I guess I don’t have to worry about my job going away quite yet. This is what Twitter’s AI thingy thinks is currently happening in the industry I work in.
I think we're talking past each other.I mean, this is a problem for human beings as well. Circa three hundred years ago, there were people confidently asserting that combustion is a phlogiston-dependent phenomenon. And, without pushing too hard against board rules, I'm sure you're aware of many contemporary examples of large numbers of human confidently and sincerely affirming falsehoods.
The reason that people can tell the difference between truth and falsehood - when they can - is not because of their mastery of semantics as well as syntax, but because of their mastery of evidence. It's quite a while (close to 30 years) since I worked on these issues; but I think a couple of points can be made.
First, what Quine called occasion sentences must be pretty important. These can be identified by syntax, I think, at least to a significant extent. But AI doesn't have epistemic access to the occasions of their utterance.
Second, when it comes to what Quine called eternal sentences, human being closely correlate their credence to these with their credence to the speaker. My understanding of the way these AI models "learn" is that speaker identity does not figure into it, and that they are not grouping sentences in speaker-relative bundles. So eg they might note that sentences about the moon are often correlated with sentences about NASA, but (as I understand it) they don't weight sentences about the moon in terms of their production by NASA compared to Wallace (who travels with Gromit to the moon because their larder is empty of cheese).
I'm definitely not an expert in AI, and as I said I'm out of date in epistemological literature. But on the face of it, these problems of warrant seem more significant than the issue of semantics vs syntax.
Right. Which is not a problem in linguistics (syntax vs semantics). It's a problem in epistemology (evidence/warrant).
There are companies doing exactly this. Training their own AIs with reliable data. Reliable as defined by trusted humans. All this AI crap that we see isn't being trained, it's being dumped in the pool of social media and left to itself and to being misused by humans with no thought or care about training it.The only way to teach an AI how to avoid this would be to train it to only truly listen to trusted sources, and then it would only be as reliable as the sources it drew upon—and would have some issues if those sources are too few, as it might not have enough training data to spit out meaningful results. In theory though, you could make one designed to collate and summarize existing news reports.
As said, these translators are good enough. I use them when travelling in foreign countries and when doing simple labor and transactions with people who don't speak English on occasion. Admittedly, they are not good enough to translate technical content, books, and journalistic articles etc.The companies that make machine translation software have been saying the same thing for two decades. They keep throwing money into their marketing, and people who aren't translators believe them. I predict the same thing for AI.
It would not surprise me if certain media companies are not training their own AIs for their own use. Won't make the journalists happy, but I think it's still probably happening.What would be nice is if AI could build up a list of trusted sources, or research and find those people for an industry and then conduct follow up fact checking with these people. That is time-consuming work human “journalists” rarely do anymore. The article could then quote and list their sources. If a source wished to be anonymous, it would need to verify with a minimum consensus or label those facts as potentially untrustworthy.
Yep, as said, there are companies doing exactly this.This would require judgement and discernment that "AIs" do not possess. At best, they could be used to check scraped data from various human-picked trusted sources and collect them together for analysis.
Generative AI does indeed pose an immediate threat to some people's jobs. Turns out, those people are comedians.I guess I don’t have to worry about my job going away quite yet. This is what Twitter’s AI thingy thinks is currently happening in the industry I work in.
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Just you wait until my party of dwarven attorneys with their baby dragon paralegals lay siege!“Once I have drunk the blood of all the town’s virgins, I will destroy all accessibility ramps to my lair. Mwahahaha!”
We're also running out of high-quality "natural" training data, which means we may even start growing a whole additional layer of "we don't know what's going on" by training one AI to generate high-quality fictitious data so that a second AI can use it as seed data for whatever the user actually wants to see happen. Personally, I'm real skeptical that such "synthetic" data can achieve anywhere near the same results as real-world data.
I was reading an article just yesterday that if AI ever reaches the same level of understanding as humans, it will actually teach other AIs much better and faster than a human can, and without breaks or weekends. Although, in principle, the usual gpt chat is enough for me for now. As an eassy helper it also copes quite well, especially if you tweak it a little. I wonder what this will lead to in the end, whether we will all be left without work. Well, of course, I don’t really want to get a matrix or terminator scenario. But these are already horror storiesAI trained on AI data tends to do much worse - it increases hallucination and inaccuracies.