generativeai
By David Linthicum According to Forrester’s Infrastructure Cloud Survey in 2023, 79% of about 1,300 enterprise cloud decision-makers surveyed said their organizations are implementing private clouds. Additionally, IDC forecasts that global spending on private, dedicated cloud services, including hosted private clouds, will hit $20.4 billion in 2024 and will at least double by 2027. In addition, global spending on enterprise private cloud infrastructure, including hardware, software, and support services, will be $51.8 billion in 2024 and grow to $66.4 billion in 2027, according to IDC. Of cour...
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By Paul Krill Google has expanded on its Gemma family of AI models, introducing the PaliGemma vision-language model (VLM) and announcing Gemma 2, the next generation of Gemma models based on a new architecture. The company also released the LLM Comparator in open source, an addition to its Responsible Generative AI Toolkit. Google announced the new products on May 14. The company described PaliGemma as a powerful open VLM inspired by the Pali-3 vision-language models, intended to be smaller, faster, and stronger. Built on components from the SigLIP vision model, PaliGemma is designed for a ran...
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By Jignesh Patel The hype and awe around generative AI have waned to some extent. “Generalist” large language models (LLMs) like GPT-4, Gemini (formerly Bard), and Llama whip up smart-sounding sentences, but their thin domain expertise, hallucinations, lack of emotional intelligence, and obliviousness to current events can lead to terrible surprises. Generative AI exceeded our expectations until we needed it to be dependable, not just amusing. In response, domain-specific LLMs have emerged, aiming to provide more credible answers. These LLM “specialists” include LEGAL-BERT for law, BloombergGP...
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By Matt Asay “AI models currently shine at helping so-so coders get more stuff done that works in the time they have,” argues engineer David Showalter. But is that right? Showalter was responding to Santiago Valdarrama’s contention that large language models (LLMs) are untrustworthy coding assistants. Valdarrama says, “Until LLMs give us the same guarantees [as programming languages, which consistently get computers to respond to commands], they’ll be condemned to be eternal ‘cool demos,’ useless for most serious applications.” He is correct that LLMs are decidedly inconsistent in how they res...
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By Martin Heller Back in the ancient days of machine learning, before you could use large language models (LLMs) as foundations for tuned models, you essentially had to train every possible machine learning model on all of your data to find the best (or least bad) fit. By ancient, I mean prior to the seminal paper on the transformer neural network architecture, “Attention is all you need,” in 2017. Yes, most of us continued to blindly train every possible machine learning model for years after that. It was because only hyper-scalers and venture-funded AI companies had access to enough GPUs or ...
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By Paul Krill Red Hat is extending its Lightspeed generative AI technology to work with the company’s Red Hat OpenShift hybrid cloud application platform as well as with Red Hat Enterprise Linux (RHEL). Announced May 7, Red Hat OpenShift Lightspeed and Red Hat Enterprise Linux Lightspeed will offer intelligent, natural language processing capabilities, intended to make OpenShift and RHEL easier for novices to use and more efficient for experienced professionals, Red Hat said. Red Hat OpenShift Lightspeed is slated for availability in late 2024. Red Hat Enterprise Linux is in the planning stage...
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By Paul Krill In a bid to “deepen the public conversation about how AI models should behave,” AI company OpenAI has introduced Model Spec, a document that shares the company’s approach to shaping desired model behavior. Model Spec, now in a first draft, was introduced May 8. The document specifies OpenAI’s approach to shaping desired model behavior and how the company evaluates trade-offs when conflicts arise. The approach includes objectives, rules, and default behaviors that will guide OpenAI’s researchers and AI trainers who work on reinforcement learning from human feedback (RLHF). The com...
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By Simon Bisson Both extremely promising and extremely risky, generative AI has distinct failure modes that we need to defend against to protect our users and our code. We’ve all seen the news, where chatbots are encouraged to be insulting or racist, or large language models (LLMs) are exploited for malicious purposes, and where outputs are at best fanciful and at worst dangerous. None of this is particularly surprising. It’s possible to craft complex prompts that force undesired outputs, pushing the input window past the guidelines and guardrails we’re using. At the same time, we can see outp...
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By Paul Krill Red Hat has unveiled Podman AI Lab, an extension to the Podman Desktop graphical interface that lets developers build generative AI-powered applications in containers. Announced May 7, Podman AI Lab is intended to make it easier to develop with AI in a local environment. The Podman AI Lab extension supports the adoption of generative AI for building intelligent applications or enhancing their workflow using AI-augmented development capabilities, Red Had said. Instructions for installing the Podman AI Lab extension can be found at developers.redhat.com. Podman AI Lab’s companion, ...
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By Paul Krill Red Hat has launched Red Hat Enterprise Linux AI (RHEL AI), described as a foundation model platform that allows users to more seamlessly develop and deploy generative AI models. Announced May 7 and available now as a developer preview, RHEL AI includes the Granite family of open-source large language models (LLMs) from IBM, InstructLab model alignment tools based on the LAB (Large-Scale Alignment for Chatbots) methodology, and a community-driven approach to model development through the InstructLab project, Red Hat said. The entire solution is packaged as a bootable RHEL image f...
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