NVIDIA

MAY 1, 2024

enhances the performance of AI systems across various metrics like accuracy, explainability and fairness. In this episode of the NVIDIA AI Podcast , recorded live at GTC 2024, host Noah Kravitz sits down with Adam Wenchel, cofounder and CEO of Arthur, to discuss the challenges and opportunities of deploying generative AI.

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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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Unite.AI

SEPTEMBER 13, 2023

Increasingly though, large datasets and the muddled pathways by which AI models generate their outputs are obscuring the explainability that hospitals and healthcare providers require to trace and prevent potential inaccuracies. In this context, explainability refers to the ability to understand any given LLM’s logic pathways.

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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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Dlabs.ai

AUGUST 16, 2022

This is the challenge that explainable AI solves. Explainable artificial intelligence shows how a model arrives at a conclusion. What is explainable AI? Explainable artificial intelligence (or XAI, for short) is a process that helps people understand an AI model’s output. Let’s begin.

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DataRobot Blog

FEBRUARY 14, 2022

With any AI solution , you want it to be accurate. But just as important, you want it to be explainable. Explainability requirements continue after the model has been deployed and is making predictions. DataRobot offers end-to-end explainability to make sure models are transparent at all stages of their lifecycle.

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Mlearning.ai

DECEMBER 31, 2023

Principles of Explainable AI( Source ) Imagine a world where artificial intelligence (AI) not only makes decisions but also explains them as clearly as a human expert. This isn’t a scene from a sci-fi movie; it’s the emerging reality of Explainable AI (XAI). What is Explainable AI?

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AI News

OCTOBER 8, 2024

As AI increasingly influences decisions that impact human rights and well-being, systems have to comprehend ethical and legal norms. “The question that I investigate is, how do we get this kind of information, this normative understanding of the world, into a machine that could be a robot, a chatbot, anything like that?”

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Marktechpost

JULY 22, 2024

This lack of explainability is a gap in academic interest and a practical concern. Analyzing the decision-making process of AI models is essential for building trust and reliability, particularly in identifying and addressing hidden biases. This is a critical limitation as the demand for explainable AI grows.

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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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Unite.AI

AUGUST 25, 2023

Can focusing on Explainable AI (XAI) ever address this? To engineers, explainable AI is currently thought of as a group of technological constraints and practices, aimed at making the models more transparent to people working on them. They need explainability to be able to push back in their own defense.

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Heartbeat

SEPTEMBER 13, 2023

Source: ResearchGate Explainability refers to the ability to understand and evaluate the decisions and reasoning underlying the predictions from AI models (Castillo, 2021). Explainability techniques aim to reveal the inner workings of AI systems by offering insights into their predictions. What is Explainability?

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Unite.AI

JUNE 26, 2023

When the patient independently uses an AI tool, an accident can be their fault. AI gone wrong isn’t always due to a technical error. For instance, maybe a doctor thoroughly explains an AI tool to their patient, but they ignore safety instructions or input incorrect data.

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Heartbeat

SEPTEMBER 7, 2023

In the ever-evolving landscape of machine learning and artificial intelligence, understanding and explaining the decisions made by models have become paramount. Enter Comet , that streamlines the model development process and strongly emphasizes model interpretability and explainability. Why Does It Matter?

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IBM Journey to AI blog

OCTOBER 12, 2023

But the implementation of AI is only one piece of the puzzle. Checkpoints can be created throughout the AI lifecycle to prevent or mitigate bias and drift. Documentation can also be generated and maintained with information such as a model’s data origins, training methods and behaviors.

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IBM Journey to AI blog

OCTOBER 30, 2024

Moreover, LRRs and other industry frameworks, such as the National Institute of Standards and Technology (NIST), Information Technology Infrastructure Library (ITIL), and Control Objectives for Information and Related Technologies (COBIT), are constantly evolving. Furthermore, watsonx.ai

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IBM Journey to AI blog

AUGUST 9, 2024

Predictive AI models enhance the speed and precision of predictive analytics and are typically used for business forecasting to project sales, estimate product or service demand, personalize customer experiences and optimize logistics. What’s the difference between generative AI and predictive AI?

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Marktechpost

AUGUST 13, 2024

However, the challenge lies in integrating and explaining multimodal data from various sources, such as sensors and images. AI models are often sensitive to small changes, necessitating a focus on trustworthy AI that emphasizes explainability and robustness.

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AI News

APRIL 25, 2024

He outlined a litany of potential pitfalls that must be carefully navigated—from AI hallucinations and emissions of falsehoods, to data privacy violations and intellectual property leaks from training on proprietary information. Pryon also emphasises explainable AI and verifiable attribution of knowledge sources.

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Marktechpost

AUGUST 24, 2024

EXplainable AI (XAI) has become a critical research domain since AI systems have progressed to being deployed in essential sectors such as health, finance, and criminal justice. The intrinsic complexity—the so-called “black boxes”—given by AI models makes research in the field of XAI difficult.

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Unite.AI

AUGUST 13, 2024

This allowed Microsoft’s representatives to attend board meetings and access confidential information. The close ties between the two companies and Microsoft's ability to access confidential information attracted scrutiny from regulators regarding fair competition and market practices. However, they would not possess voting rights.

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Unite.AI

SEPTEMBER 20, 2024

Its real-time trend analysis, investment evaluations, risk assessments, and automation features empower financial professionals to make informed choices efficiently. For example, Palmyra-Fin can detect links between geopolitical events and stock prices and can thus help professionals stay informed in rapidly evolving markets.

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Unite.AI

DECEMBER 13, 2023

The future involves human-AI collaboration to tackle evolving trends and threats in 2024. Importance of Staying Updated on Trends Staying updated on AI trends is crucial because it keeps you informed about the latest advancements, ensuring you remain at the forefront of technological innovation.

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IBM Journey to AI blog

OCTOBER 16, 2023

Foundation models are widely used for ML tasks like classification and entity extraction, as well as generative AI tasks such as translation, summarization and creating realistic content. The development and use of these models explain the enormous amount of recent AI breakthroughs. Increase trust in AI outcomes.

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IBM Journey to AI blog

MARCH 7, 2023

The specific approach we took required the use of both AI and edge computing to create a predictive model that could process years of anonymized data to help doctors make informed decisions. We wanted to be able to help them observe and monitor the thousands of data points available to make informed decisions.

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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. This step confirms that the images contain relevant information for the task.

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IBM Journey to AI blog

OCTOBER 17, 2024

Forecasting for future decision-making: Cognos Analytics also includes powerful forecasting capabilities, enabling users to make informed decisions based on predictive insights. Users can track these metrics visually or receive updates automatically, enabling faster, more informed decision-making.

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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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Unite.AI

JULY 14, 2024

Issues with Transparency: Users were not informed exactly how their data would be used, creating a trust deficit. Additionally, Meta argued that it had timely informed users through various communication channels and that its AI practices seek to enhance user experience without compromising privacy.

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Unite.AI

JULY 2, 2024

For AI systems, particularly those involving automated decision-making, this means ensuring that users are informed about how their data will be used and that they consent to said use. This adds a layer of complexity for AI systems that rely on automated processes and large-scale data analytics.

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IBM Journey to AI blog

SEPTEMBER 24, 2024

Abhisesh Silwal, a systems scientist at Carnegie Mellon University whose research focuses on AI and robotics in agriculture, thinks so. Guerena’s project, called Artemis, uses AI and computer vision to speed up the phenotyping process. We get tired, lose our focus, or just physically can’t see all that we need to.

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IBM Journey to AI blog

JANUARY 25, 2024

The goal of these forms is to capture critical information about AI models so that governance teams can assess and address their risks before deploying them. How are you making your model explainable? A common refrain might be, “How could my model be unfair if it is not gathering personally identifiable information (PII)?”

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Aiiot Talk

JULY 7, 2023

AI is today’s most advanced form of predictive maintenance, using algorithms to automate performance and sensor data analysis. This information serves as a baseline for comparison so the algorithm can identify unusual activity. IoT sensors that detect performance outside expected margins trigger the AI to alert maintenance personnel.

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IBM Journey to AI blog

APRIL 11, 2023

Although some third-party vendor information may be proprietary, the evaluation team should still review these processes and establish safeguards for vendors. It is crucial that proprietary AI is transparent, and the team should work to include diversity, equity, and inclusion in the hiring process.

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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

NOVEMBER 30, 2023

Researchers from Lund University and Halmstad University conducted a review on explainable AI in poverty estimation through satellite imagery and deep machine learning. The review underscores the significance of explainability for wider dissemination and acceptance within the development community.

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IBM Journey to AI blog

FEBRUARY 8, 2024

Not complying with the EU AI Act can be costly: 7.5 of a company’s total worldwide annual turnover (whichever is higher) for the supply of incorrect information. 15 million euros or 3% of a company’s total worldwide annual turnover (whichever is higher) for violations of the EU AI Act’s obligations.

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