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Deci delivers breakthrough inference performance on Intel’s 4th Gen Sapphire Rapids CPU

Deci, the deep learning company building the next generation of AI, announced a breakthrough performance on Intel’s newly released 4th Gen Intel® Xeon® Scalable processors, code-named Sapphire Rapids. By optimizing the AI models which run on Intel’s new hardware, Deci enables AI developers to achieve GPU-like inference performance on CPUs in production for both Computer Vision and Natural Language Processing (NLP) tasks.

Got It AI Develops AI to Identify and Address ChatGPT Hallucinations for Enterprise Applications

Got It AI, the Autonomous Conversational AI company, announced an innovative new “Truth Checker” AI that can identify when ChatGPT is hallucinating (generating fabricated answers) when answering user questions over a large set of articles or knowledge base. This innovation makes it possible to deploy ChatGPT-like experiences without the risk of providing incorrect responses to users or employees.

AI-generated Texts Could Increase People’s Exposure to Threats

Research finds that large language models are susceptible to abuse through creative prompt engineering, forcing people to become even more skeptical about what they read.

New Research Shows that 77% of Businesses Using Natural Language Processing Expect to Increase Investment

More than three-quarters of businesses with active natural language processing (NLP) projects plan to increase spending on in the next 12 to 18 months, according to new data from expert.ai, a leading company in artificial intelligence (AI) for language understanding. The finding is one of many data points culled from a recent survey and detailed in expert.ai’s new report, The 2023 Expert NLP Survey Report: Trends driving NLP Investment and Innovation.

Big Data Industry Predictions for 2023

Welcome to insideBIGDATA’s annual technology predictions round-up! The big data industry has significant inertia moving into 2023. In order to give our valued readers a pulse on important new trends leading into next year, we here at insideBIGDATA heard from all our friends across the vendor ecosystem to get their insights, reflections and predictions for what may be coming. We were very encouraged to hear such exciting perspectives.

Research Highlights: R&R: Metric-guided Adversarial Sentence Generation

Large language models are a hot topic in AI research right now. But there’s a hotter, more significant problem looming: we might run out of data to train them on … as early as 2026. Kalyan Veeramachaneni and the team at MIT Data-to-AI Lab may have found the solution: in their new paper on Rewrite and Rollback (“R&R: Metric-Guided Adversarial Sentence Generation”), an R&R framework can tweak and turn low-quality (from sources like Twitter and 4Chan) into high-quality data (texts from sources like Wikipedia and industry websites) by rewriting meaningful sentences and thereby adding to the amount of the right type of data to test and train language models on.

Chung-Ang University Researchers Develop Algorithm for Optimal Decision Making under Heavy-tailed Noisy Rewards

Researchers from South Korean Chung-Ang University propose methods that theoretically guarantee minimal loss for worst case scenarios with minimal prior information for heavy-tailed reward distributions.

The Ultimate Guide for Computer Vision Deployment on NVIDIA Jetson

Our friends from the Deci Team offer “The Ultimate Guide for Computer Vision Deployment on NVIDIA Jetson” which is perfect if you’re running or planning to run computer vision applications on NVIDIA Jetson devices. Written by Deci’s deep learning engineers for deep learning engineers, here’s what you’ll learn: (i) Best practices and technical know-how on model selection, training, optimization and deployment on Jetson, (ii) Tips for selecting the right NVIDIA Jetson for your use case, (iii) Code, tools, further resources for every recommendation provided

The Move Toward Green Machine Learning

A new study suggests tactics for machine learning engineers to cut their carbon emissions. Led by David Patterson, researchers at Google and UC Berkeley found that AI developers can shrink a model’s carbon footprint a thousand-fold by streamlining architecture, upgrading hardware, and using efficient data centers. 

Synthesis AI Expands Product Offerings to Enable the Development of Advanced Human-Centric Computer Vision Models

Synthesis AI, a pioneer in synthetic data technologies, released Synthesis Humans and Synthesis Scenarios, two new products that represent the broadest and deepest offerings of human-centric synthetic data. Through a proprietary combination of generative AI and cinematic CGI pipelines, the Synthesis platform can programmatically create vast amounts of perfectly labeled image data at orders of magnitude increased speed and reduced cost compared to current approaches.