AIOps, (for artificial intelligence for IT operations) is the application of artificial intelligence (AI) to enhance IT operations. Specifically, AIOps uses big data, analytics, and machine learning capabilities to do collect and aggregate the huge and ever-increasing volumes of operations data generated by multiple IT infrastructure components, applications, and performance-monitoring tools. It also enables the intelligent sift of ‘signals’ out of the ‘noise’ to identify significant events and patterns related to system performance and availability issues. The combined capabilities of ML and analytics help diagnose root causes and report them to IT for rapid response and remediation—or, in some cases, automatically resolve these issues without human intervention.

By replacing multiple separate, manual IT operations tools with a single, intelligent, and automated IT operations platform, AIOps enables IT operations teams to respond more quickly—even proactively—to slowdowns and outages, with a lot less effort.

It bridges the gap between an increasingly diverse, dynamic, and difficult-to-monitor IT landscape, on the one hand, and user expectations for little or no interruption in application performance and availability, on the other. Most experts consider AIOps to be the future of IT operations management.

However, the world of AIOps presents a duality. On the one hand, it’s an emerging technology that for the first time mashes up operations and AI. On the other, many of the solutions in this space are traditional tools that have been updated to leverage AI. This mix of old and new, traditional players and startups, makes this space particularly interesting. According to a report, here are key highlights of the prevailing landscape of AIOps.

The AIOps tools in the market today are on a spectrum with regard to the use of AI. While some make use of knowledge engines systemically in the monitoring and management of cloud and non-cloud systems, most tools leverage AI as an afterthought, not driving much of the functionality of the tool.

Enterprises are typically adopting AIOps as an upgrade to existing ops tools, and are remaining brand loyal. This means that the upstarts in the AIOps space will find it difficult to break into a market where the established players are in essence selling with the same basic message: AI integrated with management and monitoring that you trust. Considering this, we may see a consolidation next year as the market focuses on a handful of players, down from the two dozen or so relevant players today.

There seem to be two directions in AIOps: self-healing and not self-healing. Some AIOps systems are able to heal issues with systems that are managed and/or monitored. This means that if the tool finds an issue, a process is launched to attempt to correct the problem, for instance restarting a server or a network hub. Other solutions are more passive, alerting users about an issue, but without taking automated corrective action. The trend is toward active, or self-healing, AIOps tools.

These tools are all about the data. They store data as they monitor systems and can determine issues that need immediate attention, such as a down storage server. Or, they can deeply analyze historical data to determine trends that may portend a failure or other potential issue. The lifeblood of any AI system is the data needed to train the AI model, and this is the opportunity presented to AIOps tools. Monitored cloud or on-premises systems spin-off gigabytes of data each week, and that data can be fed into analytic systems augmented by AI.

Enterprises that wish to leverage these tools should be careful to understand their capabilities, and should also test the tools across both enterprise cloud and non-cloud platforms. There have been compatibility issues reported, most discovered after deployment.

Many of these tools are moving to an “on-demand” model, meaning that they will offer cloud-based services. This is an opportunity for those that have, or will have, the majority of their systems on public clouds. However, it may not be a good model for those that still have the majority of systems on-premises.

Gartner believes Artificial Intelligence (AI) security will be a top strategic technology trend in 2020, and that enterprises must gain awareness of AI’s impact on the security space. However, many enterprise IT leaders still lack a comprehensive understanding of the technology and what the technology can realistically achieve today. It is important for leaders to question exasperated Marketing claims and over-hyped promises associated with AI so that there is no confusion as to the technology’s defining capabilities.

IT leaders should take a step back and consider if their company and team is at a high enough level of security maturity to adopt advanced technology such as AI successfully. The organization’s business goals and current focuses should align with the capabilities that AI can provide. 

A study conducted by Widmeyer revealed that IT executives in the U.S. believe that AI will significantly change security over the next several years, enabling IT teams to evolve their capabilities as quickly as their adversaries. 

Of course, AI can enhance cybersecurity and increase effectiveness, but it cannot solve every threat and cannot replace live security analysts yet. Today, security teams use modern Machine Learning (ML) in conjunction with automation, to minimize false positives and increase productivity.

As adoption of AI in security continues to increase, it is critical that enterprise IT leaders face the current realities and misconceptions of AI, such as:

Artificial Intelligence as a Silver Bullet

AI is not a solution; it is an enhancement. Many IT decision leaders mistakenly consider AI a silver bullet that can solve all their current IT security challenges without fully understanding how to use the technology and what its limitations are. We have seen AI reduce the complexity of the security analyst’s job by enabling automation, triggering the delivery of cyber incident context, and prioritizing fixes. Yet, security vendors continue to tout further, exasperated AI-enabled capabilities of their solution without being able to point to AI’s specific outcomes.

If Artificial Intelligence is identified as the key, standalone method for protecting an organization from cyberthreats, the overpromise of AI coupled with the inability to clearly identify its accomplishments, can have a very negative impact on the strength of an organization’s security program and on the reputation of the security leader. In this situation, Chief Information Security Officers (CISO) will, unfortunately, realize that AI has limitations and the technology alone is unable to deliver aspired results. 

This is especially concerning given that 48% of enterprises say their budgets for AI in cybersecurity will increase by 29 percent this year, according to Capgemini.

Read more: Improve Your Bottom Line With Contract Automation and AI

Automation Versus Artificial Intelligence

We have seen progress surrounding AI in the security industry, such as the enhanced use of ML technology to recognize behaviors and find security anomalies. In most cases, security technology can now correlate the irregular behavior with threat intelligence and contextual data from other systems. It can also use automated investigative actions to provide an analyst with a strong picture of something being bad or not with minimal human intervention. 

A security leader should consider the types of ML models in use, the biases of those models, the capabilities possible through automation, and if their solution is intelligent enough to build integrations or collect necessary data from non-AI assets. 

AI can handle a bulk of the work of a Security Analyst but not all of it. As a society, we still do not have enough trust in AI to take it to the next level — which would be fully trusting AI to take corrective actions towards those anomalies it identified. Those actions still require human intervention and judgment.

Read more: The Nucleus of Statistical AI: Feature Engineering Practicalities for Machine Learning

Biased Decisions and Human Error

It is important to consider that AI can make bad or wrong decisions. Given that humans themselves create and train the models that achieve AI, it can make biased decisions based on the information it receives.

Models can produce a desired outcome for an attacker, and security teams should prepare for malicious insiders to try to exploit AI biases. Such destructive intent to influence AI’s bias can prove to be extremely damaging, especially in the legal sector. 

By feeding AI false information, bad actors can trick AI to implicate someone of a crime more directly. As an example, just last year, a judge ordered Amazon to turn over Echo recordings in a double murder case. In instances such as these, a hacker has the potential to wrongfully influence ML models and manipulate AI to put an innocent person in prison. In making AI more human, the likelihood of mistakes will increase.

What’s more, IT decision-makers must take into consideration that attackers are utilizing AI and ML as an offensive capability. AI has become an important tool for attackers, and according to Forrester’s Using AI for Evil report, mainstream AI-powered hacking is just a matter of time.

AI can be leveraged for good and for evil, and it is important to understand the technology’s shortcomings and adversarial potential.

The Future of AI in Cybersecurity

Though it is critical to acknowledge AI’s realistic capabilities and its current limitations, it is also important to consider how far AI can take us. Applying AI throughout the threat lifecycle will eventually automate and enhance entire categories of Security Operations Center (SOC) activity. AI has the potential to provide clear visibility into user-based threats and enable increasingly effective detection of real threats.

There are many challenges IT decision-makers face when over-estimating what Artificial Intelligence alone can realistically achieve and how it impacts their security strategies right now. Security leaders must acknowledge these challenges and truths if organizations wish to reap the benefits of AI today and for years to come.

Read more: AI in Cybersecurity: Applications in Various Fields


Xinhua, the Chinese state news agency, has released its latest artificial intelligence (AI) 3D news anchor. The AI anchor joins a list of growing virtual presenters that are being developed by the agency.

The AI news anchor is named Xin Xiaowei, and it is modeled after Zhao Wanwei, who is one of the agency’s human news presenters. 

According to the search engine Sogou, who co-developed the technology, the AI anchor utilizes “multi-modal recognition and synthesis, facial recognition and animation and transfer learning.”


The video released by Sogou shows Xin Xiaowei speaking on set about how the anchor can “intelligently imitate human voices, facial expressions, lip movements and mannerisms.” 

Previous Virtual Presenters

Xin Xiaowei is not the only virtual presenter that has been developed by Xinhua and the Beijing-based Sogou. It joins a growing list that includes their 2018 digital anchor Qiu Hao and 2019 Russian-speaking version

In 2018, the pair debuted two different AI news anchors, identical to each other in appearance, at the World Internet Conference. The two versions’ biggest difference was language, with one speaking English and the other Mandarin. 

Both of the 2018 models were based on Zhang Zhao, who was another human-anchor like Zhao Wanwei. 

In order to develop these first models, hours of video footage was used to replicate the movements, expressions, and other features of real-life anchors. 

According to a report released by Xinhua in 2018, “AI anchors have officially become members of Xinhua‘s reporting team. Together with other anchors, they will bring you authoritative, timely and accurate news information in Chinese and English.”

The 2018 AI news anchors were used on various distribution channels including WeChat, the TV webpage, Weibo, and Xinhua’s English and Chinese Apps. 

The Russian-speaking anchor was released at the St. Petersburg International Economic Forum 2019. It was developed through a different partnership than the other two versions, with Xinhua working with Russia’s leading news agency, ITAR-TASS. 

The announcement came as the two nations celebrated their 70th year of diplomatic relations. 

China's State News Agency Introduces New Artificial Intelligence Anchor

First Russian-Speaking AI News Anchor. Image: Sogou Inc. 

ITAR-TASS Partnership

ITAT-TASS is one of the largest news organizations in the world, consisting of a network of businesses, media organizations, diplomatic missions, and financial and research institutions. They have over 1,500 reporters present in more than 63 countries. 

“We are very excited to launch the world’s first Russian-speaking AI News Anchor,” said Xiaochuan Wang, CEO of Sogou back in 2018. “The development of the Russian-speaking AI News Anchor allows us to share the benefits of Sogou’s leading AI technologies with more diverse audiences around the world. As one of the world’s largest news organizations, ITAR-TASS is an ideal partner for Sogou, and we look forward to introducing this new AI News Anchor to Russian-speaking audiences.”

The Spread of AI Personalities

The newest AI anchor coming from Xinhua and Soguo highlights the increasing presence of AI-personalities, especially in the realm of media. The technology is improving at such a rapid rate that it will soon be undetectable when put next to a real-life human presenter. 

The use of these AI-anchors could dramatically alter the media landscape, but it is really just a part of the larger takeover of AI in the industry. Whether it is AI writers, news anchors, or some other use for the technology, it is going to become increasingly difficult to differentiate between what is human-based and what is AI-based. 


  • 85% of enterprises are evaluating or using artificial intelligence in production today.
  • 55% of all enterprises adopting AI today are using TensorFlow as their primary development tool.
  • 73% of enterprises with the most advanced AI adoption levels say supervised learning is the most popular machine learning technique (73%).
  • Human-in-the-loop AI models are considerably more popular among enterprises with advanced AI expertise compared to their peers.
  • Enterprise's enthusiasm for AI is growing, with 62% increasing their spending last year, according to a recent MIT Sloan Management Review study.


These and many other insights are from O'Reilly's recently published research, AI Adoption in the Enterprise 2020 available for download here (20 pp., PDF, free, opt-in). The survey is based on interviews with 1,388 respondents from 25 industries, with 17% of total respondents from the software industry. 30% of respondents are data scientists, data engineers, AIOps engineers, or their managers. 70% of all respondents are in technology roles. For additional information on the methodology, please see pages 2 and 3 of the study, downloadable here.

Additional insights from the study showing enterprises' growing adoption of AI include the following:


  • In 2020, AI is being adopted evenly across enterprises, with R&D leading all departments by a wide margin. O'Reilly's survey finds that enterprises are stabilizing their adoption patterns for AI across a wide variety of functional areas. Nine to twelve functional areas included in the survey have over 10% adoption. It's fascinating to watch how IT is adopting AI to improve ITSM performance for example. AI has the potential for redefining enterprises, making them more customer-driven, adaptive, and capable of generating and sharing intelligence faster than ever before. Guiding the transformation of an enterprise takes a framework that both can create knowledge while staying focused on the customer and provides an understanding of the entire customer journey. BMC's Autonomous Digital Enterprise (ADE) shows potential in this area, as its structure enables all departments of an enterprise to contribute and share AI-driven insights about customers and provide a transcendent customer experience. The following is a ranking of the functional areas of enterprises where AI is used today:


AI Is The Uncertainty Cure Enterprises Want In 2020


Supervised machine learning algorithms is the most popular machine learning technique in enterprises today. 73% of enterprises with advanced expertise in AI are making extensive use of supervised machine learning techniques to interpret, classify, and analyze the large data sets they've accumulated over years of operations. Enterprises who are evaluating AI are using deep learning techniques in their pilots, making this area of machine learning most popular with enterprises running AI pilots today.

AI Is The Uncertainty Cure Enterprises Want In 2020



  • TensorFlow continues to be the most popular development tool across all enterprises evaluating and using AI in production today. The O'Reilly research team found that 55% of all enterprises in 2019 and 2020 place a high priority on TensorFlow expertise, making it the most popular tool two years in a row. TensorFlow is integral to completing deep learning and neural network projects, further evidence of how enterprises are adopting AI to solve increasingly complex problems. The following is an analysis of the AI tools enterprises are using today: 


AI Is The Uncertainty Cure Enterprises Want In 2020



  • 53% of advanced enterprises using AI today say the greatest risk when building and deploying Machine Learning models is unexpected outcomes and predictions. The more experienced an enterprise is using AI, the more they're likely to anticipate unexpected outcomes and work on making models more transparent. The most advanced enterprises adopting AI today are also far more likely to include steps during model building to improve fairness, ethics, and limit or control biases. 


AI Is The Uncertainty Cure Enterprises Want In 2020



  • Of the 15% of enterprises who are considering AI, one in five or 22% says that a lack of institutional support is slowing down adoption efforts. The most significant barrier to overcome is changing a company culture that doesn't recognize the value of AI. Conversely, the most successful AI implementations are known for the strong support for senior management they receive. Closing the skills gap is the third greatest impediment to making progress with AI. Comparing bottlenecks holding back AI adoption across the entire survey sample versus enterprises who have reached a level of AI maturity shows how significant the skills gap continues to be. The O'Reilly research team found that selecting the right machine learning technique for the job has more than three-quarters (78%) of respondents selecting at least two of ML techniques, 59%, using at least three, and 39% choosing at least four.


AI Is The Uncertainty Cure Enterprises Want In 2020


Source: Forbes

Russian researchers have revealed that artificial intelligence (AI) is able to infer people’s personality from ‘selfie’ photographs better than human raters do. Also Read - Early Signs of Glaucoma Progression to Blindness Can be Spotted by AI

The study, published in the journal Scientific Reports, revealed that personality predictions based on female faces appeared to be more reliable than those for male faces. Also Read - Kazakhstan Woman Goes to Celebrate End of Lockdown, Falls To Death While Posing For Photo On A Cliff

The technology can be used to find the ‘best matches’ in customer service, dating or online tutoring, the researchers from HSE University and Open University in Russia, said. Also Read - Mumbai Students Develop AI Tool Which Could Help Diagnose Covid-19 Through Voice


Studies asking human raters to make personality judgments based on photographs have produced inconsistent results, suggesting that our judgments are too unreliable to be of any practical importance.

According to the study, there are strong theoretical and evolutionary arguments to suggest that some information about personality characteristics, particularly, those essential for social communication, might be conveyed by the human face.

After all, face and behaviour are both shaped by genes and hormones, and social experiences resulting from one’s appearance may affect one’s personality development.

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