How to Get People to Pay Attention

A machine-generated signal often fails to hold our attention for very long. However, when the signal is someone else’s ongoing creation, engagement can be very high.

In this case, Smart built a video capture mechanism that converts dance moves into a “Do Not Walk” signal.

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“Non-invasive” Direct Neural Interface

This tech will lead to some interesting new approaches to controlling things (or people!) over the internet.

Our non-invasive interface, demonstrated originally in August 2013, combines electroencephalography (EEG) for recording brain signals with transcranial magnetic stimulation (TMS) for delivering information to the brain. We illustrate our method using a visuomotor task in which two humans must cooperate through direct brain-to-brain communication to achieve a desired goal in a computer game.

from A Direct Brain-to-Brain Interface in Humans

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Dutch Defibrillator Drone

There’s an interesting article on Nerdoholic about a flying defibrillator.

The drone tracks emergency mobile calls and uses the GPS to navigate. Once at the scene, an operator, like a paramedic, can watch, talk and instruct those helping the victim by using an on-board camera connected to a control room via a livestream webcam.

Drones can clearly be more than a way to convey the material; they can also project the expertise of an emergency medical professional. There are many imaginable cases where it would be difficult to pre-position materials and expertise that would be required to save a life, from floating devices to blood clotting agents to chemical burn treatments.

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Implication Blindness

The Three Breakthroughs That Have Finally Unleashed AI on the World by Kevin Kelly is an interesting read. Unfortunately, like a lot of tech writers, Kelly speculates about what has seemingly arrived without helping us understand the important outcomes looming just ahead.

The AI on the horizon looks more like Amazon Web Services—cheap, reliable, industrial-grade digital smartness running behind everything, and almost invisible except when it blinks off. This common utility will serve you as much IQ as you want but no more than you need. Like all utilities, AI will be supremely boring, even as it transforms the Internet, the global economy, and civilization.

In this case, the intention may have been to shock us with how ordinary AI seems to be, but the effect is to give us the impression that this technology is benign, helpful, and not likely to do anything unexpected. That is what boring, helpful things do. But technology like AI may provide far more than some transformative boost to the current economy or civilization. As people involved in info tech widely believe, and as Kelly seems to later touch on, AI even has the potential to unleash unforeseen forces upon itself.

In writing about microprocessors, observers may have casually stated that the technology powering missiles and getting astronauts to the moon was “supremely boring” even if it offered transformative utility. But that view seriously underestimates the long-term implications of ever-improving microprocessors!

Technology can be thought of as just a snapshot of what is really going on. And tech can be an amazing thing, so it is easy to miss the implications. What is far more important than the thing is the trend. What matters in microprocessors is Moore’s Law.

So does AI have some aspect that does more than just transform the current environment? Does AI have some characteristic that leads to something entirely unexpected?

Cloud computing obeys the law of increasing returns, sometimes called the network effect, which holds that the value of a network increases much faster as it grows bigger. The bigger the network, the more attractive it is to new users, which makes it even bigger, and thus more attractive, and so on. A cloud that serves AI will obey the same law. The more people who use an AI, the smarter it gets. The smarter it gets, the more people use it. The more people that use it, the smarter it gets. Once a company enters this virtuous cycle, it tends to grow so big, so fast, that it overwhelms any upstart competitors.

Kevin Kelly’s interpretation of the network effect is also problematic. He positions it as a force that primarily widens a technology’s reach and adoption, potentially powering a company’s rise and destruction of its competitors. With this sort of reasoning, AI will follow cloud computing and the web before it and be widely adopted.

But this is again focusing on the surface outcomes over the deeper implications. The concept of network effect actually describes the creation of extra value with each additional user or component of a system. And far beyond that, it describes how something unexpected and tremendous may emerge.

Far more important than companies getting rich or users getting access are the deep implications of value-generation — the new relationships created, new technologies produced, new efficiencies and scale. All of these are happening in a situation which involves a network effect. And they just might lead to the next network effect!

Unlocking value in the unsearchable web is how companies like Google brought us to this cloud AI scenario in the first place. To take it a step further, tracing the entire history of information tech back to the earliest days leads us to see a whole network of network effects.

So, it follows that if we believe that AI technology has network effect characteristics, then a more critical set of question are: what new relationships, what new technologies, what new network effects are now possible?

This line of thinking takes you to a far more interesting, far more potentially scary place.

In the grandest irony of all, the greatest benefit of an everyday, utilitarian AI will not be increased productivity or an economics of abundance or a new way of doing science—although all those will happen. The greatest benefit of the arrival of artificial intelligence is that AIs will help define humanity. We need AIs to tell us who we are.

Now this cherry on top is classic Wired Magazine reading material. It sounds great, it seems deep, and it helps us forget that most of the article has missed the fractal for the equation.

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