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01 Jun

Mistral AI Unveils Codestral, Its First GenAI Model For Developers

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(whiteMocca/Shutterstock) Mistral AI, one of Europe’s premier artificial intelligence startups, has marked its entry into the programming and development space with the launch of Codestral, an open-weight generative AI model explicitly designed for code generation tasks. Trained on a dataset of 80 programming languages, Codestral is designed for various coding functions and can complete any partial code using a fill-in-the-middle mechanism, according to a blog post released by Mistral. Developers can also use the model as a learning tool to improve their coding skills and minimize errors. Mistral claims that Codestral outperforms other AI models in coding tasks, including CodeLlama…

01 Jun

Delta Sharing and The Emergence of the Lakehouse Customer Data Platform (CDP)

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Special thanks to Caleb Benningfield and Sam Malissa at Amperity for their valuable insights and contributions to this blog. Today, businesses face a significant challenge in handling a greater volume and complexity of customer data to power personalization at scale while also staying compliant with privacy regulations. This means prioritizing data quality and implementing an effective governance layer, but existing tools and methods that businesses used to rely on aren't up to the challenge.To address this challenge, many businesses have transitioned from cloud data warehouses and data lakes to a data lakehouse architecture. The data lakehouse combines the best of what…

01 Jun

Conformal Recursive Feature Elimination

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arXiv:2405.19429v1 Announce Type: new Abstract: Unlike traditional statistical methods, Conformal Prediction (CP) allows for the determination of valid and accurate confidence levels associated with individual predictions based only on exchangeability of the data. We here introduce a new feature selection method that takes advantage of the CP framework. Our proposal, named Conformal Recursive Feature Elimination (CRFE), identifies and recursively removes features that increase the non-conformity of a dataset. We also present an automatic stopping criterion for CRFE, as well as a new index to measure consistency between subsets of features. CRFE selections are compared to the classical Recursive Feature Elimination…

01 Jun

A Full-duplex Speech Dialogue Scheme Based On Large Language Models

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arXiv:2405.19487v1 Announce Type: new Abstract: We present a generative dialogue system capable of operating in a full-duplex manner, allowing for seamless interaction. It is based on a large language model (LLM) carefully aligned to be aware of a perception module, a motor function module, and the concept of a simple finite state machine (called neural FSM) with two states. The perception and motor function modules operate simultaneously, allowing the system to simultaneously speak and listen to the user. The LLM generates textual tokens for inquiry responses and makes autonomous decisions to start responding to, wait for, or interrupt the user…

01 Jun

Career Notes for May 2024

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(metamorworks/Shutterstock) In this monthly feature, we’ll keep you up-to-date on the latest career developments for individuals in the big data community. Whether it’s a promotion, new company hire, or even an accolade, we’ve got the details. Check in each month for an updated list and you may even come across someone you know, or better yet, yourself! Matt Garman Matt Garman AWS announced that starting June 3, Matt Garman will be its new CEO, replacing Adam Selipsky, who announced his resignation earlier in the month. Garman joined AWS as a college intern in 2005 when he was still in college,…

01 Jun

Empowering Data Teams with Snowplow for First-Party Digital Event Data Collection

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With more and more customer interactions moving into the digital domain, it's increasingly important that organizations develop insights into online customer behaviors. In the past, many organizations relied on third-party data collectors for this, but growing privacy concerns, the need for more timely access to data and requirements for customized information collection are driving many organizations to move this capability in-house. Using customer data infrastructure (CDI) platforms such as Snowplow coupled with the real-time data processing and predictive capabilities of Databricks, these organizations can develop deeper, richer, more timely and more privacy-aware insights that allow them to maximize the potential…

31 May

On the Convergence of Multi-objective Optimization under Generalized Smoothness

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arXiv:2405.19440v1 Announce Type: new Abstract: Multi-objective optimization (MOO) is receiving more attention in various fields such as multi-task learning. Recent works provide some effective algorithms with theoretical analysis but they are limited by the standard $L$-smooth or bounded-gradient assumptions, which are typically unsatisfactory for neural networks, such as recurrent neural networks (RNNs) and transformers. In this paper, we study a more general and realistic class of $ell$-smooth loss functions, where $ell$ is a general non-decreasing function of gradient norm. We develop two novel single-loop algorithms for $ell$-smooth MOO problems, Generalized Smooth Multi-objective Gradient descent (GSMGrad) and its stochastic variant, Stochastic…

31 May

Diffusion Policy Attacker: Crafting Adversarial Attacks for Diffusion-based Policies

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arXiv:2405.19424v1 Announce Type: new Abstract: Diffusion models (DMs) have emerged as a promising approach for behavior cloning (BC). Diffusion policies (DP) based on DMs have elevated BC performance to new heights, demonstrating robust efficacy across diverse tasks, coupled with their inherent flexibility and ease of implementation. Despite the increasing adoption of DP as a foundation for policy generation, the critical issue of safety remains largely unexplored. While previous attempts have targeted deep policy networks, DP used diffusion models as the policy network, making it ineffective to be attacked using previous methods because of its chained structure and randomness injected. In…

31 May

Critical Learning Periods: Leveraging Early Training Dynamics for Efficient Data Pruning

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[Submitted on 29 May 2024] View a PDF of the paper titled Critical Learning Periods: Leveraging Early Training Dynamics for Efficient Data Pruning, by Everlyn Asiko Chimoto and 5 other authors View PDF HTML (experimental) Abstract:Neural Machine Translation models are extremely data and compute-hungry. However, not all data points contribute equally to model training and generalization. Data pruning to remove the low-value data points has the benefit of drastically reducing the compute budget without significant drop in model performance. In this paper, we propose a new data pruning technique: Checkpoints Across Time (CAT), that leverages early model training dynamics to…

31 May

How Delta Sharing Enables Secure End-to-End Collaboration | Databricks Blog

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In today's digital landscape, secure data sharing is critical to operational efficiency and innovation. Databricks and the Linux Foundation developed Delta Sharing as the first open source approach to data sharing across data, analytics and AI. Databricks provides secure data exchange, facilitating seamless sharing across platforms, clouds and regions. Enterprises of all sizes trust Delta Sharing, which supports a broad spectrum of applications and diverse data formats. This flexibility makes it a reliable tool for organizations seeking to harness the full potential of their data assets.In this blog, we will review Delta Sharing's security architecture through three different sharing scenarios—…