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Machine Learning and AI: No Fear of World Domination…But Exciting Possibilities for Government


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Greg Inks - Cloud ServicesBy Greg Inks, Cloud Services Practice Lead

One of the more thought-provoking movies in recent decades told the story of a time when a super computer (Skynet) became self-aware and took over the Earth (queue the theme music from “The Terminator”: duh da duh duh da dum).

The intersection of ever-increasing computing power and sophisticated programming was used to teach a computer to program itself and resulted in…the ultimate cloud system. In the film, it was in the form of an elastic computing platform that could control everything from IoT (internet of things) devices like drones/UAVs (unmanned aerial vehicle) to sophisticated computational platforms for academia; from healthcare systems prescribing treatments or cures for diseases to factories building everyday tools.  Does this sound familiar?

This scenario provided the ideal story line for a sci-fi classic. However, life often imitates fiction, so we are beginning to see these solutions come to life with the Cloud. You know it as Machine Learning…and we’re more and more about it these days.

However, is this “real” AI (artificial intelligence) or just marketing talk from Cloud providers?

Let’s start with a definition of machine learning. Then we will discuss where it is today and its potential for government and agencies that support their initiatives.

Machine Learning, Defined

Machine learning is actually an older (in computer science, anyway) that is often used to refer to just about anything that spikes a person’s emotional response to a computer thinking like a human. Modern day computer science would define machine learning as having a computer with the ability to learn or operate without being explicitly programmed. There is a very important subtle difference there.

Machine Learning Curiosity GraphicFor a computer to think like a human, the system would need to have a problem that it wants to solve (curiosity), then the cognition to ask a question, analyze to determine an answer, make a judgement about the validity of that answer, take action, and repeat the cycle. This cycle is often associated with self-awareness – the curiosity aspect that living organisms have (through either voluntary cognition or involuntary genetic response to stimuli). Computers, at least at the time of this article, lack this curiosity aspect.

However, computers are rapidly approaching the power to drive the cycle of discovery much faster than humans (and that can be scary!). That is machine learning. Simply put, if we can give a computer a model by which it can analyze data and make a deterministic evaluation over possible answers, it can take action on those answers and even learn from the results as to the fit of the answer against the data. If you can do this over a sufficiently large data set, so that the statistics (yes, statistics, along with linear algebra and differential equations) supply an answer that becomes relevant against a data model, you have actually trained a model against your data without writing any code. Computers are fantastic at applying patterns in this manner and now have the power to drive these calculations a break-neck speed over internet-scale amounts of data. Humans, however, cannot!

Machine Learning: A Real-Life Government Application

This might sound complicated, so consider this:

State and local governments rely on impact studies to determine how to better handle a problem. This is always done from a “what’s already happened” point of view, which, while helpful, is not ideal. Rather than working from a “past” perspective—and deciding how to do things differently next time—governments can use AI to build impact “what-ifs” to help them prepare for, prevent, and be proactive regarding an impending or possible change in the economy. For example, how will the community be impacted if a large business closes?

And here is an example for the Federal government. The VA can use predictive analytics to reach out to those returning from combat in an effort to prevent or mitigate the impact of PTSD, rather than identifying and beginning treatment after problems have already begun.

This is how the computer does it: A human loads an evaluation model into a system like Microsoft Azure’s Machine Learning in the Cortana Data Platform. This model is then trained against thousands or even millions of data points.

I use the term, “trained” because Cortana uses the model to calculate deterministic answers to the various sets of properties it has access to, and determines which ones really made a difference in selection. In short: Cortana has now learned. All the while, it is continuing to train the model against new data that is coming in, and discarding old answers that are no longer statistically relevant. It feels like artificial intelligence, but it is really math – done on such a scale that no human could possibly do those calculations real time –like magic.

So, will computers take over?

The short answer is no. What makes the computer so great at machine learning is obviously Cloud-scale computing power. But people have something computers don’t—emotions. Computers can’t take into consideration all those little things we just “know.” And they lack the ability (at least today) to see the trends outside of the explicit model that was setup or ask the follow up questions about “why.”

Until we can build systems that can apply a machine learning algorithm on top of the results of another machine learning algorithm, and build a neural network of computation, the system will not be able to evaluate and predict all behaviors – for now. Given a sufficient amount of time, enough computational power, and the right type of recursive evaluation models, computers will likely be able to develop curiosity and start to ask its own questions.

But Governments Can Benefit Today

For now, however, we can take advantage of machine learning to make ourselves better at our existing jobs. We are sitting on tops of petabytes of data; the history of everything that we have done over the decades. Any government or agency has burning questions that can be answered with the right machine-learning models.

The ability to accelerate our decision cycles to address our own curiosity is available to us right now—through the Cloud.

Do you want to take your government—and your community—beyond business intelligence and analytics? To make decisions based on what’s coming rather than what has already happened? AKA can help, offering expert Cloud Services that will help you develop and execute your Cloud strategy, including native cloud development that can bring Machine Learning into that strategy.

Ready to get started? Talk to the Cloud experts at AKA about how you can start capitalizing now on the power of Azure and predictive insights.

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