Unite.AI

DECEMBER 13, 2023

AI transforms cybersecurity by boosting defense and offense. However, challenges include the rise of AI-driven attacks and privacy issues. Responsible AI use is crucial. The future involves human-AI collaboration to tackle evolving trends and threats in 2024.

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Explainability AI AI Responsible AI 290

Unite.AI

JANUARY 2, 2025

Transparency allows AI decisions to be explained, understood, and verified. Developers can identify and correct biases when AI systems are explainable, creating fairer outcomes. For example, biased hiring algorithms trained on historical data have been found to favor male candidates for leadership roles.

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AI Developer AI Development Explainability AI 130

Unite.AI

APRIL 9, 2024

As organizations strive for responsible and effective AI, Composite AI stands at the forefront, bridging the gap between complexity and clarity. The Need for Explainability The demand for Explainable AI arises from the opacity of AI systems, which creates a significant trust gap between users and these algorithms.

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Composite AI Explainability Black Box AI Explainable AI 182

Unite.AI

DECEMBER 1, 2023

Machine learning , a subset of AI, involves three components: algorithms, training data, and the resulting model. An algorithm, essentially a set of procedures, learns to identify patterns from a large set of examples (training data). This obscurity makes it challenging to understand the AI's decision-making process.

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Unite.AI

FEBRUARY 17, 2025

The wide availability of affordable, highly effective predictive and generative AI has addressed the next level of more complex business problems requiring specialized domain expertise, enterprise-class security, and the ability to integrate diverse data sources. The bank also projects cost savings with SymphonyAI on Microsoft Azure of 3.5m

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Automation Responsible AI Generative AI AI Modeling 147

IBM Journey to AI blog

OCTOBER 12, 2023

But the implementation of AI is only one piece of the puzzle. The tasks behind efficient, responsible AI lifecycle management The continuous application of AI and the ability to benefit from its ongoing use require the persistent management of a dynamic and intricate AI lifecycle—and doing so efficiently and responsibly.

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Data Scientist Metadata Explainability Responsible AI 234

Unite.AI

MAY 27, 2024

Detecting fraud with AI Traditional fraud detection methods rely on rule-based systems that can only identify pre-programmed patterns. Also, ML algorithms can learn and adapt to new fraud tactics, making them more effective at combating emerging threats and helping enterprises stay ahead of evolving cyber risks.

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Machine Learning Explainability Algorithm AI 223

Google Research AI blog

MARCH 16, 2023

The Impact Lab team, part of Google’s Responsible AI Team , employs a range of interdisciplinary methodologies to ensure critical and rich analysis of the potential implications of technology development. We examine systemic social issues and generate useful artifacts for responsible AI development.

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NVIDIA

OCTOBER 18, 2023

Responsible AI is hot on its heels. Julia Stoyanovich, associate professor of computer science and engineering at NYU and director of the university’s Center for Responsible AI , wants to make the terms “AI” and “responsible AI” synonymous. Artificial intelligence is now a household term.

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Responsible AI Deep Learning AI AI 95

Aiiot Talk

NOVEMBER 30, 2023

Now that the novelty of artificial intelligence has worn off, people are focusing on its responsible use. Ethical algorithms have become a chief concern for many businesses and regulatory agencies. Its output is easily explainable and traceable, meaning you can hold it accountable and verify its conclusions.

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Algorithm Artificial Intelligence Artificial Intelligence AI 233

Unite.AI

NOVEMBER 25, 2024

For AI and large language model (LLM) engineers , design patterns help build robust, scalable, and maintainable systems that handle complex workflows efficiently. This article dives into design patterns in Python, focusing on their relevance in AI and LLM -based systems. forms, REST API responses).

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Chatbots Life

MAY 23, 2023

Responsible AI — deployment framework I asked ChatGPT and Bard to share their thoughts on what policies governments have to put in place to ensure responsible AI implementations in their countries. They should also work to raise awareness of the importance of responsible AI among businesses and organizations.

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Responsible AI Chatbots AI AI 82

Google Research AI blog

FEBRUARY 10, 2023

Robust algorithm design is the backbone of systems across Google, particularly for our ML and AI models. Hence, developing algorithms with improved efficiency, performance and speed remains a high priority as it empowers services ranging from Search and Ads to Maps and YouTube. You can find other posts in the series here.)

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Algorithm Neural Network Auto-classification ML 110

Unite.AI

MARCH 27, 2024

Back then, people dreamed of what it could do, but now, with lots of data and powerful computers, AI has become even more advanced. Along the journey, many important moments have helped shape AI into what it is today. Today, AI benefits from the convergence of advanced algorithms, computational power, and the abundance of data.

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Natural Language Processing AI AI Algorithm 278

IBM Journey to AI blog

JANUARY 15, 2024

The differences between generative AI and traditional AI To understand the unique challenges that are posed by generative AI compared to traditional AI, it helps to understand their fundamental differences. Teams should have the ability to comprehend and manage the AI lifecycle effectively.

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Generative AI Responsible AI Neural Network Natural Language Processing 200

Marktechpost

APRIL 21, 2024

Introduction to Generative AI: This course provides an introductory overview of Generative AI, explaining what it is and how it differs from traditional machine learning methods. Participants will learn about the applications of Generative AI and explore tools developed by Google to create their own AI-driven applications.

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IBM Journey to AI blog

JANUARY 10, 2024

In this article, we’ll discuss how AI technology functions and lay out the advantages and disadvantages of artificial intelligence as they compare to traditional computing methods. AI operates on three fundamental components: data, algorithms and computing power. What is artificial intelligence and how does it work?

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Artificial Intelligence Artificial Intelligence Neural Network Algorithm 229

Unite.AI

JULY 1, 2024

It doesn’t matter if you are an online consumer or a business using that information to make key decisions – responsible AI systems allow all of us to fully and better understand information, as you need to ensure what is coming out of Generative AI is accurate and reliable.

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Explainability Software Engineer AI Modeling Responsible AI 173

AWS Machine Learning Blog

NOVEMBER 26, 2024

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API. The following screenshot shows the response that we get from the LLM (truncated for brevity).

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LLM Convolutional Neural Networks Metadata Explainability 97

Flipboard

APRIL 1, 2025

To improve factual accuracy of large language model (LLM) responses, AWS announced Amazon Bedrock Automated Reasoning checks (in gated preview) at AWS re:Invent 2024. Explainable validation results Each validation check produces detailed findings that indicate whether content is Valid, Invalid, or No Data.

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Automation Generative AI AI AI 164

AWS Machine Learning Blog

MARCH 21, 2025

Amazon Bedrock is a fully managed service that provides a single API to access and use various high-performing foundation models (FMs) from leading AI companies. It offers a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI practices. samples/2003.10304/page_5.png"

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Metadata Convolutional Neural Networks Generative AI Data Scientist 110

NYU Center for Data Science

OCTOBER 4, 2024

Black brings her expertise in responsible AI, algorithmic fairness, and technology policy to address critical challenges at the intersection of machine learning and societal impact. This entry is part of our Meet the Faculty blog series, which introduces and highlights faculty who have recently joined CDS. By Stephen Thomas

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Pickl AI

FEBRUARY 17, 2025

Algorithmic bias can result in unfair outcomes, necessitating careful management. Transparency in AI systems fosters trust and enhances human-AI collaboration. ML algorithms can efficiently identify patterns and trends in large datasets, significantly reducing the time and effort needed for analysis.

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Machine Learning Algorithm Explainability Data Quality 52

NYU Center for Data Science

JUNE 16, 2023

A PhD candidate in the Machine Learning Group at the University of Cambridge advised by Adrian Weller , Umang will continue to pursue research in trustworthy machine learning, responsible artificial intelligence, and human-machine collaboration at NYU. For these reasons, I am excited to start my academic journey at NYU. By Meryl Phair

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Pickl AI

OCTOBER 8, 2024

Summary: This blog discusses Explainable Artificial Intelligence (XAI) and its critical role in fostering trust in AI systems. One of the most effective ways to build this trust is through Explainable Artificial Intelligence (XAI). What is Explainable AI (XAI)? What is Explainable AI (XAI)?

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Artificial Intelligence Artificial Intelligence Explainability Explainable AI 52

Unite.AI

JUNE 7, 2024

However, it is one of many realities that we must consider as AI is integrated into society. Being Human in the Age of AI , MIT professor Max Tegmark explains his perspective on how to keep AI beneficial to society. In his book, Life 3.0:

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Generative AI AI AI Automation 162

NYU Center for Data Science

DECEMBER 12, 2023

In an era where algorithms determine everything from creditworthiness to carceral sentencing, the imperative for responsible innovation has never been more urgent. The Center for Responsible AI (NYU R/AI) is leading this charge by embedding ethical considerations into the fabric of artificial intelligence research and development.

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TransOrg Analytics

FEBRUARY 11, 2025

These are just a few ways Artificial Intelligence (AI) silently influences our daily lives. As AI continues integrating into every aspect of society, the need for Explainable AI (XAI) becomes increasingly important. What is Explainable AI? Why is Explainable AI Important?

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Explainable AI Explainability Convolutional Neural Networks Neural Network 52

Unite.AI

OCTOBER 25, 2024

In mortgage requisition intake, AI optimizes efficiency by automating the analysis of requisition data, leading to faster processing times. Fraud detection has become more robust with advanced AI algorithms that help identify and prevent fraudulent activities, thereby safeguarding assets and reducing risks.

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Automation Explainability Responsible AI AI Automation 147

ODSC - Open Data Science

MARCH 12, 2025

However, with this growth came concerns around misinformation, ethical AI usage, and data privacy, fueling discussions around responsible AI deployment. The Decline of Traditional MachineLearning 20182020: Algorithms like random forests, SVMs, and gradient boosting were frequent discussion points.

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Data Science ETL Machine Learning AI Engineer 52

NVIDIA

MARCH 1, 2024

They have a simple goal: to enable trust and transparency in AI and support the work of partners, customers and developers. Privacy: Complying With Regulations, Safeguarding Data AI is often described as data hungry. Often, the more data an algorithm is trained on, the more accurate its predictions.

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Unite.AI

MAY 30, 2024

A seasoned internet technology developer since 2001, Benjamin is also an SEO expert who has earned over $20 million in profits reverse engineering Google search algorithm updates. Can you explain how DataGenn INVEST leverages Google’s Gemini model and MoE models to predict intraday trading movements?

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Artificial Intelligence Artificial Intelligence Machine Learning AI 147

AWS Machine Learning Blog

JANUARY 31, 2025

Increasingly, FMs are completing tasks that were previously solved by supervised learning, which is a subset of machine learning (ML) that involves training algorithms using a labeled dataset. His primary focus lies in building responsible AI systems, using techniques such as RAG, multi-agent systems, and model fine-tuning.

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Prompt Engineer Prompt Engineering Data Scientist Large Language Models 79

Unite.AI

SEPTEMBER 2, 2024

Examples of such policies include the EU's AI Act , which aims to regulate high-risk AI applications, and the U.S. Algorithmic Accountability Act , which focuses on transparency and fairness in AI systems.

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Generative AI Chatbots AI AI 225

Viso.ai

FEBRUARY 12, 2024

True to its name, Explainable AI refers to the tools and methods that explain AI systems and how they arrive at a certain output. Artificial Intelligence (AI) models assist across various domains, from regression-based forecasting models to complex object detection algorithms in deep learning.

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Explainable AI Explainability Deep Learning Neural Network 52

Becoming Human

APRIL 19, 2024

The rise of AI consulting services AI consulting services have emerged as a key player in the digital transformation landscape. Businesses are leveraging the expertise of AI consultants to navigate the complexities of implementing AI solutions, from developing custom algorithms to integrating off-the-shelf AI tools.

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AI AI Responsible AI Artificial Intelligence 102

IBM Journey to AI blog

SEPTEMBER 3, 2024

With deepfake detection tech evolving at such a rapid pace, it’s important to keep potential algorithmic biases in mind. Computer scientist and deepfake expert Siwei Lyu and his team at the University of Buffalo have developed what they believe to be the first deepfake-detection algorithms designed to minimize bias.

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