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FasterViT for Image Classification
24 May

FasterViT for Image Classification

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FasterViT is a family of Vision Transformer models that is both fast and provides better accuracy than other ViT models. It combines the local representation learning of CNNs and the global learning properties of ViTs. In this article, we will cover the FasterViT model for image classification. Figure 1. FasterViT architecture, throughput, and benchmark on ImageNet1K. We will go through image inference using the pretrained network along with a brief of its architectural components. Furthermore, we will also fine-tune a FasterViT model for image classification. We will cover the following topics in this article We will start with a discussion…
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Enterprises Have Just Two Years to Harness the Full Potential of GenAI: Genpact and HFS Report
24 May

Enterprises Have Just Two Years to Harness the Full Potential of GenAI: Genpact and HFS Report

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(Berit Kessler/Shutterstock) The advent of GenAI has proven to be the first real innovation to disrupt industry since the advent of the internet. While GenAI is only over a year old, it has left enterprises scrambling to gain a competitive advantage. However, the window of opportunity for these enterprises may be shorter than anticipated. Enterprises have only two years to adopt GenAI before competitive disadvantages emerge, according to a new report by Genpact and HFS Research. The report also highlights that only 5% of enterprises have mature GenAI initiatives, signaling an urgent need for acceleration of GenAI adoption.  Genpact is…
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Data Machina #248
24 May

Data Machina #248

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Jailbreaking AI Models: It’s easy. Hundreds of millions of dollars have been thrown at AI Safety & Alignment over the years. Despite that, jailbreaking LLMs in April 2024 is easy. Oddly enough, as the LLM models become more capable and sophisticated, the jailbreaking attacks are becoming easier to perform, more effective, and frequent. Gary Marcus - who is hypercritical about LLMs and current AI trends- just published this very opinionated post: An unending array of jailbreaking attacks could be the death of LLMs.I often speak to colleagues and clients about the “LLM jailbreaking elephant in the room.” And they all…
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From Constant Firefighting to Innovation: How Databricks’s Money Team Halved Their Ops Burden in One Year!
24 May

From Constant Firefighting to Innovation: How Databricks’s Money Team Halved Their Ops Burden in One Year!

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In the last year, the Databricks Money Engineering Team has embarked on an exhilarating journey, achieving nearly double our operational efficiency. We are excited to share this transformative experience with you, highlighting the specific strategies that fueled our success. In this post, we will discuss how introducing an Ops Czar reduced operational burden while at the same time empowered our engineering team. We will discuss pragmatism and Databricks first principles."In Unity, Strength": How Collective Effort and Strategic Efficiency Doubled Our CapabilitiesThe Money team is at the heart of commercializing Databricks's products, such as Workflows and Notebooks. We handle everything from…
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Score-CDM: Score-Weighted Convolutional Diffusion Model for Multivariate Time Series Imputation
24 May

Score-CDM: Score-Weighted Convolutional Diffusion Model for Multivariate Time Series Imputation

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arXiv:2405.13075v1 Announce Type: new Abstract: Multivariant time series (MTS) data are usually incomplete in real scenarios, and imputing the incomplete MTS is practically important to facilitate various time series mining tasks. Recently, diffusion model-based MTS imputation methods have achieved promising results by utilizing CNN or attention mechanisms for temporal feature learning. However, it is hard to adaptively trade off the diverse effects of local and global temporal features by simply combining CNN and attention. To address this issue, we propose a Score-weighted Convolutional Diffusion Model (Score-CDM for short), whose backbone consists of a Score-weighted Convolution Module (SCM) and an Adaptive…
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NieR: Normal-Based Lighting Scene Rendering
24 May

NieR: Normal-Based Lighting Scene Rendering

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arXiv:2405.13097v1 Announce Type: new Abstract: In real-world road scenes, diverse material properties lead to complex light reflection phenomena, making accurate color reproduction crucial for enhancing the realism and safety of simulated driving environments. However, existing methods often struggle to capture the full spectrum of lighting effects, particularly in dynamic scenarios where viewpoint changes induce significant material color variations. To address this challenge, we introduce NieR (Normal-Based Lighting Scene Rendering), a novel framework that takes into account the nuances of light reflection on diverse material surfaces, leading to more precise rendering. To simulate the lighting synthesis process, we present the LD…
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An Assessment of Model-On-Model Deception
24 May

An Assessment of Model-On-Model Deception

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[Submitted on 10 May 2024] View a PDF of the paper titled An Assessment of Model-On-Model Deception, by Julius Heitkoetter and 2 other authors View PDF Abstract:The trustworthiness of highly capable language models is put at risk when they are able to produce deceptive outputs. Moreover, when models are vulnerable to deception it undermines reliability. In this paper, we introduce a method to investigate complex, model-on-model deceptive scenarios. We create a dataset of over 10,000 misleading explanations by asking Llama-2 7B, 13B, 70B, and GPT-3.5 to justify the wrong answer for questions in the MMLU. We find that, when models…
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FasterViT for Semantic Segmentation
24 May

FasterViT for Semantic Segmentation

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In this article, we will modify the FasterViT model for semantic segmentation. FasterViT is a family of CNN-Transformer hybrid models for deep learning based computer vision tasks. The FasterViT models are faster and more accurate on several computer vision benchmarks, particularly the ImageNet dataset. We can also modify the model for semantic segmentation to get excellent results on a custom dataset. Although it is not straightforward and requires several changes to the architecture, it is possible. In this article, we will cover the architectural details and the changes we must make to the FasterViT model for semantic segmentation. Figure 1.…
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Fujitsu Chosen For GENIAC Project To Enhance Reliability Of GenAI in Business Applications
24 May

Fujitsu Chosen For GENIAC Project To Enhance Reliability Of GenAI in Business Applications

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(issaro prakalung/Shutterstock) Fujitsu, one of the leading technology and business solutions providers, has been chosen for the research and development project for the enhanced infrastructures for post-5G information and communication systems. This project is part of the Generative AI Accelerator Challenge (GENIAC) initiative by Japan’s New Energy and Industrial Technology Development Organization (NEDO).  The goal of the GENIAC project is to enhance Japan’s capabilities to harness the transformative power of GenAI by bringing together the knowledge of stakeholders in Japan and other countries. Fujitsu will be responsible for R&D on GenAI technologies with a focus on combining knowledge graphs with…
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