Perception, consciousness, self-awareness, and volition are key attributes that define human intelligence. By harnessing these, the human mind builds an intellectual capacity which is characterized by exhibiting apt reactions and making right decisions at the opportune time.
For decades, the greatest minds have been debating on “What makes one human more intelligent than the other?”
Several theories in psychology have attempted to put forth the factors that cumulatively form a distinct human cognitive system. But the inference is comprehensible – multiple biological and psychological factors combine to form human intelligence. There is no single element which energizes the human mind and helps it get smarter.
Today however, we talk about intellect in a broad manner. The earlier theories of human intellect, cognition, and adaptation still remain valid, but the focus now lies in instilling the said theories into machines and making them perform as efficiently as the human mind. The long contemplated Automation and Machine learning fundamentals are now finding solid ground.
Since the inception of the Automation, industry reactions have remained dispersed.
‘How can machines overrun human intelligence?’
‘What will happen to the human workforce if machines compositely address all needs?’
These ideas are unorthodox. Automation technology should walk hand in hand with the interests of the human workforce. Similar to how human intelligence requires the maturity of biological and psychological attributes as inputs, Automation technology also requires devised complex algorithms and an overarching automation framework to interpret the results produced. When applied correctly, these approaches can enable machines to make logical, probability-based decisions, and undertake thoughtful tasks.
Building upon the idea that there is no one factor which enables human minds to get smarter, even the desired outputs of Automation depend upon a variety of inputs fed in the form of algorithms and software.
Why Automation technology is the new Truth
The data influx in the modern world has made enterprises realize two things. First; even a byte of data may be critical to the enterprise; second; not all data is productive and a careful analysis and modeling are required through robust automation solutions to extract mission critical data from the available data set.
According to IDC, every person online will create 1.7 megabytes of new data every second by 2020. In a dynamic world characterized by shorter innovation cycles and even shorter lead times, technology leaders find it difficult to deploy human intellect to assess the vast amount of information. Since data is perishable and its value is time-sensitive, enterprises must make sense of the data before its shelf life expires by deploying automation solutions. Human intellect is essential but not sufficient to accomplish the aforesaid task.
At this juncture, organizations are quickly realizing the importance of leveraging automation solutions, in order to create a bridge between the cyber and the physical world. Infusing the principles like cognition, prediction, and self-awareness into machines has to a large extent, helped with this endeavor.
Why Enterprise Networks require a prudent strategic planning
Digital Darwinism is unkind to those who procrastinate. 52% of the firms on the Fortune 500 list in 2002 are out. Technology is evolving faster than enterprises can comprehend, creating a new economic landscape which continues to exert pressure across every business aspect. The following trends have heavily impacted the enterprise network -
- Changing traffic patterns: The conventional north-south traffic pattern paradigm has changed its direction. Today; when we talk about traffic flow in the data center, the applications encompass multiple servers and devices creating a stream of an east-west machine to machine traffic. In addition to this, the inception of BYOD has put a significant burden on the enterprise network as employees want a mobility independent access to the corporate applications through the device of their choice. This change has made enterprises rethink their existing legacy architectures and incorporate private cloud, public cloud, or a mix of both to strengthen the bottom-line economics. Though virtualization promises to save costs, it requires stringent traffic management and bandwidth usage reworking.
- Adoption of the cloud: Cloud adoption brings along a plethora of benefits like scalability, scope, economic advantages, speed, agility, infinite elasticity, flexibility, and innovation. Understanding the benefits, most enterprises have either moved their data centres to the cloud or are planning to make the change because of the proliferation of apps, reduced costs, and dynamic traffic transit. But the cloud adoption also brings along integration challenges, process deviations and technology change. Through fundamentals of cognitive intelligence and data modeling, automation technologies can help organizations envision seamless cloud adoption.
- “Big data” and the phenomena of “Things”: It is beyond doubt that IoT ushers a huge opportunity for enterprises to increase their revenues, cut costs, and improve operating efficiencies and throughput. But collecting the massive amounts of data won’t yield results without the embodiment of intelligence to collect, manage, analyze, and model the available data into sensible information which the enterprise can use for decision making.
Considering the current scenario, there is an imminent need to design a well-crafted network automation framework that provides solutions based on scripts, tools, and platforms. Moreover, in order to maintain business continuity and drive down operational costs, companies should implement real-time, proactive, and self-healing networks – delivering enhanced end user experience.