Daily Tech Digest - July 02, 2024

The Changing Role of the Chief Data Officer

The chief data officer originally played more “defense” than “offense.” The position focused on data security, fraud protection, and Data Governance, and tended to attract people from a technical or legal background. CDOs now may take on a more offensive strategy, proactively finding ways to extract value from the data for the benefit of the wider business, and may come from an analytics or business background. Of course, in reality, the choice between offense and defense is a false one, as companies must do both. ... Major trends for CDOs in the future will include incorporating cutting-edge technology, such as generative AI, large language models, machine learning, and increasingly sophisticated forms of automation. The role is also spreading to a wider variety of industry sectors, such as healthcare, the private sector, and higher education. One of the major challenges is already in progress: responding to the COVID-19 pandemic. The pandemic hugely shook global supply chains, created new business markets, and also radically changed the nature of business itself. 

Duplicate Tech: A Bottom-Line Issue Worth Resolving

The patchwork nature of combined technologies can hinder processes and cause data fragmentation or loss. Moreover, differing cybersecurity capabilities among technologies can expose the organization to increased risk of cyberattacks, as older or less secure systems may be more vulnerable to breaches. Retaining multiple technologies may initially seem prudent in a merger or acquisition, but ultimately it proves detrimental. The drawbacks — from duplicated data and disconnected processes to inefficiencies and security vulnerabilities — far outweigh any perceived benefits, highlighting the critical need for streamlined, unified IT systems. ... There are compelling reasons to remove the dead weight of duplicate technologies and adopt a singular technology. The first step in eliminating tech redundancy is to evaluate existing technologies to determine which tools best align with current and future business needs. A collaborative approach with all relevant stakeholders is recommended to ensure the chosen solution supports organizational goals and avoids unnecessary repetition.

Disability community has long wrestled with 'helpful' technologies—lessons for everyone in dealing with AI

This disability community perspective can be invaluable in approaching new technologies that can assist both disabled and nondisabled people. You can't substitute pretending to be disabled for the experience of actually being disabled, but accessibility can benefit everyone. This is sometimes called the curb-cut effect after the ways that putting a ramp in a curb to help a wheelchair user access the sidewalk also benefits people with strollers, rolling suitcases and bicycles. ... Disability advocates have long battled this type of well-meaning but intrusive assistance—for example, by putting spikes on wheelchair handles to keep people from pushing a person in a wheelchair without being asked to or advocating for services that keep the disabled person in control. The disabled community instead offers a model of assistance as a collaborative effort. Applying this to AI can help to ensure that new AI tools support human autonomy rather than taking over. A key goal of my lab's work is to develop AI-powered assistive robotics that treat the user as an equal partner. We have shown that this model is not just valuable, but inevitable. 

What is the Role of Explainable AI (XAI) In Security?

XAI in cybersecurity is like a colleague who never stops working. While AI helps automatically detect and respond to rapidly evolving threats, XAI helps security professionals understand how these decisions are being made. “Explainable AI sheds light on the inner workings of AI models, making them transparent and trustworthy. Revealing the why behind the models’ predictions, XAI empowers the analysts to make informed decisions. It also enables fast adaptation by exposing insights that lead to quick fine-tuning or new strategies in the face of advanced threats. And most importantly, XAI facilitates collaboration between humans and AI, creating a context in which human intuition complements computational power.,” Kolcsár added. ... With XAI working behind the scenes, security teams can quickly discover the root cause of a security alert and initiate a more targeted response, minimizing the overall damage caused by an attack and limiting resource wastage. As transparency allows security professionals to understand how AI models adapt to rapidly evolving threats, they can also ensure that security measures are consistently effective. 

10 ways AI can make IT more productive

By infusing AI into business processes, enterprises can achieve levels of productivity, efficiency, consistency, and scale that were unimaginable a decade ago, says Jim Liddle, CIO at hybrid cloud storage provider Nasuni. He observes that mundane repetitive tasks, such as data entry and collection, can be easily handled 24/7 by intelligent AI algorithms. “Complex business decisions, such as fraud detection and price optimization, can now be made in real-time based on huge amounts of data,” Liddle states. “Workflows that spanned days or weeks can now be completed in hours or minutes.”  “Enterprises have long sought to drive efficiency and scale through automation, first with simple programmatic rules-based systems and later with more advanced algorithmic software,” Liddle says.  ... “By reducing boilerplating, teams can save time on repetitive tasks while automated and enhanced documentation keeps pace with code changes and project developments.” He notes that AI can also automatically create pull requests and integrate with project management software. Additionally, AI can generate suggestions to resolve bugs, propose new features, and improve code reviews.

How Tomorrow's Smart Cities Will Think For Themselves

When creating a cognitive city, the fundamental need is to move the computing power to where data is generated: where people live, work and travel. That applies whether you’re building a totally new smart city or retrofitting technology to a pre-existing ‘brownfield’ city. Either way, edge is key here. You’re dealing with information from sensors in rubbish bins, drains, and cameras in traffic lights. ... But in years to come the city itself will respond dynamically to the changing physical world, adjusting energy use in real-time to respond to the weather, for example. The evolution of monitoring has come from a machine-to-machine foundation, with the introduction of the Internet of Things (IoT) and now artificial intelligence (AI) becoming transformational in enabling smart technologies to become dynamic. Emerging AI technologies such as large language models will also play a role going forward, making it easy for both city planners and ordinary citizens to interact with the city they live in. Edge will be the key ingredient which gives us effective control of these cities of the future.

Serverless cloud technology fades away

The meaning of serverless computing became diluted over time. Originally coined to describe a model where developers could run code without provisioning or managing servers, it has since been applied to a wide range of services that do not fit its original definition. This led to a confusing loss of precision. It’s crucial to focus on the functional characteristics of serverless computing. The elements of serverless—agility, cost-efficiency, and the ability to rapidly deploy and scale applications—remain valuable. It’s important to concentrate on how these characteristics contribute to achieving business goals rather than becoming fixated on the specific technologies in use. Serverless technology will continue to fade into the background due to the rise of other cloud computing paradigms, such as edge computing and microclouds. ... The explosion of generative AI also contributed to the shifting landscape. Cloud providers are deeply invested in enabling AI-driven solutions, which often require specialized computer resources and significant data management capabilities, areas where traditional serverless models may not always excel.

Infrastructure-as-code and its game-changing impact on rapid solutions development

Automation is one of the main benefits of adopting an IaC approach. By automating infrastructure provisioning, IaC allows configuration to be accomplished at a faster pace. Automation also reduces the risk of errors that can result from manual coding, empowering greater consistency by standardizing the development and deployment of the infrastructure. ... Developers can rapidly assemble and deploy its infrastructure blocks, reusing them as needed throughout the development process. When adjustments are needed, developers can simply update the code the blocks are built on rather than making manual one-off changes to infrastructure components. Testing and tracking are more streamlined with IaC since the IaC code serves as a centralized and readily accessible source for documentation on the infrastructure. It also streamlines the testing process, allowing for automated unit testing of compliance, validation, and other processes before deploying. Additionally, IaC empowers developers to take advantage of the benefits provided by cloud computing. It facilitates direct interaction with the cloud’s exposed API, allowing developers to dynamically provision, manage, and orchestrate resources.

What is Multimodal AI? Here’s Everything You Need to Know

Multimodal AI describes artificial intelligence systems that can simultaneously process and interpret data from various sources such as text, images, audio, and video. Unlike traditional AI models that depend on a single type of data, multimodal AI provides a holistic approach to data processing. ... Although multimodal AI and generative AI share similarities, they differ fundamentally. For instance, generative AI focuses on creating new content from a single type of prompt, such as creating images from textual descriptions. In contrast, multimodal AI processes and understands different sensory inputs, allowing users to input various data types and receive multimodal outputs. ... Multimodal AI represents a significant advancement in the field of artificial intelligence. Therefore, by understanding and leveraging this advanced technology, data scientists and AI professionals can pave the way for more sophisticated, context-aware, and human-like AI systems, ultimately enriching our interaction with technology and the world around us. 

Excel Enthusiast to Supply Chain Innovator – The Journey to Building One of the Largest Analytic Platforms

While ChatGPT has helped raise awareness about AI capabilities, explaining how to integrate AI has presented challenges, especially when managing over 200 different data analytic reports. To address the different uses, Miranda has simplified AI into three categories: rule-based AI, learning AI (machine learning), and generative AI. Generative AI has emerged as the most dynamic tool among the three for executing and recording data analytics. Its versatility and adaptability make it particularly effective in capturing and processing diverse data sets, contributing to more comprehensive analytics outcomes. Miranda says, “People in analytics might not jump out of bed excited to tackle documentation, but it's a critical aspect of our work. Without proper documentation, we risk becoming a single point of failure, which is something we want to avoid.” ... These recordings are then converted into transcripts and securely stored in a containerized environment, streamlining the documentation process while ensuring data security. Because of process automation, Miranda says that the organization generated 240,000 work hours last year, and they anticipate even more this year.

Quote for the day:

"Life is like riding a bicycle. To keep your balance you must keep moving." -- Albert Einstein

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