Daily Tech Digest - June 27, 2024

Is AI killing freelance jobs?

Work that has previously been done by humans, such as copywriting and developing code, is being replicated by AI-powered tools like ChatGPT and Copilot, leading many workers to anticipate that these tools may well swipe their jobs out from under them. And one population appears to be especially vulnerable: freelancers. ... While writing and coding roles were the most heavily affected freelance positions, they weren’t the only ones. For instance, the researchers found a 17% decrease in postings related to image creation following the release of DALL-E. Of course, the study is limited by its short-term outlook. Still, the researchers found that the trend of replacing freelancers has only increased over time. After splitting their nine months of analysis into three-month segments, each progressive segment saw further declines in the number of freelance job openings. Zhu fears that the number of freelance opportunities will not rebound. “We can’t say much about the long-term impact, but as far as what we examined, this short-term substitution effect was going deeper and deeper, and the demands didn’t come back,” Zhu says.

Can data centers keep up with AI demands?

As the cloud market has matured, leaders have started to view their IT infrastructure through the lens of ‘cloud economics.’ This means studying the cost, business impact, and resource usage of a cloud IT platform in order to collaborate across departments and determine the value of cloud investments. It can be a particularly valuable process for companies looking to introduce and optimize AI workloads, as well as reduce energy consumption. ... As the demand for these technologies continues to grow, businesses need to prioritize environmental responsibility when adopting and integrating AI into their organizations. It is essential that companies understand the impact of their technology choices and take steps to minimize their carbon footprint. Investing in knowledge around the benefits of the cloud is also crucial for companies looking to transition to sustainable technologies. Tech leaders should educate themselves and their teams about how the cloud can help them achieve their business goals while also reducing their environmental impact. As newer technologies like AI continue to grow, companies must prepare for the best ways to handle workloads. 

Building a Bulletproof Disaster Recovery Plan

A lot of companies can't effectively recover because they haven't planned their tech stack around the need for data recovery, which should be central to core technology choices. When building a plan, companies should understand the different ways that applications across an organization’s infrastructure are going to fail and how to restore them. ... When developing the plan, prioritizing the key objectives and systems is crucial to ensure teams don't waste time on nonessential operations. Then, ensure that the right people understand these priorities by building out and training your incident response teams with clear roles and responsibilities. Determine who understands the infrastructure and what data needs to be prioritized. Finally, ensure they're available 24/7, including with emergency contacts and after-hours contact information. While storage backups are a critical part of disaster recovery, they should not be considered the entire plan. While essential for data restoration, they require meticulous planning regarding storage solutions, versioning, and the nuances of cold storage. 

How are business leaders responding to the AI revolution?

While AI provides a potential treasure trove of possibilities, particularly when it comes to effectively using data, business leaders must tread carefully when it comes to risks around data privacy and ethical implications. ‌While the advancements of generative AI have been consistently in the news, so too have the setbacks major tech companies are facing when it comes to data use. ... “Controls are critical,” he said. “Data privileges may need to be extended or expanded to get the full value across ecosystems. However, this brings inherent risks of unintentional data transmission and data not being used for the purpose intended, so organisations must ensure strong controls and platforms that can highlight and visualise anomalies that may require attention.” ... “Enterprises must be courageous around shutting down automation and AI models that while showing some short-term gain may cause commercial and reputational damage in the future if left unchecked.” He warned that a current skills shortage in the area of AI might hold businesses back. 

AI development on a Copilot+ PC? Not yet

Although the Copilot+ PC platform (and the associated Copilot Runtime) shows a lot of promise, the toolchain is still fragmented. As it stands, it’s hard to go from model to code to application without having to step out of your IDE. However, it’s possible to see how a future release of the AI Toolkit for Visual Studio Code can bundle the QNN ONNX runtimes, as well as make them available to use through DirectML for .NET application development. That future release needs to be sooner rather than later, as devices are already in developers’ hands. Getting AI inference onto local devices is an important step in reducing the load on Azure data centers. Yes, the current state of Arm64 AI development on Windows is disappointing, but that’s more because it’s possible to see what it could be, not because of a lack of tools. Many necessary elements are here; what’s needed is a way to bundle them to give us an end-to-end AI application development platform so we can get the most out of the hardware. For now, it might be best to stick with the Copilot Runtime and the built-in Phi-Silica model with its ready-to-use APIs.

The Role of AI in Low- and No-Code Development

While AI is invaluable for generating code, it's also useful in your low- and no-code applications. Many low- and no-code platforms allow you to build and deploy AI-enabled applications. They abstract away the complexity of adding capabilities like natural language processing, computer vision, and AI APIs from your app. Users expect applications to offer features like voice prompts, chatbots, and image recognition. Developing these capabilities "from scratch" takes time, even for experienced developers, so many platforms offer modules that make it easy to add them with little or no code. For example, Microsoft has low-code tools for building Power Virtual Agents (now part of its Copilot Studio) on Azure. These agents can plug into a wide variety of skills backed by Azure services and drive them using a chat interface. Low- and no-code platforms like Amazon SageMaker and Google's Teachable Machine manage tasks like preparing data, training custom machine learning (ML) models, and deploying AI applications. 

The 5 Worst Anti-Patterns in API Management

As a modern Head of Platform Engineering, you strongly believe in Infrastructure as Code (IaC). Managing and provisioning your resources in declarative configuration files is a modern and great design pattern for reducing costs and risks. Naturally, you will make this a strong foundation while designing your infrastructure. During your API journey, you will be tempted to take some shortcuts because it can be quicker in the short term to configure a component directly in the API management UI than setting up a clean IaC process. Or it might be more accessible, at first, to change the production runtime configuration manually instead of deploying an updated configuration from a Git commit workflow. Of course, you can always fix it later, but deep inside, those kludges stay there forever. Or worse, your API management product needs to provide a consistent IaC user experience. Some components need to be configured in the UI. Some parts use YAML, others use XML, and you even have proprietary configuration formats. 

Ownership and Human Involvement in Interface Design

When an interface needs to be built between two applications with different owners, without any human involvement, we have the Application Integration scenario. Application Integration is similar to IPC in some respects; for example, the asynchronous broker-based choice I would make in IPC, I would also make for Application Integration for more or less the same reasons. However, in this case, there is another reason to avoid synchronous technologies: ownership and separation of responsibilities. When you have to integrate your application with another one, there are two main facts you need to consider: a) Your knowledge of the other application and how it works is usually low or even nonexistent, and b) Your control of how the other application behaves is again low or nonexistent. The most robust approach to application integration (again, a personal opinion!) is the approach shown in Figure 3. Each of the two applications to be integrated provides a public interface. The public interface should be a contract. This contract can be a B2B agreement between the two application owners.

Reports show ebbing faith in banks that ignore AI fraud threat

The ninth edition of its Global Fraud Report says businesses are worried about the rate at which digital fraud is evolving and how established fraud threats such as phishing may be amplified by generative AI. Forty-five percent of companies are worried about generative AI’s ability to create more sophisticated synthetic identities. Generative AI and machine learning are named as the leading trends in identity verification – both the engine for, and potential solution to, a veritable avalanche of fraud. IDology cites recent reports from the Association of Certified Fraud Examiners (ACFE), which say businesses worldwide lose an estimated 5 percent of their annual revenues to fraud. “Fraud is changing every year alongside growing customer expectations,” writes James Bruni, managing director of IDology, in the report’s introduction. “The ability to successfully balance fraud prevention with friction is essential for building customer loyalty and driving revenue.” “As generative AI fuels fraud and customer expectations grow, multi-layered digital identity verification is essential for successfully balancing fraud prevention with friction to drive loyalty and grow revenue.”

What IT Leaders Can Learn From Shadow IT

Despite its shady reputation, shadow IT is frequently more in tune with day-to-day business needs than many existing enterprise-deployed solutions, observes Jason Stockinger, a cyber leader at Royal Caribbean Group, where he's responsible for shoreside and shipboard cyber security. "When shadow IT surfaces, organization technology leaders should work with business leaders to ensure alignment with goals and deadlines," he advises via email. ... When assessing a shadow IT tool's potential value, it's crucial to evaluate how it might be successfully integrated into the official enterprise IT ecosystem. "This integration must prioritize the organization's ability to safely adopt and incorporate the tool without exposing itself to various risks, including those related to users, data, business, cyber, and legal compliance," Ramezanian says. "Balancing innovation with risk management is paramount for organizations to harness productivity opportunities while safeguarding their interests." IT leaders might also consider turning to their vendors for support. "Current software provider licensing may afford the opportunity to add similar functionality to official tools," Orr says.

Quote for the day:

"Ninety percent of leadership is the ability to communicate something people want." -- Dianne Feinstein

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