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Donostia, Spain April 8, 2025 Multiverse Computing today released two new AI models compressed by CompactifAI, Multiverse’s AI compressor: 80 percent compressed versions of Llama 3.1-8B and Llama 3.3-70B.
Wells Fargos generative AI assistant, Fargo, surpassed 245 million interactions in 2024 using a model-agnostic architecture powered by Googles Flash 2.0. The banks privacy-forward orchestration approach offers a blueprint for regulated industries looking to scale AI safely and efficiently.
Palo Alto, April 8, 2025 Vectara, a platform for enterprise Retrieval-Augmented Generation (RAG) and AI-powered agents and assistants, today announced the launch of Open RAG Eval, its open-source RAG evaluation framework.
Document-heavy workflows slow down productivity, bury institutional knowledge, and drain resources. But with the right AI implementation, these inefficiencies become opportunities for transformation. So how do you identify where to start and how to succeed? Learn how to develop a clear, practical roadmap for leveraging AI to streamline processes, automate knowledge work, and unlock real operational gains.
Black box AI models have revolutionized how decisions are made across multiple industries, yet few fully understand the intricacies behind these systems. These models often process vast amounts of data, producing outputs that can significantly impact operational processes, organizational strategies, and even individual lives. However, the opacity of how these decisions are reached raises concerns about bias, accountability, and transparency.
As Large Language Models (LLMs) continue to advance quickly, one of their most sight after applications is in RAG systems. Retrieval-Augmented Generation, or RAG connects these models to external information sources, thereby increasing their usability. This helps ground their answers to facts, making them more reliable. In this article, we will compare the performance and […] The post LLaMA 4 vs.
Nowadays, everyone across AI and related communities talks about generative AI models, particularly the large language models (LLMs) behind widespread applications like ChatGPT, as if they have completely taken over the field of machine learning.
Nowadays, everyone across AI and related communities talks about generative AI models, particularly the large language models (LLMs) behind widespread applications like ChatGPT, as if they have completely taken over the field of machine learning.
Large Language Models (LLMs) have become integral to modern AI applications, but evaluating their capabilities remains a challenge. Traditional benchmarks have long been the standard for measuring LLM performance, but with the rapid evolution of AI, many are questioning their continued relevance. Are these benchmarks still a reliable indicator of the real-world performance of LLMs?
Active learning in machine learning is a fascinating approach that allows algorithms to actively engage in the learning process. Instead of passively receiving information, these systems identify which data points are most helpful for refining their models, making them particularly efficient in training with limited labeled data. This adaptability is essential in todays data-driven environment, where acquiring labeled data can be resource-intensive.
Good April day to you! It was a wild week for more than the HPC-AI sector last week, heres a brief (7:39) look at some key developments: U.S. tariffs, the technology sector and advanced chips, Intel-TSMC.
AI accelerators are transforming the landscape of technology by providing specialized hardware optimized for artificial intelligence tasks. As organizations increasingly rely on AI to enhance operations and analysis, the demand for efficient data processing grows. These accelerators not only speed up computational processes but also enhance energy efficiency, making them a game-changer in various industries.
Speaker: Ben Epstein, Stealth Founder & CTO | Tony Karrer, Founder & CTO, Aggregage
When tasked with building a fundamentally new product line with deeper insights than previously achievable for a high-value client, Ben Epstein and his team faced a significant challenge: how to harness LLMs to produce consistent, high-accuracy outputs at scale. In this new session, Ben will share how he and his team engineered a system (based on proven software engineering approaches) that employs reproducible test variations (via temperature 0 and fixed seeds), and enables non-LLM evaluation m
Nvidia NIM, or Nvidia Inference Machine, represents a significant leap forward in the deployment of AI models. By leveraging the unparalleled power of Nvidia GPUs, NIM enhances inference performance, making it a pivotal tool for industries where real-time predictions are crucial. This technology is designed to streamline the integration and operational efficiency of AI applications, catering to a variety of sectors, including automotive, healthcare, and finance.
The ML stack is an essential framework for any data scientist or machine learning engineer. With the ability to streamline processes ranging from data preparation to model deployment and monitoring, it enables teams to efficiently convert raw data into actionable insights. Understanding the components and benefits of an ML stack can empower professionals to harness the true potential of machine learning technologies.
In the accounting world, staying ahead means embracing the tools that allow you to work smarter, not harder. Outdated processes and disconnected systems can hold your organization back, but the right technologies can help you streamline operations, boost productivity, and improve client delivery. Dive into the strategies and innovations transforming accounting practices.
Google Search Labs is an exciting initiative that opens the door to a new realm of interactive possibilities within Google Search. Since its launch on May 10, 2023, at the Google I/O conference, it invites users to engage with experimental features designed to enrich their search experience. Users now have the chance to participate in shaping the future of search technology by providing feedback and insights on these developing features.
A prominent American academic working in Thailand has been charged with insulting the monarchy, in a rare case of a foreign national being charged under the kingdoms strict lese majeste law.
Text generation inference represents a fascinating frontier in artificial intelligence, where machines not only process language but also create new content that mimics human writing. This technology has opened a plethora of applications, impacting industries ranging from customer service to creative writing. Understanding how this process worksincluding the algorithms and large language models behind itcan help us appreciate the capabilities and considerations of AI text generation.
Speaker: Chris Townsend, VP of Product Marketing, Wellspring
Over the past decade, companies have embraced innovation with enthusiasm—Chief Innovation Officers have been hired, and in-house incubators, accelerators, and co-creation labs have been launched. CEOs have spoken with passion about “making everyone an innovator” and the need “to disrupt our own business.” But after years of experimentation, senior leaders are asking: Is this still just an experiment, or are we in it for the long haul?
Bittensor: The Cutting Edge of Decentralized AI Infrastructure As artificial intelligence becomes increasingly central to the global digital economy, decentralized alternatives are beginning to challenge the dominance of corporate-led AI development.
Data science techniques are the backbone of modern analytics, enabling professionals to transform raw data into meaningful insights. By employing various methodologies, analysts uncover hidden patterns, predict outcomes, and support data-driven decision-making. Understanding these techniques can enhance a data scientist’s toolkit, making it easier to navigate the complexities of big data.
Solar power has doubled in just three years, according to thinktank Ember, but rising electricity demand from air conditioning, AI and electric vehicles means electricity from fossil fuel sources still grew.
Conversational agents have transformed the way we interact with technology, bridging gaps between humans and machines. These intelligent systems not only respond to queries with remarkable accuracy but also learn from interactions to improve user experiences over time. The evolution of conversational agents has led to their widespread use in customer service, e-commerce, and even healthcare, making them indispensable tools in various industries.
In this new webinar, Tamara Fingerlin, Developer Advocate, will walk you through many Airflow best practices and advanced features that can help you make your pipelines more manageable, adaptive, and robust. She'll focus on how to write best-in-class Airflow DAGs using the latest Airflow features like dynamic task mapping and data-driven scheduling!
Researchers are studying why the energy factories are moving between cells and whether the process can be harnessed to treat cancer and other diseases. Researchers are studying why the energy factories are moving between cells and whether the process can be harnessed to treat cancer and other diseases.
Google Cloud unveiled its seventh-generation Tensor Processing Unit (TPU) called Ironwood on Wednesday, a custom AI accelerator that the company claims delivers more than 24 times the computing power of the worlds fastest supercomputer when deployed at scale.
Many software teams have migrated their testing and production workloads to the cloud, yet development environments often remain tied to outdated local setups, limiting efficiency and growth. This is where Coder comes in. In our 101 Coder webinar, you’ll explore how cloud-based development environments can unlock new levels of productivity. Discover how to transition from local setups to a secure, cloud-powered ecosystem with ease.
A new company, Deep Cogito, has emerged from stealth with a family of openly available AI models that can be switched between reasoning and non-reasoning modes.
Large enterprises face unique challenges in optimizing their Business Intelligence (BI) output due to the sheer scale and complexity of their operations. Unlike smaller organizations, where basic BI features and simple dashboards might suffice, enterprises must manage vast amounts of data from diverse sources. What are the top modern BI use cases for enterprise businesses to help you get a leg up on the competition?
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