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Apple Acquires 'Dark Data' Machine Learning Company Lattice Data
Apple recently paid around $200 million to acquire Lattice Data, a firm that aims to turn unstructured "dark data" such as text and images into structured data that can then be handled with traditional data analysis tools. News of the acquisition comes from TechCrunch, and Apple has essentially confirmed the acquisition by issuing its standard statement on the topic.
Lattice uses machine learning techniques to take mass amounts of initially unusable data and turn it into properly labeled and categorized data that can be used for AI, medical research, and more.
Lattice uses machine learning techniques to take mass amounts of initially unusable data and turn it into properly labeled and categorized data that can be used for AI, medical research, and more.
It’s unclear who Lattice has been working with, or how Apple would intend to use the technology. Our guess is that there is an AI play here: Our source said that Lattice had been “talking to other tech companies about enhancing their AI assistants,” including Amazon’s Alexa and Samsung’s Bixby, and had recently spent time in South Korea.TechCrunch says the deal closed "a couple of weeks ago," with roughly 20 Lattice engineers having joined Apple.
Tags: Apple acquisition, Lattice Data
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24 months ago
The valley is running out of ridiculous synonyms to attract VC money. "Dark data?" Next will be "deep space dense data matter".
24 months ago
Dynamic Unilateral Machine Planning
DUMP
LOL. Just added that to my LinkedIn skills
24 months ago
I feel like I just watched an advertisement for "snake oil"....
If the conveyance of something's usefulness cannot be explained in such a way that is easily understood, then 1) it is either BS, or 2) the only people who DO understand it, were not involved in the attempt to explain it.
If the conveyance of something's usefulness cannot be explained in such a way that is easily understood, then 1) it is either BS, or 2) the only people who DO understand it, were not involved in the attempt to explain it.
24 months ago
The valley is running out of ridiculous synonyms to attract VC money. "Dark data?" Next will be "deep space dense data matter".
PhaseNeutral Anti-Matter Data
PAMD
24 months ago
As some background, one of the major problems dealing with unstructured data is extracting meaningful information out of it.
As an example, let's use a PDF file of a newspaper article. First, you need to extract the text out of it. Then, you have to figure out what the entities are, and entities generally are people, places, and things. Then you need to deal with time relevance.
To do most of those things you need to train your NLP engine to recognize entities. If you train them with too little data they'll get too specialized. If you train them with too much data they get too generalized.
What is an entity? It could be a name. What's a name? Sometimes it's firstname, lastname. Sometimes it's lastname, firstname. Both of those are valid representations of a canonical name. The trick is to recognize that both of those are really referring to the same thing.
When two entities have the same referent (not sure what the technical term is), then the engine needs to find more context. Is the Saddam Hussein you're talking about the former dictator of Iraq or the guy who owns the Shawarma place down the block?
Entity stuff is still a major problem in the academic world, and there's still a lot of stuff to figure out. All the solutions today are pretty clunky. In real life it's faster to just get a mechanical turk to do entity-ization for you, because people are still way faster and more accurate than computers.
I suppose if this approach works it'd be good, because it would allow you to actually query for most of the stuff that's out there in web pages, etc and return "an answer."
As an example, let's use a PDF file of a newspaper article. First, you need to extract the text out of it. Then, you have to figure out what the entities are, and entities generally are people, places, and things. Then you need to deal with time relevance.
To do most of those things you need to train your NLP engine to recognize entities. If you train them with too little data they'll get too specialized. If you train them with too much data they get too generalized.
What is an entity? It could be a name. What's a name? Sometimes it's firstname, lastname. Sometimes it's lastname, firstname. Both of those are valid representations of a canonical name. The trick is to recognize that both of those are really referring to the same thing.
When two entities have the same referent (not sure what the technical term is), then the engine needs to find more context. Is the Saddam Hussein you're talking about the former dictator of Iraq or the guy who owns the Shawarma place down the block?
Entity stuff is still a major problem in the academic world, and there's still a lot of stuff to figure out. All the solutions today are pretty clunky. In real life it's faster to just get a mechanical turk to do entity-ization for you, because people are still way faster and more accurate than computers.
I suppose if this approach works it'd be good, because it would allow you to actually query for most of the stuff that's out there in web pages, etc and return "an answer."
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