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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Towards AI

AUGUST 31, 2023

Last Updated on September 1, 2023 by Editorial Team Author(s): Louis Bouchard Originally published on Towards AI. An introduction to explainable AI This member-only story is on us. Powerful artificial intelligence models like DALLE or ChatGPT are super useful and fun to use. Upgrade to access all of Medium.

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Unite.AI

JUNE 26, 2023

At the root of AI mistakes like these is the nature of AI models themselves. Most AI today use “black box” logic, meaning no one can see how the algorithm makes decisions. Black box AI lack transparency, leading to risks like logic bias , discrimination and inaccurate results.

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Unite.AI

OCTOBER 20, 2023

AI models in production. Today, seven in 10 companies are experimenting with generative AI, meaning that the number of AI models in production will skyrocket over the coming years. As a result, industry discussions around responsible AI have taken on greater urgency. In 2022, companies had an average of 3.8

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Unite.AI

MARCH 18, 2024

As generative AI technology advances, there's been a significant increase in AI-generated content. This content often fills the gap when data is scarce or diversifies the training material for AI models, sometimes without full recognition of its implications.

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AI News

MARCH 7, 2024

Indeed, as Anthropic prompt engineer Alex Albert pointed out, during the testing phase of Claude 3 Opus, the most potent LLM (large language model) variant, the model exhibited signs of awareness that it was being evaluated. In addition, editorial guidance on AI has been updated to note that ‘all AI usage has active human oversight.’

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IBM Journey to AI blog

JANUARY 25, 2024

How might this insight affect evaluation of AI models? Model (in)accuracy To quote a common aphorism, all models are wrong. This holds true in the areas of statistics, science and AI. Models created with a lack of domain expertise can lead to erroneous outputs. How are you making your model explainable?

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IBM Journey to AI blog

JUNE 26, 2023

Concerns to consider with off the shelf generative AI models include: Internet data is not always fair and accurate At the heart of much of generative AI today is vast amounts of data from sources such as Wikipedia, websites, articles, image or audio files, etc. What is watsonx.governance?

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Towards AI

JULY 22, 2023

So, don’t worry, this is where Explainable AI, also known as XAI, comes in. HEALTHCARE WITH AI: SOURCE: [link] Let’s go through some instances to help you understand why Explainable AI is so important: Imagine a healthcare system in which, instead of speaking with a doctor, you interact with an AI system to assist you with diagnosis.

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IBM Journey to AI blog

MARCH 7, 2023

Using IBM Watson Studio , a service available on IBM Cloud Pak for Data , to train and monitor the AI solution’s machine learning models, Innocens BV could help doctors by providing data driven insights that are associated with a potential onset of sepsis.

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NVIDIA

MARCH 1, 2024

Developers of trustworthy AI understand that no model is perfect, and take steps to help customers and the general public understand how the technology was built, its intended use cases and its limitations.

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IBM Journey to AI blog

MAY 9, 2023

A lack of confidence to operationalize AI Many organizations struggle when adopting AI. According to Gartner , 54% of models are stuck in pre-production because there is not an automated process to manage these pipelines and there is a need to ensure the AI models can be trusted.

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LevelAI

DECEMBER 5, 2023

Level AI stands out as a pioneer in this domain, setting new standards for enterprise-ready generative AI. AI Benchmarking for Impact and Effectiveness Level AI recognizes that traditional benchmarking methods fall short when evaluating generative AI models.

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IBM Journey to AI blog

OCTOBER 16, 2023

It encompasses risk management and regulatory compliance and guides how AI is managed within an organization. Foundation models: The power of curated datasets Foundation models , also known as “transformers,” are modern, large-scale AI models trained on large amounts of raw, unlabeled data.

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LevelAI

DECEMBER 5, 2023

Level AI stands out as a pioneer in this domain, setting new standards for enterprise-ready generative AI. AI Benchmarking for Impact and Effectiveness Level AI recognizes that traditional benchmarking methods fall short when evaluating generative AI models.

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IBM Journey to AI blog

MARCH 4, 2024

Operations Incidents occur, even in an AI-first world. However, an AI+ enterprise uses AI not only to delight customers but also to solve IT problems. The scale and impact of next-generation AI emphasize the importance of governance and risk controls.

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Unite.AI

DECEMBER 13, 2023

The need for explainability in AI algorithms becomes important in meeting compliance requirements. Organizations must showcase how AI-driven decisions are made, making explainable AI models important. These models clearly understand the decision-making process, facilitating regulatory compliance audits.

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Dlabs.ai

JULY 19, 2023

This problem becomes particularly pronounced when employees are unsure why an AI tool makes specific recommendations or decisions and could lead to reluctance to implement the AI’s suggestions. Possible solution: Explainable AI Fortunately, a promising solution exists in the form of Explainable AI.

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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). For instance, human experts bring domain knowledge and expertise that can complement AI systems. However, there are slight differences between them.

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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.

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IBM Journey to AI blog

AUGUST 11, 2023

Generative AI is a type of deep-learning model that takes raw data, processes it and “learns” to generate probable outputs. In other words, the AI model uses a simplified representation of the training data to create a new work that’s similar, but not identical, to the original data.

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DataRobot Blog

JUNE 23, 2022

Replacing these with a more accurate (and rational) AI/ML model in an existing system or process is generally straightforward, because there is a context and environment in which the new model can succeed. DATAROBOT AI CLOUD. Too often the new model fails the “so what” test. using an AI model.

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IBM Journey to AI blog

JULY 17, 2023

Generative AI has the potential to significantly disrupt customer care, leveraging large language models (LLMs) and deep learning techniques designed to understand complex inquiries and offer to generate more human-like conversational responses. Watsonx.data allows scaling of AI workloads using customer data. Watsonx.ai

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Defined.ai blog

AUGUST 10, 2023

Businesses can transform raw numbers into actionable insights by applying AI. For instance, an AI model can predict future sales based on past data, helping businesses plan better. Interpreting these requires a keen understanding of your business context and the specific problem the AI was set to solve.

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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

APRIL 9, 2024

The adoption of Artificial Intelligence (AI) has increased rapidly across domains such as healthcare, finance, and legal systems. However, this surge in AI usage has raised concerns about transparency and accountability. Composite AI is a cutting-edge approach to holistically tackling complex business problems.

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Pickl AI

APRIL 8, 2024

Overfitting AI models trained on datasets that are not representative of the real world can become overfitted. This means the model performs well on the training data but fails to generalize to new situations. How Can We Ensure the Transparency of AI Systems?

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ODSC - Open Data Science

JUNE 1, 2023

Top ODSC East 2023 Virtual Sessions Available to Watch for Free With topics ranging from explainable AI to delivering data-driven presentations, these are the top virtual sessions from ODSC East that you can watch for free.

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ODSC - Open Data Science

FEBRUARY 22, 2024

Join this upcoming training session which will demonstrate how you can use low-code/no-code programming platforms to effectively integrate geospatial analysis with a variety of AI algorithms, including machine learning, deep learning, and Explainable AI. Get your ODSC East Pass today to save 50%.

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IBM Journey to AI blog

JULY 6, 2023

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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Dlabs.ai

JULY 25, 2023

” [ Read also: Explainable AI: A Way To Explain How Your AI Model Work s ] 6. Scrutinize the vendor’s developmental processes, fairness safeguards, transparency, and ethics policies to reduce algorithmic bias. Avoid systems that operate as a “black box.” Document your selection process.

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Heartbeat

SEPTEMBER 7, 2023

Let’s start by understanding why transparency in AI is not just an option but a necessity in today’s world. The Need for Model Interpretability and Explainability In the age of AI, models impact our lives in countless ways. Consider healthcare, where AI models are being used for disease diagnosis.

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IBM Journey to AI blog

OCTOBER 12, 2023

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. Here’s what’s involved in making that happen.

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DataRobot Blog

MARCH 15, 2022

In addition, as more decisions are guided by machine learning, there’s the prerequisite to monitor, assess, and explain AI model performance against the constant of changing data (volumes fluctuate, casemix varies, clinical system configuration changes, and so on).

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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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Towards AI

APRIL 2, 2024

X’s Grok Chatbot Will Soon Get an Upgraded Model, Grok-1.5 has announced an upgraded version of its AI model, Grok-1.5. This new model is expected to power the Grok chatbot on X. shows improved performance over the previous Grok-1 model on math and coding benchmarks. As per the official blog, Grok-1.5

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IBM Journey to AI blog

FEBRUARY 8, 2024

For industries providing essential services to clients such as insurance, banking and retail, the law requires the use of a fundamental rights impact assessment that details how the use of AI will affect the rights of customers. Higher risk tiers have more transparency requirements including model evaluation, documentation and reporting.

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