Tracking animals without markers in the wild
29 May

Tracking animals without markers in the wild

stp2y0 CommentsGenAI
Researchers from the Cluster of Excellence Collective Behaviour developed a computer vision framework for posture estimation and identity tracking which they can use in indoor environments as well as in the wild. They have thus taken an important step towards markerless tracking of animals in the wild using computer vision and machine learning. Two pigeons are pecking grains in a park in Konstanz. A third pigeon flies in. There are four cameras in the immediate vicinity. Doctoral students Alex Chan and Urs Waldmann from the Cluster of Excellence Collective Behaviour at the University of Konstanz are filming the scene. After…
Read More
The work perk that GenZ really wants
29 May

The work perk that GenZ really wants

stp2y0 CommentsRAG models
For some young workers, having flexibility in their jobs is nearly as important as how much they get paid.In a survey of Gen Zers in the US by the career platform iHire, 81.3% said choice over "when, where, and how" they would do a job was either "extremely" or "very" important.That was nearly in line with the 82.2% who said having a potential employer offer a "fair and competitive" salary was either extremely or very important.The survey involved nearly 1,100 Gen Zers and took place in March and April.The findings highlight what's a priority for some among the youngest slice…
Read More
On margin-based generalization prediction in deep neural networks
29 May

On margin-based generalization prediction in deep neural networks

stp2y0 CommentsViral News
[Submitted on 20 May 2024] View a PDF of the paper titled On margin-based generalization prediction in deep neural networks, by Coenraad Mouton View PDF Abstract:Understanding generalization in deep neural networks is an active area of research. A promising avenue of exploration has been that of margin measurements: the shortest distance to the decision boundary for a given sample or that sample's representation internal to the network. Margin-based complexity measures have been shown to be correlated with the generalization ability of deep neural networks in some circumstances but not others. The reasons behind the success or failure of these metrics…
Read More
29 May

Prodigy | 0.58.0 | ZenML Documentation

stp2y0 CommentsNews
Annotating data using Prodigy.Prodigy is a modern annotation tool for creating training and evaluation data for machine learning models. You can also use Prodigy to help you inspect and clean your data, do error analysis and develop rule-based systems to use in combination with your statistical models.Prodigy is a paid annotation tool. You will need a Prodigy is a paid tool. A license is required to download and use it with ZenML.The Prodigy Python library includes a range of pre-built workflows and command-line commands for various tasks, and well-documented components for implementing your own workflow scripts. Your scripts can specify…
Read More
IBM Storage Perpetual offers long-term upgrades, low cost – SiliconANGLE
29 May

IBM Storage Perpetual offers long-term upgrades, low cost – SiliconANGLE

stp2y0 CommentsChat-GPT, ,
As the wave of artificial intelligence technology opens possibilities and constrains budgets, IBM Corp. is offering an IBM Storage Assurance Perpetual model, a price-protected subscription for businesses wanting modern storage without breaking the bank. “What’s really unique and awesome about this Perpetual model is that it’s on client-owned infrastructure … that’s what’s really exciting for clients here, is it’s assets that they own but still get that price protection flexibility on,” said Audrey O’Donnell (pictured), product manager of IBM FlashSystem at IBM Corp. IBM’s Audrey O’Donnell talks to theCUBE about new storage options and AI’s impact on company storage methods.…
Read More
How to train your ViT for OOD Detection
29 May

How to train your ViT for OOD Detection

stp2y0 CommentsViral News
arXiv:2405.17447v1 Announce Type: new Abstract: VisionTransformers have been shown to be powerful out-of-distribution detectors for ImageNet-scale settings when finetuned from publicly available checkpoints, often outperforming other model types on popular benchmarks. In this work, we investigate the impact of both the pretraining and finetuning scheme on the performance of ViTs on this task by analyzing a large pool of models. We find that the exact type of pretraining has a strong impact on which method works well and on OOD detection performance in general. We further show that certain training schemes might only be effective for a specific type of…
Read More
SambaNova breaks Llama 3 speed record with 1,000 tokens per second
29 May

SambaNova breaks Llama 3 speed record with 1,000 tokens per second

stp2y0 CommentsChat-GPT
Time's almost up! There's only one week left to request an invite to The AI Impact Tour on June 5th. Don't miss out on this incredible opportunity to explore various methods for auditing AI models. Find out how you can attend here. There is no one simple speedometer to measure the speed of a generative AI model, but one of the leading approaches is by measuring how many tokens per second a model handles. Today, SambaNova Systems announced that it has achieved a new milestone in terms of gen AI performance, hitting a whopping 1,000 tokens per second with the…
Read More
Incremental Comprehension of Garden-Path Sentences by Large Language Models: Semantic Interpretation, Syntactic Re-Analysis, and Attention
29 May

Incremental Comprehension of Garden-Path Sentences by Large Language Models: Semantic Interpretation, Syntactic Re-Analysis, and Attention

stp2y0 CommentsViral News
arXiv:2405.16042v1 Announce Type: new Abstract: When reading temporarily ambiguous garden-path sentences, misinterpretations sometimes linger past the point of disambiguation. This phenomenon has traditionally been studied in psycholinguistic experiments using online measures such as reading times and offline measures such as comprehension questions. Here, we investigate the processing of garden-path sentences and the fate of lingering misinterpretations using four large language models (LLMs): GPT-2, LLaMA-2, Flan-T5, and RoBERTa. The overall goal is to evaluate whether humans and LLMs are aligned in their processing of garden-path sentences and in the lingering misinterpretations past the point of disambiguation, especially when extra-syntactic information (e.g.,…
Read More
No widgets found. Go to Widget page and add the widget in Offcanvas Sidebar Widget Area.