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I was hoping this was a question about applying NLP to coding tasks, but based on the answers it is about voice to text for the special use case of coders. I a

I was hoping this was a question about applying NLP to coding tasks, but based on the answers it is about voice to text for the special use case of coders. I am not a coder, I am a writer. I wonder why all these AI people are trying to create things that will displace my means of earning a living instead of something that will create applications? Why can't I tell my Mac: "Computer: take this collection of files and extract all the addresses of people in Indiana."

smt88 6y ago
> I wonder why all these AI people are trying to create things that will displace my means of earning a living instead of something that will create applications? AI writing is currently terrible. If your writing income can be replaced by AI, you're likely summarizing simple topics (e.g. unemotional, analysis-free facts of a recent event, like a change in a stock price). In that case, your job isn't safe anyway, because there are $2/hr humans in other countries that can also take your job. Note that all software is capable of eliminating jobs, whether it's automating writing or something else. > Why can't I tell my Mac: "Computer: take this collection of files and extract all the addresses of people in Indiana." NLP is nowhere near being able to generally understand requests like this. Try talking to your Google Assistant and doing anything other than a simple lookup. It fails spectacularly. Think about all the cognition in this task: first it has to understand what you're asking it. Then it has to figure out which collection of files you mean. Then it has to understand what an "address" is and what it looks like. Finally, it has to have a concept of "Indiana" and an understanding that an address can be in Indiana. In terms of effort, it's far more efficient for you to learn the simple programming skills to do this yourself (or to use a purpose-built tool) than to create AI that does this generally, which will cost at least billions of dollars more in research time.
- sdenton4 6y ago
  I dunno... English to sql query is probably easier than English to French. You don't actually need to understand that Indiana is a place, just recognize that it's one of those tokens that's getting stuffed into the where clause... The tough part is really probably the implicit knowledge of what the database columns are called. Is Indiana a "state" field or a substring in an address field? Depends on the database. This mixing of "soft" ml queries with "hard" requirements is a really interesting space, which I think will produce massive results if we can get it under control. It's similar to the problems in the AI dungeon master space... And probably tutoring, as well...
  - retrac 6y ago
    > I dunno... English to sql query is probably easier than English to French. You can have substantial success in transforming English to French without understanding what is said. Some big grammar rulebooks, some dictionaries, and a few terabytes of sentence-aligned parallel texts and data on statistical colocations in the languages will get you most of the way. It's far from 100% accurate but the human brain is surprisingly good at filling in the gaps with the mangled grammar and odd turns of phrase. I'm not so sure the same is true about English to SQL query. The exact meaning of the English must be understood, in context, in order to construct the query correctly. No room for error or fuzziness. The database engine doesn't have human cognition to fall back on should the query be slightly malformed or ambiguous.
phailhaus 6y ago
> I wonder why all these AI people are trying to create things that will displace my means of earning a living instead of something that will create applications? I think this has to do with overestimating the power of current ML techniques combined with severely underestimating the complexity of jobs like writing. This tends to result in solutions that "overreach" and end up disappointing. If we scale back a bit, we could build some really fantastic tools that work with people. One great example of this is the movie Spiderman: Into the Spiderverse. In order to draw the comic-book lines on faces, they used a tool that would learn over time from the artists themselves. It would guess, the artist would adjust, it would learn. This is the perfect fit for ML: try to help the human, not replace them.

smt88 6y ago

> I wonder why all these AI people are trying to create things that will displace my means of earning a living instead of something that will create applications? AI writing is currently terrible. If your writing income can be replaced by AI, you're likely summarizing simple topics (e.g. unemotional, analysis-free facts of a recent event, like a change in a stock price). In that case, your job isn't safe anyway, because there are $2/hr humans in other countries that can also take your job. Note that all software is capable of eliminating jobs, whether it's automating writing or something else. > Why can't I tell my Mac: "Computer: take this collection of files and extract all the addresses of people in Indiana." NLP is nowhere near being able to generally understand requests like this. Try talking to your Google Assistant and doing anything other than a simple lookup. It fails spectacularly. Think about all the cognition in this task: first it has to understand what you're asking it. Then it has to figure out which collection of files you mean. Then it has to understand what an "address" is and what it looks like. Finally, it has to have a concept of "Indiana" and an understanding that an address can be in Indiana. In terms of effort, it's far more efficient for you to learn the simple programming skills to do this yourself (or to use a purpose-built tool) than to create AI that does this generally, which will cost at least billions of dollars more in research time.

sdenton4 6y ago
I dunno... English to sql query is probably easier than English to French. You don't actually need to understand that Indiana is a place, just recognize that it's one of those tokens that's getting stuffed into the where clause... The tough part is really probably the implicit knowledge of what the database columns are called. Is Indiana a "state" field or a substring in an address field? Depends on the database. This mixing of "soft" ml queries with "hard" requirements is a really interesting space, which I think will produce massive results if we can get it under control. It's similar to the problems in the AI dungeon master space... And probably tutoring, as well...
- retrac 6y ago
  > I dunno... English to sql query is probably easier than English to French. You can have substantial success in transforming English to French without understanding what is said. Some big grammar rulebooks, some dictionaries, and a few terabytes of sentence-aligned parallel texts and data on statistical colocations in the languages will get you most of the way. It's far from 100% accurate but the human brain is surprisingly good at filling in the gaps with the mangled grammar and odd turns of phrase. I'm not so sure the same is true about English to SQL query. The exact meaning of the English must be understood, in context, in order to construct the query correctly. No room for error or fuzziness. The database engine doesn't have human cognition to fall back on should the query be slightly malformed or ambiguous.

phailhaus 6y ago

> I wonder why all these AI people are trying to create things that will displace my means of earning a living instead of something that will create applications? I think this has to do with overestimating the power of current ML techniques combined with severely underestimating the complexity of jobs like writing. This tends to result in solutions that "overreach" and end up disappointing. If we scale back a bit, we could build some really fantastic tools that work with people. One great example of this is the movie Spiderman: Into the Spiderverse. In order to draw the comic-book lines on faces, they used a tool that would learn over time from the artists themselves. It would guess, the artist would adjust, it would learn. This is the perfect fit for ML: try to help the human, not replace them.