Daily Tech Digest - January 22, 2021

Why it's vital that AI is able to explain the decisions it makes

The effort to open up the black box is called explainable AI. My research group at the AI Institute at the University of South Carolina is interested in developing explainable AI. To accomplish this, we work heavily with the Rubik’s Cube. The Rubik’s Cube is basically a pathfinding problem: Find a path from point A – a scrambled Rubik’s Cube – to point B – a solved Rubik’s Cube. Other pathfinding problems include navigation, theorem proving and chemical synthesis. My lab has set up a website where anyone can see how our AI algorithm solves the Rubik’s Cube; however, a person would be hard-pressed to learn how to solve the cube from this website. This is because the computer cannot tell you the logic behind its solutions. Solutions to the Rubik’s Cube can be broken down into a few generalized steps – the first step, for example, could be to form a cross while the second step could be to put the corner pieces in place. While the Rubik’s Cube itself has over 10 to the 19th power possible combinations, a generalized step-by-step guide is very easy to remember and is applicable in many different scenarios. Approaching a problem by breaking it down into steps is often the default manner in which people explain things to one another.

Why KubeEdge is my favorite open source project of 2020

The KubeEdge architecture allows autonomy on an edge computing layer, which solves network latency and velocity problems. This enables you to manage and orchestrate containers in a core data center as well as manage millions of mobile devices through an autonomous edge computing layer. This is possible because of how KubeEdge uses a combination of the message bus (in the Cloud and Edge components) and the Edge component's data store to allow the edge node to be independent. Through caching, data is synchronized with the local datastore every time a handshake happens. Similar principles are applied to edge devices that require persistency. KubeEdge handles machine-to-machine (M2M) communication differently from other edge platform solutions. KubeEdge uses Eclipse Mosquitto, a popular open source MQTT broker from the Eclipse Foundation. Mosquitto enables WebSocket communication between the edge and the master nodes. Most importantly, Mosquitto allows developers to author custom logic and enable resource-constrained device communication at the edge.

DevOps, DevApps and the Death of Infrastructure

The godfather of the DevOps movement, Patrick Debois, often speaks about how we are moving to a more service-oriented or serviceful intranet. I have been calling this riff on DevOps deployment methodology, DevApps. This is an emerging design pattern where cloud native applications are a combination of bespoke services (like Twilio, Salesforce, and many others) alongside custom software deployed as functions on scale-to-zero web services like Amazon Lambda. Services are being managed with Terraform, just as the services of the past had been managed by Chef or Puppet. Once organizations tackle the well-accepted practice to automate deployment, the next frontier is to create applications that are composable via automated means. What we’re talking about here is layering integration-as-code on top of infrastructure-as-code. With a wide variety of cloud services at their disposal, application developers need not worry about the latter — just the former. At TriggerMesh, we are seeing more and more organizations looking to create applications that are configured with automated workflows on the fly.

5 Qualities Of Highly Engaged Teams

Trust is not just the cornerstone of leadership. It is also a fundamental building block in high-performance teams. When teams trust each other, it gives them more confidence in their abilities. They know they will get support when needed. Also, they will be willing to provide support to teams in need. This collaboration and cooperation help the sharing of best practices, which brings the level of the whole team, or teams higher. Trust is one of those reflexive qualities; the more the leader shows trust, the more they will be trusted. The more we trust our teams, the more they will trust themselves and each other. Leaders need to be the role model when it comes to this but also need to go that extra step to providing support and also to ask for it. Leaders who can show this vulnerability make it ok for their teams to ask for help when needed, as well as give it. Teams that consistently deliver are teams that feel empowered, teams that understand what needs to be done and have the tools to achieve it. This empowerment boost self-confidence and belief that the teams will reach their goals. Being engaged is great, but if you’re empowered, this can lead to frustration and disengagement.

Four key real world intelligent automation trends for 2021

In 2021, there will be an overdue re-think of how organisations choose RPA and intelligent automation technologies. We’ll see greater selection rigour fuelling more informed assessments of these technologies’ abilities to successfully operate and scale in large, demanding, front-to back-office enterprise environments, where performance, security, flexibility, resilience, usability, and governance are required. ... For a RPA or intelligent automation programme to really deliver, a strategy and purpose is needed. This could be improving data quality, operational efficiency, process quality and employee empowerment, or enhancing stakeholder experiences by providing quicker, more accurate responses. By examining the experiences and proven outcomes experienced by those organisations with mature automation programs, we’ll see more meaningful methods of measuring the impact of RPA and intelligent automation. ... This year, there will also be a greater understanding of which vendor software robots really possess the ability to be ‘the’ catalyst for digital transformation. These robots are typically pre-built, smart, highly productive and self-organising processing resources, that perform joined up, data-driven work across multiple operating environments of complex, disjointed, difficult to modify legacy systems and manual workflows.

Why North Korea Excels in Cybercrime

The cybercrime market's size and the scarcity of effective protection continue to be a mouth-watering lure for North Korean cyber groups. The country's cyber operations carry little risk, don't cost much, and can produce lucrative results. Nam Jae-joon, the former director of South Korea's National Intelligence Service, reports that Kim Jong Un himself said that cyber capabilities are just as important as nuclear power and that "cyber warfare, along with nuclear weapons and missiles, is an 'all-purpose sword' that guarantees our [North Korea's] military's capability to strike relentlessly." Other reports note that in May 2020, the North Koreans recruited at least 100 top-notch science and technology university graduates into its military forces to oversee tactical planning systems. Mirim College, dubbed the University of Automation, churns out approximately 100 hackers annually. Defectors have testified that its students learn to dismantle Microsoft Windows operating systems, build malicious computer viruses, and write code in a variety of programming languages. The focus on Windows may explain the infamous North Korean-led 2017 WannaCry ransomware cyberattack, which wrought havoc in more than 300,000 computers across 150 countries by exploiting vulnerabilities in the popular operating system.

To see the future more clearly, find your blind spots

There are multiple causes for the blind spots. One is a persistent state of denial, described in four parts by an emergency management professional after Hurricane Katrina: “One is, it won’t happen. Two is, if it does happen, it won’t happen to me. Three: If it does happen to me, it won’t be that bad. And four: If it happens to me and it’s bad, there’s nothing I can do to stop it anyway.” To this, I’m sure we can now add a fifth rationalization: “It won’t happen again.” Denial, however, has never been a successful strategy. An additional cause of blind spots is an overreliance on available data. Executives have benefited greatly from increased insights derived through analytics and other sophisticated methods of pattern recognition. The limitation of these tools, however, is that they can’t detect the “dog that didn’t bark,” a reference to a Sherlock Holmes case in which the crucial clue is not what happened but what did not. Leading is, in part, about bringing an organization into the future, and so executives should sharpen their thinking to include not only what they can see clearly but also what they can’t. A third cause is conditions that can tightly bind thinking.

Being Future Ready Is The Only Way To Survive In Data Science Field

There are three key skills for any data scientist– a stronghold on mathematics and statistics. Secondly, you need a programming language base for different tasks such as data processing, storage, etc. Lastly, domain knowledge. When you are working in a company, you must think about what value you are adding. Having acquired these skills next comes constant upgradation and upskilling. There is a sea of resources available online. For example, Coursera and EDx are good sources for theoretical introductions to a variety of topics. For a more practical approach, aspirants may check Datacamp and Udemy. I would also suggest using Kaggle, participating in hackathons, and undertaking internships to gain an edge. It is also important to think from the perspective of being ready for future challenges, given this field’s dynamic nature. It does get difficult to catch up with every new model or concept. I find it difficult too. What I tend to do is I try to look at the bigger picture, and once a tech starts picking pace, I spend time understanding it. The secret lies in following a broad macro trend, not just in DS but in complete tech space.

How to implement a DevOps toolchain

A good DevOps toolchain is a progression of different DevOps tools used to address a specific business challenge. Connected in a chain, they guarantee a profitable cycle between the front-end and back-end developers, quality analyzers, and customers. The goal is to automate development and deployment processes to ensure the rapid, reliable, and budget-friendly delivery of innovative solutions. We found out that building a successful DevOps toolchain is not a simple undertaking. It takes experimentation and nonstop refinement to guarantee that essential processes are fully automated. A DevOps toolchain automates all of the technical elements in your workflow. It also gets different teams on the same page so that you can focus on a business strategy to drive your organization into the future. We have come to identify five all the more valid benefits in support of the DevOps toolchain implementation. ... A fully enabled and properly implemented DevOps toolchain propels your innovation initiatives from start to end and ensures prompt deployment. Your toolchain will look different than this, depending on your requirements, but I hope seeing our workflow gives you a sense of how to approach automation as a solution.

3 Essential Steps to Exploit the Full Power of AI

A key to generating a good ROI is in executing data, automation, analytics and AI initiatives. Close to 23% of respondents have already set up or are in the process of setting up an AI Center of Excellence that shares and coordinates resources across different areas of the company. This number has risen from 18% just a year back. Also, nearly 19% of companies have a company-wide AI leader who oversees AI strategy and governance. The reason why such an integrated delivery model makes sense is the convergence of the cloud infrastructure that provides the storage and compute, the data that is the raw material for the analysis, the automation that operates on the technology infrastructure, the analytics that operates on the data to generate better insights, and the AI that enhances both the automation and the analytics resulted in decreased costs and better revenues. In large (greater than $1 billion revenues) companies the existing data and analytics group have expanded their remit to include AI. Companies that currently have separate centers of excellence (COE) for analytics and/or automation and/or AI must integrate, or the very least, coordinate their initiatives. Doing so would provide more seamless integration and yield better ROI. Companies that are just starting their journey in analytics and AI can start with an analytics or automation COE that expands to include AI capabilities.

Quote for the day:

"Our expectation in ourselves must be higher than our expectation in others." -- Victor Manuel Rivera

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