘uniqueness’ of the generated content – let me use the term solution from now on..
There is a lot of work being done in these areas, some in research institutions and some in companies applying them in specific contexts.
I had an opportunity recently to have a conversation with the founder of a startup that is currently in stealth mode, working on a ‘domain aware, large language model based’ generative AI solution.
A very interesting conversation that touches upon many of the points as above.
You can listen to this conversation as a podcast in 2 parts here:
https://pm-powerconsulting.com/blog/the-potential-of-large-language-models-with-steven-aberle/
https://pm-powerconsulting.com/blog/episode-221/
Or watch the conversation as a video in 2 parts here:
https://www.youtube.com/watch?v=86fGLa9ljso
https://www.youtube.com/watch?v=f9DnDNUwFBs
Do share your comments and experiences with the emerging applications of GAN, Transformers etc.