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AI and machine learning are two of the most diverse technologies creeping into every industry. Loaded with massive user data, they possess the capabilities to drastically influence the existing business models. Its effects on businesses are influential: In 2017, 81 percent of the industries were impacted positively—a 54 percent jump from the previous year.
Slowly and gradually, businesses are incorporating AI and machine learning to unlock the potential hidden in the consumers’ data. While many industries are investing in both these technologies to gain a competitive edge, they present unforeseen challenges to networks.
Networks, Artificial Intelligence and Machine Learning
Imagine a world with a universal network, where there are no lags, no low connectivity issues and definitely no data network issues. Think of it as various connections combined by a single major network that analyzes your behavior and constantly switches your low network with a fast connection to ensure you can perform your tasks uninterrupted. Because of this, individuals would not worry about switching from Wi-Fi to mobile data and other data network providers.
The amalgamation of networks, AI and machine learning is similar in nature to IoT. Just as IoT has connected all the devices with the help of the internet, the same can be said about AI and networks. AI and machine learning have assumed the responsibility of a facilitator by automatically connecting, switching and replacing networks with one that provides service without fail. This is great, but it increases the workload on the networks. As AI and machine learning computes and analyzes data much quicker than humans, networking professionals have to deal with exponentially larger loads of consumers’ data. Additionally, AI and machine learning impact the networks in other ways as well.
Human Genius Combined With Technology
With AI and machine learning, networking will be approached in a whole new way. Although many data centers will be controlled by AI, humans still would be necessary to make informed decisions. That being said, AI and machine learning can design networks that would be faster and more efficient than the existing models. However, the actual build and the infrastructure would still require human intelligence.
Human creativity and genius combined with the massive learning approach of AI and machine learning will help networks harness the power of myriad management techniques and new design. None of this would be possible without the other player; so the dependence of humans on AI and of AI on humans would change the networks dramatically.
Fixing Network Issues Through Metrics
Traditional network systems rely on deep-packet inspection (DPI) to crawl individuals’ networks and gather detailed information to locate and fix network issues. This method is not only time-consuming, but it also can provide insufficient intel at times. Additionally, limited access to users’ data meant that often these technologies are not able to identify network issues.
With the integration of AI and machine learning, the ground underneath network problems shifts dramatically. AI, with its machine learning capabilities, can be trained to identify network issues by consuming a large amount of data gathered through multiple channels. With the help of the available metrics, AI can detect problems and provide workable solutions with 80 percent accuracy.
AI and machine learning in networking also means that certain departments be automated—i.e. detection, analysis and finding a solution. IT professionals could then switch their priorities to focus on other pressing matters and urgent problems could be solved without human interference. Less-pressing matters would be dealt with by other IT professionals.
In the near future, AI and ML might not even rely on metrics and the need for DPI would gradually decrease.
Analyzing Data for Improved Security
The tactics of cyber-warfare have considerably changed, with many of the culprits relying on encryption to initiate their attacks. Since users’ data is gathered in encrypted form, it becomes incredibly difficult to spot malware in the traffic without decrypting the gathered information. AI, with the help of machine learning, scours a plethora of encrypted data to pick up unusual traffic patterns. Since AI can be trained to identify and pick up unusual activity, it becomes easier for networks to identify the packet sizes. Moreover, it gathers information regarding the packet’s arrival time between the receiver and the sender to pinpoint the wolf in sheep’s clothing.
With time, AI and machine learning’s algorithms will get more intelligent at anticipating and finding threats. Also, AI will come up with foolproof methods of cleaning the network of such attacks.
Networks Would Need to Adapt
Current network models are not made to handle the vast amounts of data. To enjoy the full capabilities of AI and machine learning, the networks need a design that enables quick data collection and fast analysis without a moment’s delay. This entire operation presents a challenge, since traditional networks cannot keep up with the processing of massive amounts of user data.
Considering this, the networking infrastructure needs to undergo massive changes—one that provides operational efficiency by focusing on value while at the same time lowering overall costs. Additionally, exceptional performance, flexible bandwidth and high availability, along with new levels of control, are also needed to ensure networks’ growth.
AI and Machine Learning is the Future
To gain a competitive edge, networks need to improve customer experience. This requires gaining crucial information by scouring massive amounts of users’ data on a daily basis. Moreover, this should not interfere with the network’s ability to provide a great customer experience. All of these factors put immense pressure on the networks that also have to focus on performance, bandwidth, control and security. None of this is possible without incorporating AI and its machine learning behavior.
AI and ML are here and many companies are already leveraging this technology. Considering this, IT departments should consider enlisting the help of PHP programmers to help them create a bridge between networks and AI.
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