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For all the happiness and sharing, real money is involved here. In July GitHub received $100 million from the venture capital firm Andreessen Horowitz. This early in most software companies’ lives, $20 million would be a fortune.

GitHub’s popularity has also made it an important way for companies to recruit engineers, because some of the best people in the business are showing their work or dissecting the work of others inside some of the public pull requests.

Mr. Preston-Werner thinks the way open source requires a high degree of trust and collaboration among relative equals (plus a few high-level managers who define the scope of a job and make final decisions) can be extended more broadly, even into government.

“For now this is about code, but we can make the burden of decision-making into an opportunity,” he said. “It would be useful if you could capture the process of decision-making, and see who suggested the decisions that created a law or a bill.”

Can this really be extended across a large, complex organization, however?

As complex as an open-source project may be, it is also based on a single, well-defined outcome, and an engineering task that is generally free of concepts like fairness and justice, about which people can debate endlessly.

Google once prided itself on few managers and fast action, but has found that getting big can also involve lots more meetings.

Still, these fast-rising successes may be on to something more than simply universalizing the means of their own good fortune. An early guru of the Information Age, Peter Drucker, wrote often in the latter part of his career of the need for managers to define tasks, and for workers to seek fulfillment before profits.

A class can have subclasses that represent concepts that are more specific than the superclass.

Here we discuss general issues to consider and offer one possible process for developing an ontology. We describe an iterative approach to ontology development: we start with a rough first pass at the ontology. We then revise and refine the evolving ontology and fill in the details. Along the way, we discuss the modeling decisions that a designer needs to make, as well as the pros, cons, and implications of different solutions.

In practical terms, developing an ontology includes: �         defining classes in the ontology, �         arranging the classes in a taxonomic (subclass–superclass) hierarchy, �         defining slots and describing allowed values for these slots, �         filling in the values for slots for instances.

We can then create a knowledge base by defining individual instances of these classes filling in specific slot value information and additional slot restrictions.

Slots describe properties of classes and instances:

some fundamental rules in ontology design

There is no one correct way to model a domain— there are always viable alternatives. The best solution almost always depends on the application that you have in mind and the extensions that you anticipate. 2)      Ontology development is necessarily an iterative process. 3)      Concepts in the ontology should be close to objects (physical or logical) and relationships in your domain of interest. These are most likely to be nouns (objects) or verbs (relationships) in sentences that describe your domain.

how detailed or general the ontology is going to be

what we are going to use the ontology for

concepts in the ontology must reflect this reality

We suggest starting the development of an ontology by defining its domain and scope. That is, answer several basic questions: �         What is the domain that the ontology will cover? �         For what  we are going to use the ontology? �         For what types of questions the information in the ontology should provide answers? �         Who will use and maintain the ontology?

plan to use

domain

If the people who will maintain the ontology describe the domain in a language that is different from the language of the ontology users, we may need to provide the mapping between the languages.

One of the ways to determine the scope of the ontology is to sketch a list of questions that a knowledge base based on the ontology should be able to answer, competency questions

These competency questions are just a sketch and do not need to be exhaustive.

We heard that the government should be more focussed on helping innovative firms to grow and, particularly, on serving the needs of small and medium-sized enterprises (SMEs)

greater cooperation with provincial programs

innovation support is too narrowly focussed on R&D – more support is needed for other activities along the continuum from ideas to commercially useful innovation

more productive and internationally competitive economy

whole-of-government program delivery vehicle – the Industrial Research and Innovation Council (IRIC)

SR&ED program should be simplified

includes non-labour costs, such as materials and capital equipment, the calculation of which can be highly complex

the base for the tax credit should be labour-related costs, and the tax credit rate should be adjusted upward

fund direct support measures for SMEs

promoting the growth of firms

facilitating access by such firms to an increased supply of risk capital at both the start-up and later stages of their growth.

building public–private research collaborations

National Research Council (NRC) should become independent collaborative research organizations

become affiliates of universities

create opportunity and demand for leading-edge goods

encouragement of innovation in the Canadian economy should become a stated objective of procurement policies and programs.

the government needs to establish business innovation as a whole-of-government priority

put innovation at the centre of the government's economic strategy

Innovation Advisory Committee (IAC) – a body with a whole-of-government focus that would oversee the realization of our proposed action plan, as well as serve as a permanent mechanism to promote the refinement and improvement of the government's business innovation programs going forward.

focus resources where market forces are unlikely to operate effectively or efficiently and, in that context, address the full range of business innovation activities, including research, development, commercialization and collaboration with other key actors in the innovation ecosystem

the closer the activity being supported is to market, and therefore the more likely it is that the recipient firm will capture most of the benefit for itself.

specific sectors

of strategic importance

concentrated in particular regions

succeed in the arena of global competition

the bell curve described the wealth and income distribution of American society

As the technology boom of the 1990s increased productivity, many assumed that the rising water level of the economy was raising all those middle class boats. But a different phenomenon has also occurred. The wealthy have gained substantially over the past two decades while the middle class has remained stagnant in real income, and the poor are simply poorer.

America is turning into a power-curve society: one where there are a relative few at the top and a gradually declining curve with a long tail of relatively poorer people.

For the first time since the end of World War II, the middle class is apparently doing worse, not better, than previous generations.

an alarming trend

What is the role of technology in these developments?

a sweeping look at the relationship between innovation and productivity

New Economy of Personal Information

Power-Curve Society

the future of jobs

the report covers the social, policy and leadership implications of the “Power-Curve Society,”

World Wide Web

as businesses struggle to come to terms with this revolution, a new set of structural innovations is washing over businesses, organizations and government, forcing near-constant adaptation and change. It is no exaggeration to say that the explosion of innovative technologies and their dense interconnections is inventing a new kind of economy.

the new technologies are clearly driving economic growth and higher productivity, the distribution of these benefits is skewed in worrisome ways.

the networked economy seems to be producing a “power-curve” distribution, sometimes known as a “winner-take-all” economy

Economic and social insecurity is widespread.

major component of this new economy, Big Data, and the coming personal data revolution fomenting beneath it that seeks to put individuals, and not companies or governments, at the forefront. Companies in the power-curve economy rely heavily on big databases of personal information to improve their marketing, product design, and corporate strategies. The unanswered question is whether the multiplying reservoirs of personal data will be used to benefit individuals as consumers and citizens, or whether large Internet companies will control and monetize Big Data for their private gain.

Why are winner-take-all dynamics so powerful?

appear to be eroding the economic security of the middle class

A special concern is whether information and communications technologies are actually eliminating more jobs than they are creating—and in what countries and occupations.

How is the power-curve economy opening up opportunities or shutting them down?

Is it polarizing income and wealth distributions? How is it changing the nature of work and traditional organizations and altering family and personal life?

many observers fear a wave of social and political disruption if a society’s basic commitments to fairness, individual opportunity and democratic values cannot be honored

what role government should play in balancing these sometimes-conflicting priorities. How might educational policies, research and development, and immigration policies need to be altered?

The Innovation Economy

Conventional economics says that progress comes from new infusions of capital, whether financial, physical or human. But those are not necessarily the things that drive innovation

What drives innovation are new tools and then the use of those new tools in new ways.”

at least 50 percent of the acceleration of productivity over these years has been due to ICT

economists have developed a number of proxy metrics for innovation, such as research and development expenditures.

Atkinson believes that economists both underestimate and overestimate the scale and scope of innovation.

Calculating the magnitude of innovation is also difficult because many innovations now require less capital than they did previously.

Others scholars

see innovation as going in cycles, not steady trajectories.

A conventional approach is to see innovation as a linear, exponential phenomenon

leads to gross errors

Atkinson

believes that technological innovation follows the path of an “S-curve,” with a gradual increase accelerating to a rapid, steep increase, before it levels out at a higher level. One implication of this pattern, he said, is that “you maximize the ability to improve technology as it becomes more diffused.” This helps explain why it can take several decades to unlock the full productive potential of an innovation.

innovation keeps getting harder. It was pretty easy to invent stuff in your garage back in 1895. But the technical and scientific challenges today are huge.”

costs of innovation have plummeted, making it far easier and cheaper for more people to launch their own startup businesses and pursue their unconventional ideas

innovation costs are plummeting

Atkinson conceded such cost-efficiencies, but wonders if “the real question is that problems are getting more complicated more quickly than the solutions that might enable them.

we may need to parse the different stages of innovation: “The cost of innovation generally hasn’t dropped,” he argued. “What has become less expensive is the replication and diffusion of innovation.”

what is meant by “innovation,”

“invention plus implementation.”

A lot of barriers to innovation can be found in the lack of financing, organizational support systems, regulation and public policies.

90 percent of innovation costs involve organizational capital,”

there is a serious mismatch between the pace of innovation unleashed by Moore’s Law and our institutional and social capacity to adapt.

This raises the question of whether old institutions can adapt—or whether innovation will therefore arise through other channels entirely. “Existing institutions are often run by followers of conventional wisdom,”

The best way to identify new sources of innovation, as Arizona State University President Michael Crow has advised, is to “go to the edge and ignore the center.”

Paradoxically, one of the most potent barriers to innovation is the accelerating pace of innovation itself.

Institutions and social practice cannot keep up with the constant waves of new technologies

“We are moving into an era of constant instability,”

“and the half-life of a skill today is about five years.”

Part of the problem, he continued, is that our economy is based on “push-based models” in which we try to build systems for scalable efficiencies, which in turn demands predictability.

The real challenge is how to achieve radical institutional innovations that prepare us to live in periods of constant two- or three-year cycles of change. We have to be able to pick up new ideas all the time.”

pace of innovation is a major story in our economy today.

The App Economy consists of a core company that creates and maintains a platform (such as Blackberry, Facebook or the iPhone), which in turn spawns an ecosystem of big and small companies that produce apps and/or mobile devices for that platform

tied this success back to the open, innovative infrastructure and competition in the U.S. for mobile devices

standard

The App Economy illustrates the rapid, fluid speed of innovation in a networked environment

crowdsourcing model

winning submissions are

globally distributed in an absolute sense

problem-solving is a global, Long Tail phenomenon

As a technical matter, then, many of the legacy barriers to innovation are falling.

small businesses are becoming more comfortable using such systems to improve their marketing and lower their costs; and, vast new pools of personal data are becoming extremely useful in sharpening business strategies and marketing.

Another great boost to innovation in some business sectors is the ability to forge ahead without advance permission or regulation,

“In bio-fabs, for example, it’s not the cost of innovation that is high, it’s the cost of regulation,”

This notion of “permissionless innovation” is crucial,

“In Europe and China, the law holds that unless something is explicitly permitted, it is prohibited. But in the U.S., where common law rather than Continental law prevails, it’s the opposite