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Is Use More Important than Ownership?

Posted By Administration, Tuesday, January 29, 2019
Updated: Wednesday, February 27, 2019

Tim Morgan publishes his second blog post in our Emerging Fellows program by comparing the concept of ‘use’ against the ‘ownership’ notion. The views expressed are those of the author and not necessarily those of the APF or its other members. 


When artificial intelligence pioneer Marvin Minsky was a graduate student in 1952, he constructed a simple machine which he kept on his desk. Acclaimed scientist and writer Arthur C. Clarke once saw the device, finding it both sinister and fascinating. The machine had a single switch. When activated, the device did one thing: it would raise a small arm and flip the switch back to the OFF position. This “Useless Machine” is an automation with only a single use: it turns itself off.

Minsky understood a fundamental aspect of automation: use is inextricably bound up with the structure, operating rules, and intent of the automation. His Useless Machine was a minimalist example of that active fusion of intent and capability. No matter who owned the machine or operated it, it would always lead to the same result. It would turn itself off.

We appear to see two contradictory trends at work with automation and capital. There is a long history of owners ceding use of their capital to intermediaries like banks, managers, and companies. Automation is clearly amplifying that trend via everything from programmatically traded stocks and commodities to “lights-out” automation of shipment planning and warehouse management. Even hobbyist gardeners can now buy an autonomous gardening robot like FarmBot to seed, weed, and feed their backyard garden based on the bot’s sophisticated algorithms and design.

Conversely, we also see automation pushing direct control back to the owners. That same FarmBot gives its owner the tools to plan the garden they want, schedule watering that adapts to local weather, and alert them to problems. The average owner has far more sophisticated control over the quality of their home-grown food than if they had bought it from intermediaries like local farmers, a local supermarket, or grown it the old fashioned way.

Can we reconcile automation simultaneously degrading ownership and amplifying it? We can if we consider how use plays out via the automation. Owners get more control over the use of their capital by embedding rules and algorithms into the capital itself. A factory produces goods. A fully automated factory would produce goods exactly the way the owner wants. The automation allows the owner to use the capital the way they see fit, restoring more direct control. The flip side is that automation is increasingly dependent on outside integration. The automated control is spreading out into the vast network of supporting services like cloud storage, software tools, and data services like weather reporting or goods pricing.

The resolution to the contradiction is that Ownership and Use are fusing together via dependence on a vast network of embedded, actively changing automation infrastructure. With each piece of capital automation, we add its capabilities to the networks of the world. This amplifies the capabilities of other items, creating new opportunities and synergies across the network.

Automation is creating a new world of Networkable Capital that is both amplifying control and spreading out the benefits to others in a seemingly magical halo of interdependent capabilities. That bodes well for bringing the benefits of Networked Capitalism to everyone in the future.

Now if we can only keep it from shutting itself off.

© Tim Morgan 2019

Tags:  automation  machine  network 

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Artificial Intelligence and Us

Posted By Administration, Thursday, March 22, 2018
Updated: Sunday, February 24, 2019

Monica Porteanu has written her third installment in our Emerging Fellows program. Here, she questions the effects of artificial intelligence on society. The views expressed are those of the author and not necessarily those of the APF or its other members.

“Will AI take over the world?” is a common question across many news outlets these days. “Artificial Intelligence will best humans at everything by 2060, experts say,” predicts one of them. “More than 70% of US fears robots taking over our lives, survey finds,” describes another. Most of all, “how long will it take for your job to be automated?” seems to be the question on everyone’s mind. Opposing views are also present, arguing about “The great tech panic: robots won’t take all our jobs.” How do we reconcile these views into what Artificial Intelligence is and can be?

The term “Artificial Intelligence” was coined in the 1950s, intending to describe the ability of machines to perform tasks at a human intelligence level. Today, the definition encompasses more nuanced meanings, especially when considering the level of human cognition. In this regard, there seem to be four categories: (1) automation; (2) machine learning using artificial neural networks; (3) deep learning; (4) and beyond.

Automation represents a low cognitive process that is repeatable, having well-defined sequences of actions that are pre-programmed into machine behaviour. The machine is a passive executor of what is being instructed to accomplish. Its ability to complete complex computations fast and without error is superior to humans. Automation can be applied on a large scale, with numerous examples from manufacturing production lines, to, more recently, interactions with customers, such as onboarding operations. It has the most concrete social impact, as it does take away jobs as we know them today. However, it also opens the opportunity for humans to do what they are better at than machines are: empathy, critical thinking, and creativity. The key to staying ahead of automation is, as Garry Kasparov puts it, “human ambition.”

Machine learning using artificial neural networks requires a more sophisticated, yet still moderate level of cognition. The machine can mimic repeatable but personalized activities, while learning from each interaction, and utilizing increasing amounts of data. It reacts to events based on what was instructed to be accomplished. In other words, it can present a solution to a problem as posed, recommend tasks, or take simple actions. For example, it can automatically set up preferences at home, adjust ambient environment parameters based on these preferences, turn appliances on/off, or keep track of our grocery list. This stage has developed in leaps and bounds during the last decade or so, achieving results in recognition and even digitization of image, face, or speech. However, the machine still has difficulty perceiving at a level comparable to a human. Although we are still irritated by recommendations gone wrong or irrelevant comments coming from the chat box, we allow this type of artificial intelligence into our lives, without yet understanding its concrete positive and negative impacts.

The leap to deep learning is the phase that debuted only a few years ago. With big visions at the forefront, deep learning aims to build capacity for a machine to solve problems without being told how. Such machines mimic the brain, through layers of artificial neurons that connect with and send signals to each other in the network. Initial results are astounding. For example, the machine has been able to beat humans at Go, the complex ancient Chinese game, whose number of alternative positions surpasses the atoms in the universe. However, it seems we have yet to uncover what is happening inside these deep neural networks. Scientists are currently investigating adversarial examples, in which the difference between what the human and machine sees is extreme (e.g., turtle versus gun).

Beyond deep learning is yet an area for even bigger dreams in which, perhaps, machines will surpass the human brain capacity, being able to create symbol systems (e.g., language, money, time, religion, governance) and with that, structurally alter every aspect of the life as we know it.

It seems we are now somewhere during the development of the second category, machine learning, and in the early stages of the third one, deep learning.

We have been warned that “Artificial Intelligence will best humans at everything by 2060.” With the many and contradicting opinions though, one could wonder, what will human capacity be in 2060? How will our brain functions evolve, and with that, where will our creativity, empathy, ambition, and critical thinking take us?

 



© Monica Porteanu 2018

Tags:  artificial intelligence  machine  society 

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