Welcome to The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters.
This week: Nvidia’s CEO grabs headlines by saying your career is toast, and Intel is still fighting. (more…)
Tag: generative AI
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No, Dev Jobs Aren’t Dead: AI Means ‘Everyone’s a Programmer’? ¦ Interesting Intel VPUs
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Logz.io Taps AI to Surface Incident Response Recommendations
Logz.io this week added a supervised machine learning capability to its observability platform that reduces mean-time-to-remediation by surfacing recommendations for resolving incidents.
Asaf Yigal, vice president of product for Logz.io, said the Alert Recommendation capability added to the Logz.io Open360 platform uses artificial intelligence (AI) to model the steps a DevOps team needs to complete to resolve an incident.
The goal is to reduce the amount of time required to resolve incidents at a time when IT environments are becoming increasingly complex, he added.
In fact, a recent Logz.io survey found 75% of respondents said it currently takes them hours to resolve production issues, with only 14% satisfied with their current mean-time-to-resolution (MTTR). A total of 41% specifically cited monitoring and observability of Kubernetes environments as a primary challenge.
Alert Recommendation is the latest in a series of investments in AI that Logz.io has made. Previously, Logz.io integrated the ChatGPT generative artificial intelligence (AI) platform to surface links to related information and best practices for resolving IT issues.
In general, AI tools should make it possible to manage IT at a level of scale that eliminates many of the low-level data engineering and analytics tasks that previously required manual effort from a DevOps engineering team. If, for example, the observability platform is generating recommendations to address issues, there may be less of a need to create runbooks that DevOps teams typically create to address a wide range of known issues.
Along with making it simpler to discern the root cause of an issue, AI also makes it possible for less experienced members of a DevOps team to resolve an issue using guidance generated by the observability platform, noted Yidal.
In effect, AI technologies are reducing the cognitive load required to be an effective member of a DevOps team, he added.
One way or another, it’s not so much a question of whether AI will be applied to DevOps as much as it is to the degree. Many of the manual tasks that often conspire to create DevOps bottlenecks should be significantly reduced in the months ahead as more advances are made. The challenge now is determining how best to reallocate DevOps expertise in anticipation of those advances.
Of course, there’s always going to be some sense of trepidation when it comes to AI. However, many of the tasks that are about to become automated tend to be tedious. Many DevOps professionals would just as soon see those tasks become automated in the expectation that more time will become available to take on more complex challenges.
Regardless of the motivation, the way IT is managed is about to change. There may be plenty of instances where AI does not live up to its initial hype, but as AI models are exposed to more data, they will become more accurate. That doesn’t mean, however, there won’t always be a need for a DevOps engineer to ensure those algorithms are behaving as expected.
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Checkmarx Brings Generative AI to SAST and IaC Security Tools
Under an early access program, Checkmarx today made available query builder and guided automation tools that take advantage of OpenAI’s generative artificial intelligence (AI) technologies to make it simpler for developers to resolve application security issues.
AI Guided Remediation surfaces actionable remediation recommendations for vulnerability issues such as misconfigurations directly from within integrated development environments (IDEs).
Meanwhile, AI Query Builder makes it possible to use natural language text to create a query for both the Checkmarx static application security testing (SAST) and the infrastructure-as-code (IaC) security tool that creates rules for scanning code. Those rules can be easily fine-tuned or modified and queries for other use cases can easily be added.
In addition to reducing the amount of time it takes to create a query by 65%, that approach also dramatically reduces the number of false positive alerts that arise based on rules created by a security administrator.
Checkmarx CEO Sandeep Johri said these additions to the Checkmarx One Application Security Platform are aimed at improving the application security experience for developers. Most developers don’t want to be inundated by alerts that lack any real context, nor do they want to be bothered with remediation details.
It’s not likely developers will be interested in how AI could help them write more secure code from the start, but the faster a reliable fix is surfaced, the sooner developers can return to writing code, noted Johri.
In the longer term, Checkmarx will add support for multiple large language models (LLMs) beyond those provided by OpenAI to provide other AI capabilities that are based on more domain security knowledge, said Johri.
However, despite these advances, vulnerability remediation will not become fully automated using AI any time soon, he added. Instead, it will become much simpler to identify code that has either inadvertently or deliberately introduced a vulnerability, said Johri.
In fact, generative AI tools such as GitHub Copilot can themselves introduce vulnerabilities into code. As a general-purpose AI platform, the recommendations surfaced are based on a mix of instances of clean and flawed code, Johri noted. There will also be instances where cybercriminals attempt to subvert an LLM that creates code by injecting snippets loaded with malware into the samples used to train a generative AI model.
On the plus side, however, generative AI tools should narrow the divide that currently exists between application developers and cybersecurity teams as more issues are discovered and remediated before applications are deployed in a production environment. The challenge has aways been surfacing application security issues at the time developers are writing code rather than sending them a list of vulnerabilities to address weeks (sometimes even months) after a developer has moved on to another project.
Naturally, there is a lot of trepidation when it comes to all things generative AI; one area of certainty is that the benefits far outweigh the risks—especially when it comes to developing secure-by-default applications.
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GitLab Adds More AI and Cybersecurity Capabilities to CI/CD Platform
GitLab this week delivered an update to its continuous integration/continuous delivery (CI/CD) platform that adds additional generative artificial intelligence (AI) and cybersecurity capabilities.
The GitLab 16 release included cybersecurity capabilities such as centralized policy management, expanded compliance reports and controls, compliance dashboards and default Level 3 attestation for the supply-chain levels for software artifacts (SLSA) framework.
At the same time, GitLab this week also made available an emergency patch for two modules that have a path traversal flaw, tracked as CVE-2023-2825, that could allow unauthenticated users to read arbitrary files on the server under certain circumstances.
In terms of generative AI, GitLab 16 added a value stream forecasting capability that automatically generates workflow summaries for senior managers. GitLab is also adding a code refactoring capability and the ability to automatically resolve vulnerabilities to an update to GitLab 16. Previously, GitLab made available a ‘suggested reviewers’ features, an ‘explain this code’ and an ‘explain this vulnerability’ capability that are all enabled by large language models (LLMs) developed by Google. GitLab has also partnered with Oracle to provide additional AI capabilities enabled by machine learning algorithms. In 2021, GitLab acquired UnReview, a provider of a tool that uses AI to identify which expert code reviewers to assign to a project based on the quality of their previous efforts and current workloads.
David DeSanto, chief product officer at GitLab, said with 10 AI capabilities GitLab is providing more of these types of capabilities than any other DevOps platform provider. In addition, those capabilities are distributed across the entire software development life cycle rather than being focused solely on making developers more productive, he noted.
That’s critical, because if AI capabilities are only focused on developer productivity, an imbalance is created; DevOps workflows will not be able to absorb the increased amount of code that will soon be moving simultaneously through pipelines, DeSanto added.
AI will also further fuel adoption of platform engineering as a means for centralizing DevOps management. It’s becoming more apparent that infusing these types of capabilities into DevOps platforms built and maintained by an internal IT team is going to be a major challenge, he added.
There is no doubt that generative AI capabilities will soon become widely integrated across DevOps workflows. That will enable developers to build more secure code faster while at the same time making it simpler for software engineers to maintain large codebases. Just about every job function imaginable will be impacted to varying degrees. In the case of DevOps teams, the ultimate impact should involve less drudgery as many of the manual tasks that conspire to make managing DevOps workflows tedious are eliminated. In fact, AI may ultimately make DevOps, as a methodology for managing IT, more accessible to a much wider range of organizations.
As AI advances, there should still be plenty of demand for human expertise. For better or worse, the nature of the expertise required, however, will undoubtedly be at a much higher level than it has previously been as more low-level tasks are automated. The challenge is determining how and where to refocus the efforts of the DevOps teams to add higher value.
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Red Hat Adds Generative AI and Event-Driven Capabilities to Ansible
Red Hat today unveiled a technical preview of Ansible Lightspeed with IBM Watson Code Assistant, a generative artificial intelligence (AI) service specifically trained on domain-specific data collected via the Ansible automation platform.
At the same time, Red Hat is also making available an event-driven capability within version 2.4 of the Ansible platform that enables runbooks and other tasks to automatically execute when received from a third-party tool or application.
Both capabilities were announced at the Red Hat Summit and are part of an ongoing Red Hat effort to democratize IT automation.
First previewed within the Project Wisdom initiative launched by Red Hat and IBM Research last year, Ansible Lightspeed IBM Watson Code Assistant is based on a large language model (LLM) that was developed using Ansible data. Watson Code Assistant, currently in technical preview, is a service that automatically generates suggestions to improve code as developers create an application. IBM promised to make IBM Watson Code Assistant available later this year.
Red Hat is now applying that generative AI capability to the YAML code that DevOps teams need to create Ansible playbooks. IT teams can create a simple natural language prompt that will translate their domain expertise into YAML code for creating or editing Ansible Playbooks. Red Hat and IBM, however, will not automatically use the code from an organization to fine-tune their AI model without express consent.
Rich Henshall, director of product management for Red Hat Ansible, said as IT environments become more complex, it’s clear many are hitting a ceiling in terms of what can be automated using legacy custom scripts. Ansible Lightspeed IBM Watson Code Assistant, along with an event-driven capability, will make it much simpler to automate the management of IT environments at scale using a declarative framework, he added.
Ansible, for example, can now receive events from third-party tools, determine next steps and consistently initiate an automated response using Ansible playbooks. Ansible can also consume events from third-party monitoring, observability and IT analytics tools from providers such as Cisco ThousandEyes, CyberArk, Dynatrace, F5, IBM Instana, IBM Turbonomic, Palo Alto Networks and Zabbix with more to come, noted Henshall. Red Hat is also making available supplementary content for Red Hat OpenShift, Red Hat Insights, Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform and ServiceNow, in addition to enabling IT teams to create their own custom integrations.
At this juncture, it’s not clear why more IT processes have not been automated using Ansible or other frameworks. Many DevOps teams continue to rely on custom scripts that typically don’t scale and are generally poorly documented. Any time the individual who wrote those scripts leaves an organization, it’s incredibly difficult—if not impossible—for the original scripts to be maintained, noted Henshall.
One way or another, it’s clear that a more scalable approach to IT automation is required, especially with the rise of more complex cloud-native application environments. It’s no longer economically viable for most organizations to rely on legacy processes that require a small army of IT professionals to maintain.
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OpenAI to go Open Source — Elon Musk was a ‘Huge Idiot’ ¦ Mojo Risin’
Welcome to
The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters.
This week: Sources say it’s an open source future for OpenAI, and improving AI perf with Mojo. (more…)
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Boomi Brings Generative AI to Integration Platform
Boomi announced today it is applying generative artificial intelligence (AI) to enable organizations to automate integrations via a natural language interface.
Boomi CEO Steve Lucas said Boomi AI makes it much simpler for professional and citizen developers alike to create integrations at scale using generative AI platforms to invoke the low-code engine that Boomi already provides to create integrations between thousands of applications.
Those generative AI capabilities will complement a wide range of AI capabilities based on machine learning algorithms that are already embedded in the Boomi platform, he added.
Generative AI’s Impact on Integration and Automation – Steve Lucas, Boomi from Techstrong.TV on Vimeo.
Boomi has been widely used to drive business process integration via an integration platform-as-a-service (iPaaS) environment it delivers as a managed service. The low-code platform the company created significantly reduces the need to rely on professional developers to write procedural code using Java, for example, to create those integrations.
Generative AI capabilities make it possible for almost anyone of any skill level to create those integrations across multiple types of application programming interfaces (APIs), noted Lucas.
That capability also makes it possible for Boomi to notify end users when, for example, a data attribute in an application has changed and ask them if they would like to apply that change across their entire application environment.
In addition, Boomi AI can also interpret the intent of a prompt to better orchestrate processes in addition to providing an audit history in natural language that explains how decisions were made and what outcomes occurred as a result. As part of that effort, Boomi algorithms are also trained to avoid biases and unfairness with established ethical guidelines.
Boomi’s AI engine is trained using anonymized data collected from approximately 20,000 customers that have created more than 200 million integrations using the Boomi platform. Those integrations have been exposed to third-party large language models (LLMs) that the company is using to train Boomi AI, said Lucas. Boomi is not storing any customer data to train its AI models.
DevOps teams, of course, spend hours creating integrations that should be much simpler to create, test and validate with the rise of generative AI. In fact, the number of integrations being created to drive a wide range of digital business transformation initiatives should substantially increase.In the meantime, DevOps teams would be well-advised to start evaluating which tasks they manually perform today that could instead be automated by a generative AI platform. Many of the routine integrations that DevOps teams create today using low-level APIs will be increasingly automated by end users. The ultimate goal should be to identify more complex tasks that previously DevOps teams would not have attempted simply because there wasn’t enough time.
It’s still too early to determine what impact generative AI will have on the way IT is managed, but DevOps teams that are typically committed to automating as many processes as possible should be at the forefront of adoption. The challenge is going to be simply keeping track of the rate of change that might soon be enabled by these platforms.
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Google Previews Generative AI Service to Improve Code
Google is previewing an instance of a generative artificial intelligence (AI) service that surfaces code recommendations to make it possible to build high-quality applications faster.
Specifically, Duet AI for Google Cloud surfaces code recommendations in real-time, generates full functions and code blocks and suggests fixes for vulnerabilities and errors in the code that it discovers. It also promises to enable DevOps teams to create custom large language models (LLM) based on their own code. In fact, GitLab and Tabnine are, respectively, already integrating LLMs running on Google Cloud with their continuous integration/continuous delivery (CI/CD) platform and testing tools.
Chen Goldberg, vice president of engineering for Google, told attendees at the Google I/O 2023 conference that Duet AI for Google Cloud will fundamentally change how developers will be able to build applications, regardless of their skill level.
Duet AI for Google Cloud was trained using data collected from Google Cloud using sample code and documentation to fine-tune behaviors and patterns.
Duet AI for Google Cloud is available under an AI Trusted Tester Program and provides developers with many of the same capabilities that can be found in the GitHub Copilot tool that is based on the LLMs created by OpenAI.
In addition to making Duet AI for Google Cloud accessible via both its tools and managed services, Google said it also plans to integrate the service with multiple integrated development environments (IDEs).
It’s still early days as far as the use of generative AI within DevOps workflows is concerned, but it’s clear that the amount of code being created by developers is about to increase exponentially. Hopefully, AI technologies will also one day help software engineers find ways to manage the massive volume of code moving across their DevOps pipelines.
Generative AI platforms will fundamentally change the way humans interact with machines. Instead of requiring a developer to create a level of abstraction to communicate with a machine, it’s now possible for machines to understand the language humans use to communicate with each other. Using a natural language interface, developers will soon be asking generative AI platforms to not only make suggestions but also debug applications and, eventually, write the code used to create an application.
At this point, like it or not, the generative AI genie is out of the proverbial bottle. Just about every job function imaginable will be impacted to varying degrees. In the case of DevOps teams, the ultimate impact should involve less drudgery and toil as many of the manual tasks that conspire to make managing DevOps workflows tedious are eliminated.
In the meantime, DevOps teams should spend some time identifying which tasks will become automated by generative AI and other technologies that employ machine and deep learning algorithms. It doesn’t make much sense to onboard new members to a DevOps team only to discover that the tasks assigned to that individual are about to be automated. Hopefully, when all these AI technologies are finally implemented, the entire DevOps experience will be better than ever.
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New Relic Taps Generative AI to Simplify Observability
New Relic has added a tool that takes advantage of generative artificial intelligence (AI) to make it simpler to query the telemetry data collected by its observability platform via a natural language interface (NLI).
Jemiah Sius, director of developer relations for New Relic, said New Relic Grok will make the New Relic One platform more accessible to a wider range of IT professionals, especially those that haven’t mastered New Relic’s proprietary programming language that enables DevOps teams to identify the root cause of an issue.
New Relic Grok automatically pinpoints code-level errors in integrated development environments (IDEs) in addition to analyzing code, stack traces and production telemetry to suggest fixes. New Relic is providing this capability via integration with the large language models (LLMs) created by OpenAI, which is providing the foundation for a broad range of generative AI capabilities through Microsoft. New Relic has a long-standing alliance with Microsoft, added Sius.
Using plain language, anyone can generate a system or app health report complete with anomalies, issues and recent deployments in a way anyone can understand without having to create and filter dashboards, he noted.
New Relic Grok also makes it simpler to identify instrumentation gaps that can then be addressed using the instructions it provides. It can also set up missing alerts and automate alerts using the open source Terraform infrastructure-as-code (IaC) tool.
Finally, IT teams can also use New Relic Grok to manage accounts, users and user access, data retention rules, usage, billing and other administrative tasks.
New Relic previously invested in machine learning algorithms to automate observability tasks. These AI extensions to the New Relic platform are part of a larger effort to extend the reach of the New Relic One observability platform further left toward developers and IT administrators rather than just site reliability engineers (SREs), said Sius.
However, New Relic doesn’t anticipate that generative AI will eliminate the need for SREs to use its programming tool to solve more complex problems, he added.
The overall goal is to provide development teams with frictionless access to observability data at every stage of the software development life cycle to reduce mean-time-to-detection (MTTD) and mean-time-to-resolution (MTTR) of issues.
In general, most organizations are still in the early stages of achieving full-stack observability. A recent New Relic survey found only 27% of respondents have achieved full-stack observability and only 5% claimed they have a mature observability practice in place. A third (33%) of respondents also said they still primarily detect outages manually or based on complaints, the survey found. On the plus side, the survey also found nearly three-quarters of respondents said C-suite executives in their organization are advocates of observability, and more than three-quarters of respondents (78%) saw observability as a key enabler for achieving core business goals. However, more than half (52%) of respondents said they experienced high-business-impact outages once per week or more and 29% said they take more than an hour to resolve those outages.
AI, of course, promises to improve reliability. At the very least, it could make it easier to identify issues long before a major disruption occurs.
