|
|
|
|
|
|
by datpiff
1187 days ago
|
|
|
> There are too many world-changing things moving forward at the same time, and I'm only looking at such a small cut of the tech sphere. I don't know what to do with myself, I feel so thoroughly unprepared. I think the general trend is that actual useful applications are emerging from enormous models trained and owned by billion dollar companies only. Even projects that aim to run models on private consumer hardware are dependent on commercial orgs to produce them. It doesn't seem like that is likely to change. I don't think there are many positions/jobs/roles for people doing integral, foundational work that requires a deep understanding of ML. Becoming a world expert in ML will probably only open up opportunities at a dozen companies. A very shallow surface-level understanding of ML already puts you leagues ahead of the general population. In terms of job security, figuring out how to use an ML API will get you hired faster than knowing how to advance the field. We don't actually need that many Fabrice Bellards. |
|
|
One way to think about it: Today's LLMs require incredible outlays of capital and processor power (and crews of folks with doctorates), such as billion dollar companies can provide. But how is that different from what Intel brought to commodity CPUs in the '90s/'00s, or what Nvidia brought to GPUs in the '00s/'10s? Or even what Cisco and folks brought to networks?
Though we may never design an artisanal CPU/GPU/router, we get to work with them every day to make things, and to communicate. These LLMs can be that for us at this moment. Let's go out and enjoy them, and see what we can make within their (vast) domain-specific capabilities.
[takes off rose-tinted glasses]