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From Slop to Determinism – Shifting the Narrative Space

3 points by pyeri ↗ HN
What I realized today while sipping my morning cup of chai is that a large part of the present AI hype cycle is just about the 'narrative of AI'. The LLM technology itself is just a digital tool like many others that came before it but all this chatter about 'AI is the future', 'learn it or perish', 'machines will replace humans soon', etc. keeps it in the news and creates a burger out of nothing. But folks lose their energy and sleep over this which becomes a problem. And many a benign enterprises are falsely led to believe that this technology can do wonders, it's a great disservice being done by those selling these bridges.

For folks like us who are fed up of this slop narrative - instead of fighting it, it's better to try and shift the narrative towards determinism. For centuries and millennia, we have been trained to avoid and even fear fatalism (a negative aspect of determinism), and look towards future with hopes for betterment and prosperity. There is nothing wrong with having hope but for the first time in known history, the 'future will be glorious' theme isn't looking so bright, at least to a large number of folks connected to grassroots.

In order to push back against this narrative, we need to turn determinism into a force of good, or at least a force preferable to the alternative. Even in the LLM space, most of the utilitarian things are happening in the low-end, open source and local LLMs niches which are more deterministic in approach. What can we do to bring back people's interest in regular deterministic programming with C, Java and Python?

4 comments

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masking
I just tried to convince a major blockchain company that contract testing was a miracle cure for less loops in their AI agents. I’m probably right in some regards and wrong in others. But it does feel nice to use my brain to try to solve problems
sizero
Can you elaborate please? Interested in your view
NMTri1110
Nowadays, people often optimize for short feedback loops, it's every where from the way we consume, to the way we work. It's so much faster to get feedback when you build with AI, a feature only take 1-2 days compared to traditional coding which can take a few days just to debug a few things. If we could significantly shorten the feedback loop with deterministic programming (make debugging way faster, make writing code easier, make building things more high level) that would bring back people's interest. I also think that if the cons of AI is more evident, people will use it less. In AI I also like the fact that it's so easily customizable to my specific case, specific wants and needs, C, Java, Python don't have customizable functions where I can just enter my wants, needs, context and get the thing I want, if I need to get something I want, customize something I need to read docs and learn how to do that, this corresponds to longer feedback loop and less convenience.

But there are problems that needs more nuances and not necessarily deterministic, in those cases AI could help.