emergingtechnology
By John E Dunn The ability to index, query, and analyze large amounts of data from real-time sources remains a major headache for anyone involved in building database systems. Data retrieval takes time and even that assumes that the nature of the queries lie within certain parameters. Overcoming the issue is possible but can be resource-intensive and involve complex engineering. Meanwhile, the use cases for real-time data continue to multiply, not least in artificial intelligence (AI) applications connected to cybersecurity automation, fraud detection in financial services, and business analyt...
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By Matt Asay Just like big data back in 2013, we’re in the “everyone’s doing it, no one knows why” phase of generative AI (genAI). A recent McKinsey survey found that 65% of enterprises are “regularly using genAI.” Promising! In Elastic’s recent earnings call, the company noted that over 1,000 customers are paying to build genAI applications. Wow! Each of the big cloud companies, as well as Oracle, has talked up how genAI is driving cloud spend. Amazing! Maybe. Maybe not. Peel back the headlines and we’re still seeing genAI as aspirational, not necessarily transformational for most companies. ...
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By David Linthicum European banking executives are increasingly concerned about the growing dependence of big U.S. tech firms on integrated artificial intelligence (AI) in financial services. Substantial computing power is needed for AI, and many banks believe they will struggle to operate AI independently. They fear a shift towards “Big Tech” and “Big Cloud” due to AI adoption requirements. These concerns were widely discussed at a recent fintech conference in Amsterdam. I wish I had been there to remind them that they have direct control of the technology they deploy, and large companies and...
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By Nick Hodges In a past column, I talked about how the fat cats of social media profit from all of the value that normal folks like you and I provide. Social media sites have complete control over the attention-creating value that we provide for them, and give us little to no say over how that content is managed. They can even take away access to our profiles. All the money generated by our work ends up in their coffers. I mentioned Substack as a place with low take rates, and how content producers there are able to benefit much more directly from the value that they create because they use a...
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By Scott McCarty The term “inflection point” is overused, but it certainly applies to the current state of artificial intelligence. Technology providers—and the companies that depend on them—can choose one of two roads to AI development: proprietary or open source. This dichotomy has existed for decades, with both sides achieving great levels of success. However, I would argue that the stakes for AI are higher than we’ve ever seen, and that the open source model is critical for the productive, economically feasible, and safe productization and consumption of AI. And, in terms of open source, t...
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By Matt Asay Everyone is doing AI, but no one knows why. That’s an overstatement, of course, but it feels like the market has hit peak hype without peak productivity. As Monte Carlo CEO Barr Moses highlights from a recent Wakefield survey, 91% of data leaders are building AI applications, but two-thirds of that same group said they don’t trust their data to large language models (LLMs). In other words, they’re building AI on sand. To be successful, we need to move beyond the confusing hype and help enterprises make sense of AI. In other words, we need more trust (open models) and fewer moving ...
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By David Linthicum In the rapidly evolving field of cloud computing, the emergence of generative AI agents, or more colloquially, agentic AI, heralds a potential paradigm shift in how we do AI in the cloud—even before we fully capitalize on generative AI’s true potential. Just as cloud computing transformed the tech landscape, agentic AI has the potential to revolutionize our approach to generative AI architecture by introducing autonomy, intelligence, and efficiency. Before we delve deeper, it’s important to understand that agentic AI is not a one-size-fits-all solution for all AI deployments...
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By Matt Asay Everyone wants in on the AI boom. For now, however, you can probably count on one hand the number of vendors cashing in. The most obvious one is Nvidia, of course. Nvidia has earned nation-state levels of cash for its GPUs ($26 billion in the first quarter of 2024 alone). Beyond Nvidia are the big three cloud vendors and OpenAI. Beyond that cast of five, however, it’s pretty hard to find many—yet. That “yet” is the key here. We are absolutely in a frothy period for AI, where vendors are selling “hopium” and enterprises are buying just enough to fuel proofs of concept, without much...
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By Matt Asay Everyone wants in on the AI boom. For now, however, you can probably count on one hand the number of vendors cashing in. The most obvious one is Nvidia, of course, Nvidia has earned nation-state levels of cash for its GPUs ($26 billion in the first quarter of 2024 alone). Beyond Nvidia are the big cloud vendors and OpenAI. Beyond that cast of five, however, it’s pretty hard to find many—yet. That “yet” is the key here. We are absolutely in a frothy period for AI, where vendors are selling “hopium” and enterprises are buying just enough to fuel proofs of concept, without much produ...
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By Matt Asay Strap in, the AI revolution has hit overdrive!!! Except, of course, that it hasn’t, and it won’t anytime soon, despite what you’ve read in countless breathless editorials. It’s not that AI isn’t important, or that it doesn’t have the potential to change everything. It is and it does, but it’s simply not going to happen as fast as we think. The reason is people. It’s always people. The hubris of forecastsThe Wall Street Journal columnist Christopher Mims reminds us of this in his latest column. He says that we all fall prey to the “all-too-common error of technological determinism—...
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