Alexander Lovell on LinkedIn: Encryption At Rest: Whose Threat Model Is It Anyway? (2024)

🚀 Forrester evaluates AI foundation models for language! 10 providers, 21 criteria—because who needs just benchmarks, right? Vendor strategy, tooling, scalability, and innovation are the new buzzwords. If you're serious about AI, this report is your new bedtime reading. Curious who's delivering on promises? Dive into this detailed analysis! 🔗 [Check it out here](https://lnkd.in/eaqZga8H)

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It’s going to be fun watching everything that’s old in #security be new again with #ai and #agents *grabs popcorn*

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The AI that I wanted: does dishes and cooks while I write poetry and make art. The #AI that I got: writes poetry and makes art while I do dishes and cook.

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📊🚨 Before diving into #AI for customer service, consider this: AI requires significant initial investment and ongoing maintenance. Data integration, model training, and infrastructure can cost millions of $$$, depending on complexity. Meanwhile, AI typically reduces customer service expenses by only 20-30%. Factor in these costs to see if the investment truly pays off. #AIcosts #CustomerService #TechInsights

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The real #ai arms race https://lnkd.in/g6EE8dqJ

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Has anyone here done a master’s program focused specifically on AI (rather than a CS degree with AI emphasis)? I’m considering one but with some hesitation it may prove too specialized…

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Andrej Karpathy's new project, "llm.c," offers a fascinating approach to training large language models directly in C/CUDA, without relying on extensive frameworks like #PyTorch. This minimalist method not only makes the process lightweight and efficient but also accessible for deeper learning and customization. I’m just getting started and already I found it remarkably efficient and enlightening, perfectly suited for anyone looking to deeply understand and engage with AI development. #AI #MachineLearning #TechTrendshttps://lnkd.in/gvsSsbxH

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Hot take: By 2030, graph neural nets (GNNs) will be more important than language models. Why? GNNs are set to become the backbone of next-generation AI systems, surpassing traditional neural network architectures in tasks that require capturing relational and structural information. GNNs excel in understanding the intricate relationships and interdependencies within data, enabling more accurate predictions and insights. As a result, we’re likely to see GNNs becoming increasingly integral in industries like healthcare, finance, and cybersecurity, where understanding complex relationships is crucial. With advancements in scalability and efficiency, GNNs are becoming more accessible, allowing for broader adoption and innovation across various sectors. So, if you're interested in staying ahead of the AI curve, keep an eye on the growth of graph neural nets. #gnn #neuralnet #ai #llm

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