Recently, I was having a discussion on the potential effects of large scale adoption of LLMs (and AI in general), and one of the risks was a move towards uniformity/homogeneity, or a loss of randomness in the human experience. (1)
Basically, if algorithms are designed to give you ‘the most likely’ or ‘the best’ answer (this may not always have to be the case (2)), then everyone would get similar answers and be driven to same things.
Add to this the fact that as more people use LLMs, more and more content on the internet will be LLM generated, and therefore the training data used for LLMs will include a higher percentage of LLM created data as opposed to human created data.
Fear not!
A data
scientist at NTUC fairprice in Singapore has managed to build a machine (apply an algo) that
gives very interesting answers:
The AI
built by NTUC understands that, after a meal, you can use 2 similar products,
similar in the sense that you, as a human, have a choice to do 1 of 2 things:
- do the dishes using the sponge, or
- have a piece of chocolate
There still is hope!
Or despair: “mummy/daddy, it’s not me! It’s the machine who told me I could do either one since they are similar”
- https://www.linkedin.com/feed/update/urn:li:activity:7134835884893290497/
- https://www.linkedin.com/feed/update/urn:li:activity:7124551149268963328/
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