Facebook expected to revise plans for Messenger Bot API as failure rate hits 70%
theregister.co.uk
theregister.co.uk
Is this an article or an opinion piece? It reads very weirdly but I can't pinpoint it.
30% in a year is solid, especially with the proliferation of tools for building bots making it more and more accessible to tinkerers and smaller orgs. ie, use caes where complete automation may not be the primary goal.
In what way?
First, their BOT api which is the product in question, works 100% of the time. the failure rate mentioned has to so with conversation that require humans as opposed to entirely AI driven. A better Apple analogy would be Siri. if Siri failed to even acknowledge a request 70% of the time, it would indeed be catastrophic. But siri does often fail at providing relevant results and while frustrating, is entirely expected.
We could argue more, but as you and I have both noted, the main substance of the interview is not provided.
Newton's handwriting recognition worked "100%" of the time by that measure. I assume we are comparing quality of outcomes not whether the API accepts the arguments it says it does and produces data in the format it promises.
FB's third party access to the messenger platform (aka you and the article calling it the bot api) is supposedly failing 30%.
That number, if I were to guess where it came from, likely came from the fact that a chat bot app has to be submitted to Facebook for approval. That means FB has a running tally of how many chat bot accounts exists within their ecosystem.
Maybe that number is 100k.
Likewise, because messages are passed to FB to then be passed to the user, FB has a runny tally of the sentimental analysis or even the blocking analysis (users have the option to block bot accounts).
From there they can ascertain that 70,000 bot accounts result in negative interactions, discontinued use, or results in the user banning the bot.
This is the equivalent of Apple opening up third party access to the Siri platform and seeing that developers and users don't like the 70% of the ways to interact with Siri. Or Amazon saying that users don't like 70% of the Alexa Skills available.
I seem to remember certain websites automatically get a lower weight on HN when they get upvoted a lot - shouldn't this be applied to theregister too?
Doesn't look like a particularly popular source to me.
Tinfoil hat on: a smashing success for them is controlling everyones online life.
See: the Whatsapp acquisition - burning a double digit number of billions on a profitable platform just to remove their one source of income and USP so as to get even more juicy metadata and eyeballs.
(Yes, I was a huge fan of Whatsapp. Yes, so much that I expected them to manage to stay true to their ideals after the acquisition.
Yes, I tried to believe Facebook actually just wanted a part in what Whatsapp was about to become.
Fool me once something something )
As someone who works in NLP I don't think there will be an AI winter. There will be a Gartner "valley of despair", abut no winter.
That doesn't mean NLP is solved though.
A computer doesn't need to know how you feel about snow, to tell you that it's snowing outside, and you'll probably want to remember snow chains.
Embedding the word "snow" in a sufficiently rich abstraction tells you what snow is, in so far as anyone else can articulate it anyway.
I think you have an artificially high threshold for what a useful level of understanding is.
What do you mean by this?
(I could have written my comment more politely. Didn't mean to offend.)
When we place the word "snow" into a vectorspace of words (along with the other words), we're saying something about what that word means relative to the other words, with those relationships stemming from the experience of people in the real world. "Snow" is similar to "ice" along these dimensions and is similar to "powder" along those dimensions. Ideally, we want something like "snow = ice - solid + powder". All the possible equations for snow in the embedding gives you, essentially, the different ways in which people understand snow.
The utility of these structures for NLP is fundamentally that they embed something of the understanding people have for the real world in their embedding of the words. So NLP structures must capture something of "understanding" if they're genuinely useful for NLP.
So I agree with the person I was replying to in that you can't have NLP without understanding, because NLP is fundamentally about understanding, but I think they're implying a higher level of understanding than is actually required for most uses of NLP and more over, that it has to be an independent understanding, rather than a secondary derived one.
This is unsupervised learning of word embeddings that shows the "topics" related to the word "snow" and how closely they are related. It shows pretty reasonable understanding of the world - certainly enough to build useful things with it.
We are already there.
There's the hugely popular Tenserflow but it's for developers and Facebook also released nice libraries (e.g for Torch)
Have you, perchance, used Google Search?
That's great for very common searches, but makes it much harder to find anything that Google doesn't expect (or want) you to look for.
Granted, I've got no data to back that up. I'm probably in the minority of users here, but I pretty sure I'm not the only one.
In ML landscape, DL had changed fundermentally the expectation of what algorithm could achieve
These are answerable questions, but there are a lot of them and I can see why Facebook would choose to focus on 1-1 interactions first (especially since those are more likely to become paying interactions).
Personally I would love group bots. They can enable a lot of fun interactions, and it's certainly more fun to "play with" a novel bot with your friends than by yourself.
"The Information is, for sure, the most thoughtful / smartest tech coverage." SAM ALTMAN PRESIDENT OF Y COMBINATOR
lol