Earlier quoted context omitted.
I use the Portal de Archivos Españoles [1] for Spanish colonial documents. Each country has their own archive but the Spanish one has the most content (35 million digitized pages) The hard part is knowing where to look since most of the images haven’t gone through HRT/OCR or indexing so you have to understand Spanish colonial administration and go through the collections to find stuff. [1] https://pares.cultura.gob.e…
Want to collab on a database and some clustering and analysis? I’m a data scientist at FAIR with an interest in antiquarian docs and books
A new Google model is nearly perfect on automated handwriting recognition
191–200 of 328 posts
Re: A new Google model is nearly perfect on automated handwriting recognition
#192Earlier quoted context omitted.
I'm skeptical because my entire identity is basically built around being a software engineer and thinking my IQ and intelligence is higher than other people. If this AI stuff is real then it basically destroys my entire identity so I choose the most convenient conclusion. Basically we all know that AI is just a stochastic parrot autocomplete. That's all it is. Anyone who doesn't agree with me is of lesser intelligenc…
> [...] my entire identity is basically built around [...] thinking my IQ and intelligence is higher than other people. Well, there's your first problem.
Re: A new Google model is nearly perfect on automated handwriting recognition
#193Re: A new Google model is nearly perfect on automated handwriting recognition
#194Rgd the "14 lb 5 oz" point in the article, the simpler explanation than the hypothesis there that it back calculated the weight is that there seems to be a space between 14 and 5 - i.e. It reads more like "14 5" than "145"?
Re: A new Google model is nearly perfect on automated handwriting recognition
#195Re: A new Google model is nearly perfect on automated handwriting recognition
#196Re: A new Google model is nearly perfect on automated handwriting recognition
#197Earlier quoted context omitted.
Predicting the next word requires understanding, they're not separate things. If you don't know what comes after the next word, then you don't know what the next word should be. So the task implicitly forces a more long-horizon understanding of the future sequence.
This is utterly wrong. Predicting the next word requires a large sample of data made into a statistical model. It has nothing to do with "understanding", which implies it knows why rather than what.
Re: A new Google model is nearly perfect on automated handwriting recognition
#198Earlier quoted context omitted.
Ilya Sustkever was on a podcast, saying to imagine a mystery novel where at the end it says “and the killer is: (name)”. Saying it’s just a statistical model generating the next most likely word, how can it do that in this case if it doesn’t have some understanding of all the clues, etc. A specific name is not statistically likely to appear
It can't do that without the answer to who did it being in the training data. I think the reason people keep falling for this illusion is that they can't really imagine how vast the training dataset is. In all cases where it appears to answer a question like the one you posed, it's regurgitating the answer from its training data in a way that creates an illusion of using logic to answer it.
Re: A new Google model is nearly perfect on automated handwriting recognition
#199Earlier quoted context omitted.
I'm skeptical that they're actually capable of making something novel. There are thousands of hobby operating systems and video game emulators on github for it to train off of so it's not particularly surprising that it can copy somebody else's homework.
I remain confused but still somewhat interested as to a definition of "novel", given how often this idea is wielded in the AI context. How is everyone so good at identifying "novel"? For example, I can't wrap my head around how a) a human could come up with a piece of writing that inarguably reads "novel" writing, while b) an AI could be guaranteed to not be able to do the same, under the same standard.
Re: A new Google model is nearly perfect on automated handwriting recognition
#200Earlier quoted context omitted.
> LLMs are great, but they're not (yet?) as capable as our best and brightest (and in many ways, lag behind the average human) in most respects, so why would you expect such genius now ? I'm not expecting novel scientific theories today . What I am expecting are signs and hints of such genius. Something that points in the direction that all tech CEOs are claiming we're headed in. So far I haven't seen any of this yet…
>I'm not expecting novel scientific theories today. What I am expecting are signs and hints of such genius. Like I said, what exactly would you be expecting to see with the capabilities that exist today ? It's not a gotcha, it's a genuine question. >And, I'm sorry, I don't buy the excuse that these tools are not "yet" as capable as the best and brightest humans. There's nothing to buy or not buy. They simply aren't.…
And like I said, "signs and hints" of superhuman intelligence. I don't know what that looks like since I'm merely human, but I sure know that I haven't seen it yet.
> There's nothing to buy or not buy. They simply aren't. They are unable to do a lot of the things these people do.
This claim is directly opposed to claims by Sam Altman and his cohort, which I'll repeat:
> we have recently built systems that are smarter than people in many ways, and are able to significantly amplify the output of people using them.
So which is it? If they're "smarter than people in many ways", where is the product of that superhuman intelligence? If they're able to "significantly amplify the output of people using them", then all of humanity should be empowered to produce incredible results that were previously only achievable by a limited number of people. In hands of the best and brightest humans, it should empower them to produce results previously unreachable by humanity.
Yet all positive applications of this technology show that it excels at finding and producing data patterns, and nothing more than that. Those experience reports by Terence Tao are prime examples of this. The system was fed a lot of contextual information, and after being coaxed by highly intelligent humans, was able to find and produce patterns that were difficult to see by humans. This is hardly a showcase of intelligence that you and others think it is. Including those highly intelligent humans, some of whom have a lot to gain from pushing this narrative.
We have seen similar reports by programmers as well[1]. Yet I'm continually amazed that these highly intelligent people are surprised that a pattern finding and producing system was able to successfully find and produce useful patterns, and then interpret that as a showcase of intelligence. So much so that I start to feel suspicious about the intentions and biases of those people.
To be clear: I'm not saying that these systems can't be very useful in the right hands, and potentially revolutionize many industries. Ultimately many real-world problems can be modeled as statistical problems where a pattern recognition system can excel. What I am saying is that there's a very large gap from the utility of such tools, and the extraordinary claims that they have intelligence, let alone superhuman and general intelligence. So far I have seen no evidence of the latter, despite of the overwhelming marketing euphoria we're going through.
> Well it's a good thing that's not true then
In the world outside of the "AI" tech bubble, that is very much the reality.