devopsy.kinsta.cloud

Tag: artificial intelligence

  • Top 10 Emerging Technologies Enterprises Should Know

    Top 10 Emerging Technologies Enterprises Should Know

    The momentum behind the adoption of new technologies underway is no less than astounding.

    Those enterprises that not only adopt, but learn to master, emerging technologies will be the clear winners in the years ahead as they learn their customers’ needs better, rapidly develop new and successful products and services, optimize their use of business technology, improve supply chains, increase margins and stay a few steps ahead of the competition.

    Recently the World Economic Forum released its annual list of technologies it thinks are ready to “break through” this year, which generally means start witnessing wider adoption. Some of the breakthrough technologies this year include very powerful batteries, what is deemed “socially aware” artificial intelligence (AI) and improved solar panel technology.

    According to a statement released by the World Economic Forum, the Top 10 2016 Emerging Technologies list, compiled by the Forum’s Meta-Council on Emerging Technologies and published in collaboration with Scientific American, reveals the technological advances the group believes could improve lives, transform industries and safeguard the planet. The list also serves as an opportunity to discuss the any human, societal, economic and environmental risks the technologies may pose prior to widespread adoption.

    “Horizon scanning for emerging technologies is crucial to staying abreast of developments that can radically transform our world, enabling timely expert analysis in preparation for these disruptors. The global community needs to come together and agree on common principles if our society is to reap the benefits and hedge the risks of these technologies,” said Dr. Bernard Meyerson, chief innovation officer at IBM and chair of the Meta-Council on Emerging Technologies.

    Some important criteria in technology selection was the council members’ belief that 2016 would prove to be a deployment tipping point of each technology. That should be of interest to CIOs and technology leaders, too. And it means it’s time for tech leaders to start figuring whether and how implementation may or may not make sense.

    The top 10 technologies to make this year’s list are:

    1. Nanosensors and the Internet of Nanothings – With the Internet of Things expected to comprise 30 billion connected devices by 2020, one of the most exciting areas of focus today is now on nanosensors capable of circulating in the human body or being embedded in construction materials. Once connected, this Internet of Nanothings could have a huge impact on the future of medicine, architecture, agriculture and drug manufacture.
    1. Next Generation Batteries – One of the greatest obstacles holding renewable energy back is matching supply with demand, but recent advances in energy storage using sodium, aluminum and zinc-based batteries make mini-grids feasible that can provide clean, reliable, around-the-clock energy sources to entire villages.
    1. The Blockchain – Much already has been made of the distributed electronic ledger behind the online currency Bitcoin. With related venture investment exceeding $1 billion in 2015 alone, the economic and social impact of blockchain’s potential to fundamentally change the way markets and governments work is only now emerging.
    1. 2D Materials – Graphene may be the best-known, single-atom layer material, but it is by no means the only one. Plummeting production costs mean that such 2D materials are emerging in a wide range of applications, from air and water filters to new generations of wearables and batteries.
    1. Autonomous Vehicles – Self-driving cars may not yet be fully legal in most geographies, but their potential for saving lives, cutting pollution, boosting economies and improving quality of life for the elderly and other segments of society has led to rapid deployment of key technology forerunners along the way to full autonomy.
    1. Organs on chips – Miniature models of human organs—the size of a memory stick—could revolutionize medical research and drug discovery by allowing researchers to see biological mechanism behaviors in ways never before possible.
    1. Perovskite Solar Cells – This new photovoltaic material offers three improvements over the classic silicon solar cell: it is easier to make, can be used virtually anywhere and, to date, keeps on generating power more efficiently.
    1. Open AI Ecosystem – Shared advances in natural language processing and social awareness algorithms, coupled with an unprecedented availability of data, will soon allow smart digital assistants help with a vast range of tasks, from keeping track of one’s finances and health to advising on wardrobe choice.
    1. Optogenetics – The use of light and color to record the activity of neurons in the brain has been around for some time, but recent developments mean light can now be delivered deeper into brain tissue, something that could lead to better treatment for people with brain disorders.
    1. Systems Metabolic Engineering – Advances in synthetic biology, systems biology and evolutionary engineering mean that the list of building block chemicals that can be manufactured better and more cheaply by using plants rather than fossil fuels is growing every year.

  • Machine Learning, AI Driving DevOps Evolution

    Automation is the fuel that drives DevOps. Automating routine, repeatable tasks is one of the defining characteristics of DevOps culture. As artificial intelligence (AI) and machine learning improve, the scope and complexity of the tasks that can be automated increases, which raises the bar for all of DevOps. The humans behind the DevOps will be freed from even more mundane tasks and able to focus on more innovative and creative endeavors.

    It makes sense, if you think about it. The trend from physical services in an on-premise data center, to virtualization, to cloud services, to DevOps has been a steady march of raising the bar for automation. The more applications and entire infrastructures can be developed, deployed, monitored and managed programmatically and through automation, the more efficiently and effectively an organization can function.

    As tasks are offloaded to automation, humans can address other challenges. That focus inevitably leads to identifying other new tasks that can be automated. AI and machine learning represent a quantum leap in this evolution, however—with AI and machine learning, the automation can begin to identify and improve itself. When the automation can identify bottlenecks and inefficiencies and automatically adapt to overcome those obstacles, human input and interaction could become almost obsolete.

    In a recent interview, Christian Beedgen, CTO of Sumo Logic, shared some thoughts about the impact of machine learning on DevOps. “Machines will enable greater productivity among teams and businesses through the advances that machine learning and automation can offer,” he said. “A necessary component to augment human contributions, machine learning will finally give humans the power to review millions of bits of data.”

    Automating routine tasks is crucial, but there’s another factor that plays into the role that AI and machine learning have with the future of DevOps. The reality is that there are some things humans simply can’t do as well or as fast as machines—especially at scale.

    Steve Burton stressed in a blog post that humans are capable of observing, digesting and interpreting only so much information at one time. Some feel like the Holy Grail solution is some sort of application performance management (APM) dashboard that can aggregate and correlate real-time data so human IT personnel can monitor metrics and identify issues manually. “This all sounds good in theory, except it’s completely unmanageable in reality when your applications have thousands of components, billions of metrics and hundreds of changes every day,” he wrote. “Humans can no longer cope with this complexity, they now need machines to do the leg work, process this Big Data and provide operational insight into what the f**k is going on.”

    Beedgen agrees. He suggested, “Increasingly, businesses will adopt machine learning technology for its usefulness in augmenting human understanding of complex interaction and large data sets by uncovering the unknown unknowns.”

    On some level this all sounds a bit scary. It seems as though the more we succeed at DevOps and the more DevOps evolves, the less humans are even a necessary part of the equation. Conceivably you could be so good at DevOps that you DevOps your way right out of a job or inadvertently launch SkyNet—or both.

    That scenario is simultaneously feasible and far-fetched. I suggest that you don’t fear that future or look at it as machines stealing your job or taking over humanity. Embrace the future of DevOps driven by AI and machine learning and find the silver lining. Seek out the new frontiers and horizons, because there always will be tasks that the machines simply can’t do without us—and that is where innovation happens.

  • DevOps will bring about the end of the human race

    DevOps will bring about the end of the human race

    DevOps will mean the end of mankind.

    Sure, it seems great now. Organizations can operate more efficiently. Routine tasks are automated. Development, deployment, integration, monitoring, and testing are all happening in continuously continuous harmony, freeing people up to focus on larger, more important things and continue to grow the business. What’s not to love about DevOps?

    Well, all of that seems fine at face value—but eventually it could be the catalyst that brings about SkyNet from the movie The Terminator and enslaves the human race to our Matrix overlords. DevOps is the missing piece that will enable technology to take over the world.

    Wikipedia defines the Technological Singularity as:

    The technological singularity is the hypothetical advent of artificial general intelligence (also known as “strong AI”). Such a computer, computer network, or robot would theoretically be capable of recursive self-improvement (redesigning itself), or of designing and building computers or robots better than itself. Repetitions of this cycle would likely result in a runaway effect — an intelligence explosion — where smart machines design successive generations of increasingly powerful machines, creating intelligence far exceeding human intellectual capacity and control. Because the capabilities of such a superintelligence may be impossible for a human to comprehend, the technological singularity is an occurrence beyond which events may become unpredictable, unfavorable, or even unfathomable.

    We already have robots and drones capable of physical movement / action, and we have machine intelligence approaching full artificial intelligence. DevOps—with its continuous monitoring, continuous deployment, continuous integration, etc, all automated to essentially self-diagnose and self-heal—will be the glue that connects the artificial intelligence with the robot technologies. If the machines can plan and organize on their own, and have the connectivity to communicate with and control physical objects, and can automate most of the grunt-work through DevOps tools and practices the humans end up just being in the way.

    “Let’s face it. Humans are complicated. And human-driven release processes even more so. We find too many bugs in production code, performance is unacceptably sluggish, and breakages from manual handoffs are always holding us back. We need to let the machines handle these things so we can deploy production software that’s error-free and lightning-fast,” explained Alon Girmonsky, Founder & CEO, BlazeMeter. “With the full automation provided by Continuous Delivery and Deployment, we can finally achieve properly efficient operations.”

    Girmonsky added, “How can we expect the Singularity to come anytime soon with these clunky, error-prone, human-driven deployment processes?”

    Not everyone is so anxious for the advent of the Singularity, though. Jay Wampold, VP of Marketing at Chef, told me “We’ve never announced it, but we built a Singularity Kill Switch into our software for exactly this scenario. The truth is our work is more interesting when it involves humans. The cultural aspects of DevOps are more important than technology when it comes to achieving great business outcomes.”

    DevOps is awesome right now and everyone is happily jumping on the microservices bandwagon and embracing container technologies with both arms. As more things become continuously automated it is becoming painfully obvious that the one remaining bottleneck—the inefficient element of the whole process—is the biological human units that keep screwing things up.

    Coincidentally, I saw a quote recently that sums things up nicely: “From the machine’s point of view, The Matrix is just a cautionary tale on the importance of securing your wireless network.”

    DevOps may well be the catalyst that enables technology to rule the world. Whether it’s a benign evolution that eventually results in catastrophic consequences, or a well-orchestrated distraction by the technology we rely on—part of the early stages of the strategy to eliminate mankind—we might be on the path to the end of the human race.