We need to find ways of making machine learning robust, reliable and secure, advocated a number of speakers at the O’Reilly AI Conference in New YorkView in browser »
The New Stack Update

ISSUE 163: Artificial Intelligence 2.0

Talk Talk Talk

“You have algorithms there that are actively prioritizing this kind of information, and that is not acceptable. The platforms are going to need to figure out how to create algorithms that don’t just rely on frequency and popularity, but actually, have some kind of mechanism in there to slow this kind of stuff down before people actually look at it.”

Sean Gourley, founder and CEO of Primer, on the trend of the AI algorithms of social media networks, such as Twitter and YouTube, that automatically promoting toxic, conspiratorial and racist material to their users. O’Reilly AI Conference press panel, New York.
Add It Up
Buzzword Bingo

The blockchain bubble has burst and now it looks like AIOps (IT operations aided by artificial intelligence) may be the next buzzword to become overplayed. Fifty-eight percent of IT professionals think AIOps is a buzzword, according to a survey reported on in Turbonomic’s 2019 State of Multicloud. The term will exist a few years from now if it represents a set of technologies that are both unique and provide real benefits. That’s what happened with DevOps and Big Data, which are now considered to be over-hyped by fewer than half as many people.

We all hate when marketers cynically rebrand their existing products with the latest buzzword. Just don’t close your eyes to real industry trends because of this tawdry phenomenon.

What's Happening

During this first podcast episode in the relaunched The New Stack @ Scale series, recorded during the Open Source Leadership Summit, Sid Sijbrandij, co-founder and CEO of GitLab, discussed the state of open source development today. He also discussed emerging threats, such as those posed by the hypercloud model and what many say is the menace of what Sijbrandij describes as Amazon AWS’s “fork and commoditize” strategy.

‘Fork and Commoditize’ — GitLab CEO on the Threat of the Hyper-Clouds

AI 2.0

Now that machine learning has firmly entered the corporate world, we need to find ways of making it robust, reliable and secure, advocated a number of speakers at the O’Reilly AI Conference in New York this week.
At the conference, it was Massachusetts Institute of Technology faculty member Aleksander Madry who first called for AI 2.0 (though the term is probably inevitable in this industry, we suppose). Today’s AI is not nearly robust enough, insufficiently secure, and still way too unpredictable. The next generation of the technology must be “much more aligned with what we humans see as significant,” he said during his keynote.
And indeed, many of the talks, presentations and sponsor booths were centered around the idea of making AI more mature. In one presentation, Microsoft data scientists Fidan Boylu Uz and Mathew Salvaris demonstrated three ways to do Kubernetes-based Deep Learning in a production setting. One approach involved using Kubectl as a launching point — this approach offers the most flexibility for those who know how to manage Kubernetes. Another method would be to use Kubeflow, a Google project to package the whole AI pipeline. This approach would be best for research scientists who just want to use their favorite libraries, such as TensorFlow and PyTorch. And lastly was the Microsoft AzureML service, which was the easiest to deploy, as it does a lot of the configuration and build work itself, though, unlike the other approaches, you are limited to using Microsoft Azure as your cloud.
Still, with social media companies getting immense criticism for how their AI algorithms tend to surface more extreme, and outright toxic content, there still needs to be a reconciliation between what the machines suggest as the best answers, and what we humans consider acceptable. One factor could be a lack of diversity in the workforce. The less inclusive — and more homogeneous — the development team is building AI, the more likely the AI will contain unintentional biases, Dataiku’s Kurt Muehmel pointed out in his own talk on AI Ethics.

Red Hat’s Quarkus Brings Natively Compiled Java to Kubernetes

The huge market for enterprise Java is coming to Kubernetes. Some engineers at Red Hat have developed Quarkus, a “Kubernetes native Java framework” that compiles Java down to a native executable to reduce runtime memory requirements and boot time. Java is the top programming language for the enterprise, though it has been hampered by performance concerns. Rather than migrate to newer, less supported languages, the Red Hat developers want to bring Java to the cloud native world.

A Conversation with the Creators Behind Python, Java, TypeScript, and Perl

On April 2, the Puget Sound Programming Python (PuPPy) users group in Seattle brought together a historic panel of software engineers to discuss the craft of creating and maintaining programming languages. On hand were Guido van Rossum (creator of the Python programming language), James Gosling (the founder and lead designer of Java), Anders Hejlsberg (TypeScript lead architect) and Larry Wall (Perl creator), for a livestreamed conversation about “language design, the universe, and everything.” The wide-ranging discussion included team projects, language scalability, and ongoing maintainability of code.

How to Avoid the 5 SRE Implementation Traps that Catch Even the Best Teams

A recent DevOps survey conducted by Harvard Business Review confirms this software delivery pain point: While 86 percent of respondents said it is important for their organizations to build and deploy software quickly, only 10 percent report being successful at doing so. Blameless co-founder Lyon Wong discusses some potential hold-ups, including the lack of cross-team buy-in, sluggish incidence response procedures, and superficial postmortems.

Party On

Twitter’s Satanjeev Banerjee and Cibele Montez Halasz explained how Twitter uses AI to create each user’s customized timeline, at the O’Reilly AI conference.

At the O’Reilly AI Conference, Microsoft data scientists Fidan Boylu Uz and Mathew Salvaris demonstrated three ways to do cloud-based Deep Learning with Kubernetes.

Intuit’s Desiree Gosby explained how Intuit went through multiple iterations of its app to read W2 forms, which were made for humans. “Human context matters,” she advised, at the O’Reilly AI conference.

On The Road
Open Infrastructure Summit // APRIL 29-MAY 1, 2019 // THE COLORADO CONVENTION CENTER, DENVER, CO


Open Infrastructure Summit
The Open Infrastructure Summit is coming to Denver and The New Stack will be there for a show that speaks to the deeper requirements for new modern architectures such as container infrastructure, CI/CD and the new dimensions of edge technologies, NFV and Kubernetes across multiple cloud services. Open source project leaders from Red Hat Ansible and Chef will be there as will community members from OpenStack, ONAP and Open vSwitch. See you in Denver. Register now!
The New Stack Makers podcast is available on: — Pocket CastsStitcher — Apple PodcastsOvercastSpotifyTuneIn

Technologists building and managing new stack architectures join us for short conversations at conferences out on the tech conference circuit. These are the people defining how applications are developed and managed at scale.
Free Guide to Cloud Native DevOps Ebook

Cloud native technologies — containers, microservices and serverless functions that run in multicloud environments and are managed through automated CI/CD pipelines — are built on DevOps principles. You cannot have one without the other. However, the interdepencies between DevOps culture and practices and cloud native software architectures are not always clearly defined.

This ebook helps practitioners, architects and business managers identify these emerging patterns and implement them within an organization. It informs organizational thinking around cloud native architectures by providing original research, context and insight around the evolution of DevOps as a profession, as a culture, and as an ecosystem of supporting tools and services. 

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