Machine Learning Becomes Mainstream: How to Increase Your Competitive Advantage

First there was big data – extremely large data sets that made it possible to use data analytics to reveal patterns and trends, allowing businesses to improve customer relations and production efficiency. Then came fast data analytics – the application of big data analytics in real-time to help solve issues with customer relations, security, and other challenges before they became problems. Now, with machine learning, the concepts of big data and fast data analytics can be used in combination with artificial intelligence (AI) to avoid these problems and challenges in the first place.

So what is machine learning, and how can it help your business? Machine learning is a subset of AI that lets computers “learn” without explicitly being programmed. Through machine learning, computers can develop the ability to learn through experience and search through data sets to detect patterns and trends. Instead of extracting that information for human comprehension and application, it will use it to adjust its own program actions.

What does that mean for your business? Machine learning can be used across industries, including but not limited to healthcare, automotive, financial services, cloud service providers, and more. With machine learning, professionals and businesses in these industries can get improved performance in a number of areas, including:

  • Image classification and detection
  • Fraud detection
  • Facial detection/recognition
  • Image recognition/tagging
  • Big data pattern detection
  • Network intrusion detection
  • Targeted ads
  • Gaming
  • Check processing
  • Computer server monitoring

In their raw data, large and small data sets hide numerous patterns and insights. Machine learning gives businesses, organizations, and institutions the ability to discover trends and patterns faster than ever before. Practical applications include:

  • Genome mapping
  • Enhanced automobile safety
  • Oil reserves exploration

Intel has worked relentlessly to develop libraries and reference architectures that not only enable machine learning but allow it to truly take flight and give businesses and organizations the competitive edge they need to succeed.
In fact, according to a recent study by Bain [1], companies that use machine learning and analytics are:

  • Twice as likely to make data-driven decisions.
  • Five times as likely to make decisions faster than competitors.
  • Three times as likely to have faster execution on those decisions.
  • Twice as likely to have top-quartile financial results.

Machine learning is giving businesses competitive advantages.

In other words, predictive data analytics and machine learning are becoming necessities for businesses that wish to succeed in today’s market. The right machine learning strategy can put your business ahead of the competition, reduce your TCO, and give you the edge your business needs to succeed.

Background on Predictive Analytics and Machine Learning

You already know that machine learning is essentially a form of data analytics, but where did it come from and how has it evolved to become what it is today? In the past couple of decades, we have seen a rapid expansion and evolution of information technology. In 1995, data storage cost around $1000/GB; by 2014 that cost had plummeted to $0.03/GB [2]. With access to larger and larger data sets, data scientists have made major advances in neural networks, which have led to better accuracy in modeling and analytics.

As we mentioned earlier, the combination of data and analytics opens up unique opportunities for businesses. Now that machine learning is entering the mainstream, the next step along the path is predictive analytics, which goes above and beyond previous analytics capabilities.

The Path to Predictive Analytics

With predictive analytics, companies can see more than just “what happened” or “what will happen in the future.”

Machine learning is a part of predictive analytics, and it is made up of deep learning and statistical/other machine learning. For deep learning, algorithms are applied that allow for multiple layers of learning more and more complex representations of data. For statistical/other machine learning, statistical algorithms and algorithms based on other techniques are applied to help machines estimate functions from learned examples.

Essentially, machine learning allows computers to train by building a mathematical model based on one or more data sets. Then those computers are scored when they may make predictions based on the available data. So when should you apply machine learning?

There are a number of times when applying machine learning can give you a competitive advantage. Some prominent examples include:

  • When there is no available human expertise on a subject. Recent navigation to Pluto relied on machine learning, as there was no human expertise on this course.
  • When humans cannot explain their abilities or expertise. How do you recognize someone’s voice? Speech recognition is a deep-seated skill, but there are so many factors in play that you cannot say why or how you recognize someone’s voice.
  • When solutions change over time. Early in a rush-hour commute,the drive is clear. An hour later, there’s a wreck, the freeway comes to a standstill, and side streets become more congested as well. The best route to getting to work on time changes by the minute.
  • When solutions vary from one case to another. Every medical case is different. Patients have allergies to medications, multiple symptoms, family histories of certain diseases, etc. Solutions must be found on an individual basis.

These are just a few of the uses that you’ll find across industries and institutions for machine learning. Not only is the demand for machine learning growing, though, but there is now an evolving ecosystem of software dedicated to furthering machine learning and giving businesses and organizations the benefits of instantaneous, predictive analytics.

An evolving ecosystem of machine learning software.*

In this ecosystem, Intel is the most widely deployed platform for the purposes of machine learning. Intel® Xeon® and Intel® Xeon Phi™ CPUs provide the most competitive and cost efficient performance for most machine learning frameworks.

Challenges to Adoption of Machine Learning

There are a few barriers to adoption of machine learning that businesses need to overcome to take advantage of predictive analytics. These include:

  • Understanding how much data is necessary
  • Adapting and using current data sets
  • Hiring data scientists to create the best machine learning strategy for your business
  • Understanding potential needs for new infrastructure vs. using your existing infrastructure.

With the right machine learning strategy, the barriers to adoption are actually fairly low. And, when you consider the reduced TCO and increased efficiency throughout your business, you can see how the transition can pay for itself in very little time. As well, Intel is dedicated to establishing a developer and data science community to exchange thought leadership ideas across disciplines of advanced analytics. Through these articles and information exchanges, we hope to further help businesses and organizations understand the power of predictive analytics and machine learning”.

What is your opinion and how do you apply data analytics and machine learning? Let us know what you think.

About the Authors:

Nidhi Chappell is the director of machine learning strategy at Intel Corporation. Connect with Nidhi on LinkedIn and Twitter to find out more about how machine learning can give your business a competitive edge.

 

Ronald van Loon is director at Adversitement. If you would like to read more from Ronald van Loon on the possibilities of Big Data please click ‘Follow’ and connect on LinkedIn and Twitter.

 

 

Intel, the Intel logo, Intel Xeon, and Intel Xeon Phi are trademarks of Intel Corporation in the U.S. and/or other countries. *Other names and brands may be claimed as the property of others. © Intel Corporation.

Sources:
[1] http://www.bain.com/publications/articles/big_data_the_organizational_challenge.aspx
[2] http://www.mkomo.com/cost-per-gigabyte-update

Source: Machine Learning Becomes Mainstream: How to Increase Your Competitive Advantage

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Global Study Reveals Businesses and Countries Vulnerable Due to Shortage of Cybersecurity Talent

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Intel Corporation
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Santa Clara, CA 95054-1549

Global Study Reveals Businesses and Countries Vulnerable Due to Shortage of Cybersecurity Talent

82 Percent of IT Professionals Confirm Shortfall in Cybersecurity Workforce 

News Highlights:

  • New report by Intel Security and CSIS reveals current cybersecurity talent crisis
  • Cybersecurity skills shortage is worse than talent deficits in other IT professions.
  • Shortage in cybersecurity skills is responsible for significant damages.
  • Talent shortage is largest for individuals with highly technical skills.
  • Hands-on training and practical training are perceived as better ways to develop skills than through traditional education resources.

Dubai, United Arab Emirates – August 01, 2016 – Intel Security, in partnership with the Center for Strategic and International Studies (CSIS), recently released Hacking the Skills Shortage, a global report outlining the talent shortage crisis impacting the cybersecurity industry across both companies and nations. A majority of respondents (82 percent) admit to a shortage of cybersecurity skills, with 71 percent of respondents citing this shortage as responsible for direct and measurable damage to organizations whose lack of talent makes them more desirable hacking targets.

“A shortage of people with cybersecurity skills results in direct damage to companies, including the loss of proprietary data and IP,” said James A Lewis, senior vice president and director of the Strategic Technologies Program at CSIS. “This is a global problem; a majority of respondents in all countries surveyed could link their workforce shortage to damage to their organization.”

Despite 1 in 4 respondents confirming their organizations have lost proprietary data as a result of their cybersecurity skills gap, there are no signs of this workforce shortage abating in the near-term. Respondents surveyed estimate an average of 15 percent of cybersecurity positions in their company will go unfilled by 2020. With the increase in cloud, mobile computing and the Internet of Things, as well as advanced targeted cyberattacks and cyberterrorism across the globe, the need for a stronger cybersecurity workforce is critical.

Raj Samani
Raj Samani, VP & CTO, EMEA, Intel Security

“The security industry has talked at length about how to address the storm of hacks and breaches, but government and the private sector haven’t brought enough urgency to solving the cybersecurity talent shortage,” said Raj Samani, VP & CTO, EMEA, Intel Security. “To address this workforce crisis, we need to foster new education models, accelerate the availability of training opportunities, and we need to deliver deeper automation so that talent is put to its best use on the frontline. Finally, we absolutely must diversify our ranks.”

The demand for cybersecurity professionals is outpacing the supply of qualified workers, with highly technical skills the most in need across all countries surveyed. In fact, skills such as intrusion detection, secure software development and attack mitigation were found to be far more valued than softer skills including collaboration, leadership and effective communication.

This report studies four dimensions that comprise the cybersecurity talent shortage, which include:

  1. Cybersecurity Spending: The size and growth of cybersecurity budgets reveals how countries and companies prioritize cybersecurity. Unsurprisingly, countries and industry sectors that spend more on cybersecurity are better placed to deal with the workforce shortage, which according to 71 percent of respondents, has resulted in direct and measurable damage to their organization’s security networks.
  2. Education and Training: Only 23 percent of respondents say education programs are preparing students to enter the industry. This report reveals non-traditional methods of practical learning, such as hands-on training, gaming and technology exercises and hackathons, may be a more effective way to acquire and grow cybersecurity skills. More than half of respondents believe that the cybersecurity skills shortage is worse than talent deficits in other IT professions, placing an emphasis on continuous education and training opportunities.
  3. Employer Dynamics: While salary is unsurprisingly the top motivating factor in recruitment, other incentives are important in recruiting and retaining top talent, such as training, growth opportunities and reputation of the employer’s IT department. Almost half of respondents cite lack of training or qualification sponsorship as common reasons for talent departure.

Recommendations for Moving Forward:

  • Redefine minimum credentials for entry-level cybersecurity jobs: accept non-traditional sources of education
  • Diversify the cybersecurity field
  • Provide more opportunities for external training
  • Identify technology that can provide intelligent security automation
  • Collect attack data and develop better metrics to quickly identify threats

For more information on these findings, along with Intel Security’s proposed recommendations, read the full report: Hacking the Skills Shortage: A study of the international shortage in cybersecurity skills.

About Intel Security:

Intel Security, with its McAfee product line, is dedicated to making the digital world safer and more secure for everyone. Intel Security is a division of Intel Corporation. Learn more at www.intelsecurity.com.

Intel and the Intel logo are trademarks of Intel Corporation in the United States and other countries.

*Other names and brands may be claimed as the property of others.

Contacts:

Vernon SaldanhaVernon Saldanha

Procre8 (on behalf of Intel Security)

vernon@procre8.biz