Unite.AI

FEBRUARY 6, 2025

AI and machine learning (ML) are reshaping industries and unlocking new opportunities at an incredible pace. There are countless routes to becoming an artificial intelligence (AI) expert, and each persons journey will be shaped by unique experiences, setbacks, and growth. The legal considerations of AI are a given.

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AI News

MARCH 5, 2025

Regulatory challenges and the new AI standard ISO 42001 Tony Porter, former Surveillance Camera Commissioner for the UK Home Office, provided insights into regulatory challenges surrounding AI transparency. “AI explainability means understanding why a specific object or change was detected.

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Google Research AI blog

APRIL 19, 2023

This transformative potential requires us to be responsible not only in how we advance our technology, but also in how we envision which technologies to build, and how we assess the social impact AI and ML-enabled technologies have on the world.

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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.

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AWS Machine Learning Blog

NOVEMBER 29, 2023

These challenges include some that were common before generative AI, such as bias and explainability, and new ones unique to foundation models (FMs), including hallucination and toxicity. Guardrails drive consistency in how FMs on Amazon Bedrock respond to undesirable and harmful content within applications.

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AWS Machine Learning Blog

OCTOBER 16, 2024

Amazon SageMaker supports geospatial machine learning (ML) capabilities, allowing data scientists and ML engineers to build, train, and deploy ML models using geospatial data. SageMaker Processing provisions cluster resources for you to run city-, country-, or continent-scale geospatial ML workloads.

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Flipboard

FEBRUARY 19, 2025

Additionally, we discuss some of the responsible AI framework that customers should consider adopting as trust and responsible AI implementation remain crucial for successful AI adoption. But first, we explain technical architecture that makes Alfred such a powerful tool for Andurils workforce.

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AWS Machine Learning Blog

NOVEMBER 7, 2024

In this second part, we expand the solution and show to further accelerate innovation by centralizing common Generative AI components. We also dive deeper into access patterns, governance, responsible AI, observability, and common solution designs like Retrieval Augmented Generation. This logic sits in a hybrid search component.

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ODSC - Open Data Science

OCTOBER 21, 2024

As AI systems become increasingly embedded in critical decision-making processes and in domains that are governed by a web of complex regulatory requirements, the need for responsible AI practices has never been more urgent. But let’s first take a look at some of the tools for ML evaluation that are popular for responsible AI.

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Unite.AI

FEBRUARY 7, 2024

Machine learning (ML) is a powerful technology that can solve complex problems and deliver customer value. However, ML models are challenging to develop and deploy. This is why Machine Learning Operations (MLOps) has emerged as a paradigm to offer scalable and measurable values to Artificial Intelligence (AI) driven businesses.

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Marktechpost

AUGUST 9, 2024

The System Card provides a comprehensive framework for understanding and assessing GPT-4o’s capabilities, offering a more robust solution for the safe deployment of advanced AI systems. Check out the Paper and Details. All credit for this research goes to the researchers of this project.

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Unite.AI

FEBRUARY 5, 2025

By 2025, it is estimated that 85% of all enterprise applications will feature AI-powered capabilities. This shift is driven by increasing computational power, advancements in machine learning (ML), and the growing availability of high-quality data.

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Marktechpost

JANUARY 17, 2024

The NIST AI Risk Management Framework and AI Trustworthiness taxonomy have indicated that these operational characteristics are necessary for trustworthy AI. A comprehensive taxonomy of AML attacks that covers systems that use both Generative AI and Predictive AI has been presented.

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AWS Machine Learning Blog

OCTOBER 29, 2024

This engine uses artificial intelligence (AI) and machine learning (ML) services and generative AI on AWS to extract transcripts, produce a summary, and provide a sentiment for the call. This post provides guidance on how you can create a video insights and summarization engine using AWS AI/ML services.

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Flipboard

JANUARY 22, 2025

With this new capability, businesses can build more intuitive and responsive AI solutions that better serve their customers needs. About the Authors Christian Kamwangala is an AI/ML and Generative AI Specialist Solutions Architect at AWS, based in Paris, France.

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Marktechpost

NOVEMBER 16, 2024

Introduction to Generative AI Learning Path Specialization This course offers a comprehensive introduction to generative AI, covering large language models (LLMs), their applications, and ethical considerations. The learning path comprises three courses: Generative AI, Large Language Models, and Responsible AI.

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Google Research AI blog

MAY 15, 2023

Last year, in a step toward more inclusive computer vision systems, Google's Responsible AI and Human-Centered Technology team in Research partnered with Dr. Ellis Monk to openly release the Monk Skin Tone (MST) Scale, a skin tone scale that captures a broad spectrum of skin tones. The images are not to be used to train ML models.

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IBM Journey to AI blog

MAY 9, 2023

Challenges around managing risk and reputation Customers, employees and shareholders expect organizations to use AI responsibly, and government entities are starting to demand it. Responsible AI use is critical, especially as more and more organizations share concerns about potential damage to their brand when implementing AI.

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AWS Machine Learning Blog

JANUARY 28, 2025

By investing in robust evaluation practices, companies can maximize the benefits of LLMs while maintaining responsible AI implementation and minimizing potential drawbacks. To support robust generative AI application development, its essential to keep track of models, prompt templates, and datasets used throughout the process.

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IBM Journey to AI blog

DECEMBER 20, 2023

Machine learning (ML) technologies can drive decision-making in virtually all industries, from healthcare to human resources to finance and in myriad use cases, like computer vision , large language models (LLMs), speech recognition, self-driving cars and more. However, the growing influence of ML isn’t without complications.

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Marktechpost

MAY 30, 2024

Introduction to AI and Machine Learning on Google Cloud This course introduces Google Cloud’s AI and ML offerings for predictive and generative projects, covering technologies, products, and tools across the data-to-AI lifecycle. It includes labs on feature engineering with BigQuery ML, Keras, and TensorFlow.

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Google Research AI blog

MAY 18, 2023

It’s a mission that is particularly timely given the emergence of generative AI and chatbots. Our inspiration this year is "changing the way people think about what THEY can do with AI.” For example, we recently published new AI Explorables on the topics of model confidence and unintended biases.

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Unite.AI

JULY 11, 2023

Introduction To Generative AI Image Source Course difficulty: Beginner-level Completion time: ~ 45 minutes Prerequisites: No What will AI enthusiasts learn? What is Generative Artificial Intelligence, how it works, what its applications are, and how it differs from standard machine learning (ML) techniques.

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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. About the Authors Mithil Shah is a Principal AI/ML Solution Architect at Amazon Web Services.

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AWS Machine Learning Blog

FEBRUARY 27, 2023

Model tuning is the experimental process of finding the optimal parameters and configurations for a machine learning (ML) model that result in the best possible desired outcome with a validation dataset. Single objective optimization with a performance metric is the most common approach for tuning ML models.

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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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AWS Machine Learning Blog

OCTOBER 29, 2024

Ishan Singh is a Generative AI Data Scientist at Amazon Web Services, where he helps customers build innovative and responsible generative AI solutions and products. With a strong background in AI/ML, Ishan specializes in building Generative AI solutions that drive business value.

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Google Research AI blog

FEBRUARY 2, 2023

Great machine learning (ML) research requires great systems. In this post, we provide an overview of the numerous advances made across Google this past year in systems for ML that enable us to support the serving and training of complex models while easing the complexity of implementation for end users.

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Unite.AI

JULY 24, 2024

This innovation laid the groundwork for further advancements in AI across Apple’s product line. In 2017, Apple introduced Core ML , a machine learning framework that allowed developers to integrate AI capabilities into their apps. The company's proactive handling of these issues set a benchmark for the industry.

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AWS Machine Learning Blog

NOVEMBER 14, 2024

Core benefits of Amazon Bedrock and Amazon Location Service Amazon Bedrock provides capabilities to build generative AI applications with security, privacy, and responsible AI practices. Being serverless, it allows secure integration and deployment of generative AI capabilities without managing infrastructure.

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ODSC - Open Data Science

MARCH 12, 2025

The next wave of advancements, including fine-tuned LLMs and multimodal AI, has enabled creative applications in content creation, coding assistance, and conversational agents. However, with this growth came concerns around misinformation, ethical AI usage, and data privacy, fueling discussions around responsible AI deployment.

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AWS Machine Learning Blog

MARCH 14, 2025

This post focuses on RAG evaluation with Amazon Bedrock Knowledge Bases, provides a guide to set up the feature, discusses nuances to consider as you evaluate your prompts and responses, and finally discusses best practices. With a strong background in AI/ML, Ishan specializes in building Generative AI solutions that drive business value.

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AWS Machine Learning Blog

JANUARY 29, 2025

Centralized model In a centralized operating model, all generative AI activities go through a central generative artificial intelligence and machine learning (AI/ML) team that provisions and manages end-to-end AI workflows, models, and data across the enterprise.

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John Snow Labs

JANUARY 21, 2025

NLP Logix, a leading artificial intelligence (AI) and machine learning (ML) consultancy has announced a strategic technology partnership with John Snow Labs, a premier provider of healthcare AI solutions. Together, were helping organizations transform how they operate the right way.

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AWS Machine Learning Blog

FEBRUARY 21, 2025

Chaithanya Maisagoni is a Senior Software Development Engineer (AI/ML) in Amazons Worldwide Returns and ReCommerce organization. He specializes in building scalable machine learning infrastructure, distributed systems, and containerization technologies.

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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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Unite.AI

APRIL 9, 2024

Composite AI is a cutting-edge approach to holistically tackling complex business problems. These techniques include Machine Learning (ML), deep learning , Natural Language Processing (NLP) , Computer Vision (CV) , descriptive statistics, and knowledge graphs. Transparency is fundamental for responsible AI usage.

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