As the chart shows, the McKinsey folks believe that the most economically significant technologies over the next decade-plus will be those already well underway in their development — the mobile Internet, largely in place in the adv
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Indeed, maybe the single biggest takeaway from the study is this: The things that will have the greatest impact on the economy in the medium term aren’t the ones that seem to most excite the imagination and public interest. Instead, the potentially powerful innovations are mostly those that have been evolving for many years in new ways.
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The real economic benefits of innovation, at least over the near term, come not from the flashy, mind-blowing ideas, but from clever combinations of technologies that are just maturing with those that have been around for ages.
Wrong tense. Search has been sucked into business intelligence as a subordinate or utility function.
Consultants and “experts” suggest that search and business intelligence are converging. Information Builders, based in New York City, suggests that the alleged convergence looks like two equally-sized markets merging like a math book’s illustration of a Venn diagram. The picture is symmetrical and appears to make sense. In my opinion, the presentation of “worlds’ merging” in an orderly manner is misleading at best and downright silly at worst.
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In the somewhat untidy worlds of search and business intelligence, not much has changed. Terminology and the fervent belief that new phrases will solve information problems is more important than tackling more fundamental, less zippy issues.
We have entered an era of same old, same old, and there is no turning back. The problem of computational limits force systems to work as they have for many years. The methods and the math is the same. The content types and the marketing lingo are different.
You refer several times to what you call “unethical private sector parties whose only focus is on money in, not intelligence out.” This is what corrupted Truman's initiative. Covens like this go way back in history, but in the US more recently it coalesced around the group that set up the Fed, expanded to include globetrotters like Wild Bill Donovan and the Dulles Brothers. They, in turn, brought in collusion with Meyer Lansky, a merger of Mafia with highest level Masons, and a very powerful group of attorneys (Paul Helliwell, etc), financiers (Averell Harriman, etc), who were vigorously engaged in financing the Bolsheviks, the Nazis, the Fascists, Imperialists, and eventually Neo-Cons. While financing these groups to put power into dictatorship-of-the-State (everyone else being disposable serfs), they profited from moving heroin and other drugs, and siphoned off all the gold they could to personal offshore caches. In effect, this created a covert government-within-a-government, enabled them to take over mass media, and to use pharmaceuticals to stupefy the general public. The formal government of the US is now 90% corrupted into what's been called “a parliament of whores”. Huge sums provided to “rescue” Greece, etc., vanish. The coven has brought together as many fascist foreign governments as possible, so in effect it is attempting to set up its “invisible government” as the new global government. In doing so it has subverted the UN, and virtually all global organizations such as the FAO. This is why OSINT is having such a difficult time, reversing the poisonous current. The last thing the coven wants is an honest, moral, and open source of truthful information for the public.
In the Dutch media there wqs a discussion today about an opinion poll about support for conspiracy theories organized by a Dutch university research group. The researchers used the same arguments as in the NYT piece. If you ask sensible questions and don't believe the official narrative you must be crazy and have low self worth.
Phi Beta Iota: The only useful part of the NYT article is this first comment by a sane person:
Pat Nyack, NY. Part of the conspiracy cohort is made up of those of us who grew up during Watergate; the lies told about the number of enemy deaths during the Viet Nam war; the actions of major corporations in places like Chile in the 70's and 80's. Some of us stood in public spaces in the heart of our universities with the guns of the National Guard trained on us. These were all factual happenings, many uncovered and reported on by this very paper. Our childhood was built upon the illusion that America was mostly just a large Mayberry. These revelations shook our basic beliefs right to the ground. It's hard to step back from that cliff once you've been pushed to the edge of it.
It’s simple. Authorities invented the idea that other people have issues with authority.
Psychiatrists rank right up there among the elitists setting the standards. They, for example, have concocted a little fictional doodad called Oppositional Defiance Disorder. And magically, they never accuse their professional colleagues of having it. No.
Why should they? They amuse themselves by deciding when civilians are overly defiant and need pacification (drugs).
But we’re also talking about character structure here, because psychiatrists turn out to be exactly the people who want to slap labels like ODD on others. They like that. So they labor in universities and hospitals and earn their degrees and state-issued licenses, knowing that soon they will have that power.
Having gained it, there is nothing to be defiant about. They’re sitting on top of the heap, which they call science.
It’s quite a racket.
Full post below the line — both humorous and frightening.
Interview with Physicist and Consciousness Researcher Matt Pulver on the topics of Theoretical Physics, the Sociology of Science, and Consciousness/Perception. Matt works with Dr. Paul LaViolette in modeling Subquantum Kinetics; Dr. Paul's novel systems approach to microphysics and cosmology. Matt is also the coordinator of Project Camelot's Blue Science, an undertaking intended to expand public awareness of ‘Censored Science'. Reference Abstracted Outline below for effective skimming:
0.min-6.min: Matt's Academic experience; Project Camelot/Blue Science; Funding in Academia; the Independent Researcher; Kerry Cassidy
6.min-30.min: Collaberative work with Dr. Paul LaViolette; Predictions of Subquantum Kinetics; String Theory as a Brain Drain; Particles as Dissipative Structures; Gravity; Unaccounted Core Energy in Planets/Stars; Genic Energy; Galactic Superwaves; Pioneer Anomaly; Was There a Big Bang; Red Shift as a Doppler Effect; Red Shift as Tired Light Effect; Micro Physics as an Open System Phenomenon
31.min-37.min: Science in Academia; Was the Ether Disproven; Politics of the Master Game; Black Projects; Phil Schneider and Underground Bases; Sociology of Science; Thrive Movie
38.min-54.min: Ra and the Law of One; Space for Spirituality; Gödel's Theorem; Philosophy/History of Mathematics; Consciousness; False-Flags; What Is Truth; Russell's Paradox; Formal Number Theory; Shortcomings/Limitations of Mathematical Logic/Proofs; Qualitative vs. Quantitative Thinking
55.min-1.hour.2.min: Structure of Consciousness; Altered Perception; Limitations of Awareness; Archetypes and Artifacts; Embracing Paradox and Holism
1.hour.3.min-1.hour.21.min: Where Does Consciousness Come From; Self-Awareness and God; Electrogravitics; Reverse Engineering B2-Bomber; T. Townsend Brown; Anti-Gravity; Cloaking/Stealth; Philadelphia Experiment; Gravitational Potential/Vector Force; The Scalar Ether; Over-Unity Prototypes
By encouraging employees to both seek and provide help, rewarding givers, and screening out takers, companies can reap significant and lasting benefits.
McKinsey & Company, April 2013
After the tragic events of 9/11, a team of Harvard psychologists quietly “invaded” the US intelligence system. The team, led by Richard Hackman, wanted to determine what makes intelligence units effective. By surveying, interviewing, and observing hundreds of analysts across 64 different intelligence groups, the researchers ranked those units from best to worst.
Then they identified what they thought was a comprehensive list of factors that drive a unit’s effectiveness—only to discover, after parsing the data, that the most important factor wasn’t on their list. The critical factor wasn’t having stable team membership and the right number of people. It wasn’t having a vision that is clear, challenging, and meaningful. Nor was it well-defined roles and responsibilities; appropriate rewards, recognition, and resources; or strong leadership.
Rather, the single strongest predictor of group effectiveness was the amount of help that analysts gave to each other. In the highest-performing teams, analysts invested extensive time and energy in coaching, teaching, and consulting with their colleagues. These contributions helped analysts question their own assumptions, fill gaps in their knowledge, gain access to novel perspectives, and recognize patterns in seemingly disconnected threads of information. In the lowest-rated units, analysts exchanged little help and struggled to make sense of tangled webs of data. Just knowing the amount of help-giving that occurred allowed the Harvard researchers to predict the effectiveness rank of nearly every unit accurately.
The importance of helping-behavior for organizational effectiveness stretches far beyond intelligence work. Evidence from studies led by Indiana University’s Philip Podsakoff demonstrates that the frequency with which employees help one another predicts sales revenues in pharmaceutical units and retail stores; profits, costs, and customer service in banks; creativity in consulting and engineering firms; productivity in paper mills; and revenues, operating efficiency, customer satisfaction, and performance quality in restaurants.
Next week I am doing an invited talk in London. My subject is search and Big Data. I will be digging into this notion in this month’s Honk newsletter and adding some business intelligence related comments at an Information Today conference in New York later this month. (I have chopped the number of talks I am giving this year because at my age air travel and the number of 20 somethings at certain programs makes me jumpy.)
I want to highlight one point in my upcoming London talk; namely, the financial challenge which companies face when they embrace Big Data and then want to search the information in the system and search the Big Data system’s outputs.
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Notice that precision and recall has not improved significantly over the last 30 years. I anticipate that many search vendors will tell me that their systems deliver excellent precision and recall. I am not convinced. The data which I have reviewed show that over a period of 10 years most systems hit the 80 to 85 percent precision and recall level for content which is about a topic. Content collections composed of scientific, technical, and medical information where the terminology is reasonably constrained can do better. I have seen scores above 90 percent. However, for general collections, precision and recall has not been improving relative to the advances in other disciplines; for example, converting structured data outputs to fancy graphics.
I don’t want to squabble about precision and recall. The main point is that when an organization mashes Big Data with search, two curves must be considered. The first is the complexity curve. The idea is that search is a reasonably difficult system to implement in an effective manner. The addition of a Big Data system adds another complex task. When two complex tasks are undertaken at the same time, the costs go up.