recentpopularlog in


Apple acquires ‘dark data’ company Lattice Data | iLounge News
Apple has acquired “dark data” company Lattice Data for around $200 million, TechCrunch reports. Founded in 2015, the company has produced its own AI-enabled inference engine to convert unstructured “dark data” into more structured and usable information. A source claims Apple closed the deal a couple of weeks ago, adding around 20 of the company’s engineers to its ranks. Apple confirmed the acquisition with its usual blanket statement, but gave no indication of how the technology would be used. The company has boosted its research into AI, with CEO Tim Cook sharing his hopes that the technology will transform the way people use their phones “in ways that most people don’t even think about.” The company recently joined the Partnership on AI and began allowing employees to publish their AI research.
analytics  apple  dark_data  data  intelligence  AI/ML 
may 2017 by rgl7194
Apple acquires AI company Lattice Data, a specialist in unstructured ‘dark data’, for $200M | TechCrunch
As large tech companies gear up to make a stronger push into machine learning and artificial intelligence, Apple has acquired a company to fill out its own capabilities in the area.
Specifically, Apple has picked up Lattice Data, a company that applies an AI-enabled inference engine to take unstructured, “dark” data and turn it into structured (and more usable) information. We’ve heard from a single source that Apple has paid a price of around $200 million.
The deal was closed a couple of weeks ago, the source said, and about 20 engineers have joined the larger company.
apple  data  analytics  intelligence  dark_data  AI/ML 
may 2017 by rgl7194
Apple acquires 'dark data' intelligence company Lattice Data | iMore
Lattice Data, a company that specializes in extracting useful information from unstructured data now belongs to Apple.
Ingrid Lunden, reporting for TechCrunch...
A lot of data analysis in general, and "big data" in specific, has to do with the analysis of structured data: nice, orderly tables all stacked up in neat rows and columns. Unstructured data is everything else.
So, almost like sifting for gold once all the easy-to-pluck nuggets have been harvested, extracting value from "dark data" has traditionally been much harder.
But now we have artificial intelligence and machine learning.
AI and ML have become huge buzzwords in the tech industry since last year, when several prominent companies took to their keynote stages to act like they'd just invented the terms.
Apple's been using ML and AI for years in everything from Siri to battery life management. What the company does with "dark data" will be interesting to see.
apple  data  analytics  intelligence  dark_data  AI/ML 
may 2017 by rgl7194
Lattice turns massive amounts of "dark" data such as text and images, into "structured" data, the kind used by a traditional database.
artificial_intelligence  analyze  dark_data 
april 2017 by dpcat237
More foreign buyers snapping up Canadian condos: CMHC - The Globe and Mail
The Globe and Mail
Published Thursday, Dec. 03, 2015

The federal housing agency said it has struggled to get a full picture of foreign purchaser activity in the housing market and that its numbers are far lower than those in other studies. ....

The only data that CMHC has on foreign buyers are from surveys of property managers and condo boards, who are asked to identify non-resident condo owners. The survey doesn’t measure the number of overall home sales in a given year to foreign investors, nor whether foreign owners are buying units for themselves and family members or purely as speculative investments. CMHC also includes Canadians who now live abroad but who still own property in the country as part of its definition of foreign owner.....“We’re trying to work with other people and make an effort to get these data gaps solved so we can have more information about what some are saying is an important part of the market,” Mr. Dugan said. “You can see from our data that the rate of foreign ownership seems relatively low, certainly not the kind of levels that some other studies might suggests.
real_estate  CMHC  offshore  data  condominiums  information_gaps  housing  dark_data 
december 2015 by jerryking
How Not to Drown in Numbers -

If you’re trying to build a self-driving car or detect whether a picture has a cat in it, big data is amazing. But here’s a secret: If you’re trying to make important decisions about your health, wealth or happiness, big data is not enough.

The problem is this: The things we can measure are never exactly what we care about. Just trying to get a single, easy-to-measure number higher and higher (or lower and lower) doesn’t actually help us make the right choice. For this reason, the key question isn’t “What did I measure?” but “What did I miss?”...So what can big data do to help us make big decisions? One of us, Alex, is a data scientist at Facebook. The other, Seth, is a former data scientist at Google. There is a special sauce necessary to making big data work: surveys and the judgment of humans — two seemingly old-fashioned approaches that we will call small data....For one thing, many teams ended up going overboard on data. It was easy to measure offense and pitching, so some organizations ended up underestimating the importance of defense, which is harder to measure. In fact, in his book “The Signal and the Noise,” Nate Silver of estimates that the Oakland A’s were giving up 8 to 10 wins per year in the mid-1990s because of their lousy defense.

And data-driven teams found out the hard way that scouts were actually important...We are optimists about the potential of data to improve human lives. But the world is incredibly complicated. No one data set, no matter how big, is going to tell us exactly what we need. The new mountains of blunt data sets make human creativity, judgment, intuition and expertise more valuable, not less.

From Market Research: Safety Not Always in Numbers | Qualtrics ☑
Author: Qualtrics|July 28, 2010

Albert Einstein once said, “Not everything that can be counted counts, and not everything that counts can be counted.” [Warning of the danger of overquantification) Although many market research experts would say that quantitative research is the safest bet when one has limited resources, it can be dangerous to assume that it is always the best option.
human_ingenuity  data  analytics  small_data  massive_data_sets  data_driven  information_overload  dark_data  measurements  creativity  judgment  intuition  Nate_Silver  expertise  datasets  information_gaps  unknowns  underestimation  infoliteracy  overlooked_opportunities  sense-making  easy-to-measure  Albert_Einstein  special_sauce  metrics  overlooked  defensive_tactics  emotional_intelligence  EQ  soft_skills  overquantification  false_confidence 
may 2015 by jerryking
The Data Companies Wish They Had About Customers - WSJ
March 23, 2014 | WSJ | by Max Taves.

We asked companies what data they wish they had—and how they would use it. Here's what they said....
(A) has a huge appetite for data. Every hour, the mail-order snack business digests 15,000 user ratings about its foods, which it uses to better understand what its customers like or dislike and to predict what else they might like to try...more data could help him understand customers' tastes even better. Among the information he wants most is data about customers' dietary habits, such as what they buy at grocery stores, as well as better information about what they look at on Graze's own site. And because the dietary needs of children change rapidly, he'd like to know if his customers have children and, if so, their ages.
(B) Energy-----Energy consumption is among its customers' main concerns, says CEO William Lynch. For instance, the company offers a product giving homeowners the real-time ability to see things like how many kilowatts it takes to heat the hot tub in Jan. Because of privacy concerns, Savant doesn't collect homeowners' energy data. But if the company knew more about customers' energy use, it could help create customized plans to conserve energy. "We could make recommendations on how to set up your thermostat to save a lot of money,
(C) Banking-----the Bank of the West would like "predictive life-event data" about its customers—like graduation, vacation or retirement plans—to create products more relevant to their financial needs...At this point, collecting that breadth of data is a logistical and regulatory challenge, requiring very different sources both inside and outside the bank.
(D) Appliances-----Whirlpool Corp.has a vast reach in American households—but wants to know more about its customers and how they actually use its products. Real-time use data could not only help shape the future designs of Whirlpool products, but also help the company predict when they're likely to fail.
(E) Healthcare----Explorys creates software for health-care companies to store, access and make sense of their data. It holds a huge trove of clinical, financial and operational information—but would like access to data about patients at home, such as their current blood-sugar and oxygen levels, weight, heart rates and respiratory health. Having access to that information could help providers predict things like hospitalizations, missed appointments and readmissions and proactively reach out to patients,
(F) Healthcare----By analyzing patient data, Carolinas HealthCare System of Charlotte, N.C., can predict readmission rates with 80% accuracy,
(G) Law----law firms that specialize in defense work are typically reactive, however some are working towards becoming more proactive, coveting an ability to predict lawsuits—and prevent them.How? By analyzing reams of contracts and looking for common traits and language that often lead to problems.
(H) Defense---BAE Systems PLC invests heavily in protecting itself from cyberattacks. But it says better data from its suppliers could help improve its defenses...if its suppliers get cyberattacked, its own h/w and s/w could be compromised. But "those suppliers are smaller businesses with lesser investments in their security," ...A lack of trust among suppliers, even those that aren't direct competitors, means only a small percentage of them disclose the data showing the cyberattacks on their systems. Sharing that data, he says, would strengthen the security of every product BAE makes. [BAE is expressing recognition of its vulnerability to network risk].
data  data_driven  massive_data_sets  Graze  banking  cyber_security  BAE  law_firms  Whirlpool  genomics  social_data  appliances  sense-making  predictive_analytics  dark_data  insights  customer_insights  real-time  design  failure  cyberattacks  hiring-a-product-to-do-a-specific-job  network_risk  shifting_tastes  self-protection  distrust  supply_chains 
november 2014 by jerryking

Copy this bookmark:

to read