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03 Jun

Global Survey By McKinsey: GenAI Adoption Starting To Deliver Value

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Investments in GenAI are beginning to create value for organizations according to a new global survey by McKinsey, a leading management consultancy firm. While 2023 was a year of investing in GenAI initiatives, 2024 is about deriving business value from this new technology. In just one and half years since OpenAI’s ChatGPT was launched, 65% of organizations are now regularly using AI, according to the McKinsey report. This is nearly double the percentage from last year’s survey. The report suggests that organizations are now using AI in more parts of the business. More than half of the respondents shared that…

03 Jun

Data + AI Strategy: Platform Focus

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The secret to good AI is great data. As AI adoption soars, the data platform is the most important component of any enterprise's technology stack. It’s increasingly clear that Generative AI systems won’t be one monolithic, but rather a combination of many different components that must work together. And while data is one of the most important pieces, there are many other functions required for enterprises to actually deploy the models into the real-world. That’s why, when businesses are looking to build the foundational platform that will support the breadth of their data and AI needs, they should keep three core pillars…

03 Jun

Back to the Basics on Predicting Transfer Performance

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arXiv:2405.20420v1 Announce Type: new Abstract: In the evolving landscape of deep learning, selecting the best pre-trained models from a growing number of choices is a challenge. Transferability scorers propose alleviating this scenario, but their recent proliferation, ironically, poses the challenge of their own assessment. In this work, we propose both robust benchmark guidelines for transferability scorers, and a well-founded technique to combine multiple scorers, which we show consistently improves their results. We extensively evaluate 13 scorers from literature across 11 datasets, comprising generalist, fine-grained, and medical imaging datasets. We show that few scorers match the predictive performance of the simple…

03 Jun

Slight Corruption in Pre-training Data Makes Better Diffusion Models

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03 Jun

Automated Focused Feedback Generation for Scientific Writing Assistance

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arXiv:2405.20477v1 Announce Type: new Abstract: Scientific writing is a challenging task, particularly for novice researchers who often rely on feedback from experienced peers. Recent work has primarily focused on improving surface form and style rather than manuscript content. In this paper, we propose a novel task: automated focused feedback generation for scientific writing assistance. We present SWIF$^{2}$T: a Scientific WrIting Focused Feedback Tool. It is designed to generate specific, actionable and coherent comments, which identify weaknesses in a scientific paper and/or propose revisions to it. Our approach consists of four components - planner, investigator, reviewer and controller - leveraging multiple…

03 Jun

Snowflake Embraces Open Data with Polaris Catalog

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(monticello/Shutterstock) On the first day of its Data Cloud Summit today, Snowflake unveiled Polaris, a new data catalog for data stored in the Apache Iceberg format. In addition to contributing Polaris to the open source community, the catalog also enables Snowflake customers to use open compute engines with their Iceberg-based Snowflake data, including Apache Spark, Apache Flink, Presto, Trino, and Dremio. The launch of Polaris represents a significant embrace of open source and open data on the part of Snowflake, which grew its business predominantly through a closed data stack, including proprietary table format and a proprietary SQL processing engine.…

03 Jun

How AI Will Impact Cybersecurity and Its Implications for SIEM

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Artificial Intelligence (AI) is changing the way various industries operate, and cybersecurity is no exception. Over the years, cyber threats have been complex and frequent, and the need for advanced, adaptive security measures is greater than ever. AI and Machine Learning (ML) offer powerful tools to enhance cybersecurity defenses, but they also bring new challenges and risks. This article examines how AI will impact cybersecurity, highlighting its implications for Security Information and Event Management (SIEM) systems. Main Challenges Cybersecurity Faces Today Imagine a situation where an organization is facing a complex, multi-vector cyber attack, and AI is incorporated into the…

03 Jun

Enhancing Antibiotic Stewardship using a Natural Language Approach for Better Feature Representation

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arXiv:2405.20419v1 Announce Type: new Abstract: The rapid emergence of antibiotic-resistant bacteria is recognized as a global healthcare crisis, undermining the efficacy of life-saving antibiotics. This crisis is driven by the improper and overuse of antibiotics, which escalates bacterial resistance. In response, this study explores the use of clinical decision support systems, enhanced through the integration of electronic health records (EHRs), to improve antibiotic stewardship. However, EHR systems present numerous data-level challenges, complicating the effective synthesis and utilization of data. In this work, we transform EHR data into a serialized textual representation and employ pretrained foundation models to demonstrate how this…

03 Jun

Is Synthetic Data all We Need? Benchmarking the Robustness of Models Trained with Synthetic Images

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[Submitted on 30 May 2024] View a PDF of the paper titled Is Synthetic Data all We Need? Benchmarking the Robustness of Models Trained with Synthetic Images, by Krishnakant Singh and 4 other authors View PDF HTML (experimental) Abstract:A long-standing challenge in developing machine learning approaches has been the lack of high-quality labeled data. Recently, models trained with purely synthetic data, here termed synthetic clones, generated using large-scale pre-trained diffusion models have shown promising results in overcoming this annotation bottleneck. As these synthetic clone models progress, they are likely to be deployed in challenging real-world settings, yet their suitability remains…

03 Jun

Extending the Massive Text Embedding Benchmark to French

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arXiv:2405.20468v1 Announce Type: new Abstract: In recent years, numerous embedding models have been made available and widely used for various NLP tasks. Choosing a model that performs well for several tasks in English has been largely simplified by the Massive Text Embedding Benchmark (MTEB), but extensions to other languages remain challenging. This is why we expand MTEB to propose the first massive benchmark of sentence embeddings for French. Not only we gather 22 existing datasets in an easy-to-use interface, but we also create three new French datasets for a global evaluation over 8 different tasks. We perform a large scale…