Even state-of-the-art systems struggle to have a human-like conversation without tripping up, clearly. But as these systems improve, questions are arising about what the experience should ultimately look like. Values, dialects, and social norms vary across cultures, ethnicities, races, and even sexual identities, presenting a major challenge in designing a chatbot that works well for all potential users. An ostensibly “safe,” polite, and agreeable chatbot might be perceived as overly accommodating to one person but exclusionary to another. Another unsolved problem is how chatbots should treat controversial topics like religion, illegal activities, conspiracy theories, and politics — or whether they should opine about these at all. A recent paper coauthored by researchers at Meta explores the potential harm that might arise from chatbots that give poor advice, particularly in the medical or psychological realms. In a prime example, OpenAI’s GPT-3 language model can be prompted to tell a person to commit suicide.
Modern data integration technologies focus on advanced automation, connected data intelligence and persona-based interactive tooling, helping organisations to accelerate various use cases and other data integration requirements. Distributed hybrid and multicloud data is creating new integration challenges. Data lives everywhere, so centralising it into data lakes or data hubs to support business insights is no longer practical, especially with the explosion of data at the edge. Forrester expects the adoption of data integration systems to proliferate in the coming years as organisations look for supporting insights across multicloud, hybrid cloud and edge environments. Artificial intelligence (AI) is driving the next level of data integration solutions. New and innovative AI features are helping enterprises to automate data integration functions, including data ingestion, classification, processing, security and transformation. Although AI capabilities within data integration are still emerging, areas that technology architecture and delivery leaders can leverage today include the ability to discover connected data, classify and categorise sensitive data, identify duplicates and orchestrate silos.
Microservices application architecture is taking root across the enterprise ecosystem. Organizing and efficiently operating microservices in multicloud environments and making their data available in near-real time are some of the key challenges enterprise architects confront with this design. Thanks to developments in event-driven architecture (EDA) platforms (such as Apache Kafka) and data-management techniques (such as data mesh and data fabrics), designing microservices-based applications has become much easier. However, to ensure that microservices-based applications perform at requisite levels, you must consider critical non-functional requirements (NFRs) in the design. NFRs address how a system operates, rather than how the system functions (the functional requirements). The most important NFRs involve performance, resiliency, availability, scalability, and security. This article describes designing for performance, which entails low-latency processing of events and high throughput. Future articles will address the other NFRs.
“It’s important for our security teams to have visibility into all aspects of cloud applications, on-prem applications, network, services, systems, databases, accounts, third-party providers, etc. to help fortify our cybersecurity defenses,” Karki explains. “Having a complete, accurate and appropriately prioritized inventory of all our hardware, software, and supply chain assets enables our security teams to take a systematic approach to knowing what needs to be safeguarded, what controls to implement to protect, defend, and respond against any adverse events, and how to identify and produce metrics that tell the full story about our current security posture.” ... Although the complexity of that mesh has been growing for years, Van Horenbeeck says events during the past two years such as SolarWinds and Log4j have reinforced for him the criticality of understanding all the moving parts that make up his company’s technology ecosystem. To that end, Van Horenbeeck has invested in technology to gain a fuller understanding of his own company’s IT environment.
Blockchain and Distributed Ledger Technology (DLT) have been on a downward swing in the hype cycle. The lack of clarity about why some data needs to be on a decentralised network at all remains, as does the suspicion that other ventures may just be offloading energy costs onto customers – no minor concern as prices soar. Meanwhile some recent NFT releases have made non-fungible tokens seem like a satire on the digital economy – a Situationist joke. But one area where blockchain may have useful applications is establishing a secure digital identity, according to a techUK seminar this week. The Zoom event brought together four DLT luminaries, from finance, government, agriculture, and digital identity itself. The intention was to challenge misconceptions and set out a viable route to market, according to Laura Foster, techUK’s Programme Manager for Technology and Innovation. However, she then passed the chair to potentially the most interesting speaker, Genevieve Leveille, key founder and CEO of AgriLedger – a distributed-ledger app for the farming supply chain, who did a good job.
One principle of agile EA is not to boil the ocean by collecting every bit of information about an organisation before providing insights or recommendations, says Gordon Barnett, principal analyst at Forrester. To speed the process, agile EA practitioners refer to a “minimum viable architecture” or “just enough architecture” to meet an urgent business problem, making frequent changes to the EA process as needed. But, Barnett warns, the key is to choose the right elements to include to ensure that such a minimal architecture doesn’t limit its future usefulness. For organisations that are heavily reliant on SaaS applications and the cloud, “a minimum viable architecture helps hold together that distributed ecosystem” with technology standards and more collaborative governance models, even if it doesn’t provide a central repository of the distributed assets that now support the business, Gartner’s Blosch adds. At SYNLABS Jones began by concentrating on “the key pieces of information we needed to understand the business in terms of the application portfolio” and narrowed his search to, at most, “20 pieces of information about an application.”
Intelligent automation is a subset of artificial intelligence (AI). It is the computerization of processes traditionally carried out by people. Moving beyond current automation technologies (such as robotic process automation), intelligent automation replicates more complex processes — especially those that involve human decision-making. It gives organizations the opportunity to increase efficiency, improve customer experience and generate new revenues through automated digital products and services. But organizations that take to the sky with intelligent automation programs — without properly understanding the success factors — risk dropping quickly back down to Earth. As François Candelon, Rodolphe Charme di Carlo, Midas De Bondt and Theodoros Evgeniou wrote in Harvard Business Review, "For most of the past decade, public concerns about digital technology have focused on the potential abuse of personal data." But now, "attention is shifting to how data is used by the software."
When it's about starting up new to the technology, there's obviously a strong pull towards the pre, and like, how do we connect with our supply chain, and CI/CDs, and what gets deployed there? Where is the source of truth of configurations of resiliency? Is it in my Git and my Git stuff? Is it in a separate system? Where should I change what? How should it change? A lot of the challenges are around setting up those organizational processes in terms of who changes what, where, and how does that get approved? Ultimately, then it gets to Nora's world, which is, if things do go wrong, who's accountable? How do I recover? Who's alerted? How quickly? Simple things to nail home at one point, which is, we do certain things like, ok, every service, there should be a team that owns it. It should have an owner. It's a very simple concept. You will be surprised how not implemented it is, like our lack of implementation of that. I've heard stories of, this went down, and we went down tracking, and we ended up at a service of like, who wrote this? This dude left three years ago.
Having entire organisations working remotely is a relatively new phenomenon, but traditional security advice remains very relevant. Remote workers should consider the technologies available to support their remote security needs, such as a password manager that allows you to use a variety of passwords and rotate them often, without having to remember them all, but which makes it difficult for hackers to access your different accounts based on a single master password that could have been compromised when relied upon too often. Equally, employees can upgrade their applications and tools to improve their privacy posture. Search engines and browsers such as DuckDuckGo, Brave Browser and Ecosia that give you more control over your privacy exposure can help minimise the risk of attack and personal information loss. Network firewalls are another tool to consider, which can help upgrade a home network into an environment more consistent with that of the office by monitoring network traffic, blocking malicious websites and allowing you to moderate how others access resources.
The problem with identifying top candidates often lies in how a short list is generated. Traditionally, the focus is on who the leader is without significant weight put on what skills he or she needs to deliver on the company’s strategy. If succession discussions are to be transformed into more of an upstream process for the board—and members are to have a clear understanding of what the company needs before discussing the best candidates—then the process must account for three distinct and entirely predictable challenges. Because they are predictable, these challenges can be anticipated and overcome. First, start with the what and not the who. Doing so will lay out a more realistic and substantive framework. Second, from this vantage point, try to explicitly minimize the noise in the boardroom. Ensure that the directors are using shared, contextual definitions of core jargon, such as strategy, agility, transformation, and execution. Third, root the follow-on analyses of the candidates in that shared understanding, and base any assessments on a factual evaluation of their track records and demonstrated potential in order to minimize the bias of the decision-makers themselves.
Quote for the day:
"Leadership is the art of giving people a platform for spreading ideas that work" -- Seth Godin