In light of the federal cybersecurity workforce shortage, turning to machines and automation to help secure federal systems and networks is no longer a suggestion; it’s a necessity.

This shortage can be attributed to several factors, one of which is that a lot of person-power is spent on mundane tasks that don’t need to be done by a human. This leads to higher levels of turnover in more junior roles — namely tier-one security operations center (SOC) operators and researchers.

Fortunately, these junior roles are the easiest and most logical to automate with human-machine teaming. A recent Pathfinder report commissioned by McAfee, via 451 Research, explores this topic and describes how human-machine teaming makes for sustainable endpoint security in all enterprises, including government. Artificial intelligence and machine learning can help with more mundane tasks, while leaving higher-level human thinking for more sophisticated attacks, changing the way cyber professionals do their job for the better.

That’s an important caveat, because machines are only as good as the humans creating and using them. Federal cybersecurity workers shouldn’t worry about job security with artificial intelligence looming. In fact, it’s quite the opposite: They should be excited, as their jobs should become more interesting and challenging with automation taking over lower-level tasks. The optimum state of federal cybersecurity is not simply automation, artificial intelligence or machine learning; it’s human-machine teaming.

Despite the 2017 National Defense Authorization Act directing a more limited use of lowest-price technically acceptable (LPTA) contracts, the government continues to leverage these contracts heavily for cybersecurity efforts. Going forward, they will need to leverage machine learning and automation for low-price, lower-skilled activities, reserving human intellect for the higher-order efforts.

This concept is not without precedent. Machines helped us win World War II through cryptanalysis and codebreaking; in the same way, machines can help us defend our systems from modern-day adversaries. The Allies still required Alan Turing and his team. They still needed Joseph Rochefort and his cryptanalysts. Imagine the state of the world if the government continued to work on the enemy’s ciphers and codes manually without involving machines. The Battle of the Atlantic and the Battle of Midway would likely have resulted in significantly different outcomes. Like cryptanalysts in WWII, we need to think differently about cybersecurity today.

Attackers now focus on vulnerable endpoints as the preferred point of entry for malware, as endpoints are not confined to the data center, with its layers of security under the watchful eye of security teams. With the increased use of public and hybrid clouds, the network becomes even more diverse and complex, not to mention the coming mass-propagation of the Internet of Things (IoT) sensors and control devices. Humans simply can’t keep up today, even the best of them. Tomorrow will be even more challenging. This is where machine learning will be key.

Machine learning provides the fastest way to identify new attacks and push that information to endpoint security platforms. Machines are excellent at repetitive tasks, such as making calculations across broad swaths of data, crunching big data sets and drawing statistical inferences based on that data, all at rapid speed. With the help of machine learning, security teams may have greater insight into who the attackers are (basic attribution), what methods they’re using, and how successful those methods are. Despite this, it’s imperative to remember that machines lack the ability to put data into context like humans can, or understand the implications of events. Context is of critical importance in cyber operations and not something as well suited to machines.

Machine learning is a long way from perfect, but it’s making significant gains and worth the effort. Of course, the results derived are always subject to the variables humans submit for calculation and any unknowns that we didn’t calculate in the equation. The models are only as good as the human-provided inputs; as we know, machines don’t think for themselves. A hybrid of human and machine will be the answer, and as technology evolves, the workload will shift.

Government organizations need to understand that today’s attacks are not as simple as finding the next event, but rather correlating events that might come from multiple sources, targeting multiple systems within multiple agencies. One or two events on their own might be benign, but taken out of isolation and viewed from a broader perspective, those events might be indicators of compromise. The job of looking across that broader perspective, correlating events, and telling the story falls to humans.

he key to human-machine teaming is using machines to do what they do best and humans to do what machines can’t do — like making sophisticated judgments and thinking quickly to solve problems. The result will yield not only more interesting federal jobs but also a more effective defensive posture for government networks. Our adversaries are using machine learning and artificial intelligence to attack us; it’s time we match their capabilities.

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From Amazon’s Alexa learning which restaurants its users like, to Apple’s iPhone predicting the next word in a text message, artificial intelligence (AI) is already having a significant influence on everyday life.

But Northwestern economist Benjamin Jones and his colleagues are now asking what happens to economic growth if artificial intelligence starts generating original thought. They are among the researchers looking at how much more human work AI can automate, including the generation of new ideas.

“If machine learning can really take over all human tasks and take over ideas of innovation, then it would be possible to get a radical change in the growth rate” of the economy, Jones told CNBC in an interview. “But the real question is going to be: can AI take over all of the essential tasks?”

Jones, along with Chad Jones of Stanford University and Philippe Aghion of the College de France wrote about their research in a paper entitled “Artificial Intelligence and Economic Growth” for the National Bureau of Economic Research earlier this month.

If rapidly-improving artificial intelligence can provide the markets with innovations to improve the workplace, some jobs could see skyrocketing wage growth while others could become obsolete.

AI activity has been accelerating, with the world’s top technology companies leading the way. Self-driving vehicles have been one popular subject of experimentation. Chipmakers including Nvidia have refined their products to better suit AI computations, while Amazon has long used AI to recommend products in its e-commerce business.

This week, Google-owned DeepMind published the latest findings of Alphago, its project in which a computer learns how to play the board game Go. This latest installment, dubbed Alpha Zero, managed to beat Google’s existing AlphaGo 100 times consecutively after only three days of training. This happened completely without human training.

Jones and his research team looked at different scenarios. The first modeled growth if people could be replaced by AI in all tasks. Other models looked at growth with partial automation. There weren’t any stark numerical findings, but the ongoing research is aimed at finding how AI can be useful in generating economic growth, as the steam engine did in the 1800s and early computer chips did in the middle 20th century.

In one model, replacing labor with artificial intelligence, the research team showed that only AI and economic capital could be required for the generation of new ideas.

Some have hypothesized that AI could enter into a rapid cycle of self-improvement, with each new cycle more intelligent than the previous one. Such a development could dramatically change the way people live.

But, Jones said, there is ongoing disagreement among economists on whether AI can — or even should — reach the point where it can generate original ideas. One of the most important lessons of their research is that economic growth may be constrained not by what humans are good at, but rather by tasks that are essential yet hard to improve.

In farming, for example, while fertilizer and combines boosted growth for a while, a finite amount of arable land has kept production bounded.

“The way to think about it is bottlenecks,” explained Jones. “We are vastly better at growing food than we were 100 years ago, but by virtue of automation it now only accounts for 2 percent of GDP.”

“We have computers that are mind-bogglingly fast,” continued Jones. “And yet, growth recently has been slower than it has been. Our limit to economic performance probably isn’t computation, right? We’re still heavily constrained by things we can’t or find harder to improve.”

Another possible bottleneck is what the researchers called “search limits.”

The idea suggests that the most obvious innovation ideas are discovered first and new ideas become increasingly harder to find. While AI could help speed up that search, it may be subject to a finite universe of new ideas.

Still, Jones said he is excited about the research.

“What’s interesting about AI is that it seems like we’re on the edge of tasks that are cognitive,” he said.

To be sure, a number of innovators and scientists don’t believe artificial intelligence is a great idea.

Tesla and SpaceX CEO Elon Musk has repeatedly warned against AI, going so far as to declare that competition for the technology will be the “most likely cause of World War III.” Earlier this year, Musk said humans must somehow merge with machines or risk becoming irrelevant in the age of AI. The billionaire is working on a company called Neuralink to do just that.

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While artificial intelligence (AI), machine learning and deep learning are often thought of as being interchangeable, they do in fact relate to very different concepts. It all began in the 1950s with AI and the idea that a computer could be made to simulate human learning and intelligence.

A subclass of that is machine learning, whereby a computer can take large amounts of data and use it begin to recognize patterns, make predictions on new data, and essentially ‘learn’ for itself. The drawback is that machine learning requires that parameters be set for what the computer needs to recognize, and those inputs can be time-consuming. And so we go one step further, into deep learning.

For example, Ripjar offers a service under the heading of ‘Analysis at the Speed of Thought’ that utilizes deep learning combined with natural language processing to analyze an organization’s internal data, in addition to information from sources like news feeds, web pages, and social media posts. These data streams are captured and monitored in real-time, in more than 160 languages, in order to provide cybersecurity, reputation management, compliance, etc. Without the capabilities of deep learning, the inputs required to get results would prove incredibly difficult. In essence, deep learning is enabling the practical application on of machine learning. So how does it work?

Inspired by the structure and activity of neurons within the human brain, deep neural networks (DNN) form the basis of deep learning. Through these algorithms, computers are able to identify features in significantly sized datasets and progress that information on through layers of the neural network, refining as it goes. This leads to a hierarchical representation of the problem.

Developer Considerations for Machine Learning 

There are many reasons why startups might struggle to fulfill their potential for financial and technological success. Among the many unique challenges they face from initial concept through to expansion, a lack of scalability can be one of the most difficult to overcome. In this section, we’ll focus on the capabilities and practical application of machine and deep learning, the frameworks and technologies you need to know about, and the ways that the community can help from the very beginning.

If you’re trying to decide whether or not to begin a machine or deep learning project, there are several points that should first be considered:

• Cost
• Need
• Organizational readiness
• Industry readiness
• Competition
• Regulations and compliance
• The pace of innovation

It may sound obvious, but the majority of startups that fail to find traction in the market do so because they’ve identified a need that doesn’t really exist – or at least not enough to be monetized.

Cost can often be the deciding factor. Can your organization afford to embark on this journey, and will your potential customers be able to afford what you’re offering? Be realistic when making these assessments. Once that’s out of the way, the second issue is one of need. It may sound obvious, but the majority of startups that fail to find traction in the market do so because they’ve identified a need that doesn’t really exist—or at least not enough to be monetized.

Readiness is a question you must ask of yourself and the industry. Is your organization ready (and able) to devote time and resources to integrating machine and deep learning into the pipeline, and is the industry ready to adopt your new solution or service? Another thing to consider is the competition. It’s an exciting time for startups, and the potential is huge, but tech heavyweights like Google and Microsoft are also looking to cash in on deep learning. It’s worth keeping that in mind when positioning yourself in the market with a specialty.

For the past five years or so, the pace of innovation within machine and deep learning has quickened significantly. Will your organization be able to keep up?

If they occur, regulation and compliance issues can slow everything down so much that it no longer becomes worth the effort. Finally, is it scalable? For the past five years or so, the pace of innovation within machine and deep learning has quickened significantly. Will your organization be able to keep up?

Where to Begin 

If you’re approaching machine or deep learning with no real experience in the design, development and employment of deep neural networks, you’re in good company. Very few organizations—and even fewer startups—come staffed with a full roster of data scientists, ready to build a platform on an enterprise scale.

One of the first points it’s important to recognize is just how accessible machine and deep learning truly are—though that shouldn’t be confused with thinking that these are easy fields to be in. Having the computing power and necessary people skills at your disposal won’t guarantee results. After giving careful consideration to the issues highlighted in the overview, the first step is to focus on the tools and infrastructure while remembering that machine and deep learning successes comes from more than the algorithms.

How to Choose a Framework

Frameworks, applications, libraries and toolkits—journeying through the world of deep learning can be daunting. The ease with which you’ll be able to build and run your application is first determined by the framework you choose. With that in mind, the five best-known frameworks are as follows:

• • 1. Caffe
    2. Tensorflow
    3. Torch
    4. Apache Mahout
    5. Microsoft Cognitive Toolkit (CNTK)

These are five of the frameworks, but you may still be wondering how to choose between them. The answer is that it really depends on what your goals are. If in doubt, it can be helpful to go with one of the more popular or supported frameworks like Caffe or Torch. The full guide covers descriptions and specifics on each of these frameworks to assist you in choosing the perfect framework for your needs.

Deploying the right kit can be critical, and the main thing is the significant advantages that GPU acceleration provides. GPUs and deep learning go together like a marriage made in heaven. The multi-layered nature of the deep neural networks means that they run best on highly parallel processors. Deep learning training and inference will, therefore, be achieved much faster on GPUs—any GPUs—from small workstations to some serious hardware. In fact, you can start developing on any GPU-based system.

The insideHPC Special Report, “Riding the Wave of Machine Learning & Deep Learning,”explains it well: ‘the high compute capability and high memory bandwidth make GPUs an ideal candidate to accelerate deep learning applications, especially when powered with NVIDIA’s Deep Learning so ware development kit (SDK) that includes CUDA® Deep Neural Network library (cuDNN), a GPU-accelerated library of primitives for deep neural networks, TensorRTTM, a high performance neural network inference engine for production deployment of deep learning applications, and CuBLAS a fast GPU-accelerated implementa on of the standard basic linear algebra subroutines.’

The NVIDIA cuBLAS library is a fast GPU-accelerated implementa on of the standard basic linear algebra subrou nes (BLAS). Using cuBLAS APIs, you can speed up your applications by deploying compute-intensive operations to a single GPU or scale up and distribute work across multi-GPU configurations efficiently.

The full guide also offers information on how developers can receive help from community resources, as well as what questions you should be asking while exploring the field of machine learning.

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