The Growing Role of Artificial Intelligence in Data Centers


According to Infosys, more than 75 percent of IT experts view artificial intelligence as a permanent strategic priority which can assist them in innovating their organization’s structure. Infosys’ survey receives credibility from the fact that the AI systems are expected to receive investments worth $57 billion by the year 2021. That being said, the implementation of AI is a complex task which requires considerable time and decision-making to go smoothly. Today, AI has initiated the transformation of all the global industries.

One of such industries is the data center industry where AI is making its mark slowly and gradually. Data centers power the operations of organizations all around the world. The data volumes are increasing daily, putting more and more strain on the hardware and software setups in the organization. Consequently, managers are forced to introduce new servers and hardware equipment so their IT infrastructure becomes powerful enough to store and process data without any issue. Currently, most of the centers are not able to maximize their output because they use legacy systems. So how is AI transforming data centers?

Energy Consumption

Energy consumption remains one of the most critical and dire issues in data centers. Bear in mind that as of now, about 6 percent of the world’s electricity is used by data centers. With the computing requirements climbing up day-by-day, it is fair to assume that the energy consumption of data centers will also increase.

On one hand, companies have to address the cost factor, and on the other hand, global warming is mounting pressure on organizations to do their part and act more ‘responsibly’ towards the environment. Particularly, the data center industry is one of those industries that are viewed negatively by the supporters of green energy.

Some data centers have attempted to address such issues by accepting renewable energy. However, there are qualms about its ineffectiveness for smaller setups. There are few companies that have resorted to AI as the answer to their common problems.

AI is being used for real-time monitoring to reduce energy consumption. Moreover, AI is used for parallel and distributed computing to achieve a greater level of productivity. Some organizations have identified and resolved networking troubleshooting via AI. Similarly, there are those who adjust their heating and cooling mechanisms via AI. Due to the widespread use of artificial intelligence, there is no need for staff members to continuously manage mundane tasks such as setting the office temperature.

Security

Security is also one of the most pressing issues for data centers. Cybercriminals have particularly set their eyes on the data centers. With the amount of sensitive data being stored in these data centers, it is not surprising that hackers try to target these centers. For instance, if a cybercriminal group succeeds in a ransomware attack on a data center then by just locking the servers, they bring the entire organization down on its knees. Dreading the losses due to downtime and reputational damage, the company has no option but to pay a ransom to save their data center from complete destruction. Unfortunately, ransom payment does not guarantee the return of data. While organizations are trying their best to infuse the most effective measures to restrict such attacks, they have found AI as an underrated ally in their proactive action against cyber attacks.

AI’s addition in the equation offers a greater level of flexibility and sophistication to protect the data and minimize the dependence of systems on manual intervention. Unlike humans, AI can be available 24/7 and may become the wall that ultimately safeguards you from a cyber attack. For instance, Darktrace—a British organization—leveraged AI to specify a normal network behavior where cyber threats are assessed and identified on the basis of a deviated activity.

Data Center Staffing

AI is also offering a chance for organizations to reduce their staff shortages so they can assign their qualified personnel to the relevant areas. It is expected that with AI in the mix, the standard tech support responsibilities in the center would be handed over to AI-based systems.  These responsibilities would include automation of routine and mundane tasks like the following:

  • Resolving any incoming issue.
  • Working on the help desk support.
  • Provision of services and resources.

Additionally, AI would provide an edge by capturing new symptoms, events, and scenarios for the generation of a functional knowledge base to aid the external and internal stakeholders to learn from the past issues and avoid repeating the same mistakes in future.

However, there will be times when human intervention would be necessary. In such cases, a connection can be established with senior staff members who can fulfill the required task through their years of experience.

Predictive Analytics

With enhanced outage monitoring, AI is providing a major advantage to data centers. AI systems are able to detect and predict any incoming data outage. They can continuously track the performance of all the servers and assess the storage operations like the utilization of disk.

All of this has been made possible through contemporary predictive analytics tools which do not only increase reliability but also are fairly easy to use. Probably the biggest advantage of predictive analytics is that it supervises the workload through optimization, lessens the burden from systems, and distributes the workload more evenly among all the hardware tools.

This modern outlook of data centers is widely different from the conventional data center practices. Traditionally, such troubleshooting was based completely on manual assistance, research, and computation—computers were merely a tool to execute and command their strategies. AI, on the other hand, positions itself as an independent player which can be seen more as a professional colleague rather than a tool.

Final Thoughts

As the management of data centers becomes tougher and more complex with the passing time, AI has been a welcome entry in the space as an IT technology. AI has improved the overall output without any notable compromise. It remains to be seen what more advancements arrive in data centers in the near future. For the time being, AI has done a marvelous job at managing data centers.

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What Is Artificial Neural Network and How Does It Work?


The whole idea behind artificial intelligence is to make a machine act like a human being. While many sub-divisions of AI originated with their own set of algorithms to mimic humans, artificial neural networks (ANNs) are AI at its purest sense; they mimic the working of the human brain, the core and complex foundation which influences and affects the thinking and reasoning of human beings.

What Is an Artificial Neural Network?

ANN is a machine learning algorithm. It is founded on the scientific knowledge about organic neural networks (working of the human brain). ANN works quite similar to how human beings analyze and review information. It is composed of several processing units which are linked together and perform parallel processing for the computation of data.

As machine learning is primarily focused on “learning,” ANNs continuously learn and adapt. The processing units in ANNs are commonly referred to as neurons or nodes. Bear in mind that neuron in biology refers to the most basic units in the human nervous system. Each node is linked via arcs which have their own weight. The artificial neural network is made up of three layers.

Input

The input layer is responsible for accepting explanatory attribute values which are collected from observations. Generally, input nodes are explanatory variables. Patterns are submitted to the network by the input layer. Subsequently, those patterns are then analyzed by the hidden layers. The input layer nodes are not involved in modifying any data. They accept individual values as inputs and then perform duplication of the value so it can be passed on to multiple outputs.

Hidden

The hidden layers modify and transform values collected from the input layer. By utilizing a technique of weight links or connections, the hidden layer initiates computation on the data. The number of hidden layers depends upon the artificial neural network; there may be one or more than one hidden layers. Nodes in this layer multiply the collected values by the weights. Weights are a predetermined set of numbers which convert the input values with the help of summation to generate an output in the form of a number.

Output

Afterward, the hidden layers are connected to an output layer which may also receive a connection directly from an input layer. It generates a result, which is associated with the response variable’s prediction. Generally, when the machine learning process is geared towards classification and its disciplines, there is a single output node. The collected data in the layer is integrated and modified for the generation of new values.

The structure of a neural network is also called topology or architecture. All the above layers of the ANN form the structure. The planned design of the structure bears utmost importance to the final findings of the ANN. At its most basic, a structure is divided into two layers which are comprised of one unit each.

The output unit also possesses two functions: combination and transfer. When there are multiple output units, then logistic or linear regression can be at work and the nature of the function ultimately decides it. ANN’s weights are actually coefficients (regression).

So what do the hidden layers do? Well, the hidden layers are incorporated into ANNs to enhance the prediction strength. However, it is recommended to add them smartly because excessive use of these layers may mean that the neural network stores all the learning data and may not able to generalize, causing an over-fitting problem. Over-fitting arises when the neural network is not able to discover patterns and is heavily reliant on its learning set to function.

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Applications

Due to their accurate predictions, ANNs have broad adoption across multiple industries.

Marketing

Modern marketing focuses on segmenting customers within well-defined and distinct groups. Each of these groups exhibits certain characters that are reflecting of its customer habits. In order to generate such segmentation, neural networks present themselves as an efficient solution for predicting strength to identify patterns in a customer’s purchasing habits.

For instance, it can analyze how much time customers take between each purchase, how much do they spend, and what do they mostly purchase. ANN’s input layer takes all the attributes like location, demographics, and other personal or financial information about a customer to generate meaningful output.

Supervised neural networks are usually trained to comprehend the link between clusters of data. On the other hand, unsupervised neural networks are used for segmentation of customers.

Forecasting

Forecasting is a part and parcel of a varied list of domains including governments, sales, finance, and other industries, especially their use in the monetary and economic aspects. Often, forecasting faces a tumbling roadblock because of its complexity. For instance, the prediction of stocks is considered difficult because the stock market addresses multiple seen and unseen factors where traditional forecasting becomes ineffective.

This conventional forecasting is founded merely on statistics. ANNs use the same statistical methods and techniques and enhances forecasting where its layers are sophisticated enough to tackle the complexity of the stock market. Moreover, in contrast to the conventional methods, ANN is non-restrictive for input values and residual distributions.

Image Processing

Since the layers in artificial neural networks are able to accept several input values and compute them flexibly to determine complex and non-linear hidden relationships, they are well-equipped to serve in image processing and character recognition. In criminal proceedings like bank frauds, fraud detection requires accurate results for character recognition because humans cannot go over thousands of samples to pinpoint a match. Here, ANNs are useful as they are able to recognize the smallest of irregularities. Similarly, ANN is used in facial recognition with positive results where they are able to improve governance and security.

Final Thoughts

The emergence of artificial neural networks has opened a whole new world of possibilities for machine learning. With their adoption in real-world industries, the algorithm has become one of the most trending and research topics in a short period of time.