Apart from technical considerations, the way a software development team is set up also plays a critical role in the software technology decision. A major advantage of containerization is the great flexibility and manageability of the overall development process. Although previous monolithic software development often had cumbersome documentation requirements, hard-to-predict timelines and complicated synchronization processes, the container-based approach can deliver a different experience. If you're able to split up the project in isolated containers, you can divide the team into smaller groups with faster iterations and address additional feature requests more easily to cater to modern agile processes. Containerization also bridges the two worlds of cloud and embedded development by aligning the underlying technology, unifying the development workflow and leveraging automation capabilities containers provide. With that, it becomes much easier to support hybrid workflows and reuse the same software. This is important for IoT projects if customers have vastly different network environments, data ownership requirements and solution approaches.
For organisations to successfully integrate intelligent automation, they must first acknowledge that transformation is necessary. It starts with making a conscious choice about what they want to achieve, based on the ‘art of the possible’. This decision is then fed into a robust and realistic intelligent automation strategy. That is the ideal, but here is the reality: Only 26 per cent of Deloitte’s survey respondents that are piloting automations – and 38 per cent of those implementing and scaling –have an enterprise-wide intelligent automation strategy. There is a clear difference between organisations piloting automations and those implementing and scaling their efforts. The latter are more likely to reimagine what they do and incorporate process change across functional boundaries. Those in the piloting stage are more likely to automate current processes, with limited change – they may have not yet taken advantage of the many technologies and techniques that can expand their field of vision and open up even more opportunities. There are other barriers to success: process fragmentation and a lack of IT readiness were ranked by survey respondents at the top of the list (consistent with responses in the past two years).
The primary driver behind XDR is its fusing of analytics with detection and response. The premise is that these functions are not and should not be separate. By bringing them together, XDR promises to deliver many benefits. The first is a precise response to threats. Instead of keeping logs in a separate silo, with XDR they can be used to immediately drive response actions with higher fidelity and greater depth knowledge into the details surrounding an incident. For example, the traditional SIEM approach is based on monitoring network log data for threats and responding on the network. Unless a threat is simple, like commodity malware that can be easily cleaned up, remediation is typically delayed until a manual investigation is performed. XDR, on the other hand, provides SOCs both the visibility and ability to not just respond but also remediate. SOC operators can take precise rather than broad actions, and not just across the network, but also the endpoint and other areas. Because XDR seeks to fuse the analysis, control and response planes, it provides a unified view of threats. Instead of forcing SOCs to use multiple interfaces to threat hunt and investigate, event data and analytics are brought together in XDR to provide the full context needed to precisely respond to an incident.
Improving the process of data-based decisions in the public sector should be seen as a priority, according to the CDEI. "Democratically-elected governments bear special duties of accountability to citizens," reads the report. "We expect the public sector to be able to justify and evidence its decisions." The stakes are high: earning the public's trust will be key to the successful deployment of AI. Yet the CDEI's report showed that up to 60% of citizens currently oppose the use of AI-infused decision-making in the criminal justice system. The vast majority of respondents (83%) are not even certain how such systems are used in the police forces in the first place, highlighting a gap in transparency that needs to be plugged. There is a lot that can be gained from AI systems if they are deployed appropriately. In fact, argued the CDEI's researchers, algorithms could be key to identifying historical human biases – and making sure they are removed from future decision-making tools. "Despite concerns about 'black box' algorithms, in some ways algorithms can be more transparent than human decisions," said the researchers. "Unlike a human, it is possible to reliably test how an algorithm responds to changes in parts of the input.
Microsoft is facing criticism for its new “Productivity Score” technology, which can measure how much individual workers use email, chat and other digital tools. But it turns out the company has even bigger ideas for using technology to monitor workers in the interest of maximizing organizational productivity. Newly surfaced Microsoft patent filings describe a system for deriving and predicting “overall quality scores” for meetings using data such as body language, facial expressions, room temperature, time of day, and number of people in the meeting. The system uses cameras, sensors, and software tools to determine, for example, “how much a participant contributes to a meeting vs performing other tasks (e.g., texting, checking email, browsing the Internet).” The “meeting insight computing system” would then predict the likelihood that a group will hold a high-quality meeting. ... Microsoft says the goal is to help organizations ensure that their workers are taking advantage of tools like shared workspaces and cloud-based file sharing to work most efficiently. This also works to Microsoft’s advantage by encouraging the use of its products such as Teams and SharePoint inside companies, making future Microsoft 365 renewals more likely.
Defining consciousness is only half the battle – and one that likely won’t be won until after we’ve aped it. The other side of of the equation is observing and measuring consciousness. We can watch a puppy react to stimulus. Even plant consciousness can be observed. But for a machine to demonstrate consciousness its observers have to be certain it isn’t merely imitating consciousness through clever mimicry. Let’s not forget that GPT-3 can blow even the most cynical of minds with its uncanny ability to seem cogent, coherent, and poignant. The Blums get around this problem by designing a system that’s only meant to demonstrate consciousness. It won’t try to act human or convince you it’s thinking. This isn’t an art project. Instead, it works a bit like a digital hourglass where each grain of sand is information. The machine sends and receives information in the form of “chunks” that contain simple pieces of information. There can be multiple chunks of information competing for mental bandwidth, but only one chunk of information is processed at a time. And, perhaps most importantly, there’s a delay in sending the next chunk. This allows chunks to compete – with the loudest, most important one often winning.
Data-based methods work well for situations where new data observed do not deviate too much from old data learned. In particular, data-intensive methods showed astonishing results in the domains of image, speech, and language understanding, and also in gaming. In fact, they are the quintessence implementation of what Economy Nobel Prize Daniel Kahneman refers to as System-1 in his theory about the mind. Based on this theory, the mind is composed of two systems: System-1 governs our perception and classification, and System-2 governs our reasoning and planning. ... To quote Daphne Koller, “the world is noisy and messy” and we need to deal with noise and uncertainty, even when data is available in quantity. Here, we enter the domain of probability theory and the best set of methods to consider is probabilistic graphical models where you model the subject under consideration. There are three kinds of probabilistic graphical models, from the least sophisticated to the most sophisticated: Bayesian networks, Markov networks, and hybrid networks. In these methods, you create a model that captures all the relevant general knowledge about the subject in quantitative, probabilistic terms, such as the cause-effect network of a troubleshooting application.
The study introduced the culture of innovation framework, which spans the dimensions of people, process, data, and technology, to assess organizations’ approach to innovation. It surveyed 439 business decision makers and 438 workers in India within a 6-month period, before and since COVID-19. The India study was part of a broader survey among 3,312 business decision makers and 3,495 workers across 15 markets in Asia Pacific region. Through the research, organizations’ maturity was mapped and as a result, organizations were grouped in four stages – traditionalist (stage 1), novice (stage 2), adaptor (stage 3) and leaders (stage 4). Leaders comprise of organizations that are the most mature in building a culture of innovation1. “Innovation is no longer an option, but a necessity. We have seen how the recent crisis has spurred the need for transformation; for organizations to adapt and innovate in order to emerge stronger,” said Rajiv Sodhi, COO, Microsoft India. “We commissioned this research to gain better understanding of the relationship between having a culture of innovation and an organization’s growth. But now, more than achieving growth, we see that having a mature culture of innovation translates to resilience, and strength to withstand economic crises to recover,” he added.
Current cybersecurity strategies tend to center around stopping potential threats from getting into your computing and communications infrastructure at all. To be successful, it requires that no employee ever click on a bad link, download the wrong file or work from an unsecured Wi-Fi network. However, this approach is not realistic nor sufficient enough in today’s world, and impossible in our collective future. That is why business leaders need to rethink their cyber strategy to adapt to our constantly changing world. In practice, the concept of cyber resilience is based on a bend-but-not-break philosophy. It understands that despite significant defensive investments and best efforts, cyber-criminals will occasionally get in. The cyber resilience approach is based on the premise that if you organize your defenses to prioritize resiliency over just computer security, you keep what’s most important going – your business. No matter what your business might be – whether it is churning out widgets or keeping the lights on – what’s key is to keep your most valuable assets unaffected and operational. Implementing this new goal, from the boardroom down, helps save money and improve results.
Don’t go too far and take all decision making away from the development squads. It’s natural to think that the more we implement and the fewer the choices developers have to make the better. However, I’ve found that there is such a thing as going too far. On one of my projects the architecture team was making all of the decisions and creating frameworks/tools to govern through code. I still recall vividly one of the developers coming to me and saying “If you architects want to make all of the decisions and tell us how to implement things, then put your cell phone number in Pager Duty! If you want me to be accountable and be woken up at 3 AM when my code breaks in production, then I am going to make the decisions.” A decentralized governance approach was necessary and the role of the architect needed to be as a boundary setter. While the Product Build Squad is designing and building the Microservices Framework and the Developer Onboarding Tool (using an Inner Source approach with contributions from other developers), development squads are already using the framework and tool. Depending on how many development squads your project/program has, you could have many Microservices with say Version 1.0 of the Microservices Framework.
Quote for the day:
"Success is often the result of taking a misstep in the right direction." - Al Bernstein