InfoWorld

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.

Angular 18, the next planned release of Google’s TypeScript-based web app development framework, is due to arrive on May 22, with features such as deferrable views and declarative control flow moving out of developer preview to a stable stage.

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.

As most IT people know, GPUs are in high demand and are critical for running and training generative AI models. The alternative cloud sector, also known as microclouds, is experiencing a significant surge. Businesses such as CoreWeave, Lambda Labs, Voltage Park, and Together AI are at the forefront of this movement. CoreWeave, which started as a cryptocurrency mining venture, has become a major provider of GPU infrastructure.

Oracle in its Spring 2024 roadmap for Java SE (Standard Edition) reconfirmed it will extend support for Java 11 through January 2032, and will support Java 8 and Java 11 on the Solaris operating system until at least December 2030 and January 2032 respectively.

The innovation hub of RSAC 2024, the RSAC Early Stage Expo was specifically designed to showcase emerging players in the information security industry. Among the 50 exhibitors crammed into the second floor booth space, seven VC-backed up-and-comers in application security and devsecops caught our eye.

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.

Back in 2014, when the wave of containers, Kubernetes, and distributed computing was breaking over the technology industry, Torkel Ödegaard was working as a platform engineer at eBay Sweden. Like other devops pioneers, Ödegaard was grappling with the new form factor of microservices and containers and struggling to climb the steep Kubernetes operations and troubleshooting learning curve. 

“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... Читать дальше...

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).