By Sanket Amberkar, Falkonry How machine learning can discover patterns hidden in time series data to unlock operational improvements The next industrial revolution is here.
Bottom Line How long before you need to consider how to incorporate nanotechnologies, energy systems, biotechnology, and quantum technologies into the business?
Though business applications for nanotechnologies, energy systems, biotechnology, and quantum technologies may seem light-years away, in reality they are approaching rapidly.
In the next three to five years, expect to see business use cases emerge and pioneering deployments accelerate around these once-futuristic technologies. With this in mind, increasing numbers of CIOs, CTOs, and business strategists are already taking exploratory steps with these and other exponential technologies.
They are sensing and scanning disruptive forces and putting in place deliberate, disciplined innovation responses. These leaders understand that waiting for exponentials to manifest as mature technology trends before taking action may be waiting too long.
Unlike other trends examined in this report that demonstrate clear business impact in the next 18 to 24 months, the exponentials we are discussing appear a bit smaller on the horizon. These are emerging technology forces that will likely manifest in a horizon 3 to 5 timeframe—between 24 and 60 months.
But when they manifest, the speed with which they impact markets will likely grow exponentially. For businesses, exponentials represent unprecedented opportunities as well as existential threats.
As such, an analysis of exponential forces is a time-honored part of our annual discussion of emerging technologies. In our Tech Trends report, for example, we collaborated with faculty at Singularity University, a leading research institution, to explore artificial intelligence, robotics, cybersecurity, and additive manufacturing.
In each force, we seek to identify precursor uses or breadcrumbs of adoption for early application to business uses.
Some if not all of these exponentials may disrupt industries in 24 months or more, but there can be competitive opportunities for early adoption. At a minimum, executives can begin contemplating how their organizations can embrace exponentials to drive innovation.
The time to prepare is now. As we have seen during the past year, political landscapes are shifting beneath our feet. News, signals, and random information come at us in torrents. At the same time, exponential technologies such as synthetic biology, advanced energy storage, nanotechnology, and quantum computing, among others, are poised to disrupt every part of our lives, every business model and market, every society.
Eventually, they may even redefine what it means to be human. What are we to make of all this? Change is happening all around us at a pace that will only accelerate. Particularly in the realm of exponentials, when you see seemingly radical innovations emerging, we often experience it emotionally.
We feel anxious about change. Our first reaction is often to cling to something that feels stable—the past.
At Singularity University, we are trying to understand where all this change is heading. Contrary to what some may see, we see a future that is hopeful and full of historic possibility.
By leveraging exponentials, we could have a future in which cancer no longer afflicts our families. Everyone—even the most pessimistic—can agree that this is a desirable goal. This is the lens through which we should all view exponentials.
Exponentials are an opportunity driver, not something to fear.
As use cases for exponentials emerge and technologies mature over the next three to five years, it will not be enough for the technology, science, academia, and business sectors to focus solely on their own goals.
Collectively, we must also help build understanding throughout society of what these technologies are and where they can take us. The future is already here.
The world around is changing every day, and will continue to do so.There will be increased use of post-packaging pasteurization with irradiation, hot water, steam, and high pressure in the future.
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Predictive analytics in retail help to control inventory and guide service levels. By Colin Wright on January 8, Filed under That’s where machine learning (a big part of predictive analytics) comes in. The right tools that include machine learning capabilities can analyze massive amounts of data—and use more interesting sources of.
* Business Analyst: Gather requirements, document use cases, analyze data to determine conceptual solution designs. Creation of reporting mock-ups, prototypes and ashio-midori.com: VP, Data Analytics and Business .
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