Unite.AI

APRIL 9, 2024

These techniques include Machine Learning (ML), deep learning , Natural Language Processing (NLP) , Computer Vision (CV) , descriptive statistics, and knowledge graphs. Composite AI plays a pivotal role in enhancing interpretability and transparency. Combining diverse AI techniques enables human-like decision-making.

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Viso.ai

MARCH 26, 2024

This drastically enhanced the capabilities of computer vision systems to recognize patterns far beyond the capability of humans. In this article, we present 7 key applications of computer vision in finance: No.1: Applications of Computer Vision in Finance No. 1: Fraud Detection and Prevention No.2:

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IBM Journey to AI blog

SEPTEMBER 24, 2024

Better, faster phenotyping In Tanzania, David Guerena, an agricultural scientist at the International Center for Tropical Agriculture, is using AI to kick plant evolution into overdrive. Guerena’s project, called Artemis, uses AI and computer vision to speed up the phenotyping process.

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Marktechpost

MARCH 11, 2024

However, understanding their information-flow dynamics, learning mechanisms, and interoperability remains challenging, limiting their applicability in sensitive domains requiring explainability. These matrices are leveraged to develop class-agnostic and class-specific tools for explainable AI of Mamba models.

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Marktechpost

JUNE 8, 2024

Google researchers introduced a novel framework, StylEx, that leverages generative AI to address the challenges in the field of medical imaging, especially focusing on the lack of explainability in AI models.

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Mlearning.ai

FEBRUARY 6, 2023

This is why we need Explainable AI (XAI). IEEE Conference on Computer Vision and Pattern Recognition 2021. Submission Suggestions Explainable AI and ChatGPT Detection was originally published in MLearning.ai The “Obvious” Solution One potential solution to the above conundrum is to identify word importance.

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Marktechpost

MAY 8, 2024

Among the main advancements in AI, seven areas stand out for their potential to revolutionize different sectors: neuromorphic computing, quantum computing for AI, Explainable AI (XAI), AI-augmented design and Creativity, Autonomous Vehicles and Robotics, AI in Cybersecurity and AI for Environmental Sustainability.

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Marktechpost

JUNE 4, 2024

Modern Deep Neural Networks (DNNs) are inherently opaque; we do not know how or why these computers arrive at the predictions they do. An emerging area of study called Explainable AI (XAI) has arisen to shed light on how DNNs make decisions in a way that humans can comprehend.

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Marktechpost

AUGUST 24, 2023

r/computervision Computer vision is the branch of AI science that focuses on creating algorithms to extract useful information from raw photos, videos, and sensor data. The subreddit has excellent computer vision and artificial intelligence content. There are about 68k members.

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IBM Journey to AI blog

JULY 6, 2023

Machine learning engineers can specialize in natural language processing and computer vision, become software engineers focused on machine learning and more. to learn more) In other words, you get the ability to operationalize data science models on any cloud while instilling trust in AI outcomes.

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NVIDIA

MARCH 1, 2024

Transparency in AI is a set of best practices, tools and design principles that helps users and other stakeholders understand how an AI model was trained and how it works. Explainable AI , or XAI, is a subset of transparency covering tools that inform stakeholders how an AI model makes certain predictions and decisions.

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NVIDIA

DECEMBER 13, 2023

AI-driven applications using deep learning with graph neural networks (GNNs), natural language processing (NLP) and computer vision can improve identity verification for know-your customer (KYC) and anti-money laundering (AML) requirements, leading to improved regulatory compliance and reduced costs.

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Heartbeat

FEBRUARY 2, 2024

Visual Question Answering (VQA) stands at the intersection of computer vision and natural language processing, posing a unique and complex challenge for artificial intelligence. is a significant benchmark dataset in computer vision and natural language processing. or Visual Question Answering version 2.0,

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Viso.ai

MAY 10, 2024

Person detection with a computer vision model Step 2: Create a Dataset for Model Training & Testing Before we can train a machine learning model, we need to have data on which to train. The example image below is from a model that was built to identify and segment people within images.

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How to Learn Machine Learning

JANUARY 21, 2023

This market growth can be attributed to factors such as increasing demand for AI-based solutions in healthcare, retail, and automotive industries, as well as rising investments from tech giants such as Google , Microsoft , and IBM. This has helped to drive innovation in the industry.

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Pickl AI

NOVEMBER 5, 2024

AI encompasses various subfields, including Machine Learning (ML), Natural Language Processing (NLP), robotics, and computer vision. Together, Data Science and AI enable organisations to analyse vast amounts of data efficiently and make informed decisions based on predictive analytics.

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Pickl AI

SEPTEMBER 17, 2024

Transfer learning can significantly reduce the time and resources required to train a model from scratch and has applications in areas such as computer vision and natural language processing. What Is the Role of Explainable AI (XAI) In Machine Learning? What Is the Role of Ensemble Methods in Machine Learning?

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Pickl AI

JANUARY 29, 2024

AI comprises Natural Language Processing, computer vision, and robotics. Emerging Trends Emerging trends in Data Science include integrating AI technologies and the rise of Explainable AI for transparent decision-making.

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Viso.ai

JULY 9, 2024

provides the leading end-to-end Computer Vision Platform Viso Suite. Global organizations like IKEA and DHL use it to build, deploy, and scale all computer vision applications in one place, with automated infrastructure. Diffusion Models Diffusion models are one of the newest models in generative AI.

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Pickl AI

APRIL 2, 2024

Computer Vision: Analyze visual data like images and videos to automate tasks, identify objects and patterns, and improve product development. Explainable AI (XAI): As AI models become more complex, there’s a growing need for interpretability.

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Dlabs.ai

MAY 22, 2022

This course is a great starting point if you want to use it to build and apply scalable models to real-world problems.

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DataRobot Blog

DECEMBER 20, 2022

The great thing about DataRobot Explainable AI is that it spans the entire platform. As the figure below shows, you can customize the image augmentation flips, rotating, and scaling images to increase the number of observations for each object in the training dataset aimed to create high performing computer vision models.

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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).

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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).

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The MLOps Blog

JUNE 27, 2023

Auto-annotation tools such as Meta’s Segment Anything Model and other AI-assisted labeling techniques. MLOps workflows for computer vision and ML teams Use-case-centric annotations. The key features of Encord Annotate include: Support for all annotation types. Easy collaboration, annotator management, and QA workflows.

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The MLOps Blog

AUGUST 11, 2023

Embeddings are utilized in computer vision tasks, NLP tasks, and statistics. The concept of Explainable AI revolves around developing models that offer inference results and a form of explanation detailing the process behind the prediction. Both these areas often demand large-scale model training.

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IBM Journey to AI blog

FEBRUARY 9, 2024

The incoming generation of interdisciplinary models, comprising proprietary models like OpenAI’s GPT-4V or Google’s Gemini, as well as open source models like LLaVa, Adept or Qwen-VL, can move freely between natural language processing (NLP) and computer vision tasks.

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