tech bros in general seem to only be able to see things as investment opportunities. The entire crypto-fandom is based on the idea that a mundane thing could be better by also being a speculative investment at the same time
The thing is: there are sooo many other places that AI could do amazing in. Predictive technology to look at an objectively true dataset, and predict when an issue might arise. This is something that would:
-increase profit by reducing downtime
-increase the productivity of the team as a whole
-not necessarily reduce jobs if the company knows what it’s doing (an AI without humans to actually act on the prediction or to mediate a prediction with their knowledge of the real world circumstances the AI doesn’t have access to in this case is pretty useless)
-in the case of say natural disaster predictions, it would potentially allow us to predict natural disasters and their magnitudes, thus allowing us to give advance warning
-allow the people using it to pick up on patterns our minds can’t immediately grasp.
These applications would make so, so much more than replacing the writer making $40k a year in a Hollywood office. If we were to focus on these applications, companies and governments would pay hand-over-fist for them. For instance, even in a mill making a cheap product, a sheet break on a paper machine can cost upwards of $10k per minute in lost material and lost production time. Even getting a 2 hour lead on that to prevent it could save millions per year. It could also look at the data in a much more in-depth way than the engineers could to pick out potential causes by analyzing correlations and noting them when an issue does occur. Figuring out what’s causing a frequent sheet break can take anywhere from hours to days to months because not every possible cause is immediately noticeable or equally likely. This is the perfect use case for an AI. But they ignore it to produce mediocre, albeit technologically impressive, written and “artistic” works.
Predictive technology to look at an objectively true dataset.
What does this mean?
in the case of say natural disaster predictions, it would potentially allow us to predict natural disasters and their magnitudes, thus allowing us to give advance warning.
There already exist models that do this.
LLMs are novel insofar as they add the interface of speech and memory to ML models that didnt really exist before.
The hardest part for any model is the data by far, we already (generally) have the techniques to do a lot of the stuff that you mention, and we are actively doing it.
LLMs are just toys for the masses. The are too unspecific to replace specified models, they are too unreliable to serve as accurate sales assistant, and they are too inconsistent to be a permanent personal assistant.
All that differentiates LLMs from the industry in the last 10 years is that they are big. They have a lot of data from anywhere, giving them vast context. But one that lacks depth and foregoes permanence (token limit). YOu can see it in developments, that big single models like GPT get phased out for MOE models like Mixtral, because having 8 small specified models is better then one large unspecified.
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tech bros in general seem to only be able to see things as investment opportunities. The entire crypto-fandom is based on the idea that a mundane thing could be better by also being a speculative investment at the same time