421 research outputs found
GPTutor: an open-source AI pair programming tool alternative to Copilot
This paper presents the latest progress of GPTutor: a ChatGPT-powered
programming tool extension in Visual Studio Code. The emergence of Large
Language Models (LLMs) has improved software development efficiency, but their
performance can be hindered by training data limitations and prompt design
issues. Existing LLM development tools often operate as black boxes, with users
unable to view the prompts used and unable to improve performance by correcting
prompts when errors occur. To address the aforementioned issues, GPTutor was
introduced as an open-source AI pair programming tool, offering an alternative
to Copilot. GPTutor empowers users to customize prompts for various programming
languages and scenarios, with support for 120+ human languages and 50+
programming languages. Users can fine-tune prompts to correct the errors from
LLM for precision and efficient code generation. At the end of the paper, we
underscore GPTutor's potential through examples, including demonstrating its
proficiency in interpreting and generating Sui-Move, a newly introduced smart
contract language, using prompt engineering
DeepOtolith v1.0: An Open-Source AI Platform for Automating Fish Age Reading from Otolith or Scale Images
Every year, marine scientists around the world read thousands of otolith or scale images to determine the age structure of commercial fish stocks. This knowledge is important for fisheries and conservation management. However, the age-reading procedure is time-consuming and costly to perform due to the specialized expertise and labor needed to identify annual growth zones in otoliths. Effective automated systems are needed to increase throughput and reduce cost. DeepOtolith is an open-source artificial intelligence (AI) platform that addresses this issue by providing a web system with a simple interface that automatically estimates fish age by combining otolith images with convolutional neural networks (CNNs), a class of deep neural networks that has been a dominant method in computer vision tasks. Users can upload otolith image data for selective fish species, and the platform returns age estimates. The estimates of multiple images can be exported to conduct conclusions or further age-related research. DeepOtolith currently contains classifiers/regressors for three fish species; however, more species will be included as related work on ageing will be tested and published soon. Herein, the architecture and functionality of the platform are presented. Current limitations and future directions are also discussed. Overall, DeepOtolith should be considered as the first step towards building a community of marine ecologists, machine learning experts, and stakeholders that will collaborate to support the conservation of fishery resources.publishedVersio
Health Care Chatbot Assistant System
Rasa stack consists of many open source AI apparatuses solely utilized in plan to make a logical chatbot. It consists of incredible APIs embedded along the Rasa stack that incorporates Natural language understanding. It incorporates the sack of words calculation helping in streamlining portrayl utilized in measurable displaying and AI stages and furthermore trend setting innovation. The proposed framework is to make an option in contrast to this ordinary strategy for visiting a clinic and making a meeting with a specialist to get analysis. From the user queries chatbot will, predicts the infection and prescribes treatment along with necessary medicine. It like wise support the utilization of this RASA stage for the client specific format according to their prerequisites and furthermore elevates in building up the system for better efficiency
MagicFusion: Boosting Text-to-Image Generation Performance by Fusing Diffusion Models
The advent of open-source AI communities has produced a cornucopia of
powerful text-guided diffusion models that are trained on various datasets.
While few explorations have been conducted on ensembling such models to combine
their strengths. In this work, we propose a simple yet effective method called
Saliency-aware Noise Blending (SNB) that can empower the fused text-guided
diffusion models to achieve more controllable generation. Specifically, we
experimentally find that the responses of classifier-free guidance are highly
related to the saliency of generated images. Thus we propose to trust different
models in their areas of expertise by blending the predicted noises of two
diffusion models in a saliency-aware manner. SNB is training-free and can be
completed within a DDIM sampling process. Additionally, it can automatically
align the semantics of two noise spaces without requiring additional
annotations such as masks. Extensive experiments show the impressive
effectiveness of SNB in various applications. Project page is available at
https://magicfusion.github.io/
AI Regulation in Europe: From the AI Act to Future Regulatory Challenges
This chapter provides a comprehensive discussion on AI regulation in the
European Union, contrasting it with the more sectoral and self-regulatory
approach in the UK. It argues for a hybrid regulatory strategy that combines
elements from both philosophies, emphasizing the need for agility and safe
harbors to ease compliance. The paper examines the AI Act as a pioneering
legislative effort to address the multifaceted challenges posed by AI,
asserting that, while the Act is a step in the right direction, it has
shortcomings that could hinder the advancement of AI technologies. The paper
also anticipates upcoming regulatory challenges, such as the management of
toxic content, environmental concerns, and hybrid threats. It advocates for
immediate action to create protocols for regulated access to high-performance,
potentially open-source AI systems. Although the AI Act is a significant
legislative milestone, it needs additional refinement and global collaboration
for the effective governance of rapidly evolving AI technologies.Comment: Final version forthcoming in: Ifeoma Ajunwa & Jeremias Adams-Prassl
(eds), Oxford Handbook of Algorithmic Governance and the Law, Oxford
University Press, 202
Towards Openness Beyond Open Access: User Journeys through 3 Open AI Collaboratives
Open Artificial Intelligence (Open source AI) collaboratives offer
alternative pathways for how AI can be developed beyond well-resourced
technology companies and who can be a part of the process. To understand how
and why they work and what additionality they bring to the landscape, we focus
on three such communities, each focused on a different kind of activity around
AI: building models (BigScience workshop), tools and ways of working (The
Turing Way), and ecosystems (Mozilla Festival's Building Trustworthy AI Working
Group). First, we document the community structures that facilitate these
distributed, volunteer-led teams, comparing the collaboration styles that drive
each group towards their specific goals. Through interviews with community
leaders, we map user journeys for how members discover, join, contribute, and
participate. Ultimately, this paper aims to highlight the diversity of AI work
and workers that have come forth through these collaborations and how they
offer a broader practice of openness to the AI space.Comment: Presented at the 2022 NeurIPS Workshop on Broadening Research
Collaborations in M
Sensemaking Practices in the Everyday Work of AI/ML Software Engineering
This paper considers sensemaking as it relates to everyday software engineering (SE) work practices and draws on a multi-year ethnographic study of SE projects at a large, global technology company building digital services infused with artificial intelligence (AI) and machine learning (ML) capabilities. Our findings highlight the breadth of sensemaking practices in AI/ML projects, noting developers' efforts to make sense of AI/ML environments (e.g., algorithms/methods and libraries), of AI/ML model ecosystems (e.g., pre-trained models and "upstream"models), and of business-AI relations (e.g., how the AI/ML service relates to the domain context and business problem at hand). This paper builds on recent scholarship drawing attention to the integral role of sensemaking in everyday SE practices by empirically investigating how and in what ways AI/ML projects present software teams with emergent sensemaking requirements and opportunities
GAIA Search: Hugging Face and Pyserini Interoperability for NLP Training Data Exploration
Noticing the urgent need to provide tools for fast and user-friendly
qualitative analysis of large-scale textual corpora of the modern NLP, we
propose to turn to the mature and well-tested methods from the domain of
Information Retrieval (IR) - a research field with a long history of tackling
TB-scale document collections. We discuss how Pyserini - a widely used toolkit
for reproducible IR research can be integrated with the Hugging Face ecosystem
of open-source AI libraries and artifacts. We leverage the existing
functionalities of both platforms while proposing novel features further
facilitating their integration. Our goal is to give NLP researchers tools that
will allow them to develop retrieval-based instrumentation for their data
analytics needs with ease and agility. We include a Jupyter Notebook-based walk
through the core interoperability features, available on GitHub at
https://github.com/huggingface/gaia. We then demonstrate how the ideas we
present can be operationalized to create a powerful tool for qualitative data
analysis in NLP. We present GAIA Search - a search engine built following
previously laid out principles, giving access to four popular large-scale text
collections. GAIA serves a dual purpose of illustrating the potential of
methodologies we discuss but also as a standalone qualitative analysis tool
that can be leveraged by NLP researchers aiming to understand datasets prior to
using them in training. GAIA is hosted live on Hugging Face Spaces -
https://huggingface.co/spaces/spacerini/gaia
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