An AI system has become the third-most important signal to Google search results
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Then give it a keyword, I use "vg" for "Verbatim Google".
Then in the Navbar I can type "vg foo bar", which will search "foo bar" verbatim. Closest thing to permanent once you get used to using keywords ( which are awesome by the way :D )
Specifically, the url you want to use is: https://www.google.com/search?q=%s&tbs=li:1
Good tip. I use a similar one with site:en.wikipedia.com and I'm Feeling Lucky to quickly jump to wikipedia articles.
PageRank was a simple, stupid algorithm that produced incredibly smart results. The exact kind of thing that sees widespread deployment with a startup. The description of this AI sounds more like an AI tool in general. It would've been much harder for Google to have started with this. The computers alone would've been prohibitive. So, we can call it an AI.
PageRank, for example, can't be the whole search algorithm since it's not even query-dependent. It would just put the same most authoritative document at the top for every query.
Similiarly, RankBrain doesn't sound like it could be the whole algorithm. It sounds like it is just a text understanding model (which wouldn't know anything about e.g. the global reputation of the document, the popularity of the document, etc.). In fact, the article explicitly confirms this:
>RankBrain has become the third-most important signal contributing to the result of a search query, he said.
I'd guess some kind of composite content quality signal and some kind of composite popularity signal sit at #1 and #2.
[1] - I don't have any insider knowledge about how Google works, but this article suggests that they were getting ready to switch from a hand-tuned model to a machine learned one in 2008: http://anand.typepad.com/datawocky/2008/05/are-human-experts...
Even if I search for a very specific query, Google will just only give me results ignoring several parts of my query, and at best one or two about my actual query.
Even duckduckgo manages to give better results often at this point.
EDIT: Just found verbatim search, at least that gives almost equal results as duckduckgo. Still not as good, but it’s okay.
It should not be hard to convert a query into a regex for content, title and URL and apply it on the index.
It’s incredibly frustrating, but if I, by accident, use Google again and see the issue, I’ll tell you.
Even Verbatim search will happily lop-off search words.
Tonight I was searching for "raf ballykelly vulcan" and after a few puzzling results realised that Ballykelly had been discarded. Since it was the airfield in question the results were therefore useless.
And with Google Maps being already worse than Bing, OSM, or even fucking Apple Maps, all Google products I actively used are now useless for me.
Does RankBrain have an intermediate form which shows its interpretation of the query? Wolfram Alpha does, and will show an explanation of how it interpreted the query. (It has to, because it may give you an numeric answer). It would be useful for Google to tell you what question they think you are asking.
Geoff Hinton - "If we can convert a sentence into a vector that captures the meaning of the sentence, then google can do much better searches, they can search based on what is being said in a document. Also, if you can convert each sentence in a document into a vector, you can then take that sequence of vectors and try and model why you get this vector after you get these vectors, that's called reasoning, that's natural reasoning, and that was kind of the core of good old fashioned AI and something they could never do because natural reasoning is a complicated business, and logic isn't a very good model of it, here we can say, well, look, if we can read every English document on the web, and turn each sentence into a thought vector, we've got plenty of data for training a system that can reason like people do. Now, you might not want to reason like people do on the web, but at least we can see what they would think."
Do we know what is the most and the second-most important signal?
Or if relevance doesn't count as signal maybe it's social sharing.
Do you experience a drastic drop in result quality when you use a public computer?
"What’s the title of the consumer at the highest level of a food chain?"
But the results page (for me) does not contain the words 'apex predator'. The top result is the wikipedia page for "Consumer (food chain)", which does contain that term.
It would have been very cool if the AI could have identified the concept described by the query. But it didn't. It just found a very relevant page for three strings in the query.
The journalist doesn't report on the results of this example. Who came up with it and why?
"what predator is at the top of the food chain?" or "what type of animal is at the top of the food chain?"
but it fails to do so if I ask "what consumer is at the top of the food chain?"
It seems like "consumer" is too ambiguous to work in this example.
AI is a stretch, but it is cool.
Probably not. My bet is that it is word2vec based
"the quick brown fox; jumped over the lazy dog"
returns hits for the version without the semicolon
I suppose for the average person this "fuzzy searching" is an improvement, but I wish I had the ability to flip a switch somewhere that says: "Please only use exactly the words I gave you, always."
Of course, being logged in all the time makes me uncomfortable...
For a technical query, you essentially want something like PageRank-weighted grep, which is, of course, what you used to get. All of the fancy NLP/fuzzy-matching stuff that Google has been adding recently, while helpful for all sorts of other things, is going to be a detriment for technical queries.
When you're doing something like googling an error message or a code snippet, you're basically querying machine-generated speech, and much of Google's recent work has been on improving querys of human-generated speech.
It seems like it should be simple to implement a little "technical query" checkbox...
"ALPN websocket handshake chrome"
The first 3 results are all acknowledged by Google to be "Missing: ALPN". I get this often with queries, where Google returns results which are missing the keywords I am most interested in.
My intention was to see if there was any progress with WebSocket handshakes over ALPN (to save a roundtrip).
From the descriptions of the results it is also not clear if any of the top 10 matches are relevant.
"does ctime always change"
Google returns:
Showing results for "does time always change"
Search instead for "does ctime always change"
None of the results are relevant because Google thinks I made a typo.
If I then search instead for "does ctime always change" the top result is:
"Does directory mtime always change when a new file ..."
Google has fuzzed ctime to match mtime which is not the same thing.
My intention was to see whether ctime would always be updated if there was any data or metadata change to a file or folder, or if there would be some edge cases where it would not change.