Unite.AI

SEPTEMBER 20, 2024

The emergence of machine learning and Natural Language Processing (NLP) in the 1990s led to a pivotal shift in AI. Its specialization makes it uniquely adept at powering AI workflows in an industry known for strict regulation and compliance standards.

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Marktechpost

MAY 5, 2024

Consequently, there’s been a notable uptick in research within the natural language processing (NLP) community, specifically targeting interpretability in language models, yielding fresh insights into their internal operations.

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ODSC - Open Data Science

MARCH 20, 2025

Day 1: Tuesday, May13th The first official day of ODSC East 2025 will be chock-full of hands-on training sessions and workshops from some of the leading experts in LLMs, Generative AI, Machine Learning, NLP, MLOps, and more.

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Mlearning.ai

FEBRUARY 6, 2023

This is why we need Explainable AI (XAI). This methodology has been used to provide explanations for sentiment classification, topic tagging, and other NLP tasks and could potentially work for chatbot-writing detection as well. Submission Suggestions Explainable AI and ChatGPT Detection was originally published in MLearning.ai

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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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How to Learn Machine Learning

APRIL 4, 2025

AI will help to strengthen defences, cybercriminal departments will utilize AI to work against phishing and deepfake attacks. Explainable AI (XAI): As AI is expanding rapidly, there is a high demand for transparency and trust in AI-driven decisions. Thus, explainable AI (XAI) comes into the picture.

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Unite.AI

JANUARY 12, 2024

Enhancing user trust via explainable AI also remains vital. Addressing these technical obstacles will be key to unlocking multimodal AI's capabilities. Meta-learning Meta-learning, or ‘learning to learn', focuses on equipping AI models with the ability to rapidly adapt to new tasks using limited data samples.

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IBM Journey to AI blog

SEPTEMBER 20, 2023

They use self-supervised learning algorithms to perform a variety of natural language processing (NLP) tasks in ways that are similar to how humans use language (see Figure 1). Large language models (LLMs) have taken the field of AI by storm.

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Marktechpost

JULY 22, 2024

Authorship Verification (AV) is critical in natural language processing (NLP), determining whether two texts share the same authorship. This is a critical limitation as the demand for explainable AI grows. This task holds immense importance across various domains, such as forensics, literature, and digital security.

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Towards AI

APRIL 2, 2024

Building A Multilingual NER App with HuggingFace This is a guide on creating an end-to-end NLP project using a RoBERTa-base model with the transformers library. Explainable AI: Thinking Like a Machine XAI, or explainable AI, has a tangible role in promoting trust and transparency and […]

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Unite.AI

DECEMBER 1, 2023

This is what led me back down the rabbit hole, and eventually back to grad school at Stanford, focusing on NLP, which is the area of using ML/AI on natural language. Snorkel AI allows enterprises to scale human-in-the-loop approaches by efficiently incorporating human judgment and subject-matter expert knowledge.

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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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Pickl AI

SEPTEMBER 17, 2024

What Is the Role of Explainable AI (XAI) In Machine Learning? Explainable AI (XAI) is a field of study that focuses on making Machine Learning models more interpretable and transparent. What Is the Role of Natural Language Processing (NLP) In Artificial Intelligence?

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Marktechpost

AUGUST 24, 2023

You can join this subreddit to be updated about trending AI updates. r/Machinelearningnews r/machinelearningnews is a community of machine learning enthusiasts/researchers/journalists/writers who share interesting news and articles about the applications of AI.

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Pickl AI

DECEMBER 10, 2024

Summary : Data Analytics trends like generative AI, edge computing, and Explainable AI redefine insights and decision-making. Key Takeaways Generative AI simplifies data insights, enabling actionable decision-making and enhancing data storytelling. Lets explore the key developments shaping this space.

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ODSC - Open Data Science

JUNE 8, 2023

We had bigger sessions on getting started with machine learning or SQL, up to advanced topics in NLP, and how to make deepfakes. Some of our most popular in-person sessions were: Data Science Software Acceleration at the Edge: Audrey Reznik Guidera | Sr.

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ODSC - Open Data Science

FEBRUARY 25, 2025

Whats Next in AI TrackExplore the Cutting-Edge Stay ahead of the curve with insights into the future of AI. AI Engineering TrackBuild Scalable AISystems Learn how to bridge the gap between AI development and software engineering.

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Viso.ai

MARCH 26, 2024

Technologies such as Optical Character Recognition (OCR) and Natural Language Processing (NLP) are foundational to this. On the other hand, NLP frameworks like BERT help in understanding the context and content of documents. AI’s benefits extend to processing unstructured data from news feeds and social media.

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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

AUGUST 14, 2024

Natural Language Processing (NLP) In the realm of Natural Language Processing , neural networks have revolutionised how machines understand and generate human language. Some notable developments include: Transformers: A novel architecture that has revolutionised NLP tasks, enabling models like BERT and GPT to achieve state-of-the-art results.

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Viso.ai

JULY 9, 2024

For example, in Natural Language Processing (NLP), the model works by predicting the next word in a sequence. What happens in generative AI is it learns the distribution of the data, and then you sample a new data point from the distribution, that is when the model generates a realistic output that represents that learned distribution.

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Pickl AI

NOVEMBER 18, 2024

Natural language processing ( NLP ) allows machines to understand, interpret, and generate human language, which powers applications like chatbots and voice assistants. Neural networks are powerful for complex tasks, such as image recognition or NLP, but may require more computational resources. Let’s explore some of the key trends.

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Bugra Akyildiz

MAY 25, 2024

Explainable AI: For complex models like deep neural networks, ChatGPT could provide explanations for model predictions, identify the most influential features, and surface potential biases or fairness issues. 2015 is cited as the original reference for the use of BPE in NLP applications. Sennrich et al.

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Pickl AI

JANUARY 13, 2025

It simplifies complex AI topics like clustering , dimensionality , and regression , providing practical examples and numeric calculations to enhance understanding. Key Features: Explains AI algorithms like clustering and regression. Key Features: Covers basic AI concepts. Minimal technical jargon.

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How to Learn Machine Learning

JANUARY 21, 2023

Moreover, advancements in Natural Language Processing (NLP) are allowing AI-powered systems to understand human speech and interact in more natural ways. In addition, the increasing availability of data is providing AI with unprecedented opportunities to learn from experience and make predictions.

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Snorkel AI

MARCH 19, 2024

Vertex AI provides a suite of tools and services that cater to the entire AI lifecycle, from data preparation to model deployment and monitoring. Its focus on reliability ensures that AI systems perform as expected, mitigating potential risks and fostering trust in AI-powered solutions.

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IBM Journey to AI blog

SEPTEMBER 3, 2024

. “If we don’t have that trust in those models, we can’t really get the benefit of that AI in enterprises,” said Kush Varshney, distinguished research scientist and senior manager at IBM Research® in an IBM AI Academy video on trust, transparency and governance in AI.

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The MLOps Blog

AUGUST 11, 2023

Embeddings are utilized in computer vision tasks, NLP tasks, and statistics. More specifically, embeddings enable neural networks to consume training data in formats that allow extracting features from the data, which is particularly important in tasks such as natural language processing (NLP) or image recognition.

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ODSC - Open Data Science

DECEMBER 27, 2024

With a background in natural language processing (NLP) at Google, where he worked on early iterations of document summarization and question-answering systems, Robert later transitioned to focus on developer tooling. His work in REST APIs and Kubernetes laid the foundation for OpenHands, combining his love for NLP and developer tools.

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Unite.AI

NOVEMBER 29, 2023

Among the techniques employed to counter false information, natural language processing (NLP) emerges as a transformative technology that skillfully deciphers patterns of deception within written content.

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Pickl AI

APRIL 2, 2024

Here are some cutting-edge applications that can give your business a competitive edge: Natural Language Processing (NLP): Extract insights from text data like customer reviews, social media conversations, and documents. Explainable AI (XAI): As AI models become more complex, there’s a growing need for interpretability.

Data Science 52

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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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