AI Weekly

JULY 27, 2023

forbes.com Ethics Data Loss Prevention and the Value of Artificial Intelligence How to create a Cisco Umbrella DLP rule for ChatGPT Cisco Umbrella multimode cloud DLP functionality is easy to deploy and manage with flexible policies incorporating pre-built, customizable data identifiers. Our Oppenheimer Moment: The Creation of A.I.

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Unite.AI

NOVEMBER 27, 2023

Advanced Image Tagging Technology: Ideal for online retailers seeking to deliver precise product recommendations. The tool utilizes generative AI, employing techniques like metric learning and multimodal embedding to create content that aligns with user needs.

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AWS Machine Learning Blog

JANUARY 17, 2023

Multimodality is a multi-disciplinary research field that addresses some of the original goals of artificial intelligence by integrating and modeling multiple modalities. Train a multimodal model with a Hugging Face sentence transformer and Scikit-learn random forest classifier. Fit a multimodal model with HPO.

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NVIDIA

JANUARY 9, 2024

Generative AI Use Cases Multimodal models are leading the new frontier in the generative AI landscape. Yet another use case is in product description generation, where generative AI can intelligently generate detailed e-commerce product descriptions that include product attributes, using meta-tags to greatly improve SEO.

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NVIDIA

JANUARY 9, 2024

The use cases ranged from multimodal shopping advisors for personalized product recommendations; adaptive advertising, promotions and pricing; product tagging and cataloging; identification of similar and complementary products; as well as deployment of brand avatars for automated customer service.

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Marktechpost

JULY 23, 2023

They present a carefully chosen fly-view multimodal dataset to address the dearth of large-scale real-world outdoor scene datasets. As seen in the figure above, the dataset consists of 33 scenes with prompt annotations, tags, and 14K calibrated photos.

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Marktechpost

AUGUST 10, 2023

The dataset comprises over 1 billion region annotations in various formats, such as semantic tags, locations, question-answering pairs, and captions. The model consists of two key components: a location-aware image tokenizer and an LLM-based decoder.

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AWS Machine Learning Blog

NOVEMBER 16, 2023

We propose using this capability with the Amazon SageMaker platform of services to improve regression model accuracy in an ML use case, and independently, for the automated tagging of visual images. In AI, the term multimodal refers to the use of a variety of media types, such as images and tabular data.

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PyImageSearch

JANUARY 1, 2024

It also boasts of being multimodal from scratch, which means it began and ended as a model capable of dealing with multiple modalities of data (image, video, sound, etc.). Multimodal Use Cases: Compared to text-only LLMs, the Gemini Pro Vision’s multimodality can be used for many new use cases.

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AWS Machine Learning Blog

NOVEMBER 1, 2023

Visual language processing (VLP) is at the forefront of generative AI, driving advancements in multimodal learning that encompasses language intelligence, vision understanding, and processing. Solution overview The proposed VLP solution integrates a suite of state-of-the-art generative AI modules to yield accurate multimodal outputs.

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NVIDIA

MARCH 18, 2024

NVIDIA Edify, a multimodal architecture for visual generative AI, is entering a new dimension. The services will feature a no-code, self-service method for companies to upload a proprietary dataset, review automatically generated tags, submit fine-tuning tasks and review the results before deploying to production.

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Artificial Corner

MAY 3, 2024

Humane AI Pin Patent During one of Chaudri's initial demos, the device demonstrated its ability to see, hear, and tag objects. Thanks to its AI development and multimodal capabilities, it can offer recommendations on food, handle calls, or schedule meetings.

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Snorkel AI

MARCH 19, 2024

Google’s thought leadership in AI is exemplified by its groundbreaking advancements in native multimodal support (Gemini), natural language processing (BERT, PaLM), computer vision (ImageNet), and deep learning (TensorFlow). Noisy data is a well-known limitation of the banking77 dataset and not uncommon with human labeling.

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Snorkel AI

MARCH 19, 2024

Google’s thought leadership in AI is exemplified by its groundbreaking advancements in native multimodal support (Gemini), natural language processing (BERT, PaLM), computer vision (ImageNet), and deep learning (TensorFlow). Noisy data is a well-known limitation of the banking77 dataset and not uncommon with human labeling.

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Viso.ai

APRIL 26, 2024

” The color “red” is tagged as an attribute of interest, “vehicles” as the object class to be detected, “moving faster than” as the action, and “speed limit” as a contextual parameter. For this, techniques that use few or single-shot architectures are particularly valuable.

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Sebastian Ruder

JUNE 6, 2022

Language diversity and multimodality Panelists and their spoken languages at the ACL 2022 keynote panel on supporting linguistic diversity. The latter is an example of how multimodal approaches can benefit language diversity. The best theme paper developed speech synthesis models for three Canadian indigenous languages.

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Snorkel AI

JUNE 12, 2023

classification, tagging, extraction). 2023, “ DataComp: In search of the next generation of multimodal datasets ”; Hoffmann et al. Snorkel Flow: For programmatic labeling of predictive AI use cases (e.g., Footnotes (1) Brants et al. 2007, “Large Language Models in Machine Translation” (2) Gadre et al.

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Snorkel AI

OCTOBER 12, 2023

DataComp: In Search of the Next Generation of Multimodal Datasets Large multimodal datasets have been instrumental in recent breakthroughs such as CLIP, Stable Diffusion, and GPT-4. Their baseline experiments show that the DataComp workflow is a promising way of improving multimodal datasets.

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Snorkel AI

JUNE 12, 2023

classification, tagging, extraction). 2023, “ DataComp: In search of the next generation of multimodal datasets ”; Hoffmann et al. Snorkel Flow: For programmatic labeling of predictive AI use cases (e.g., Footnotes (1) Brants et al. 2007, “Large Language Models in Machine Translation” (2) Gadre et al.

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Snorkel AI

OCTOBER 12, 2023

DataComp: In Search of the Next Generation of Multimodal Datasets Large multimodal datasets have been instrumental in recent breakthroughs such as CLIP, Stable Diffusion, and GPT-4. Their baseline experiments show that the DataComp workflow is a promising way of improving multimodal datasets.

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TheSequence

OCTOBER 30, 2023

Whether you’re looking to process data, generate synthetic data, or curate multimodal datasets, Adala provides the tools and framework to make it happen. Adala - Pioneering the Future of Human-AI Collaboration The quest for efficiency and quality often comes with a hefty price tag. The Environment: Defined by Data Fig.

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Google Research AI blog

DECEMBER 7, 2022

Thapliyal , Jordi Pont-Tuset , Xi Chen , Radu Soricut "Will You Find These Shortcuts?"

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John Snow Labs

MAY 8, 2024

Model Architecture The architecture of pathology-specific LLMs often incorporates multimodal learning frameworks, integrating NLP with computer vision (CV) to analyze both text and images. Advancements in Multimodal Learning: The field of multimodal learning, which combines text and image analysis, is poised for significant growth.

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AWS Machine Learning Blog

OCTOBER 17, 2023

While YOLOv5 is responsible for recognizing and tagging cows in a single image, in reality, videos consist of multiple images (frames) that change continuously. With each inference, we could obtain thousands of decomposed and tagged cow walking videos, each with the original video ID, cow ID, lameness score, and various detailed scores.

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The MLOps Blog

JANUARY 25, 2023

For example, “I love being ignored” may be tagged as a negative example, and “I can be very ambitious” can be tagged as a positive example. You can also prepare a dictionary of words and tag them into different classes so that humans labeling the data will have uniformity in the annotation.

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Marek Rei

JANUARY 17, 2018

link] They incorporate fMRI features into POS tagging, under the assumption that reading semantically/functionally different words will activate the brain in different ways. link] The authors compare different image recognition models and image data sources for multimodal word representation learning. Copenhagen. Verő, Stephen Clark.

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ODSC - Open Data Science

JANUARY 29, 2024

Spacy: Another firm favorite, Spacy can be used for tokenization, part-of-speech tagging, and named entity recognition, aiding in structuring prompts accurately. Apache Spark: Enables distributed data processing and is essential for handling massive datasets and scaling projects efficiently.

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Marek Rei

NOVEMBER 12, 2019

Both images and text are encoded and attended over jointly with a cross-modal encoder, the model is then optimized with both unimodal and multimodal tasks (masked LM, image classification, image-caption matching, visual QA). link] The paper describes a sequence tagging model for semantic role labeling. Jakarta, Trento, Depok.

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Mlearning.ai

SEPTEMBER 4, 2023

7] For industries with strict regulatory requirements, such as healthcare, finance, or defense, private instances, and business accounts can address this issue but have a hefty price tag. WRITER at MLearning.ai // AI Video // Multimodal Machine Learning Mlearning.ai Market factors: Such an adoption strategy depends on another.

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The MLOps Blog

JANUARY 5, 2023

There are certain situations where using other means of technology like NFC tags and writing down whatever is being used now is not practical, for whatever reason, and we have the technology now, or we hope to advance this technology to the level that we will be able to identify animals on an individual level from a single or multiple pictures.

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Sebastian Ruder

NOVEMBER 14, 2022

The model uses different embeddings for POS tags, stems, and affixes (Nzeyimana & Rubungo, 2022). Cross-lingual name tagging and linking for 282 languages. Bloom Library: Multimodal Datasets in 300+ Languages for a Variety of Downstream Tasks. The KinyaBERT model for Kinyarwanda. In Proceedings of EMNLP 2021. Nothman, J.,

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