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Cerebras Wafer-Scale Cluster Brings Push-Button Ease and Linear Performance Scaling to Large Language Models

Cerebras Systems, a pioneer in accelerating artificial intelligence (AI) compute, unveiled the Cerebras Wafer-Scale Cluster, delivering near-perfect linear scaling across hundreds of millions of AI-optimized compute cores while avoiding the pain of the distributed compute. With a Wafer-Scale Cluster, users can distribute even the largest language models from a Jupyter notebook running on a laptop with just a few keystrokes. This replaces months of painstaking work with clusters of graphics processing units (GPU).

Heard on the Street – 9/12/2022

Welcome to insideBIGDATA’s “Heard on the Street” round-up column! In this regular feature, we highlight thought-leadership commentaries from members of the big data ecosystem. Each edition covers the trends of the day with compelling perspectives that can provide important insights to give you a competitive advantage in the marketplace.

Deci’s Natural Language Processing (NLP) Model Achieves Breakthrough Performance at MLPerf

Deci, the deep learning company harnessing Artificial Intelligence (AI) to build better AI, announced results for its Natural Language Processing (NLP) inference model submitted to the MLPerf Inference v2.1 benchmark suite under the open submission track.

AMAX Launches GPU Servers Powered by Intel’s Newest Data Center GPU Flex Series for AI, Gaming, & Media Streaming

AMAX, a leading provider of turnkey rack-scale High Performance Computing (HPC) solutions, Deep Learning/AI applications and server appliance manufacturing, announces the new AceleMax X-122-Flex server solution featuring Intel’s next-generation Data Center GPU Flex Series, (formerly code-named Arctic Sound-M), providing the capability of a graphics processing (GPU) solution handling high density and complex workloads targeted towards media delivery, cloud gaming, AI, metaverse, and other emerging visual cloud use cases.

Research Highlights: An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion

In this regular column we take a look at highlights for breaking research topics of the day in the areas of big data, data science, machine learning, AI and deep learning. For data scientists, it’s important to keep connected with the research arm of the field in order to understand where the technology is headed. Enjoy!

Artificial Intelligence – What’s in a Name?

As the tech industry hype cycle continues to churn my in-box every day, I find myself reflecting on the meme du jour of “artificial intelligence.” My initial reaction to over-hyped terms is to resist giving them more credence than they may deserve. “AI” associated with just about everything falls in line with my reticence. Somehow all new products and/or services are related to “AI”: AI-based, AI-powered, AI-fueled, AI-motivated (oh come on!).

Research Highlights: Why Do Tree-based Models Still Outperform Deep Learning on Tabular Data?

In this regular column we take a look at highlights for breaking research topics of the day in the areas of big data, data science, machine learning, AI and deep learning. For data scientists, it’s important to keep connected with the research arm of the field in order to understand where the technology is headed. Enjoy!

A “Glass Box” Approach to Responsible Machine Learning 

In this contributed article, editorial consultant Jelani Harper suggests that machine learning doesn’t always have to be an abstruse technology. The multi-parameter and hyper-parameter methodology of complex deep neural networks, for example, is only one type of this cognitive computing manifestation. There are other machine learning varieties (and even some involving deep neural networks) in which the results of models, how they were determined, and which intricacies influenced them, are much more transparent.

Run:ai Releases Advanced Model Serving Functionality to Help Organizations Simplify AI Deployment 

Run:ai, a leader in compute orchestration for AI workloads, announced new features of its Atlas Platform, including two-step model deployment — which makes it easier and faster to get machine learning models into production. The company also announced a new integration with NVIDIA Triton Inference Server. These capabilities are particularly focused on supporting organizations in […]

Cortical.io Semantic Folding Approach Demonstrates a 2,800x Acceleration and 4,300x Increase in Energy Efficiency over BERT

Cortical.io announced its breakthrough prototype for classifying high volumes of unstructured text. Classifying documents or messages constitutes one of the most fundamental Natural Language Understanding (NLU) functions for business artificial intelligence (AI). The benchmark was carried out on two similar system setups using the same, off-the-shelve, dual AMD-Epyc server hardware. The “BERT” system, a transformer-based machine learning technique for natural language processing, was augmented by a NVidia GPU. The “Semantic Folding” approach utilized a cost comparable number of Xilinx Alveo FPGA accelerator cards.