IBM Journey to AI blog

AUGUST 11, 2023

Because the machine learning lifecycle has many complex components that reach across multiple teams, it requires close-knit collaboration to ensure that hand-offs occur efficiently, from data preparation and model training to model deployment and monitoring. Generative AI relies on foundation models to create a scalable process.

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IBM Journey to AI blog

MARCH 7, 2023

We wanted to be able to help them observe and monitor the thousands of data points available to make informed decisions. How AI powers the Innocens Project When the collaboration began, data scientists at IBM understood they were dealing with a sensitive topic and sensitive information.

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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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Marktechpost

JULY 6, 2023

On the other hand, explainable AI models are very complicated deep learning models that are too complex for humans to understand without the aid of additional methods. This is why when Explainable AI models can give a clear idea of why a decision was made but not how it arrived at that decision.

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ODSC - Open Data Science

JANUARY 29, 2025

Explainable AI(XAI) Explainable AI emphasizes transparency and interpretability, enabling users to understand how AI models arrive at decisions. Techniques such as embodied AI, multimodal learning, knowledge graphs, reinforcement learning, and explainable AI are paving the way for more grounded and reliablesystems.

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IBM Journey to AI blog

JULY 6, 2023

It processes enormous amounts of data a human wouldn’t be able to work through in a lifetime and evolves as more data is processed. Challenges of data science Across most companies, finding, cleaning and preparing the proper data for analysis can take up to 80% of a data scientist’s day.

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Unite.AI

DECEMBER 1, 2023

Data-centric AI means focusing on building better data to build better models. This stands in contrast to—but works hand-in-hand with—model-centric AI. Our primary source of signal comes from subject matter experts who collaborate with data scientists to build labeling functions.

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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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ODSC - Open Data Science

DECEMBER 7, 2023

8 Tools to Protect Sensitive Data from Unintended Leakage In order to protect themselves from unintended leakage of sensitive information, organizations employ a variety of tools that scan repositories and code continuously to identify the secrets that are hard-coded within.

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Unite.AI

JUNE 24, 2024

My training in pure mathematics has resulted in a preference for what data scientists call ‘parsimony’ — the right tool for the job, and nothing more. It’s been fascinating to see the shifting role of the data scientist and the software engineer in these last twenty years since machine learning became widespread.

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IBM Journey to AI blog

JULY 17, 2023

is a studio to train, validate, tune and deploy machine learning (ML) and foundation models for Generative AI. Watsonx.data allows scaling of AI workloads using customer data. Watsonx.governance is providing an end-to-end solution to enable responsible, transparent and explainable AI workflows. Watsonx.ai

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Towards AI

JULY 24, 2024

Well, get ready because we’re about to embark on another exciting exploration of explainable AI, this time focusing on Generative AI. Before we dive into the world of explainability in GenAI, it’s worth noting that the tone of this article, like its predecessor, is intentionally casual and approachable.

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ODSC - Open Data Science

JUNE 7, 2023

Interactive Explainable AI Meg Kurdziolek, PhD | Staff UX Researcher | Intrinsic.ai Although current explainable AI techniques have made significant progress toward enabling end-users to understand the why behind a prediction, to effectively build trust with an AI system we need to take the next step and make XAI tools interactive.

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IBM Journey to AI blog

JUNE 28, 2023

Leveraging its genomics experience, IBM has published a whitepaper, Explainable AI reveals changes in skin microbiome composition linked to phenotypic differences , and has also invested in building an accelerator, to enable researchers to perform phenotype prediction from omics data (e.g.,

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Data Analysis Data Scientist Explainable AI AI Strategy 186

Towards AI

JULY 24, 2024

Well, get ready because we’re about to embark on another exciting exploration of explainable AI, this time focusing on Generative AI. Before we dive into the world of explainability in GenAI, it’s worth noting that the tone of this article, like its predecessor, is intentionally casual and approachable.

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IBM Journey to AI blog

SEPTEMBER 20, 2023

IBM watsonx.data is a fit-for-purpose data store built on an open lakehouse architecture to scale AI workloads for all of your data, anywhere. IBM watsonx.governance is an end-to-end automated AI lifecycle governance toolkit that is built to enable responsible, transparent and explainable AI workflows.

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The MLOps Blog

JUNE 27, 2023

Some popular end-to-end MLOps platforms in 2023 Amazon SageMaker Amazon SageMaker provides a unified interface for data preprocessing, model training, and experimentation, allowing data scientists to collaborate and share code easily. Check out the Kubeflow documentation.

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ODSC - Open Data Science

FEBRUARY 24, 2023

Its goal is to help with a quick analysis of target characteristics, training vs testing data, and other such data characterization tasks. Apache Superset GitHub | Website Apache Superset is a must-try project for any ML engineer, data scientist, or data analyst.

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Pickl AI

OCTOBER 8, 2024

This blog will explore the concept of XAI, its importance in fostering trust in AI systems, its benefits, challenges, techniques, and real-world applications. What is Explainable AI (XAI)? Explainable AI refers to methods and techniques that enable human users to comprehend and interpret the decisions made by AI systems.

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ODSC - Open Data Science

FEBRUARY 25, 2025

AI Agents TrackHarness the Power of Autonomous Systems AI agents are transforming how businesses operate by performing complex tasks independently, improving productivity and decision-making. Whats Next in AI TrackExplore the Cutting-Edge Stay ahead of the curve with insights into the future of AI.

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Flipboard

JUNE 20, 2023

Understanding AI’s mysterious “opaque box” is paramount to creating explainable AI. This can be simplified by considering that AI, like all other technology, has a supply chain. These are the mathematical formulas written to simulate functions of the brain, which underlie the AI programming.

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ODSC - Open Data Science

OCTOBER 21, 2024

Responsible AI Toolbox Website For a broader look at responsible AI, the Responsible AI Toolbox website offers detailed guidance on incorporating responsible practices into AI workflows. From development through deployment, this platform ensures that responsible AI remains a core focus.

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Pickl AI

SEPTEMBER 3, 2024

Explainable AI As ANNs are increasingly used in critical applications, such as healthcare and finance, the need for transparency and interpretability has become paramount. Many data scientists specialise in neural networks and Deep Learning to tackle complex problems across various industries.

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DataRobot Blog

MAY 25, 2022

Using either the code-centric DataRobot Core or no-code Graphical User Interface (GUI), both data scientists and non-data scientists such as risk analysts, government experts, or first responders can build, compare, explain, and deploy their own models. Other Disaster Applications for DataRobot.

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ODSC - Open Data Science

FEBRUARY 27, 2024

Interactive Explainable AI Meg Kurdziolek, PhD | Staff UX Researcher | Intrinsic.ai Although current explainable AI techniques have made significant progress toward enabling end-users to understand the why behind a prediction, to effectively build trust with an AI system we need to take the next step and make XAI tools interactive.

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DataRobot Blog

MAY 12, 2022

AI will create countless opportunities for homeland security leaders to better support their communities and lead their organizations. DataRobot believes trusted, explainable AI can help generate better outcomes than either humans or machines alone. AI Cloud for Public Sector. Our team is here to help along the way.

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Pickl AI

NOVEMBER 5, 2024

According to a report by the International Data Corporation (IDC), global spending on AI systems is expected to reach $500 billion by 2027 , reflecting the increasing reliance on AI-driven solutions. Explainable AI (XAI) is crucial for building trust in automated systems. Furthermore, the U.S.

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ODSC - Open Data Science

JUNE 1, 2023

Is an AI Coding Assistant Right For You? Whether you’re a seasoned data scientist, engineer, or just getting your feet wet in the data science field, let’s take a look at how coding assistants can help data process regardless of their skill level. Well, these libraries will give you a solid start.

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Marktechpost

AUGUST 24, 2023

r/datascience It features the latest content and discussions on Data Science and its related fields. It serves as a forum for discussion and debate on matters related to the career of data scientists. The subreddit has over 817k members and active moderators.

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Pickl AI

JANUARY 29, 2024

Job Roles Data Scientist, Data Analyst , and Business Analyst are typical roles in Data Science. AI Engineer, Machine Learning Engineer, and Robotics Engineer are prominent roles in AI. ML Engineer, Data Scientist, and Research Scientist are typical roles in Machine Learning.

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How to Learn Machine Learning

JUNE 9, 2024

It is quite beneficial for organizations looking to capitalize on the potential of AI without making significant investments. Moreover, it enhances the productivity of data scientists. 2) Explainable AI Explainability AI and interpretable machine learning are the different names of the same things.

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Dlabs.ai

MAY 22, 2022

The instructors are data scientists. It’s dedicated to data scientists, and believe me: it’s run by some of the institution’s most experienced lecturers, including computer science and statistics professors. While the AI course is free, you have to pay for the final certificate.

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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. Businesses harness these innovations for real-time analytics, operational efficiency, and data democratisation, ensuring competitiveness in 2025.

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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.

Data Scientist 52

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Heartbeat

SEPTEMBER 13, 2023

Distinction Between Interpretability and Explainability Interpretability and explainability are interchangeable concepts in machine learning and artificial intelligence because they share a similar goal of explaining AI predictions. However, there are slight differences between them.

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Pickl AI

JULY 14, 2023

Robotics also witnessed advancements, with AI-powered robots becoming more capable in navigation, manipulation, and interaction with the physical world. Explainable AI and Ethical Considerations (2010s-present): As AI systems became more complex and influential, concerns about transparency, fairness, and accountability arose.

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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.

Data Scientist 52

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