INSUBCONTINENT EXCLUSIVE:
As the ways we consume content multiply and change, media creators are hard-pressed to adapt their methods to take advantage
a client and Microsoft, Google and Tensility as investors.Agolo is an AI startup focused on natural language processing; specifically, how
to take a long article, like this one, and boil it down to its most important parts (assuming there are any)
Summarization is the name of the process, as it is when you or I do it, and other bots and services do it, as well
sentences.
The AP is, of course, a huge news organization, and a fast-moving one
from the way we read them
We have a hybrid model that has algorithms pointed at each of those rules
So the system is careful to preserve meaning if not necessarily the exact wording.While the AP may not be given, as I am, to
circumlocutions, it may still be beneficial to shift things a bit, though
but you get the idea: essentially lossless compression of language.If the AP team can trust the algorithm to produce a well-worded summary
that follows their rules and only takes a quick polish by an editor, they could serve and even grow the demand for short-form content
News, among other things) means that the AI model being used has to be lightweight and quick
Even if it takes only 10 seconds to summarize every article, that gets multiplied thousands of times in the complex workings of sorting and
displaying news all over the world
So Agolo has been very focused on improving the performance of its models until they are able to turn things around very quickly and enable
an essentially real-time summary service.This has a secondary application in large enterprises and companies with large backlogs of data
like documentation and analysis
Microsoft is a good example of this: After decades of running an immense software and services empire, the number of support docs, studies,
how-tos and so on are likely choking its intranet and search may or may not be effective on such a corpus.NLP-based agents are useful for
summarizing, but part of that process is, in a way, understanding the content
forth.Not all this information is useful in all cases, of course, but it sure is if you want to digest 30 years of internal documentation
and be able to search and sort it efficiently
This is what Microsoft is using it for internally, and no doubt what it intends to apply it to as part of future product offerings or
(Semantic Scholar has applied a similar approach to journals and academic papers.)It would also be helpful for, say, an investment bank
the salient information surfaced and glanceable
One pictures this as useful for Google News, as well, in browsing coverage of a specific event or developing story.The new (undisclosed
amount of) funding has Microsoft (M12 specifically) returning, with Google (Assistant Investment Group specifically) and Tensility Venture
Partners joining for the first time
variety of data sets, and that this process contributes to the bottom line more than the time-tested method of hiring another intern or grad
student to perform the drudgery.
