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jerryking : limitations   20

Cry revolution if you like, Alexa is not listening
FEBRUARY 16, 2018 | FT | Henry Mance.

We know that a revolt against Big Tech is coming. All the ingredients are there: unaccountable elites, wealth disparities, popular discontent......We should be drawing the opposite lesson. We should be grateful for these moments when technology fails: they remind us that we are relying too much on algorithms.

Silicon Valley has created such gloriously useful products that we mostly overlook their limitations. We don’t notice that Google inevitably has a bias towards certain sources of information, or that Amazon directs us towards certain products. We forget that messaging apps draw us away from other forms of interaction. Already Snapchat has over 100m users who use it for more than 30 minutes a day on average. Already you can have Alexa listen attentively to everything you say at home, which is more than any member of your family will. 

Occasionally, however, we are confronted with the imperfections of technology. We are shown online ads for products we have already bought or for which we are biologically ineligible. We are invited to connect on LinkedIn with people we’ve never met, but who have the same name as our first line manager.....It is these moments which allow us to see that the emperor has no clothes. They demonstrate that the software is only as clever as the humans who have designed it. They remind us that the real revolutionary act is to switch off.
backlash  platforms  Snapchat  imperfections  algorithms  biases  limitations  Big_Tech 
february 2018 by jerryking
The Limits of Amazon
Jan. 1, 2018 | WSJ | By Christopher Mims.

Amazon’s core mission as a data-driven instant-gratification company. Its fanaticism for customer experience is enabled by every technology the company can get its hands on, from data centers to drones. Imagine the data-collecting power of Facebook wedded to the supply-chain empire of Wal-Mart—that’s Amazon.

There is one major problem with the idea that Amazon-will-eat-the-entire-universe, however. Amazon is good at identifying commodity products and making those as cheap and available as possible. “Your margin is my opportunity” is one of Chief Executive Jeff Bezos’s best-known bon mots. But this system isn’t very compatible with big-ticket, higher-margin items.....

How Amazon Does It
Amazon now increasingly makes its money by extracting a percentage from the sales of other sellers on its site. It has become a platform company like Facebook Inc. or Alphabet Inc.’s Google, which serve as marketplaces for businesses with less reach of their own.....Eventually, Amazon could become the ultimate platform for retail, the “retail cloud” upon which countless other online retail businesses are built....Think of Amazon as an umbrella company composed of disconnected and sometimes competing businesses, though critically they can access common infrastructure, including the retail platform and cloud services.

Ultimately, these smaller businesses must feed the core mission. Amazon’s video business isn’t just its own potential profit center; it’s also a way to keep people in Amazon’s world longer, where they spend more money,

What Amazon Can’t Do
Ultimately, the strategies that allow Amazon to continue growing will also be its limitation. “If the platform needs to be one-size-fits-all across many, many different product categories, it becomes difficult to create specific experiences for different kinds of products,”
contra-Amazon  Amazon  strengths  data_driven  instant_gratification  customer_experience  platforms  one-size-fits-all  limitations  Jeff_Bezos  weaknesses  commoditization  third-party  Christopher_Mims 
january 2018 by jerryking
ATTENTION TO DETAIL by Dave Martins and The... - Dave Martins and The Tradewinds
the two biggest concerns for me are, in macro, the Indian/black ethnic division, and, in micro, the widespread tendency to accept or even encourage the sub-standard. For someone who has lived in the developed world, for two or three decades, that discinclination or disability to pay attention to detail in the various aspects of our life, is a jolt, and adjusting to that difference is very difficult because it confronts one daily. ....It is a detail, but we don’t seem to have yet understood in Guyana that the difference between good and excellent is always, absolutely always, in the details. Here, we praise the overall structure and seem oblivious to the pieces left hanging.
More pivotally, the lack is across the board. It is not just in the things we build. It is in the presentations we give, in the shows we stage, even in the way we drive. It is rampant in the media. Without fail, every day, there are punctuation errors, or declensions wrong, or verb/subject disagreements in our newspapers, and the lack of attention to detail in how we say what we say infects the broadcast media as well..... A friend of mine, with an awareness of the problem, says that this lack of attention to detail is now part of our cultural make-up; it is a condition of who we are and what we are. It is Guyana’s sociology in 2013. Cynical as that may be, it is a contention to consider....
Guyanese  Guyana  politics  limitations  detail_oriented  ethnic_divisions  quality  standards  substandard  developed_countries  Dave_Martins  shortcomings  developing_countries  pay_attention 
december 2015 by jerryking
A 25-Question Twitter Quiz to Predict Retweets -
JULY 1, 2014 | NYT | Sendhil Mullainathan.

how “smart” algorithms are created from big data: Large data sets with known correct answers serve as a training bed and then new data serves as a test bed — not too differently from how we might learn what our co-workers find of the miracles of big data: Algorithms find information in unexpected places, uncovering “signal” in places we thought contained only “noise.”... the Achilles’ heel of prediction algorithms--being good at prediction often does not mean being better at creation. (1) One barrier is the oldest of statistical problems: Correlation is not causation.(2) an inherent paradox lies in predicting what is interesting. Rarity and novelty often contribute to interestingness — or at the least to drawing attention. But once an algorithm finds those things that draw attention and starts exploiting them, their value erodes. (3) Finally, and perhaps most perversely, some of the most predictive variables are circular....The new big-data tools, amazing as they are, are not magic. Like every great invention before them — whether antibiotics, electricity or even the computer itself — they have boundaries in which they excel and beyond which they can do little.
predictive_analytics  massive_data_sets  limitations  algorithms  Twitter  analytics  data  data_driven  Albert_Gore  Achilles’_heel  boundary_conditions  noise  signals  paradoxes  correlations  causality  counterintuitive  training_beds  test_beds  rarity  novelty  interestingness  hard_to_find 
july 2014 by jerryking
The Weakest Link
November 30, 2006 |Strategy + Business | by Nicholas G. Carr.

A product’s vulnerabilities can point the way to lucrative new business opportunities.

As John Campbell pointed out in a 1996 article in the journal of the Federal Reserve Bank of Boston, the landing gear of the early 1930s, before the O-ring was introduced, is an example of a “reverse salient.” That odd term has its origins in descriptions of warfare, where it refers to a section of an advancing military force that has fallen behind the rest of the front. This section is typically the point of weakness in an attack, the lagging element that prevents the rest of the force from accomplishing its mission. Until the reverse salient is corrected, an army’s progress comes to a halt.

Historian Thomas P. Hughes was the first to apply the term to the realm of technological innovation. As described in his book Networks of Power: Electrification in Western Society, 1880–1930 (Johns Hopkins University Press, 1983), a reverse salient often forms as a complex technological system advances: “As the system evolves toward a goal, some components fall behind or out of line. As a result of the reverse salient, growth of the entire enterprise is hampered, or thwarted, and thus remedial action is required.” In technological advance as in warfare, the reverse salient is the weak link that impedes progress.
Nicholas_Carr  problem_solving  unintended_consequences  shortcomings  limitations  vulnerabilities  revenge_effects  new_businesses  weak_links 
july 2012 by jerryking
Unforeseen consequences -
May 24, 2007 | Financial Times |By Robert Matthews.

The Germans have a word for it: Schlimmbesserung - literally, a "worse improvement". You may not recognise the word, but you'll know plenty of examples of what it means: efficiency drives that reduce efficiency, cost-cutting measures that prove punitively expensive, software upgrades that cause months of downtime.

All businesses can fall victim to such "revenge effects"....

Edward Tenner, a visiting scholar in the department of history and sociology of science at the University of Pennsylvania and author of Why Things Bite Back, the classic study of the phenomenon first published in 1996, believes there are several measures that businesses can take. Indeed, he has given lectures at Microsoft, Intel and AT&T on the subject.

Ensuring there is in-house expertise that can spot emerging revenge effects and deal with the consequences is crucial, Mr Tenner says. "Many companies fail to deal with revenge effects because they are 'outsourcing their brains'," he says. "Lean organisations are supposed to be more flexible, but they may also be giving up a lot of their capability to respond to change."

According to Mr Tenner, businesses can keep a constant watch for reports of potential revenge effects in news and research findings. This has never been easier, thanks to online tools such as Google news alerts and RSS (really simple syndication) feeds.

Even so, revenge effects have a nasty habit of affecting businesses in unexpected ways. "The precondition of vigilance is the selection and development of ability at all levels,"

thinking about the downside to new developments can save a lot of heartache. "Excessive optimism risks revenge effects," he says. "You have to be prepared to work in Murphy's Law mode - and to consider that every possible thing that can go wrong will go wrong."
unintended_consequences  books  limitations  in-house  specificity  outsourcing  unexpected  revenge_effects  Murphy's_Law  thinking_tragically  lean  adaptability  flexibility  responsiveness  change  downtime 
june 2012 by jerryking
Talent Shortage Looms Over Big Data -
April 29, 2012 | WSJ | By BEN ROONEY

Big Data's Big Problem: Little Talent

"A significant constraint on realizing value from Big Data will be a shortage of talent, particularly of people with deep expertise in statistics and machine learning, and the managers and analysts who know how to operate companies by using insights from Big Data," the report said. "We project a need for 1.5 million additional managers and analysts in the United States who can ask the right questions and consume the results of the analysis of Big Data effectively." What the industry needs is a new type of person: the data scientist.....Hilary Mason, chief scientist for the URL shortening service, says a data scientist must have three key skills. "They can take a data set and model it mathematically and understand the math required to build those models; they can actually do that, which means they have the engineering skills…and finally they are someone who can find insights and tell stories from their data. That means asking the right questions, and that is usually the hardest piece."

It is this ability to turn data into information into action that presents the most challenges. It requires a deep understanding of the business to know the questions to ask. The problem that a lot of companies face is that they don't know what they don't know, as former U.S. Defense Secretary Donald Rumsfeld would say. The job of the data scientist isn't simply to uncover lost nuggets, but discover new ones and more importantly, turn them into actions. Providing ever-larger screeds of information doesn't help anyone.

One of the earliest tests for biggish data was applying it to the battlefield. The Pentagon ran a number of field exercises of its Force XXI—a device that allows commanders to track forces on the battlefield—around the turn of the century. The hope was that giving generals "exquisite situational awareness" (i.e. knowing everything about everyone on the battlefield) would turn the art of warfare into a science. What they found was that just giving bad generals more information didn't make them good generals; they were still bad generals, just better informed.

"People have been doing data mining for years, but that was on the premise that the data was quite well behaved and lived in big relational databases," said Mr. Shadbolt. "How do you deal with data sets that might be very ragged, unreliable, with missing data?"

In the meantime, companies will have to be largely self-taught, said Nick Halstead, CEO of DataSift, one of the U.K. start-ups actually doing Big Data. When recruiting, he said that the ability to ask questions about the data is the key, not mathematical prowess. "You have to be confident at the math, but one of our top people used to be an architect".
data_scientists  massive_data_sets  talent_management  talent  Pentagon  SecDef  limitations  shortages  McKinsey  war_for_talent  recruiting  Colleges_&_Universities  situational_awareness  questions  Donald_Rumsfeld  asking_the_right_questions 
june 2012 by jerryking
The Trouble with Big Data
May 5, 2012 | | What's The Big Data?| GilPress

“With too little data, you won’t be able to make any conclusions that you trust. With loads of data you will find relationships that aren’t real… On net, having a degree in math, economics, AI, etc., isn’t enough. Tool expertise isn’t enough. You need experience in solving real world problems, because there are a lot of important limitations to the statistics that you learned in school. Big data isn’t about bits, it’s about talent.”.....The “talent” of “understanding the problem and the data applicable to it” is what makes a good scientist: The required skepticism, the development of hypotheses (models), and the un-ending quest to refute them, following the scientific method that has brought us remarkable progress over the course of the last three hundred and fifty years.
in_the_real_world  massive_data_sets  blogs  skepticism  challenges  problems  problem_solving  expertise  statistics  talent  spurious  data_quality  data_scientists  haystacks  correlations  limitations 
june 2012 by jerryking
Making Sense of Research in Engagement
Dann Hoxsey; University of Victoria
Angela Matheson; Manager Research and Development, BC Stats
While everybody is talking about employee engagement, our research
points to some limitations about what can be said and how we might
better understand the effects an engaged workforce has on organizational
performance. This research presents a cautionary tale, which suggests
that a lot of “engagement” talk is over-simplified. In this
presentation, we will tell you what we have done, what we have found,
and what you can do to avoid making an engagement faux pas.
employee_engagement  limitations  cautionary_tales 
september 2010 by jerryking
Op-Ed Columnist - The Limits of Policy -
May 3, 2010 | New York Times | By DAVID BROOKS. As Brooks
notes, "The influence of politics and policy is usually swamped by the
influence of culture, ethnicity, psychology and a dozen other factors."
" So when we’re arguing about politics, we should be aware of how
policy fits into the larger scheme of cultural and social influences.
Bad policy can decimate the social fabric, but good policy can only
modestly improve it. Therefore, the rules of policy-making should be:
(1) don’t promulgate policies that destroy social bonds. If tribes of
people are exiled from their homelands and shipped to strange, arid
lands, bad outcomes result for generations. (2), try to establish basic
security. If the govt. can establish a basic level of economic and
physical security, people may create a culture of achievement — if
you’re lucky. (3), try to use policy to strengthen relationships. The
best policies, like good preschool and military service, fortify
emotional bonds."
David_Brooks  emotional_connections  limitations  policymaking  relationships  social_fabric  tribes 
may 2010 by jerryking
CEO_Guide_to_Growth.pdf (application/pdf Object)
July 2006 | On Disruption | by Michael Urlocker. Disruptive
Innovation offers the best way for companies to grow by creating new
markets. But what defines successful disruption? What are the
limitations? Is this just another marketing buzzword? And most
importantly, what can it do for your business? These questions will be
explored in this guide.
limitations  disruption  growth  innovation  filetype:pdf  media:document 
january 2010 by jerryking
Technology Gets Personal -
JULY 18, 2008 WSJ article by Steven Zeitchik on the limits of personalization algorithms.
personalization  algorithms  recommendations  limitations 
january 2009 by jerryking

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