Daily Tech Digest - July 03, 2021

DeepMind AGI paper adds urgency to ethical AI

Despite assurances from stalwarts that AGI will benefit all of humanity, there are already real problems with today’s single-purpose narrow AI algorithms that calls this assumption into question. According to a Harvard Business Review story, when AI examples from predictive policing to automated credit scoring algorithms go unchecked, they represent a serious threat to our society. A recently published survey by Pew Research of technology innovators, developers, business and policy leaders, researchers, and activists reveals skepticism that ethical AI principles will be widely implemented by 2030. This is due to a widespread belief that businesses will prioritize profits and governments continue to surveil and control their populations. If it is so difficult to enable transparency, eliminate bias, and ensure the ethical use of today’s narrow AI, then the potential for unintended consequences from AGI appear astronomical. And that concern is just for the actual functioning of the AI. The political and economic impacts of AI could result in a range of possible outcomes, from a post-scarcity utopia to a feudal dystopia. It is possible too, that both extremes could co-exist.

Distributed DevOps Teams: Supporting Digitally Connected Teams

The teams using the visualization board were in different countries, so they needed to address digital connection across time zones. This meant a more robust process for things like retrospectives, more robust breakdown of stories into tasks, more "scheduled" time for showcase and issue resolution, etc. The team found that, while they worried a more defined process would stymie their agility, it worked well in focusing their activities productively in line with the broader objectives, without the necessity of being in constant communication. They found they needed more overlapping work time, particularly when they were in release planning and deployment. And they had to think about and plan task/work turnover to the other team at the end of each day – something they never had to do when in physical proximity. They’ve seen some team members fall back into role-based activities more often. There simply isn’t the natural communication and subsequent spark of curiosity that is truly the hallmark of team collaboration.

The Cost of Managed Kubernetes - A Comparison

Running a Kubernetes cluster in EKS, you get the possibility of using either a standard Ubuntu image as the OS for your nodes, or you can use their optimized EKS AMIs. This can help you get some better speed and performance rather than running a generic OS. Once the cluster is running, there’s no way to enable automatic upgrades of the Kubernetes version. While EKS does have excellent documentation on how to upgrade your cluster, it is a manual process. If your nodes start reporting failures, EKS doesn’t have a way of enabling auto-repair like in GKE. This means you’ll have to either monitor that yourself and manually fix nodes or set up your own system to repair broken nodes. As with GKE, you pay an administration fee of $0.10 per hour per cluster when running EKS, after which you only pay for the underlying resources. If you want to run your cluster on-prem, it’s possible to do so either by using AWS Outposts or EKS Anywhere, which launches sometime in 2021.

Resetting Your IoT Device Before Reselling It Isn't Enough, Researchers Find

Those that had reset their devices, however, hadn’t quite wiped the slate clean in the way they thought they had. Researchers found that, contrary to what Amazon says, you can actually recover a lot of sensitive personal data stored on factory reset devices. The reason for this is related to how these devices store your information using NAND flash memory—a storage medium that, due to certain processes, doesn’t actually delete the data when the device is reset. “We show that private information, including all previous passwords and tokens, remains on the flash memory, even after a factory reset. This is due to wear-leveling algorithms of the flash memory and lack of encryption,” researchers write. “An adversary with physical access to such devices (e.g., purchasing a used one) can retrieve sensitive information such as Wi-Fi credentials, the physical location of (previous) owners, and cyber-physical devices (e.g., cameras, door locks).” Granted, said hypothetical snoopers would really have to know what they were doing—and their data thieving would entail a certain amount of expertise.

Defeating Ransomware-as-a-Service? Think Intel-Sharing

In addition to technological solutions, a necessary element in building a strong cybersecurity foundation is working with all internal and external stakeholders, including law enforcement. More data helps enable more effective responses. Because of this, cybersecurity professionals must openly partner with global or regional law enforcement, like US-CERT. Sharing intelligence with law enforcement and other global security organizations is the only way to effectively take down cybercrime groups. Defeating a single ransomware incident at one organization does not reduce the overall impact within an industry or peer group. It’s a common practice for attackers to target multiple verticals, systems, companies, networks and software. To make it more difficult and resource-intensive for cybercriminals to attack, public and private entities must collaborate by sharing threat information and attack data. Private-public partnerships also help victims recover their encrypted data, ultimately reducing the risks and costs associated with the attack. Visibility increases as public and private entities band together.

Maintaining a Security Mindset for the Cloud Is Crucial

A lot of organizations are moving from traditional on-premises application deployments into one or multiple clouds. Now, those transitions carry with them architectural baggage of how to architect networking and security elements for this new cloud era, where applications are distributed all around in one multi-cloud, software-as-a-service environment or even edge computing environments. And so security is becoming very, very paramount to the success of that motion. Now, we also know that security attacks are becoming increasingly sophisticated, and that’s especially true when applications are moving to the cloud. And cloud infrastructure is not always to the same level of capabilities and features that enterprises have been used to in their on-premises environments. So, this security-oriented mindset is extremely important for building these networks that now span not only the on-premises environment, but also cloud environments. 

DevOps Automation: How Is Automation Applied In DevOps Practice

We can see the automation being carried out at every phase of the development starting from triggering of the build, carrying out unit testing, packaging, deploying on to the specified environments, carrying out build verification tests, smoke tests, acceptance test cases and finally deploying on to the final production environment. Even when we say automating test cases, it is not just the unit tests but installation tests, integration tests, user experience tests, UI tests etc. DevOps forces the operations team, in addition to development activities, to automate all their activities, like provisioning the servers, configuring the servers, configuring the networks, configuring firewalls, monitoring the application in the production system. Hence to answer what to automate, it is build trigger, compiling and building, deploying or installing, automating infrastructure set up as a coded script, environment configurations as a coded script, needless to mention testing, post-deployment life performance monitoring in life, logs monitoring, monitoring alerts, pushing notifications to live and getting alerts from live in case of any errors and warnings etc

Kubernetes-Run Analytics at the Edge: Postgres, Kafka, Debezium

Implementing databases and data analytics within cloud native applications involves several steps and tools from data ingestion, preliminary storage, to data preparation and storage for analytics and analysis. An open, adaptable architecture will help you execute this process more effectively. This architecture requires several key technologies. Container and Kubernetes platforms provide a consistent foundation for deploying databases, data analytics tools, and cloud native applications across infrastructure, as well as self-service capabilities for developers and integrated compute acceleration. PostgreSQL, Apache Kafka and Debezium can be deployed using Kubernetes Operators on Kubernetes to provide a cloud native data analytic solution that be can be used for a variety of use cases and across hybrid cloud environments — including datacenter, public cloud infrastructure, and the edge — for all stages of cloud native application development and deployment. 

DevOps Testing Tutorial: How DevOps Will Impact QA Testing?

Although there are subtle differences between Agile and DevOps Testing, those working with Agile will find DevOps a little more familiar to work with (and eventually adopt). While Agile principles are applied successfully in the development & QA iterations, it is a different story altogether (and often a bone of contention) on the operations side. DevOps proposes to rectify this gap. Now, instead of Continuous Integration, DevOps involves “Continuous Development”, where the code was written and committed to Version Control, will be built, deployed, tested and installed on the Production environment that is ready to be consumed by the end-user. This process helps everyone in the entire chain since environments and processes are standardized. Every action in the chain is automated. It also gives freedom to all the stakeholders to concentrate their efforts on designing and coding a high-quality deliverable rather than worrying about the various building, operations, and QA processes. It brings down the time-to-live drastically to about 3-4 hours, from the time code is written and committed, to deployment on production for end-user consumption.

Where Can An Agile Transformation Lead Your Company?

The rituals of Agile development are largely procedural and tactical. In contrast, organizational agile transformation is driven by and reinforces cultural norms that make staying agile possible. A development lead can compel team members to participate in the process of daily scrums and weekly sprints. Agile development doesn’t address the task of building genuine collaboration or a culture of accountability. In contrast, an agile transformation requires cultural support to move the organization into a state of resonant agility. The state, in turn, reinforces and strengthens norms of collaboration and accountability that an agile culture encourages. An agile culture takes a broader view, beyond providing a prescriptive process for building something specific. It pulls together stakeholders from multiple functional areas to tackle an issue through organic, collaborative analysis. ... Next-generation technologies are purpose-built, not broad platforms that force conformity instead of innovation. There’s no one platform or suite of tools for an agile organization. Teams work with an organic tech stack that gives them the flexibility to use the best tool for the job, and everyone’s job is different.

Quote for the day:

"Effective team leaders adjust their style to provide what the group can't provide for itself." -- Kenneth Blanchard

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