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.?

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