[mikehollinger]

> About "people still thinking LLMs are quite useless", I still believe that the problem is that most people are exposed to ChatGPT 4o that at this point for my use case (programming / design partner) is basically a useless toy....

and

> a key thing with LLMs is that their ability to help, as a tool, changes vastly based on your communication ability.

I still hold that the innovations we've seen as an industry with text transfer to the data from other domains. And there's an odd misbehavior with people that I've now seen play out twice -- back in 2017 with vision models (please don't shove a picture of a spectrogram into an object detector), and today. People are trying to coerce text models to do stuff with data series, or (again!) pictures of charts, rather than paying attention to timeseries foundation models which directly can work on the data.[1]

Further, the tricks we're seeing with encoder / decoder pipelines should work for other domains. And we're not yet recognizing that as an industry. For example, whisper or the emerging video models are getting there, but think about multi-spectral satellite data, fraud detection (a type graph problem).

There's lots of value to unlock from coding models. They're just text models. So what if you were to shove an abstract syntax tree in as the data representation, or the intermediate code from LLVM or a JVM or whatever runtime and interact with that?

[1] https://huggingface.co/ibm-granite/granite-timeseries-ttm-r1 - shout-out to some former colleagues!

[simonw]

Andrej Karpathy: https://twitter.com/karpathy/status/1835024197506187617

> It's a bit sad and confusing that LLMs ("Large Language Models") have little to do with language; It's just historical. They are highly general purpose technology for statistical modeling of token streams. A better name would be Autoregressive Transformers or something.

> They don't care if the tokens happen to represent little text chunks. It could just as well be little image patches, audio chunks, action choices, molecules, or whatever. If you can reduce your problem to that of modeling token streams (for any arbitrary vocabulary of some set of discrete tokens), you can "throw an LLM at it".

[vbezhenar]

But I need enormous amounts of learning data and enormous amount of computing to learn new models, right? So it's kind of useless advice for most people who can't just parse github repositories and teach their new model using AST tokens. They have to use existing opensourced models or API and those happened to use text.

[monero-xmr]

The environmental arguments are hilarious to me as a diehard crypto guy. The ultimate answer to “waste” of electricity arguments is that energy is a free market and people pay the price if it’s useful for them. As long as the activity isn’t illegal then training LLMs or mining bitcoins, it doesn’t matter. I pay for the electricity I use.

[Eisenstein]

Do you think that it we should make it illegal to mine coins if the majority of people think the environmental cost is too high?

[adwn]

One argument against that line of thinking is that energy production has negative externalities. If you use a lot of electricity, its price goes up, which incentivizes more electricity production, which generates more negative externalities. It will also raise the costs for other consumers of electricity.

Now that alone is not yet an argument against crypto currencies, and one person's frivolous squandering of resources is another person's essential service. But you can't simply point to the free market to absolve yourself of any responsibility for your consumption.