The application of machines to tasks once performed by human beings or, increasingly, to tasks that would otherwise be impossible. Although the term mechanization is often used to refer to the simple replacement of human labour by machines, automation generally implies the integration of machines into a self-governing system. Automation has revolutionized those areas in which it has been introduced, and there is scarcely an aspect of modern life that has been unaffected by it. — Britannica

The term “automation” was first used by Ford employee D.S. Harder in 1946
Automation isn’t changing the world, it has been changing the world
Machine learning is simply accelerating the progress at a compounding rate
In short, the machines are teaching themselves to automate faster
The more computing power we throw at it, the faster it works

Also according to Britannica:Machine learning discipline concerned with the implementation of computer software that can learn autonomously.Expert systems and data mining programs are the most common applications for improving algorithms through the use of machine learning. Among the most common approaches are the use of artificial neural networks (weighted decision paths) and genetic algorithms (symbols “bred” and culled by algorithms to produce successively fitter programs).History
Automation is not a new concept
Cotton gin
Steam engine
Industrial revolution
Computers
Quantum computing and nanotechnology

Bill Gates is often attributed with a quote:I will always choose a lazy person to do a difficult job because a lazy person will find an easy way to do it.

As a Bill Gates fan, I’ve never been able to substantiate the attribution, but it seems to be similar to a quote from a 1920 article in “Popular Science Monthly”

Bill probably didn’t say that, but it doesn’t make it a bad quote. Humans have always looked for easier ways to do hard jobs. Which brings us to, CGP Grey’s 2014 YouTube film, Human’s Need Not Apply.Humans Need Not Apply

In the film by CGP Grey he brilliantly and succinctly outlines how humans have allows innovated an easier (or lazier) way of doing things — and modern automation, machine learning and artificial intelligence is no different. We’re trying to unlock “easy mode” for complex problems.

At its core, there is absolutely nothing inherently wrong about mankind’s evolution into machine reliance, but at the foundation of it all is there is the serious threat to our society as we know it — and that is economics. As machines can do more jobs, that will mean fewer jobs for people.(0:46–1:03) Some people have been specialized to be programmers … whose job it is to build mechanical minds

Like their “dumb” mechanical forefathers, “Mechanical Minds” are making “Mechanical Brains” in less demand.(1:50–2:16) General purpose is a big deal. Think computers … when cheap-ish personal computers appeared they quickly became vital to everything

General purpose is a big deal, as machines can do more than one job investments in them will grow exponentially(3:13–31) We think of technological change as the fancy new expensive stuff, but the real change comes from last decade stuff becoming cheaper and faster…

Again, as product improvements iterate faster over time, costs drop exponentially lowering the barrier for entry more and more.

He then goes on to discuss horses, and the fact that their population peaked in 1915, just and quickly went into a free fall due to the proliferation of the automobile. There is a correlation between horses and humans in this regard, because like horses who were “workers” for centuries before the automobile, they became obsolete not that long afterward… and the important part is, they didn’t find “new jobs” when it happened. “Mechanical Minds” will eventually send humans the way of the horse, if we do not prepare.(4:26–4:59) As mechanical horses pushed horses out of the economy, mechanical minds will do the same to humans… so now does the car show us the shape of things to come.”

I’m sure you can likely predict where this is going…(5:01–5:08) … The question is not if they will replace cars, but how quickly. They don’t need to be perfect, they just need to be better than us…

The tipping point for the transportation world being thrown on its head will come down to insurance… the moment insurance costs for automated vehicles is cheaper than human driven vehicles, is the moment that every single company in the transportation industry will being making the change to autonomous vehicles.(5:53–7:03) The transportation industry in the United States employs about 3 million people, which worldwide is 70 million jobs at a minimum… economics always wins…

That is an enormous economic hit(7:04–9:08) Software bots are coming for white color jobs

Machine learning can teach software bots faster than human programmers ever could. Algorithmic trading is a big area at the forefront of this technological revolution.Investment banking

But it doesn’t stop there. Professional and specialized positions are being challenged by bots as well, doing everything from providing legal and medical advice, to creating artistic works of art, music and even completely unique and lifelike people… that’s right, Nvidia’s GANs or Generative Adversarial Networks AI can develop completely life-like 3D renders of people who don’t exist.

Their GANs are based on a machine learning research paper released by Ian J. Goodfellow in just 2014… and now they have already revolutionized machine learning and AI!Where Does Microsoft Fit Into all of This?

Microsoft is quite literally developing and supporting AI everywhere it can:
Microsoft Research
Project Brainwave
Microsoft AI - Deep Neural Network, Bing Decision Engine
Azure Datacenters (Intel FPGA)
Azure Machine Learning
Azure Logic Apps
Workflow Definition Language
Microsoft Flow
PowerApps
SharePoint
Microsoft Bot Framework
Zo AI — Zo Social Bot, Skype (and Xbox), Instagram, Group Me, Facebook
Office 365
Microsoft Teams
Data Connectors and APIs
Windows 10 IoT
Cortana
Cortana is not in trouble, she has always been powered by the Bing Decision Engine
Cortana has just been the "face" of the AI
Which is now part of the Deep Neural Network of Microsoft AI
Microsoft partnering with Amazon for Alexa was a brilliant move, as they now have the data from all of the devices where Alexa is used…

That's because… Microsoft partnering with Amazon for Alexa was a brilliant move, as they now have the data from all of the devices where Alexa is used... that’s because…

Machine learning is all about data, and no company in the world has as much real-world data and user metrics than Microsoft. Just think of the 100+ million users of Office 365 and OneDrive, and the tens of millions of daily users of Skype and Xbox Live, and the more than 1 billion Windows users worldwide. Throw on top of that the countless computations that take place at their Azure data centers which house the systems for all of those services.

This is also why we’ve seen a massive shit by Microsoft to not only fully embrace open source, but to be a thought leader and visionary with it. Microsoft now has its own BSD Unix operating system, supports Ubuntu as a subsystem on Windows 10, and recently open-sourced the Xamarin software development kit… when you think of all of that, the GitHub acquisition is a no-brainer.

Now that we’ve discussed the future, and the groundwork Microsoft has laid to build it, in the next episode we will discuss what we can do to prepare ourselves for our machine overlords. Thanks so much for tuning in, and for the tremendous support we’ve received so far. I can’t wait to see where this journey takes us over the coming months, and I am truly excited to have many of you on to share as well.

Until next time…Follow or Subscribe to Microsoft Today
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