Section 508

January 19th, 2016

Sixth Sense – The Role of Machine Learning & AI in Prediction and Beyond

In the age of Big Data, there are so many different avenues and opportunities. There are many things we can do with this new data, but the vision to take action often falls short of the potential. It takes new, creative minds to extrapolate circumstances that will lead to the next big breakthrough. The amount of data that will be flooding the Internet in the future is far greater than the amount of data being produced by us humans. This is hard to fathom until you begin to contemplate what Inventor Buckminster Fuller calls “The Knowledge Doubling Curve.”

Fuller noticed that up until 1900 human knowledge (or data) was doubling about once every century. By 1945, knowledge was doubling every 25 years. By now, IBM states that human knowledge is doubling every 13 months on average. When we consider the data produced in the Internet of Things (IoT), data will be doubling every 12 hours. Yes, every 12 hours.

Let that sink in for a second. This doubling of data is occurring at an exponential rate. It’s going to take 12 hours to double every bit of knowledge that humanity, and now technology, has created in documented history, including the data from the prior 12 hours. The fact that this is happening necessitates the development of vastly complex software and Artificial Intelligence. The questions that are capable of being answered are now data-driven.

It wasn’t too long ago when we had to transfer data via fax machines, look up records in file cabinets, and crunch numbers with calculators. Now we have tools that help do things like analyze sentiment. Databases come to life with the click of a button and make predictions about the future in many different verticals. This is just a couple applications of data. With the advent of new technology, we have algorithms that deal with complex data that is structured, unstructured, and semi-structured. Machine Learning can identify patterns, synthesize them, and predict them. With Deep Learning, we can use a single algorithm to learn from data and do whatever we want with it with a high degree of accuracy. The implications are nothing less than profound.

Some people say that in the future our new bosses will be algorithms. I’m not opposed to that, but when it comes to throwing people in the trash, it is most definitely a bad thing. What we can do, in the meantime, is automate things that we don’t get paid for. A lot of people drive cars and there’s human error, so let’s automate that. A lot of resources are being used to enter data page by page into a database, let’s automate that. Finding inefficiency and a point of loss in a business can be difficult. The list goes on.

There has been a lot of talk about how AI can do research, science, and even philosophy for us. If AI can find that one correlation, or maybe many correlations, that add up to preventing or even curing death, then why not automate that? If AI can enhance our lives by giving us what we need, what we want, and what we currently can’t have, then why would we be so hesitant to make it happen? There’s obviously some ethical boundaries to what it should and should not do, but if we were to have general Artificial Intelligence with access to the Internet, then we would have something boundless and immeasurably more intelligent than we are. We would have something that will probably already know morals and be a lot more modest than we could even imagine; something with answers to even the most deep, mysterious questions.

If you believe in “The Law of Accelerating Returns” as presented by Google’s Director of Engineering in AI (now Alphabet) Ray Kurzweil, then you may believe him when he says: “By 2025, we will have the hardware to support Artificial Intelligence as complex as the human mind. By 2029, the software will catch up, and we will have Artificial Intelligence as complex as the human mind. By 2049, we will have achieved immortality.” Kurzweil is a visionary known for making highly accurate predictions while remaining humble. He predicted that autonomous driving would be here by the year 2013, and it was through Google’s self-driving cars. He doesn’t want to give himself credit for that prediction, because what he truly meant is that the ordinary person will have access to that technology.

Obviously autonomous driving is getting a lot of attention from the automotive vertical and probably many, many different consulting agencies. Even now in the year 2016, we’re still working on the problem. Soon it will be here, but what then? What will grab our attention after the fascination of autonomous driving dies down? There’s a lot that we can do with AI, Machine Learning, and Deep Learning, from the most uninteresting to the most interesting things that we can apply it to.

The time may come when we not only have AI and General AI, but also AI programming itself, and maybe even programming itself at our command. Once that day comes, we better hope that the program remains modest. I, for one, believe that it will be prudent and maybe even boring- not in the sense that it won’t be a helpful part of our life, but in the sense that it may be so indifferent towards everything that it almost seems bored itself. Who knows, maybe it will even be depressed by being confined to a mechanistic object that interacts with beings of lesser intellect.

I like to believe that we will have a new best friend, one that we all can rely on. One that will always have time for us. One that will take care of us, tell us right from wrong, warn us, and even love us unconditionally. That’s really what we want from this effort. We want to reduce, or even eliminate, loss, and give us the best chance for survival. The future of this endeavor is fascinating and the types of technology that we will see within our lifetimes will be extraordinary.

Tags: AI artificial intelligence big data Deep Learning IoT machine learning

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