The Evolution of computing, Cloud to Edge & the impact of IOT

By now you have probably heard about Cloud computing and IOT (Internet Of Things) but what you have not heard about is the Edge and no I am not talking about the new browser from Microsoft in Windows 10. 

Edge is the space between Cloud and IOT. Let me explain this a bit more. Back in December of 2016 Peter Levine of the venture firm Andreessen Horowitz published a post with a video titled "Return to the Edge and the End of Cloud Computing" . If you have not seen the video it is worth the time. In this video he makes the argument that there is a back to the future factor between Distributed computing and centralized computing. It goes something like this:

Mainframe/Centralized/1960-1970 ? Client-Server/Distributed/1980–2000

Mobile-Cloud/Centralized/2005-2020 ? Edge Intelligence/Distributed/2020–

He says the "total addressable market" in that next pendulum swing will include the Internet of Things, with trillions of devices, starting with today's cars and drones. He also says machine learning will be required to "decipher the nuances of the real world".

This is a very interesting concept to think about. As much as we think that today is a connected world it is not completely connected. We are still having conversations around latency issues and connectivity.

Cell signal providers are trying to convince you that they have the best network and connectivity and the fact that they are advertising this message should tell you that connectivity is not a given fact.

Looking at this fact, Levine argues that most of the computing and data collection will happen at the end points on the Edge and not in central Clouds.

Most of the decision making will happen on the Edge where latency is at its lowest and proximity is at its highest. Machines will make most of the data they gather (and is gathered for them). That's because, he says, humans are bad at many decisions that machines do better, such as in driving a car. Peter has a Tesla and says "My car is a much better driver than I am." In driving for us, machine-learning systems in our things will "optimize for agility over power"

Systems in today’s planes already to this for the Pilots. They are extensions of the pilot. Gathering data and reacting to the environment and conditions in real time. Yet leave the actual flying to the pilots.

If you look today in a normal car (not a Self Driving Car) we have very sophisticated computers. IOT, Drones, Self driving cars for the first time in our history are collecting real time information from our world. Up to now computing was about inputs from humans via keyboard and output from computer based on some database or information that is already stored in a data center or a cloud. Now the data is collected in from the world in real time and it requires action and output back in real time. This changes the where the computing power happens from the Cloud to the Edge.

We are changing the cycle again from a centralized environment to a DE-centralized environment.

The learning curve for these systems consists of three loops operating in recursive cycles. The loops are Sensory loop, processing loop and execution loop.

Sensory Loop:

Data will enter these loops from sensors all over the place—cameras, depth sensors, radar, accelerometers. Already, he says, "a self-driving car generates about ten gigabytes of data per mile", and "a Lytro camera—a data center in a camera—generates 300 gigabytes of data per second." Soon running shoes will have sensors with machine-learning algorithms, he adds, and they will be truly smart, so they can tell you, for example, how well you are doing or ought to do.

Processing Loop:

The data from our smart things will be mostly unstructured, requiring more machine learning to extract relevance, do task-specific recognition, train for "deep learning", and to increase accuracy and automate what needs to be automated. This leaves the humans to do what humans do best.

Execution Loop:

As IoT devices become more sophisticated, we'll have more data accumulations and processing decisions, and in many (or most) cases, machines will make ever-more-educated choices about what to do. Again, people will get to do what people do best. And, they'll do it based on better input from the educated things that also do what machines do best on their own.

Meanwhile, the old centralized cloud will become what he calls a "training center". Since machine learning needs lots of data in order to learn, and the most relevant data comes from many places, it only makes sense for the cloud to store the important stuff, learn from everything everywhere, and push relevant learnings back out to the edge. Think of what happens (or ought to happen) when millions of cars, shoes, skis, toasters and sunglasses send edge-curated data back to clouds for deep learning, and the best and most relevant of that learning gets pushed back out to the machines and humans at the edge. Everything gets smarter—presumably.

His predictions:

1. Sensors will proliferate and produce huge volumes of geo-spacial data.

2. Existing infrastructure will back-haul relevant data while most computing happens at edges, with on-site machine learning as well.

3. We will return to peer-to-peer networks, where edge devices lessen the load on core networks and share data locally.

4. We will have less code and more math, or "data-centric computing"—not just the logical kind.

5. The next generation of programmers won't be doing just logic: IF, THEN, ELSE and the rest.

6. We'll have more mathematicians, at least in terms of talent required.

7. Also expect new programming languages addressing edge use cases.

8. The processing power of the edge will increase while prices decrease, which happens with every generation of technology.

9. Trillions of devices in the supply chain will commoditize processing power and sensors. The first LIDAR for a Google car was $7,000. The new ones are $500. They'll come down to 50 cents.

10. The entire world becomes the domain of IT. "Who will run the fleet of drones to inspect houses?" When we have remote surgery using robots, we also will need allied forms of human expertise, just to keep the whole thing running.

11. We'll have "consumer-oriented applications with enterprise manageability."

His conclusion: "There's a big disruption on the horizon. It's going to impact networking, storage, compute, programming languages and, of course, management."

All that is good as far as it goes, which is toward what companies will do. But what about the human beings who own and use this self-educating and self-actualizing machinery? Experts on that machinery will have new work, sure. And all of us will to some degree become experts on our own, just as most of us are already experts with our laptops and mobile devices. But the IoT domain knowledge we already have is confined to silos. Worse, silo-ization of smart things is accepted as the status quo.

As most of you already know I am a big supporter of Open Cultures and Open Computing. As Phil Windley put it in his blog post 3 years ago we are in the building the Compuserve-Of-Things and not the Internet-Of-Things.

His summary:

On the Net today we face a choice between freedom and captivity, independence and dependence. How we build the Internet of Things has far-reaching consequences for the humans who will use—or be used by—it. Will we push forward, connecting things using forests of silos that are reminiscent of the online services of the 1980s, or will we learn the lessons of the internet and build a true Internet of Things?

Take for example "Google Home vs. Amazon Echo—a Face-Off of Smart Speakers" by Brian X. Chen in The New York Times. Both Google Home and Amazon Echo are competitors in the "virtual assistant" space that also includes Apple's Siri and Microsoft's Cortana. All are powered by artificial intelligence and brained in the server farms of the companies that sell them. None are compatible with each other, meaning substitutable.

If progress continues on its current course, the distributed future Peter Levine projects will be built on the forest-of-silos Compuserve-of-things model we already have. Amazon, Apple, Google and Microsoft are all silo-building Compuserves that have clearly not learned the first lesson of the internet—that it was designed to work for everybody and everything, and not just so controlling giants can fence off territories where supply chains and customers can be held captive. For all their expertise in using the internet, these companies are blindered to the negative externalities of operating exclusively in their self interest, oblivious to how the internet is a tide that lifts all their economic and technological boats. In that respect, they are like coal and oil companies: expert at geology, extraction and bringing goods to market, while paying the least respect to the absolute finitude of the goods they extract from the Earth and to the harms that burning those goods cause in the world.

But none of that will matter, because the true Internet of Things is the only choice we have. If all the decisions that matter most, in real time (or close enough), need to be made at the edge, and people there need to be able to use those things expertly and casually, just like today's fighter pilots, they'll need to work for us and not just their makers. They'll be like today's cars, toasters, refrigerators and other appliances in two fundamental ways: they'll work in roughly the same ways for everybody, so the learning curve isn't steep; and they'll be substitutable. If the Apple one fails, you can get a Google one and move on.

We already have too many silo applications take text messaging for example Google by itself has 4 different applications that do not talk to each other. Currently you can find 170 messaging apps on Google Play and Apple App store that only communicates with itself. These are human created silo’s

To solve all these problems, you need to start with the individual: the individual device, the individual file, the individual human being.

If you start with central authority and central systems, you make people and things subordinate dependents, and see problems only silos can solve. All your things and people will be captive, by design. No way around it.