A general term is useful only if it subsumes related concepts in such a way that semantic value is added. If our comprehension is not increased by our chosen generalised term, then we shouldn’t use it. A common claim such as ‘they stole my intellectual property’ is singularly uninformative, since the general term ‘intellectual property’ obscures more than it illuminates. If copyright infringement is alleged, we try to identify the copyrightable concrete expression, the nature of the infringement and so on. If patent infringement is alleged, we check another set of conditions (does the ‘new’ invention replicate the design of the older one?), and so on for trademarks (does the offending symbol substantially and misleadingly resemble the protected trademark?) and trade secrets (did the enterprise attempt to keep supposedly protected information secret?) The use of the general term ‘intellectual property’ tells us precisely nothing.
The idea of intellectual property is nonsensical and pernicious - Samir Chopra | Aeon E... - 0 views
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Property is a legally constructed, historically contingent, social fact. It is founded on economic and social imperatives to distribute and manage material resources – and, thus, wealth and power. As the preface to a legal textbook puts it, legal systems of property ‘confer benefits and impose burdens’ on owners and nonowners respectively. Law defines property. It circumscribes the conditions under which legal subjects may acquire, and properly use and dispose of their property and that of others. It makes concrete the ‘natural right’ of holding property. Different sets of rules create systems with varying allocations of power for owners and others. Some grants of property rights lock in, preserve and reinforce existing relations of race, class or gender, stratifying society and creating new, entrenched, propertied classes. Law makes property part of our socially constructed reality, reconfigurable if social needs change.
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‘Property’ is a legal term with overwhelming emotive, expressive and rhetorical impact. It is regarded as the foundation of a culture and as the foundation of an economic system. It pervades our moral sense, our normative order. It has ideological weight and propaganda value. To use the term ‘intellectual property’ is to partake of property’s expressive impact in an economic and political order constructed by property’s legal rights. It is to suggest that if property is at play, then it can be stolen, and therefore must be protected with the same zeal that the homeowner guards her home against invaders and thieves.
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How to write a good software design doc - freeCodeCamp - 0 views
Gamification has a dark side - 0 views
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Gamification is the application of game elements into nongame spaces. It is the permeation of ideas and values from the sphere of play and leisure to other social spaces. It’s premised on a seductive idea: if you layer elements of games, such as rules, feedback systems, rewards and videogame-like user interfaces over reality, it will make any activity motivating, fair and (potentially) fun. ‘We are starving and games are feeding us,’ writes Jane McGonigal in Reality Is Broken (2011). ‘What if we decided to use everything we know about game design to fix what’s wrong with reality?’
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But gamification’s trapping of total fun masks that we have very little control over the games we are made to play – and hides the fact that these games are not games at all. Gamified systems are tools, not toys. They can teach complex topics, engage us with otherwise difficult problems. Or they can function as subtle systems of social control.
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The problem of the gamified workplace goes beyond micromanagement. The business ethicist Tae Wan Kim at Carnegie Mellon University in Pittsburgh warns that gamified systems have the potential to complicate and subvert ethical reasoning. He cites the example of a drowning child. If you save the child, motivated by empathy, sympathy or goodwill – that’s a morally good act. But say you gamify the situation. Say you earn points for saving drowning children. ‘Your gamified act is ethically unworthy,’ he explained to me in an email. Providing extrinsic gamified motivators, even if they work as intended, deprive us of the option to live worthy lives, Kim argues. ‘The workplace is a sacred space where we develop ourselves and help others,’ he notes. ‘Gamified workers have difficulty seeing what contributions they really make.’
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Which Industries Are Investing in Artificial Intelligence? - 0 views
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The term artificial intelligence typically refers to automation of tasks by software that previously required human levels of intelligence to perform. While machine learning is sometimes used interchangeably with AI, machine learning is just one sub-category of artificial intelligence whereby a device learns from its access to a stream of data.When we talk about AI spending, we’re typically talking about investment that companies are making in building AI capabilities. While this may change in the future, McKinsey estimates that the vast majority of spending is done internally or as an investment, and very little of it is done purchasing artificial intelligence applications from other businesses.
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62% of AI spending in 2016 was for machine learning, twice as much as the second largest category computer vision. It’s worth noting that these categories are all types of “narrow” (or “weak”) forms of AI that use data to learn about and accomplish a specific narrowly defined task. Excluded from this report is “general” (or “strong”) artificial intelligence which is more akin to trying to create a thinking human brain.
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The McKinsey survey mostly fits well as evidence supporting Cross’s framework that large profitable industries are the most fertile grounds of AI adoption. Not surprisingly, Technology is the industry with highest AI adoption and financial services also makes the top three as Cross would predict.Notably, automotive and assembly is the industry with the second highest rate of AI adoption in the McKinsey survey. This may be somewhat surprising as automotive isn’t necessarily an industry with the reputation for high margins. However, the use cases of AI for developing self-driving cars and cost savings using machine learning to improve manufacturing and procurement efficiencies are two potential drivers of this industry’s adoption.
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