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AI: How ChatGPT and Big Tech are Shaping the Future of Artificial Intelligence

Having AI in Blockchain Is the New Flex. But How Can We BUIDL Responsibly and Ethically?

ব্লকচেইনে AI থাকা হল নতুন ফ্লেক্স। কিন্তু আমরা কীভাবে দায়িত্বশীল এবং নৈতিকভাবে তৈরি করতে পারি?

KI in der Blockchain ist das neue Flex. Aber wie können wir verantwortungsbewusst und ethisch BUIDL?

La IA en la cadena de bloques es la nueva flexibilidad, pero ¿cómo podemos desarrollarla de manera responsable y ética?

L'IA dans la blockchain est la nouvelle tendance. Mais comment pouvons-nous construire de manière responsable et éthique ?

ब्लॉकचेन में AI का होना नया लचीलापन है। लेकिन हम जिम्मेदारी और नैतिकता के साथ कैसे निर्माण कर सकते हैं?

ブロックチェーンに AI を組み込むことは新たな柔軟性です。しかし、責任を持って倫理的に構築するにはどうすればよいでしょうか?

블록체인에 AI를 도입하는 것이 새로운 플렉스입니다. 하지만 어떻게 책임감 있고 윤리적으로 BUIDL을 할 수 있을까요?

Ter IA em Blockchain é o novo Flex. Mas como podemos BUIDL de forma responsável e ética?

Наличие ИИ в блокчейне — это новый гибкий подход. Но как мы можем строить ответственно и этично?

Blockchain'de Yapay Zekanın Olması Yeni Esnekliktir. Peki Sorumlu ve Etik Bir Şekilde Nasıl İNŞA Edebiliriz?

Có AI trong Blockchain là Flex mới. Nhưng làm thế nào chúng ta có thể BUIDL một cách có trách nhiệm và đạo đức?

区块链中拥有人工智能是新趋势。但我们如何才能负责任且合乎道德地构建区块链？

Logging is essential for successful DevSecOps teams. Logs are filled with the information needed to monitor and understand systems. Tracking down a defect? Trying to understand a sudden burst in questionable logins from a new region? Need to figure out why an app is crawling? Logs are that single source of truth for understanding what’s really happening. But there’s a problem that comes along with logs: the sheer amount of data. The information logged by services and applications just keeps on growing. And growing. It doesn’t take long for it to become more — much more — than can be managed. The data becomes overwhelming. Alert fatigue sets in. Data keeps growing. Human resources can’t. But there’s hope on the horizon. Innovations in AI have revolutionized the process of continuous log monitoring. AI algorithms can analyze and detect patterns within vast datasets, translate raw logs into actionable insights, and proactively alert teams to problems — all at a scale and at higher precision to assist humans. Let’s look at 8 ways that AI can maximize the value of logs. #1: Handling massive amounts of data First, the most obvious. Cloud-native environments, with their dozens (or hundreds) of distributed components, emit a massive volume of log data. In turn, this data requires high levels of expertise to sift through and analyze it all. But most organizations already face a shortage of people with the skills needed to tease out the insights from this data. Companies could train more people — but training is slow. And the growing complexity of the data tends to outpace any new skills. Here is where AI shines: It’s a scalable way to handle log data, no matter the volume and no matter the time. Humans need breaks; they clock out for the day, get sick, take PTO, and play Galaga when bosses aren’t looking. But log data still arrives in massive volumes, regardless of the time of the day or day of the week. An AI-based system with analysis, detection, and alerting is always on. #2: Automated security and access control When dealing with logs, it’s important to protect any sensitive user information and ensure that the data is only accessible to authorized team members. SecOps managers often implement fine-grained access control to ensure that the approved team members have access to (and only to) the data or metrics intended for use. AI systems can automatically identify and redact sensitive information — such as personal identifiers, financial details, or confidential business information — from log data before it’s accessed by humans. Or, as part of automated preprocessing, AI can de-identify or mask sensitive parts of data. #3: Collating data from disparate sources Merging data from various sources is a complex task. But it’s essential for effective security and operations. When logs are properly aggregated and correlated, the resulting data and metrics can give the context needed for better visibility and better troubleshooting. But this is a menial and time-consuming task … which makes it perfect for AI. AI can automatically gather information from various sources and identify patterns within the data, making its analysis easier. AI correlates data from different sources far more efficiently than a human. Modern log analytics tools leverage AI to gather log data from cloud services and on-premises environments. Log analysis and issue resolution become proactive, preventing negative impacts on the health of applications and systems. #4: Transforming raw log data Organizations depend on skilled professionals to handle and analyze log data, yet they often face overwhelming resource constraints. This is where AI can contribute significantly, by automating repetitive tasks and enhancing human capabilities. Before analysis, log data often required cleaning and preprocessing to remove errors, duplicates, or irrelevant information. AI can automate this process, ensuring the accuracy and standardization of all the data. AI can also organize log data into clusters based on similarities or classify them into predefined categories. This helps manage data more efficiently, making it easier for humans to understand and act upon the insights derived. #5: Analyzing log data A clear use case for AI within a DevSecOps strategy is the automation of repetitive and time-consuming tasks — such as data cleaning, feature selection, and model training. With AI taking on these tasks, developers can focus on other tasks. AI can sift through a mountain of data to spot duplication and anomalies — like subtle signs of a cyberattack or unusual traffic patterns — that might easily slip past human scrutiny. This yields enhanced security and operational insights. It’s more than just about handling the volume; AI is adept at detecting patterns that are too complex or too faint for the human eye to catch. For example, let’s consider logging and monitoring for a network to catch signs of data exfiltration. This kind of anomaly might manifest as an unusually high volume of data being sent to an unfamiliar external IP address during off-hours; that’s a pattern that might not immediately raise flags for a human amid thousands of legitimate data transfers happening every day. On the other hand, an AI-based system that’s trained on vast datasets of normal and malicious network behavior can identify this subtle pattern by correlating different indicators: The timing of the data transfer
The volume of data
The destination IP address
The type of data being transferred For a human, recognizing such a complex pattern requires painstaking analysis and might be missed entirely due to the sheer volume of log data. But an AI system can continuously monitor for these patterns across the entire network, detecting potential threats with precision and speed that far surpasses human capability. #6: Reducing alert fatigue Traditional infrastructure and service monitoring solutions are notoriously noisy, often generating alerts for events that don’t signify a genuine threat. Excessive unactionable alerts lead to alert fatigue. AI-based alerting intelligently filters alerts and reduces the noise, ensuring that the alerts generated are relevant and actionable. For example, traditional monitoring can’t adjust for seasonality — so a crossed threshold in the middle of a high-season weekday afternoon gets as much attention as one in the middle of the night during what should be a slow week. AI-based alerting uses historical data to continually train its models, factoring seasonality into its baselines. The result is fewer false positives and no more alert fatigue. Of course, organizations using AI-driven alerting need to rigorously test results in order to tune specificity and sensitivity. This ensures that critical events are captured effectively. #7: Proactive monitoring Given the large amounts of data an organization generates, teams often struggle to monitor all the organization’s resources proactively. AI is well-equipped to address this issue at scale. By continuously monitoring and aggregating logs from across entire environments, an AI-based tool can identify anomalies before they become widespread, allowing teams to detect potential threats in their initial stages. For example, the threat detection and investigation from Sumo Logic provides the visibility to address advanced threats before they affect operations. AI features such as this enable real-time monitoring, alerting, and data analysis across security tools, cloud infrastructures, and SaaS applications. This enables a DevSecOps team to investigate and respond to cyber threats swiftly. #8: Efficient incident response AI-driven alerting improves incident response by facilitating automatic resource allocation and gathering contextual information about an incident. This helps identify potential security threats faster, which in turn helps organizations respond more quickly. When AI-powered logging and observability platforms provide automated remediation features, teams can connect the dots: from continuously monitored logs to incident detection to remediation playbooks. Automated playbook execution means near-immediate response to an incident, whether that’s eliminating the root cause or alerting an engineer to begin an investigation. Remember, with every delay in responding to a security incident, the window for impact widens. Minimizing that delay with AI directly minimizes the impact of an incident. Privacy concerns related to AI adoption One last note: as we’ve seen, AI shows great promise as a tool for improving logging. But remember that it’s still an emerging technology. As a recent GitLab report makes clear, there are serious privacy concerns around AI adoption. While 83% of the teams surveyed said implementing AI in their development process was essential, 79% said they were highly concerned about privacy and IP when dealing with AI. But in many business contexts, AI tools need access to that private data for analysis. So take advantage of AI, but be aware and guard against any privacy concerns. Drowning in log data? AI is here to help. Logs are crucial. They’re a rich source of data for monitoring applications and infrastructure. But the multiple data sources and volume of log data — along with the sensitivity of some of that data in the logs — lead to some big challenges in managing log data, security, and privacy. AI is here to help. Modern log management and SIEM solutions are leveraging AI to automate analysis, enhance monitoring, and improve incident response. AI is making DevSecOps more efficient. And as AI solutions evolve, their role in log analysis will only grow, offering smarter, faster insights into logs. Logging is essential for successful DevSecOps teams. Logs are filled with the information needed to monitor and understand systems. Tracking down a defect? Trying to understand a sudden burst in questionable logins from a new region? Need to figure out why an app is crawling? Logs are that single source of truth for understanding what’s really happening. filled But there’s a problem that comes along with logs: the sheer amount of data. The information logged by services and applications just keeps on growing. And growing. It doesn’t take long for it to become more — much more — than can be managed. The data becomes overwhelming. Alert fatigue sets in. Data keeps growing. Human resources can’t. But there’s hope on the horizon. Innovations in AI have revolutionized the process of continuous log monitoring . AI algorithms can analyze and detect patterns within vast datasets, translate raw logs into actionable insights, and proactively alert teams to problems — all at a scale and at higher precision to assist humans. continuous log monitoring Let’s look at 8 ways that AI can maximize the value of logs. #1: Handling massive amounts of data First, the most obvious. Cloud-native environments, with their dozens (or hundreds) of distributed components, emit a massive volume of log data. In turn, this data requires high levels of expertise to sift through and analyze it all. massive But most organizations already face a shortage of people with the skills needed to tease out the insights from this data. Companies could train more people — but training is slow. And the growing complexity of the data tends to outpace any new skills. could Here is where AI shines: It’s a scalable way to handle log data, no matter the volume and no matter the time. Humans need breaks; they clock out for the day, get sick, take PTO, and play Galaga when bosses aren’t looking . But log data still arrives in massive volumes, regardless of the time of the day or day of the week. An AI-based system with analysis, detection, and alerting is always on. play Galaga when bosses aren’t looking #2: Automated security and access control When dealing with logs, it’s important to protect any sensitive user information and ensure that the data is only accessible to authorized team members. SecOps managers often implement fine-grained access control to ensure that the approved team members have access to (and only to) the data or metrics intended for use. AI systems can automatically identify and redact sensitive information — such as personal identifiers, financial details, or confidential business information — from log data before it’s accessed by humans. Or, as part of automated preprocessing, AI can de-identify or mask sensitive parts of data. #3: Collating data from disparate sources Merging data from various sources is a complex task. But it’s essential for effective security and operations. When logs are properly aggregated and correlated, the resulting data and metrics can give the context needed for better visibility and better troubleshooting. But this is a menial and time-consuming task … which makes it perfect for AI. AI can automatically gather information from various sources and identify patterns within the data, making its analysis easier. AI correlates data from different sources far more efficiently than a human. Modern log analytics tools leverage AI to gather log data from cloud services and on-premises environments. Log analysis and issue resolution become proactive, preventing negative impacts on the health of applications and systems. #4: Transforming raw log data Organizations depend on skilled professionals to handle and analyze log data, yet they often face overwhelming resource constraints. This is where AI can contribute significantly, by automating repetitive tasks and enhancing human capabilities. Before analysis, log data often required cleaning and preprocessing to remove errors, duplicates, or irrelevant information. AI can automate this process, ensuring the accuracy and standardization of all the data. AI can also organize log data into clusters based on similarities or classify them into predefined categories. This helps manage data more efficiently, making it easier for humans to understand and act upon the insights derived. #5: Analyzing log data A clear use case for AI within a DevSecOps strategy is the automation of repetitive and time-consuming tasks — such as data cleaning, feature selection, and model training. With AI taking on these tasks, developers can focus on other tasks. AI can sift through a mountain of data to spot duplication and anomalies — like subtle signs of a cyberattack or unusual traffic patterns — that might easily slip past human scrutiny. This yields enhanced security and operational insights. duplication It’s more than just about handling the volume; AI is adept at detecting patterns that are too complex or too faint for the human eye to catch. For example, let’s consider logging and monitoring for a network to catch signs of data exfiltration. This kind of anomaly might manifest as an unusually high volume of data being sent to an unfamiliar external IP address during off-hours; that’s a pattern that might not immediately raise flags for a human amid thousands of legitimate data transfers happening every day. On the other hand, an AI-based system that’s trained on vast datasets of normal and malicious network behavior can identify this subtle pattern by correlating different indicators: The timing of the data transfer The volume of data The destination IP address The type of data being transferred The timing of the data transfer The volume of data The destination IP address The type of data being transferred For a human, recognizing such a complex pattern requires painstaking analysis and might be missed entirely due to the sheer volume of log data. But an AI system can continuously monitor for these patterns across the entire network, detecting potential threats with precision and speed that far surpasses human capability. #6: Reducing alert fatigue Traditional infrastructure and service monitoring solutions are notoriously noisy, often generating alerts for events that don’t signify a genuine threat. Excessive unactionable alerts lead to alert fatigue. AI-based alerting intelligently filters alerts and reduces the noise, ensuring that the alerts generated are relevant and actionable. For example, traditional monitoring can’t adjust for seasonality — so a crossed threshold in the middle of a high-season weekday afternoon gets as much attention as one in the middle of the night during what should be a slow week. AI-based alerting uses historical data to continually train its models, factoring seasonality into its baselines. The result is fewer false positives and no more alert fatigue. Of course, organizations using AI-driven alerting need to rigorously test results in order to tune specificity and sensitivity. This ensures that critical events are captured effectively. #7: Proactive monitoring Given the large amounts of data an organization generates, teams often struggle to monitor all the organization’s resources proactively. AI is well-equipped to address this issue at scale. By continuously monitoring and aggregating logs from across entire environments, an AI-based tool can identify anomalies before they become widespread, allowing teams to detect potential threats in their initial stages. For example, the threat detection and investigation from Sumo Logic provides the visibility to address advanced threats before they affect operations. AI features such as this enable real-time monitoring, alerting, and data analysis across security tools, cloud infrastructures, and SaaS applications. This enables a DevSecOps team to investigate and respond to cyber threats swiftly. threat detection and investigation #8: Efficient incident response AI-driven alerting improves incident response by facilitating automatic resource allocation and gathering contextual information about an incident. This helps identify potential security threats faster, which in turn helps organizations respond more quickly. When AI-powered logging and observability platforms provide automated remediation features, teams can connect the dots: from continuously monitored logs to incident detection to remediation playbooks. Automated playbook execution means near-immediate response to an incident, whether that’s eliminating the root cause or alerting an engineer to begin an investigation. Remember, with every delay in responding to a security incident, the window for impact widens. Minimizing that delay with AI directly minimizes the impact of an incident. Privacy concerns related to AI adoption One last note: as we’ve seen, AI shows great promise as a tool for improving logging. But remember that it’s still an emerging technology. As a recent GitLab report makes clear, there are serious privacy concerns around AI adoption. While 83% of the teams surveyed said implementing AI in their development process was essential, 79% said they were highly concerned about privacy and IP when dealing with AI. But in many business contexts, AI tools need access to that private data for analysis. GitLab report privacy and IP So take advantage of AI, but be aware and guard against any privacy concerns. Drowning in log data? AI is here to help. Logs are crucial. They’re a rich source of data for monitoring applications and infrastructure. But the multiple data sources and volume of log data — along with the sensitivity of some of that data in the logs — lead to some big challenges in managing log data, security, and privacy. AI is here to help. Modern log management and SIEM solutions are leveraging AI to automate analysis, enhance monitoring, and improve incident response. AI is making DevSecOps more efficient. And as AI solutions evolve, their role in log analysis will only grow, offering smarter, faster insights into logs.

Maximizing Log Value with AI: 8 Ways to Revolutionize DevSecOps Monitoring

The role of a site reliability engineer (SRE) is not to be understated. With the recent wave of developments in cloud computing and artificial intelligence (AI), scalability, security, and the actual reliability of these technologies can come into question. One tenured senior site reliability engineer is Chaitanya Kanth Tummalachervu, who has provided his services across sectors ranging from medical insurance, finance, retail, the automobile industry, and even video communications. In these fields, Tummalachervu’s principal focus lies in DevOps – or developer operations – where he supports teams of coders as they work to create, support, and protect AI-powered products for businesses and consumers alike. DevOps: How Automation, Security, and Deployment Are Perfected The inner workings of a developer team can vary greatly between projects. While small, agile teams may not run into issues, more complex workplaces or large-scale projects can often be mired in clutter - both digitally and physically. Site reliability engineers, such as Tummalachervu himself, help reduce the strain on these teams by creating systems tailored to the needs of each operation. He says, “The bulk of my work is allowing for a faster, more efficient automated deployment of software. During release cycles, there’s no time to waste. Developers need the support of a high-performing system, fully automated tasks, and services that are available 24/7.” Central to his work, the field of digital artificial intelligence products can be as demanding as it is expansive. With the complexity of advanced neural networks, operations can be bogged down by miscommunication, collapsed dependencies, and delays in the development pipeline. Even worse, unsecured systems of such importance are also a prime target for malicious actors, furthering the burden on the teams supporting it. “It’s not uncommon for software releases or updates to be pushed back, especially when we’re working on new tech concepts like AI. Most of the time, these inefficiencies are caused by a lack of support, or miscommunication between the developer and operations teams,” explains Tummalachervu. Indeed, his focus on software-driven automation has reportedly increased developer productivity by 40%, with incident response time also seeing a 20% reduction as well. Expertise Delivered in Contemporary AI Platforms Of particular note is Tummalachervu’s current project, where his integration of strategic team collaboration and choice software systems has secured a premiere AI-based video streaming platform. Here, a combination of software development and IT team coordination is employed to enable update releases in a timely and secure manner. Based on cloud infrastructure accessible to the public, Tummalachervu and his team contributed through his implementation of “DevSecOps” - a sub-field of DevOps focused on ensuring full security throughout the development process. “We oversee security updates, software compatibility versions, pre-scanned deployment images, and secure our cloud Kubernetes infrastructure with a wide variety of technologies. Best practices and security standards are followed to the letter, with everything kept up-to-date,” says Tummalachervu. In addition to security and team coordination, Tummalachervu has also been commended for his utilization of Infrastructure as Code (IAC). A modern strategy involving the use of code to support computing infrastructure rather than manual tweaks and settings adjustments, IAC is seeing widespread adoption across many enterprise-level systems. It is most effective when deployed in cloud environments, as IAC-enabling tools such as Terraform, Helm, and Cloudformation are wielded by Tummalachervu to great effect. “I designed and implemented multiple failover cases while optimizing for scalability and resource usage. Human errors such as accidental resource deletion or over-provisioning were eliminated as well,” elaborates Tummalachervu. “With the right tools managing our infrastructure, operational costs are greatly reduced, and the cloud portfolio itself is kept in optimal shape. Everything stays robust and available for use.” Prior to working on said cloud-based infrastructure itself, Tummalachervu was instrumental in migrating systems and data therein, keeping his signature focus on strategy throughout each project. “One of the biggest migrations we did was with image repositories. It’s not as simple as copying one folder to another drive - migrating infrastructure from on-site environments to the cloud takes significant planning and power. But, if successful, you can reduce real estate costs, save resources, and optimize workloads - especially for dev teams,” he says. Beyond the Clouds: Contributions and Teachings In addition to his technical expertise, Tummalachervu is highly regarded in the IT community for his extensive body of work, which has been published in renowned journals throughout the academic and technology spheres. His contributions to these communities extend beyond publications; he has also conducted numerous faculty development programs, directly benefiting nearly 150 PhD scholars, professors, university students, and colleagues. “Advanced cloud computing strategies can be hard to understand, at first. We have an open-door policy for any colleagues who want to consult with me. I’ve also mentored university students and professors through Faculty Development Sessions, as well as aided in reviewing a number of scholarly articles,” Tummalachervu says. “I’ve even been asked to judge at a few hackathons and international award ceremonies.” Central to the digital world, site reliability engineers like Chaitanya Tummalachervu act as pillars for what will become the future of technology: cloud-based environments. Secured, stabilized, and maintained by SREs and their teams, the future can rest easy knowing that the global infrastructure is supported by these skilled engineers, armed with nothing but best practices and sheer determination. This piece was published as part of HackerNoon’s Business Blogging Program. For more details, please click here. The role of a site reliability engineer (SRE) is not to be understated. With the recent wave of developments in cloud computing and artificial intelligence (AI), scalability, security, and the actual reliability of these technologies can come into question. One tenured senior site reliability engineer is Chaitanya Kanth Tummalachervu , who has provided his services across sectors ranging from medical insurance, finance, retail, the automobile industry, and even video communications. In these fields, Tummalachervu’s principal focus lies in DevOps – or developer operations – where he supports teams of coders as they work to create, support, and protect AI-powered products for businesses and consumers alike. Chaitanya Kanth Tummalachervu Chaitanya Kanth Tummalachervu DevOps: How Automation, Security, and Deployment Are Perfected The inner workings of a developer team can vary greatly between projects. While small, agile teams may not run into issues, more complex workplaces or large-scale projects can often be mired in clutter - both digitally and physically. Site reliability engineers, such as Tummalachervu himself, help reduce the strain on these teams by creating systems tailored to the needs of each operation. He says, “ The bulk of my work is allowing for a faster, more efficient automated deployment of software. During release cycles, there’s no time to waste. Developers need the support of a high-performing system, fully automated tasks, and services that are available 24/7. ” The bulk of my work is allowing for a faster, more efficient automated deployment of software. During release cycles, there’s no time to waste. Developers need the support of a high-performing system, fully automated tasks, and services that are available 24/7. Central to his work, the field of digital artificial intelligence products can be as demanding as it is expansive. With the complexity of advanced neural networks, operations can be bogged down by miscommunication, collapsed dependencies, and delays in the development pipeline. Even worse, unsecured systems of such importance are also a prime target for malicious actors, furthering the burden on the teams supporting it. “ It’s not uncommon for software releases or updates to be pushed back, especially when we’re working on new tech concepts like AI. Most of the time, these inefficiencies are caused by a lack of support, or miscommunication between the developer and operations teams, ” explains Tummalachervu. Indeed, his focus on software-driven automation has reportedly increased developer productivity by 40%, with incident response time also seeing a 20% reduction as well. It’s not uncommon for software releases or updates to be pushed back, especially when we’re working on new tech concepts like AI. Most of the time, these inefficiencies are caused by a lack of support, or miscommunication between the developer and operations teams, Expertise Delivered in Contemporary AI Platforms Of particular note is Tummalachervu’s current project, where his integration of strategic team collaboration and choice software systems has secured a premiere AI-based video streaming platform. Here, a combination of software development and IT team coordination is employed to enable update releases in a timely and secure manner. Based on cloud infrastructure accessible to the public, Tummalachervu and his team contributed through his implementation of “DevSecOps” - a sub-field of DevOps focused on ensuring full security throughout the development process. “ We oversee security updates, software compatibility versions, pre-scanned deployment images, and secure our cloud Kubernetes infrastructure with a wide variety of technologies. Best practices and security standards are followed to the letter, with everything kept up-to-date, ” says Tummalachervu. We oversee security updates, software compatibility versions, pre-scanned deployment images, and secure our cloud Kubernetes infrastructure with a wide variety of technologies. Best practices and security standards are followed to the letter, with everything kept up-to-date, In addition to security and team coordination, Tummalachervu has also been commended for his utilization of Infrastructure as Code (IAC). A modern strategy involving the use of code to support computing infrastructure rather than manual tweaks and settings adjustments, IAC is seeing widespread adoption across many enterprise-level systems. It is most effective when deployed in cloud environments, as IAC-enabling tools such as Terraform, Helm, and Cloudformation are wielded by Tummalachervu to great effect. “ I designed and implemented multiple failover cases while optimizing for scalability and resource usage. Human errors such as accidental resource deletion or over-provisioning were eliminated as well,” elaborates Tummalachervu. “ With the right tools managing our infrastructure, operational costs are greatly reduced, and the cloud portfolio itself is kept in optimal shape. Everything stays robust and available for use. ” I designed and implemented multiple failover cases while optimizing for scalability and resource usage. Human errors such as accidental resource deletion or over-provisioning were eliminated as well,” With the right tools managing our infrastructure, operational costs are greatly reduced, and the cloud portfolio itself is kept in optimal shape. Everything stays robust and available for use. Prior to working on said cloud-based infrastructure itself, Tummalachervu was instrumental in migrating systems and data therein, keeping his signature focus on strategy throughout each project. “ One of the biggest migrations we did was with image repositories. It’s not as simple as copying one folder to another drive - migrating infrastructure from on-site environments to the cloud takes significant planning and power. But, if successful, you can reduce real estate costs, save resources, and optimize workloads - especially for dev teams, ” he says. One of the biggest migrations we did was with image repositories. It’s not as simple as copying one folder to another drive - migrating infrastructure from on-site environments to the cloud takes significant planning and power. But, if successful, you can reduce real estate costs, save resources, and optimize workloads - especially for dev teams, Beyond the Clouds: Contributions and Teachings In addition to his technical expertise, Tummalachervu is highly regarded in the IT community for his extensive body of work, which has been published in renowned journals throughout the academic and technology spheres. His contributions to these communities extend beyond publications; he has also conducted numerous faculty development programs, directly benefiting nearly 150 PhD scholars, professors, university students, and colleagues. “ Advanced cloud computing strategies can be hard to understand, at first. We have an open-door policy for any colleagues who want to consult with me. I’ve also mentored university students and professors through Faculty Development Sessions, as well as aided in reviewing a number of scholarly articles, ” Tummalachervu says. “ I’ve even been asked to judge at a few hackathons and international award ceremonies. ” Advanced cloud computing strategies can be hard to understand, at first. We have an open-door policy for any colleagues who want to consult with me. I’ve also mentored university students and professors through Faculty Development Sessions, as well as aided in reviewing a number of scholarly articles, I’ve even been asked to judge at a few hackathons and international award ceremonies. Central to the digital world, site reliability engineers like Chaitanya Tummalachervu act as pillars for what will become the future of technology: cloud-based environments. Secured, stabilized, and maintained by SREs and their teams, the future can rest easy knowing that the global infrastructure is supported by these skilled engineers, armed with nothing but best practices and sheer determination. This piece was published as part of HackerNoon’s Business Blogging Program. For more details, please click here. This piece was published as part of HackerNoon’s Business Blogging Program. For more details, please click here . here

Cloud Expert Chaitanya Kanth Tummalachervu Uses Complex DevOps and Cloud Architecture on AI Products

Do You Have a Digital Twin? - The World of AI Generated Identities

আপনার কি ডিজিটাল টুইন আছে? - এআই জেনারেটেড আইডেন্টিটিসের বিশ্ব

Haben Sie einen digitalen Zwilling? – Die Welt der KI-generierten Identitäten

¿Tienes un gemelo digital? - El mundo de las identidades generadas por IA

Avez-vous un jumeau numérique ? - Le monde des identités générées par l'IA

क्या आपके पास डिजिटल ट्विन है? - AI जनरेटेड आइडेंटिटीज़ की दुनिया

デジタルツインをお持ちですか? - AI 生成 ID の世界

디지털 트윈이 있나요? - AI 생성 신원의 세계

Você tem um gêmeo digital? - O mundo das identidades geradas por IA

У вас есть цифровой двойник? - Мир личностей, созданных искусственным интеллектом

Dijital İkiziniz Var mı? - Yapay Zekanın Oluşturduğu Kimlikler Dünyası

Bạn có cặp song sinh kỹ thuật số không? - Thế giới danh tính do AI tạo ra

你有数字孪生吗？ - 人工智能生成身份的世界

Professionals in the book publishing industry are terrified of artificial intelligence — and for good reason. While it isn’t all bad, it has the potential to bring the sector to its knees. Not to mention, it has already harmed many creatives. Are their fears justified or unwarranted? AI’s Emerging Role in the Publishing Industry Thanks to its versatility, AI has emerged in most parts of the publishing industry. In 2023, 47% of book publishers based in the United States used it for marketing, 25% for editorial work, and 12% for content generation. Companies can use it in writing, editing, advertising, or administrative roles — it can do almost everyone’s job to some extent. AI Isn’t the First Threat, But It’s the Biggest Many disruptors have affected the publishing industry. Most recently, e-books have started replacing physical books. According to one estimate, about 30% of pages will be digitally printed by 2028, up from 5% in 2023. Many publishing houses and self-publishers are migrating to digital. Before that, digital printing shook up the offset printing press. Although AI isn’t the first disruptor, it’s the biggest so far. The publishing sector has withstood others because they were all natural evolutions to outdated processes. Machine learning (ML) and large language models are different — they replace existing technology instead of improving it. Who needs writers, editors, and agents when algorithms can do their work for free? Writers, Editors, and Publishers Are Terrified of AI Most professionals in book publishing are terrified of generative AI. Its versatility could bring about the sector’s downfall. A handful of high-quality models can write a novel, proofread, edit, design a cover, make marketing materials, post on social media for publicity, distribute digital copies, and handle accounting. For now, AI can’t perform any of these jobs better than a human. One industry professional said the content it generates is “crude at best,” arguing it isn’t comparable to what a real writer can produce. Even so, it might not matter when it can pump out pages at an inhuman speed. AI will inevitably affect the entire industry even if every author, editor, and publisher agrees not to use it. This technology’s accessibility is its main driving force. Dozens of leading models — and thousands more in-house systems — exist. Almost anyone can use ChatGPT, Midjourney, LaMDA, or Claude to make, market, and distribute AI-generated books. The Negative Impact of AI on the Publishing Industry There’s abundant evidence of AI’s negative impacts on the book publishing industry. AI Is Stealing People’s Jobs There’s truth to the fear that AI is set to steal people’s jobs. While more job ads now stress human-written content is a hard requirement and more companies are investing in software to detect AI-generated content, the publishing sector’s pushback hasn’t been enough — and people are being forced to find new careers. Olivia Lipkin, a copywriter, said her assignments dwindled after ChatGPT came out. She even claimed management changed her title to “Olivia/ChatGPT” on the company’s messaging platform. Within a few months of the generative model’s release, Lipkin was let go without explanation. Eventually, she discovered her managers had realized AI was much cheaper. It’s Stealing Their Work, Too Algorithms can’t come up with content on their own — they must use others’ work. Since most books are copyrighted, AI companies have resorted to piracy and copyright infringement. Books3 is a data set of nearly 200,000 pirated novels used to train generative AI systems. Most of the content was published within the last 20 years, so many living authors are affected. This issue isn’t limited to bad actors and shady characters, either. OpenAI, ChatGPT’s creator, argued creating models would be impossible unless it was allowed to use copyrighted material. It claimed public domain content wouldn’t “meet the needs” of people. Regardless of the statement’s truth, it shows how unconcerned AI companies are about content theft. There Aren’t Protections Yet Despite the issue of AI-driven copyright infringement being a real threat to authors, literary agents and publishing houses, no real regulations exist to protect them yet. Recently, a 24-page document revealed that Midjourney had a database of around 16,000 individuals it allegedly stole art from. Apparently, it was a list for model training. Even though the list — which contained specific names — seems like it would prompt swift regulatory action, things aren’t as open and shut as they should be. In many countries, a lawsuit is the only recourse creators have. Realistically, there’s no guarantee they’ll be successful. Not to mention, many would have to fight against well-funded tech giants. The Positive Impact of AI on the Publishing Industry AI wouldn’t be able to disrupt the book publishing industry if it was inferior to what people currently use. In other words, a silver lining does exist — there are potential positive impacts. AI Can Take on the Mundane Work Most writers spend more time than they’d like on mundane — but necessary — tasks like proofreading and spellchecking. The same can be said about literary agents hassling publishers or marketers tweaking designs for the nth time. AI can take over these boring, repetitive duties. Since ML models evolve over time, they can create and maintain searchable databases of relevant marketing avenues, contract work or agents to query. Publishing companies can leverage AI tools to help editors check for grammar or continuity mistakes, accelerating time to market. AI Makes Human Content Stand Out Publishing AI-generated books is a get-rich-quick scheme. Natural language processing technology isn’t advanced yet — readers and literary agents can quickly identify this kind of content. Fortunately, many view it as cheap, poor quality or lazy. To avoid AI-generated books, readers must search for authors. In doing so, they’ll spotlight creators and learn to appreciate content better. Many companies are already taking advantage of this fact, using “100% human-generated content” as a selling point. The Gray Area Where Its Impact Could Go Either Way AI’s impact isn’t purely black and white — there’s a massive gray area where things could go either way. Navigating these subtleties is tricky. AI Has Editorial and Publishing Sway Book publishing is notoriously difficult. Even now-famous authors have dealt with round after round of rejections. An ML model in a decision-making role could evaluate content at an unparalleled pace, helping more people get published. It may occasionally make biased or illogical decisions, but those aren’t exactly uncommon. Numerous cases exist where a book goes through dozens of rejections before becoming a best seller — well-trained literary agents and publishers make mistakes, too. Self-Publishing Is Easier Than Ever Anybody with access to AI can ask a model to write a book, design a cover, and generate hype on social media. This technology makes self-publishing easier than ever, meaning previously overlooked and underserved groups can fulfill their dream of publishing a novel. Unfortunately, bad actors are taking advantage of the simplicity of AI-driven self-publishing. One author, Jane Friedman, found six fake titles under her name in a few months. Others claim they spend hours reporting listings just for new ones to pop up the next week. Another potential issue with AI-powered self-publishing is that authors won’t need professional help anymore. If most people go this route — which they might because getting published traditionally can be next to impossible — the sector could collapse. The Future of Book Publishing Hinges on AI Although every big disruptor causes general worry, AI is different. For one, it’s accelerating at a pace publishers can’t keep up with. It’s also like no technology people have ever seen before — what other invention can autonomously read, learn, write, edit and publish a novel? Professionals’ fear seems to be justified. For now, there’s no telling if generative models will transform, replace or topple this industry. Professionals in the book publishing industry are terrified of artificial intelligence — and for good reason. While it isn’t all bad, it has the potential to bring the sector to its knees. Not to mention, it has already harmed many creatives. Are their fears justified or unwarranted? AI’s Emerging Role in the Publishing Industry AI’s Emerging Role in the Publishing Industry Thanks to its versatility, AI has emerged in most parts of the publishing industry. In 2023, 47% of book publishers based in the United States used it for marketing, 25% for editorial work, and 12% for content generation. Companies can use it in writing, editing, advertising, or administrative roles — it can do almost everyone’s job to some extent. 47% of book publishers 47% of book publishers AI Isn’t the First Threat, But It’s the Biggest AI Isn’t the First Threat, But It’s the Biggest Many disruptors have affected the publishing industry. Most recently, e-books have started replacing physical books. According to one estimate, about 30% of pages will be digitally printed by 2028, up from 5% in 2023. Many publishing houses and self-publishers are migrating to digital. Before that, digital printing shook up the offset printing press. about 30% of pages about 30% of pages Although AI isn’t the first disruptor, it’s the biggest so far. The publishing sector has withstood others because they were all natural evolutions to outdated processes. Machine learning (ML) and large language models are different — they replace existing technology instead of improving it. Who needs writers, editors, and agents when algorithms can do their work for free? Writers, Editors, and Publishers Are Terrified of AI Most professionals in book publishing are terrified of generative AI. Its versatility could bring about the sector’s downfall. A handful of high-quality models can write a novel, proofread, edit, design a cover, make marketing materials, post on social media for publicity, distribute digital copies, and handle accounting. For now, AI can’t perform any of these jobs better than a human. One industry professional said the content it generates is “crude at best,” arguing it isn’t comparable to what a real writer can produce. Even so, it might not matter when it can pump out pages at an inhuman speed. arguing it isn’t comparable arguing it isn’t comparable AI will inevitably affect the entire industry even if every author, editor, and publisher agrees not to use it. This technology’s accessibility is its main driving force. Dozens of leading models — and thousands more in-house systems — exist. Almost anyone can use ChatGPT, Midjourney, LaMDA, or Claude to make, market, and distribute AI-generated books. The Negative Impact of AI on the Publishing Industry There’s abundant evidence of AI’s negative impacts on the book publishing industry. AI Is Stealing People’s Jobs There’s truth to the fear that AI is set to steal people’s jobs. While more job ads now stress human-written content is a hard requirement and more companies are investing in software to detect AI-generated content, the publishing sector’s pushback hasn’t been enough — and people are being forced to find new careers. is a hard requirement is a hard requirement Olivia Lipkin, a copywriter, said her assignments dwindled after ChatGPT came out. She even claimed management changed her title to “Olivia/ChatGPT” on the company’s messaging platform. Within a few months of the generative model’s release, Lipkin was let go without explanation. Eventually, she discovered her managers had realized AI was much cheaper. said her assignments dwindled said her assignments dwindled It’s Stealing Their Work, Too Algorithms can’t come up with content on their own — they must use others’ work. Since most books are copyrighted, AI companies have resorted to piracy and copyright infringement. Books3 is a data set of nearly 200,000 pirated novels used to train generative AI systems. Most of the content was published within the last 20 years, so many living authors are affected. of nearly 200,000 pirated novels of nearly 200,000 pirated novels This issue isn’t limited to bad actors and shady characters, either. OpenAI, ChatGPT’s creator, argued creating models would be impossible unless it was allowed to use copyrighted material. It claimed public domain content wouldn’t “meet the needs” of people. Regardless of the statement’s truth, it shows how unconcerned AI companies are about content theft. argued creating models would be impossible argued creating models would be impossible There Aren’t Protections Yet Despite the issue of AI-driven copyright infringement being a real threat to authors, literary agents and publishing houses, no real regulations exist to protect them yet. Recently, a 24-page document revealed that Midjourney had a database of around 16,000 individuals it allegedly stole art from. Apparently, it was a list for model training. database of around 16,000 individuals database of around 16,000 individuals Even though the list — which contained specific names — seems like it would prompt swift regulatory action, things aren’t as open and shut as they should be. In many countries, a lawsuit is the only recourse creators have. Realistically, there’s no guarantee they’ll be successful. Not to mention, many would have to fight against well-funded tech giants. The Positive Impact of AI on the Publishing Industry AI wouldn’t be able to disrupt the book publishing industry if it was inferior to what people currently use. In other words, a silver lining does exist — there are potential positive impacts. AI Can Take on the Mundane Work Most writers spend more time than they’d like on mundane — but necessary — tasks like proofreading and spellchecking. The same can be said about literary agents hassling publishers or marketers tweaking designs for the nth time. AI can take over these boring, repetitive duties. Since ML models evolve over time, they can create and maintain searchable databases of relevant marketing avenues, contract work or agents to query. Publishing companies can leverage AI tools to help editors check for grammar or continuity mistakes, accelerating time to market. AI Makes Human Content Stand Out Publishing AI-generated books is a get-rich-quick scheme. Natural language processing technology isn’t advanced yet — readers and literary agents can quickly identify this kind of content. Fortunately, many view it as cheap, poor quality or lazy. To avoid AI-generated books, readers must search for authors. In doing so, they’ll spotlight creators and learn to appreciate content better. Many companies are already taking advantage of this fact, using “100% human-generated content” as a selling point. The Gray Area Where Its Impact Could Go Either Way AI’s impact isn’t purely black and white — there’s a massive gray area where things could go either way. Navigating these subtleties is tricky. AI Has Editorial and Publishing Sway Book publishing is notoriously difficult. Even now-famous authors have dealt with round after round of rejections. An ML model in a decision-making role could evaluate content at an unparalleled pace, helping more people get published. It may occasionally make biased or illogical decisions, but those aren’t exactly uncommon. Numerous cases exist where a book goes through dozens of rejections before becoming a best seller — well-trained literary agents and publishers make mistakes, too. Self-Publishing Is Easier Than Ever Anybody with access to AI can ask a model to write a book, design a cover, and generate hype on social media. This technology makes self-publishing easier than ever, meaning previously overlooked and underserved groups can fulfill their dream of publishing a novel. Unfortunately, bad actors are taking advantage of the simplicity of AI-driven self-publishing. One author, Jane Friedman, found six fake titles under her name in a few months. Others claim they spend hours reporting listings just for new ones to pop up the next week. found six fake titles found six fake titles Another potential issue with AI-powered self-publishing is that authors won’t need professional help anymore. If most people go this route — which they might because getting published traditionally can be next to impossible — the sector could collapse. The Future of Book Publishing Hinges on AI Although every big disruptor causes general worry, AI is different. For one, it’s accelerating at a pace publishers can’t keep up with. It’s also like no technology people have ever seen before — what other invention can autonomously read, learn, write, edit and publish a novel? Professionals’ fear seems to be justified. For now, there’s no telling if generative models will transform, replace or topple this industry.

Why the Book Publishing Industry Is Terrified of AI

কেন বই প্রকাশনা শিল্প এআই নিয়ে আতঙ্কিত

Warum die Buchverlagsbranche Angst vor KI hat

Por qué la industria editorial de libros está aterrorizada por la IA

Pourquoi l'industrie de l'édition de livres est terrifiée par l'IA

पुस्तक प्रकाशन उद्योग एआई से क्यों भयभीत है?

出版業界がAIを恐れる理由

도서출판업계가 AI를 두려워하는 이유

Por que a indústria editorial de livros tem pavor de IA

Почему книгоиздательская индустрия боится искусственного интеллекта

Kitap Yayıncılığı Endüstrisi Neden Yapay Zekadan Korkuyor?

Tại sao ngành xuất bản sách lại sợ hãi AI

图书出版业为何惧怕人工智能

Developers spend weeks or even months onboarding at a new company. Getting up to speed in a new codebase takes time. During this time, the developer will have many questions (as they should)! However, those questions interrupt other team members who must stop what they’re doing to provide answers. Most engineering organizations face the dilemma of ensuring the new developer gets the support they need without slowing down the rest of the team too much. A culture of documentation is an excellent step in the right direction. However, this documentation is often fragmented across Slack messages, Notion and Confluence wikis, GitHub pull requests, and Jira tickets. How do you successfully navigate this endless sea of information? An AI startup called Unblocked is seeking to solve this problem. They’ve created a chatbot-like interface where you can ask questions and get answers to unblock yourself without interrupting anyone else. Most importantly, Unblocked can be connected to all the data sources your company uses, so the answers are tailored for you based on your actual company resources, as opposed to generic advice. I recently tried Unblocked to see how well it could help someone like me. In this article, we’ll look at some example scenarios, the questions I asked, and the answers I received. We’ll explore three general categories of information seeking: Getting a general understanding of the architecture of a new codebase Trying to understand how a feature works Troubleshooting and fixing a bug Ready to get unblocked? Example Repo In order to use Unblocked with my current employer’s data, I would need to work through our security process and get permission. Unblocked is SOC 2 compliant and isolates customer data. For now, I decided to try Unblocked with my personal projects to get a sense of its capabilities. I turned to one of my largest repos from my college years. I have one repo that contains dozens of projects I worked on throughout my computer science courses. Most of these projects I haven’t looked at in over eight years. If you’re like me, you’ve likely forgotten the details of the code you worked on even a few months ago, so coming back to this repo felt similar to re-onboarding! You can . (Please don’t judge too harshly on the code quality. I was learning for the first time!) find the entire repo we will use on GitHub Scenario One: Can You Give Me an Overview of the Codebase? As I re-acquainted myself with some of the projects in this old repo, I asked Unblocked about my repo. I started with a very generic question: “What does this app do?” Unblocked responded by telling me that this repo contains many different projects. It even described some of the projects to me. It knew I had apps about pet adoption, photography, fitness, and movie streaming. It also correctly identified that one of my apps was a web-based game. We were off to a great start. I asked a second question: “What languages, libraries, or frameworks does this repo use?” Unblocked responded with many tools I listed in my main portfolio file. It correctly called out that each project used different technologies. You’ll note at the bottom of the screenshot that Unblocked cites its sources, so you know where this information comes from. Scenario Two: How Does This Thing Work? Alright, that was a good enough intro for me. Next, I asked a specific question about one of my projects: a built with jQuery. Connect Four game Remember, the point of this trial was to see how I could use Unblocked in my day-to-day job. So, I imagined myself as a developer onboarding to a new codebase working on this game. I had a question about how the game worked. Rather than bugging one of my coworkers, I decided to ask Unblocked. I wanted to make sure there weren’t opportunities for players to cheat in my game. I asked, “In the ConnectFour app, is it possible for a player to play two pieces in a row without waiting for the other person to take their turn?” Unblocked’s response here was impressive. It was able to reference a specific code snippet that showed how the turn-taking behavior worked in the game. However, I wasn’t convinced that a player couldn’t find some way to cheat. I asked a follow-up question: “What if someone clicked the button twice really quickly before the animation finished? Then could they cheat and play two pieces at once?” Again, I was impressed with Unblocked’s response. It highlighted another code snippet that showed how I had disabled the click handler to prevent someone from clicking twice to play two pieces at once. It even found a closed GitHub issue referencing a similar possible problem but assured me that it has since been resolved. Scenario Three: Can You Help Me Fix This Bug? Let’s switch gears and consider another scenario. Let’s imagine that I’m working in a new codebase and need help fixing a bug. I might turn to a coworker to ask for help, but this seems like something Unblocked could help with, too. For the next few questions, I referenced a multiplayer game called , which I built as one of my capstone projects many years ago. This game is built with JavaScript and uses web sockets to communicate information from peer to peer. In the game, each player is a dot. They move around the screen, eating food to grow in size. Players can eat one another as well. And they have to do this while avoiding obstacles that will cut their size in half. Pixel Mania This game works really well when two to four players are online. However, the game begins to lag when the number of players increases. In this scenario, let’s imagine I’m a developer working on this project and am noticing these performance issues. I need some help. Who should I ask? Unblocked, of course! My first question was, “In PixelMania, I’m seeing performance issues when a large number of players are playing. Why is that?” Unblocked responded with a few initial thoughts. The game manages the position information of all the players, food, and balls. Unblocked theorized that the operations would take longer as the number of items increased. It’s correct, by the way. Many of the operations in the game involve looping over all the items, looking for collisions to know if you’ve eaten a piece of food, eaten another player, or been hit by a ball. Thinking about Big O Notation, these operations are at least O(n) time. Unblocked then suggested that some possible optimizations could be achieved by using “techniques such as rate limiting updates, using delta compression for updates, or implementing an area of interest management system where clients only receive updates for objects near their player.” The nice thing about Unblocked is that you can have a conversation with it, similar to ChatGPT. I had follow-up questions and wanted to treat this like a pair programming brainstorming session. I asked, “Could you explain more about the optimization techniques you suggested?” Unblocked went into detail about its five suggestions: Rate limiting updates Delta compression Areas of interest management Spatial partitioning Optimizing data structures I wanted to dig in even further. I asked, “Spatial Partitioning sounds like a good approach to me. Can you give me some advice on how I would implement that in PixelMania?” It gave me even more detailed advice! Note that it’s not just generic info about how spatial partitioning works, but it applied the advice to specific files in my app, like and . game.js Player.js After that, I asked just one more question about the data structures I used: “You also mentioned Optimizing Data Structures. Are there any data structures used in PixelMania that are used inefficiently or incorrectly and could be optimized?” Unblocked responded with some specific instances of design choices I had made and highlighted some potential drawbacks. Many of my operations were done in O(n) time, and it’s possible I could use different data structures and make better use of objects to achieve O(1) time. This could potentially improve some of the performance issues. By now, I had a pretty good idea of where to go next. If I were working on this in my job, I’d be well equipped to start making changes in the code. Conclusion Finding the right balance between asking questions and being self-reliant can be difficult. Interruptions lead to context switching, and that can be a time sink. We all want to be helpful to our coworkers, but we also need to protect our time. AI is playing an ever-increasing role in our field as developers, and it has the capacity to significantly increase our productivity in ways we’ve never seen before. Unblocked is one of these tools. By making it easier for developers to find the answers to their questions on their own, Unblocked enables us to get the right help we need when we need it. Also published . here

'What Does This App Do?' and Other Questions Unblocked's AI Can Help Answer in Onboarding

AI made its mark on the cybersecurity sector in 2020, with a value exceeding . Experts predict this number will skyrocket to $46 billion by 2027. $10 billion What's driving all this excitement? AI brings some unbeatable firepower. It can crunch massive data sets, spot patterns, and make accurate decisions. These decisions transform how we combat cyber threats. New defensive use cases for AI are emerging. Some of the biggest names in tech—Microsoft, Google, and SentinelOne—have released large language model (LLM)-based products. Big Tech and AI in Cybersecurity In April 2023, Google SEC-PaLM__,__ an AI model designed for cybersecurity. This AI can process threat intelligence data, enabling detection and analytics. introduced Meanwhile, Microsoft Security Copilot deploys GPT4 to analyze threat signals, generating summaries for human review. International payment giant PayPal uses AI to evaluate transactions in real-time; flagging any irregularities. It learns from each transaction, progressively enhancing its accuracy and effectiveness. Similarly, Mastercard's Decision Intelligence relies on artificial intelligence and machine learning to differentiate between legitimate and fraudulent transactions.  It prevents fraud without disrupting legitimate transactions, demonstrating AI's ongoing learning and improvement. BAE Systems analyzes network behavior and detects anomalies using sophisticated computer programs. And if anything appears out of the ordinary, it notifies human experts. These niche products highlight how AI can help with security. AI-powered solutions enhance efficiency, open new avenues for growth, and improve users' understanding of active threats. Let’s explore more ways companies are adopting AI in cybersecurity. Top Use Cases for AI in Cybersecurity Advanced Malware Detection We face 10.4 million malware attacks annually, costing businesses $2.5 million. Attacks are becoming increasingly difficult to detect using traditional methods. But AI, via machine learning, identifies and thwarts malicious software like Trojans, viruses, and rootkits. Google, for example, uses machine learning (ML) to identify and assess malware targeting Android devices. Google also scans over 300 billion Gmail attachments every week to ensure their safety. Here's how AI transforms malware detection: Behavioral Analysis: AI algorithms study file behavior to uncover malware-like patterns. Signature-Based and Anomaly Detection: Machine learning identifies new malware signatures, including zero-day attacks. Automated Classification: AI's data analysis capabilities enable it to find patterns and anomalies that might go unnoticed by humans. This makes it excellent at detecting new and unfamiliar malware strains that traditional antivirus software might miss. Streamlining Workflows AI-powered automation simplifies routine tasks, allowing cybersecurity teams to focus on more important matters. AI handles manual compliance tasks. It provides content for standard regulations like HIPAA, GDPR, PCI DSS, and NIST. It also uses rule-based logic to alert compliance personnel about changes and gaps in compliance. It does this by assessing the importance of alerts and then notifying the team. Next, it responds to attacks by isolating the affected systems. This goes on around the clock. Say an employee opens a malware-infested link. AI can recognize malware signatures and take action, preventing further damage. Darktrace is a popular AI-powered cyber defense software. Their AI feature “RESPOND” learns on its own and develops clever ways to fight cyber threats. And the more it learns, the better it gets. User Authentication and Access Control Traditional authentication methods relying on passwords are vulnerable to breaches because of password reuse and stolen credentials. AI offers more robust authentication solutions. It considers factors like biometrics and user behavior patterns. AI-based systems use behavioral biometrics, device fingerprinting, and contextual information for multifactor authentication. They are making it difficult for unauthorized users to gain access, even with stolen credentials. Different types of AI authentication include: Biometric Authentication: AI plays a significant role here, with examples like keystroke dynamics, behavioral biometrics, facial recognition, and voice recognition. Behavioral Biometrics: This method is becoming popular due to its continuous authentication capabilities. It tracks and verifies user behavior patterns, providing a secure and password-free authentication experience. Fighting Cybercrime Cybercrime steals about of the world's GDP. A while ago, two-factor authentication was the best way to keep things secure. Now, AI is adding more layers of protection by using data to verify identities. 1% As cyber threats evolve, firewalls and antivirus software are no longer enough. You need more intelligent tools like XDR and managed SOC/SIEM. These tools help to outpace and outsmart threats. Deep learning enables AI to stay on top of cybercrime. It searches logs, real-time messages, and transactions for anything suspicious. AI in Advanced Threat Prevention AI's real-time advanced threat detection has saved businesses Advanced threats, sometimes called advanced persistent threats (APTs), are cyberattacks that target a specific organization over a long period of time. $3.05 million. They create new ways to get in and use tools to cause severe damage. Skilled attackers with plenty of resources often launch these attacks, and they can stay hidden in a system for weeks or months. Businesses use AI to examine network traffic, user behavior, and logs to detect potential threats and act before issues escalate. Ways AI helps in advanced threat prevention: Deep Learning for Advanced Threats: AI uses deep learning to identify sophisticated threats. They can detect APTs, insider threats, and evasive attacks that traditional security measures miss. Tackling False Positives: Because they are constantly learning and improving, machine learning algorithms can distinguish legitimate activities from potential threats, reducing false positives. Enhancing Threat Intelligence: AI algorithms link known indicators of compromise (IoCs) with internal security data by analyzing threat intelligence feeds. It strengthens defenses against new threats before they happen. Scalability and Efficiency: AI-driven threat detection processes massive amounts of real-time data to uncover threats across networks. This cuts down on the amount of manual analysis and speeds up detection and response. Outsmarting Phishing Traditional phishing detection methods relied on fixed rules and lists to catch well-known tricks. But they fail against newer schemes. This is where AI-driven phishing detectors come in. Using machine learning, they dive into emails and look for signs of phishing. The algorithms learn from massive data pools to identify phishing patterns. They can even track user actions in emails. And alert the security team if someone clicks a dodgy link or gives out personal information. Endpoint Security Cybercriminals often go after endpoints like laptops and smartphones. Traditional antivirus software relies on known malware signatures. But AI looks at how malware acts to find even unknown variants. CrowdStrike's Falcon harnesses the power of AI to protect user endpoints. Falcon uses machine learning algorithms to spot potential threats even when devices are offline. Here's how it works: Scan and Isolate: AI-based security scans for malware and isolates suspicious files. Behavior Monitoring: It tracks device actions. At the same time, keep an eye out for anything unusual that could be a security threat. Blocking Access: These AI tools block unauthorized attempts. And stop attackers from grabbing sensitive data. A significant benefit of AI-based endpoint security is its ability to adapt. Takeaway As cyber threats worsen, human efforts alone are no longer enough. AI helps cybersecurity experts reduce breach risks and strengthen security by analyzing and detecting threats. While AI powered cybersecurity applications might have risks, partnerships between humans and machines create a safer, better future.

AI-Powered Cybersecurity: Top Use Cases in 2023

- HackerNoon, the independent technology publishing platform, is pleased to announce the nominees for its annual “ ” award, featuring the most promising and innovative companies in the Seattle startup ecosystem. Seattle, WA - May 23,2023 Startups of the Year The list, compiled by the HackerNoon team, showcases that are making significant contributions to the Seattle economy and driving innovation in their respective industries. 200+ startups https://www.youtube.com/watch?v=ZqIW54yMsNc&t=2s&embedable=true Nominations are open. You can nominate your company as the . startup of the year in Seattle here The for Startups of the Year 2023 include: nominated Startups in Seattle Xealth Diagrid Twelve Labs XetData Coda Derisk pyq Logixboard Otto Robotics Protect AI Augment AI Proxi XetData Bungee Tech InsightStudios Pipe17 Coffee Meets Bagel Symbl.ai DefenseStorm Ozette Karat Kevala Moment Veeve Observa Cakework Forum3 Hal9 MotherDuck Read SBL Spiral “We are thrilled to recognize the top startups in Seattle and celebrate their achievements,” said . “Starting and sustaining a company is difficult. We’re honored to showcase their work and impact.” David Smooke, HackerNoon Founder & CEO is a community-driven voting platform that recognizes startups that are changing technology and the world for the better. Starting May 8th, 2023, the internet can nominate and vote for the best ventures in their city. The voting period will remain open until December 31, 2023, and winners will be announced in January 2024 after a comprehensive evaluation by the HackerNoon team. Startups of the Year 2023 In its , a total of 44,427 startups were nominated in 16 different regions, and the community cast 234,344 votes during a 6-month long campaign in favor of budding companies that were growing and trending across every major city in the world. The received perks from sponsors and a free from HackerNoon. previous iteration 1,382 winners tech company news page As a proponent of the open data movement, HackerNoon releases the unique social data gathered from Startups of the Year on GitHub as “ ” for valuable insights. /where-startups-trend Apart from validation from the world's greenest tech publication, all nominees relevant to their domain, region, and funding. They will also receive an and a free version of their very own on HackerNoon. These pages include business overviews (such as head count, founding year, bio, social links, and domain ranking) coupled with dynamic updates every time the company is mentioned in HackerNoon stories or relevant headlines around the web. get free interviews amazing discount package designed for their startup Tech Company News Page People can , and vote once per day per award for who they think should be Startup of the Year all the way up to December 31st. until June 8th nominate their favorite startups In addition to the award itself, winners receive exclusive interviews, an exclusive NFT from HackerNoon, and a .Tech Domain for life. About HackerNoon HackerNoon is a technology publication that aims to empower developers, entrepreneurs, and tech enthusiasts to share their knowledge and expertise with a global audience. With a readership of over 4M+ monthly visitors, HackerNoon provides a platform for startups and innovators to showcase their work and connect with a vibrant community of like-minded individuals. Did you know that companies that actively engage in blogging experience a whopping 67% increase in leads? Take advantage of HackerNoon's ' ' to kickstart your own company's blogging journey. Startups Package Startups Of The Year 2023 is sponsored by . .Tech Domains For more information about the “Startups of the Year 2023” list and the nominated companies, please visit our . FAQ page Nominations are open. You can nominate your company as the . startup of the year in Seattle here

Startups of The Year 2023: 200+ Startups Nominated in Seattle

To start with, this article will be slightly different from others. Why? This time, I won’t be analyzing recent events but rather trying to imagine what the future bears for us and what changes we are going to face with the real arrival of AI, VR, and AR. Yes, you may argue that we use all the mentioned technologies. Artificial intelligence is increasingly applied just everywhere: education, IT, healthcare - whatever industry you take, you can find how AI can improve it. And indeed, according to , from 2020 to 2021, AI adoption in companies in developed countries has grown from 50 to over 56%. McKinsey And in companies with headquarters located in emerging economies, the growth is even more impressive - from 45% in 2020 to 57% in 2021. Source: https://www.mckinsey.com/capabilities/quantumblack/our-insights/global-survey-the-state-of-ai-in-2021 AI adoption also means AI development, so with time, artificial intelligence systems get smarter. Take ChatGPT, for example. It already can support chats and write meaningful texts, and it even can code! In my opinion, the best acknowledgment of its abilities is the fact that it. some schools forbid AI Use Cases But where can AI be used? Practically everywhere if one doesn’t require non-standard solutions of creativity. If you have a look at the scheme below, you will see that frequency of applications (the number) depends directly on how much the system can rely on already available data and patterns. Source: McKinsey So, even though we can speculate on further AI development and its possible taking over of our place, it still lacks many things. Whenever it comes to a non-standard situation of expressing creativity, AI fails. But can it already serve to make us experience new virtual worlds? AI alone is not sufficient here. But we are already observing the development of such technologies as virtual reality and augmented reality. Combined with AI, they may enable us to experience worlds like those created by in his science-fiction novels. William Gibson But again, how far are we from it? VR and AR - We are Working On Them Many corporations have been working on developing VR and AR sets for several years. Every time Microsoft, Meta, or Google announces the arrival of a new VR set, mass media starts yelling about the arrival of virtual worlds. And every time, it ends with nothing. Source: CNBC Now, is working on a new mixed-reality headset. The company claims that this headset will be suitable for wearing all day long, and it may eventually replace laptops and smartphones by making them obsolete. Apple The headset is supposed to deliver the most realistic experience: it will be packed with all the new technologies such as a flexible OLED screen, dual 4K displays, and cameras that analyze the wearer’s eye movements, body indicators, and the external environment. Can this device make a shift to virtual world adoption? Well, I doubt it. First of all, its price will be far from affordable to the majority of people. Second, the battery charge is going to last about 2 hours only. And finally, who would want to wear a device all day long, especially considering that it won’t work properly outdoors? It may still become a hit once Apple finds a way to reduce the price, extend the battery charge, and add more apps for the set to work with. But for now, there is no way to push this headset to mass adoption. Is Apple putting its reputation on the line? I don’t think so; eventually, they may find a way to make something unique that will beat all their competitors. But the problem is that for now, there is no high demand for such sets, and the applications of virtual reality are still very limited. Brain-Machine Interfaces and Brain-Computer Interfaces What is needed though to make it work and finally to make us live virtual experiences as if they were real ones? Source: ALS Association Something very practical is needed. I believe that the future of virtual experiences is in brain-computer interfaces or brain-machine interfaces. They establish direct communication between a human brain and an external device - a gadget or a tool. For now, BCIs are used mostly in the medical field and still aren’t applied widely. But what if somebody finds a viable way and resources to implement BCIs in daily life? With them, we will be able to move from all those bulky keyboards, mice, and other control elements to managing devices directly with the brain. Even though the idea of BCIs is at least 50 years old, the technology still hasn’t advanced much. So, we are also waiting for what will come from this side. VR and AI Isn’t Everything Let’s assume that a perfect VR set is developed, and AI has progressed enough to enable us to experience a life-like virtual world. Would it be sufficient for mass adoption? People don’t like to be detached from the real world; therefore, they won’t use VR combined with AI. Therefore, something is needed that would help people to keep that connection to reality. This is Augmented Reality. Combined with VR, AR can make the breakthrough that will change the way we work, interact, and live. That’s why when we speak about the world of the future, we mention these two technologies together. Source: WEF As you can see, it is expected that the predicted market size for VR/AR software for various use cases will grow significantly by 2025. Are There Any Dangers? Whenever something new is invented, many people meet it with fear. The same can be told about artificial intelligence, virtual reality, and augmented reality. And indeed, while the benefits of these technologies and their combination are undeniable, there are some problems to solve before we can use them freely. How will the AI-, VR, and AR-enabled virtual worlds be regulated? What will be the law ruling them, and how human is it going to be? How crimes committed in a virtual world shall be treated, and whether they shall be considered crimes? These questions aren’t unanswered yet. Another problem that is getting traction today is copyright protection. Shall AI-generated works be protected, and if yes, what legislation shall apply? This question arises because AI doesn’t create original content, but it generates the content based on the information that is already available. And, in most cases, is copyright protected. It is unclear how to protect identity in a virtual world. As long as we are going to use avatars, what happens if somebody steals an identity, and whether it shall be considered a crime? Will We Be There Soon? Even when technological obstacles are eliminated, we still have to think about how to handle all those challenges that are not connected to technology. Only when we know how to do it and develop proper mechanisms to regulate those matters can we advance with the adoption of virtual worlds.

AI, VR, AR - Are We Going to Live in the World Created by William Gibson?

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