Dec 17, 2020

The future of IT monitoring lies in AIOps

Daniela Streng
3 min
There is one continuing variable that will always be dependable: IT infrastructures are becoming increasingly complicated
There is one continuing variable that will always be dependable: IT infrastructures are becoming increasingly complicated...

Trends in tech innovation can be difficult to predict, but there is one continuing variable that will always be dependable: IT infrastructures are becoming increasingly complicated. Enterprise IT systems now rely upon a range of intertwined assets that have to work in perfect synergy –  infrastructure that lies across multiple clouds, on-premises, the connections in between, and Software-as-a-Service (SaaS) applications, only to name a few.

In a perfect world, these tangled systems would always work in harmony, each intricate piece performing its specified role in a manner that impacts varying layers of the system. However, this is seldom the case. It is up to IT teams to keep these labyrinthine mechanisms ticking, and thereby prevent outage and brownout events that could disrupt company operations. 

After all, when outage events do occur, the damage can be far-reaching. Not only does disruption to company operations inevitably waste time and money, but outage events are liable to make headlines in the media, resulting in irrevocable brand damage. Inconvenienced customers will also simply choose competitors if they are unable to properly engage with a company’s service or product.

The responsibility of IT monitoring and preventing outages falls upon beleaguered IT teams, who alone must navigate the ever-more complex enterprise IT environment and spot issues before they result in major problems. The task is one of monitoring and interpreting issues in the data, but the data produced by modern IT infrastructure is vast and teams are often undermanned.

AIOps to the rescue

Fortunately for IT teams – and the enterprises that depend on them – there is a natural solution to this issue: AIOps. True to its name, AIOps is the combination of artificial intelligence (AI) and IT operations. AIOps technology combines data science and machine learning (ML) to identify, troubleshoot and resolve issues developing in the IT ecosystem. Traditionally, AIOps involves automation and has the capacity to reduce manual work for stretched IT teams.

A key functionality of AIOps within complicated IT enterprise systems is its ability to process and interpret vast pools of data without manual input from IT teams. AIOps monitoring solutions can also remediate issues before they result in the problems that cause outages and brownouts.

Given the massive benefits of an AIOps approach to IT monitoring, the trend towards AI solutions is already becoming clear. Gartner, coiner of the term “AIOps,” predicts that large enterprise use of AIOps and digital experience monitoring tools used to monitor applications and IT infrastructures will increase to 30 percent by 2023. Gartner further predicts that the global AI-derived business value will reach nearly $3.9 trillion by 2022.

This is a change that will come to benefit overstretched enterprise IT teams. Instead of having to pore over the complicated IT environment, and the vast deluge of data it emits, teams can put their trust in AI-enhanced monitoring solutions. This will not only prevent the company from suffering an embarrassing outage event, but will leave IT teams with more time for innovation and improvement of the IT infrastructure.

By Daniela Streng, LogicMonitor VP & GM EMEA

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Jun 17, 2021

Chinese Firm Taigusys Launches Emotion-Recognition System

Elise Leise
3 min
Critics claim that new AI emotion-recognition platforms like Taigusys could infringe on Chinese citizens’ rights

In a detailed investigative report, the Guardian reported that Chinese tech company Taigusys can now monitor facial expressions. The company claims that it can track fake smiles, chart genuine emotions, and help police curtail security threats. ‘Ordinary people here in China aren’t happy about this technology, but they have no choice. If the police say there have to be cameras in a community, people will just have to live with it’, said Chen Wei, company founder and chairman. ‘There’s always that demand, and we’re here to fulfil it’. 


Who Will Use the Data? 

As of right now, the emotion-recognition market is supposed to be worth US$36bn by 2023—which hints at rapid global adoption. Taigusys counts Huawei, China Mobile, China Unicom, and PetroChina among its 36 clients, but none of them has yet revealed if they’ve purchased the new AI. In addition, Taigusys will likely implement the technology in Chinese prisons, schools, and nursing homes.


It’s not likely that emotion-recognition AI will stay within the realm of private enterprise. President Xi Jinping has promoted ‘positive energy’ among citizens and intimated that negative expressions are no good for a healthy society. If the Chinese central government continues to gain control over private companies’ tech data, national officials could use emotional data for ideological purposes—and target ‘unhappy’ or ‘suspicious’ citizens. 


How Does It Work? 

Taigusys’s AI will track facial muscle movements, body motions, and other biometric data to infer how a person is feeling, collecting massive amounts of personal data for machine learning purposes. If an individual displays too much negative emotion, the platform can recommend him or her for what’s termed ‘emotional support’—and what may end up being much worse. 


Can We Really Detect Human Emotions? 

This is still up for debate, but many critics say no. Psychologists still debate whether human emotions can be separated into basic emotions such as fear, joy, and surprise across cultures or whether something more complex is at stake. Many claim that AI emotion-reading technology is not only unethical but inaccurate since facial expressions don’t necessarily indicate someone’s true emotional state. 


In addition, Taigusys’s facial tracking system could promote racial bias. One of the company’s systems classes faces as ‘yellow, white, or black’; another distinguishes between Uyghur and Han Chinese; and sometimes, the technology picks up certain ethnic features better than others. 


Is China the Only One? 

Not a chance. Other countries have also tried to decode and use emotions. In 2007, the U.S. Transportation Security Administration (TSA) launched a heavily contested training programme (SPOT) that taught airport personnel to monitor passengers for signs of stress, deception, and fear. But China as a nation rarely discusses bias, and as a result, its AI-based discrimination could be more dangerous. 


‘That Chinese conceptions of race are going to be built into technology and exported to other parts of the world is troubling, particularly since there isn’t the kind of critical discourse [about racism and ethnicity in China] that we’re having in the United States’, said Shazeda Ahmed, an AI researcher at New York University (NYU)


Taigusys’s founder points out, on the other hand, that its system can help prevent tragic violence, citing a 2020 stabbing of 41 people in Guangxi Province. Yet top academics remain unconvinced. As Sandra Wachter, associate professor and senior research fellow at the University of Oxford’s Internet Institute, said: ‘[If this continues], we will see a clash with fundamental human rights, such as free expression and the right to privacy’. 


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