IBM Journey to AI blog

JULY 6, 2023

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is data science? This post will dive deeper into the nuances of each field.

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Towards AI

MARCH 17, 2024

Alongside this, there is a second boom in XAI or Explainable AI. Explainable AI is focused on helping us poor, computationally inefficient humans understand how AI “thinks.” We will then explore some techniques for building glass-box or explainable models.

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ODSC - Open Data Science

NOVEMBER 30, 2023

Healthcare systems are implementing AI, and patients and clinicians want to know how it works in detail. Explainable AI might be the solution everyone needs to develop a healthier, more trusting relationship with technology while expediting essential medical care in a highly demanding world. What Is Explainable AI?

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IBM Journey to AI blog

AUGUST 11, 2023

Today, 35% of companies report using AI in their business, which includes ML, and an additional 42% reported they are exploring AI, according to the IBM Global AI Adoption Index 2022. MLOps is the next evolution of data analysis and deep learning. Looking to scale the impact of AI across your business?

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Towards AI

NOVEMBER 1, 2023

Last Updated on November 2, 2023 by Editorial Team Author(s): Ahmed Fessi Originally published on Towards AI.

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ODSC - Open Data Science

MARCH 20, 2025

From May 13th to 15th, ODSC East 2025 is bringing together the brightest minds in AI and data science for an unparalleled learning and networking experience. With 150+ expert-led sessions, hands-on workshops, and cutting-edge talks, youll gain the skills and insights needed to stay ahead in the rapidly evolving AI landscape.

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AI Weekly

OCTOBER 3, 2024

radiologybusiness.com Research How cold hard data science harnesses AI with Wolfram Research It’s sometimes difficult to distinguish the reality of technology from the hype and marketing messages that bombard our inboxes daily.

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Towards AI

OCTOBER 19, 2024

Yet, for all their sophistication, they often can’t explain their choices — this lack of transparency isn’t just frustrating — it’s increasingly problematic as AI becomes more integrated into critical areas of our lives. What is Explainability AI (XAI)? It’s particularly useful in natural language processing [3].

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Unite.AI

APRIL 9, 2024

Composite AI plays a pivotal role in enhancing interpretability and transparency. Combining diverse AI techniques enables human-like decision-making. Key benefits include: reducing the necessity of large data science teams. Explainability is essential for accountability, fairness, and user confidence.

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IBM Journey to AI blog

MAY 9, 2023

Success in delivering scalable enterprise AI necessitates the use of tools and processes that are specifically made for building, deploying, monitoring and retraining AI models. Consistent principles guiding the design, development, deployment and monitoring of models are critical in driving responsible, transparent and explainable AI.

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Towards AI

MAY 6, 2024

A typical SHAP Plot — Image by Author In Part 1 of Data Science Case Study — Credit Default Prediction, we have talked about feature engineering, model training, model evaluation and classification threshold selection. Model Explainability Our model can make predictions by feeding into it the features. Let’s jump in!

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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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ODSC - Open Data Science

MARCH 10, 2025

AI and data science are advancing at a lightning-fast pace with new skills and applications popping up left and right. Walk away with practical approaches to designing robust evaluation frameworks that ensure AI systems are measurable, reliable, and deployment-ready.

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Mlearning.ai

FEBRUARY 6, 2023

This is why we need Explainable AI (XAI). Attention mechanisms have often been touted as an in-built explanation mechanism, allowing any Transformer to be inherently explainable. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. And I agree to an extent.

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IBM Journey to AI blog

FEBRUARY 8, 2023

Manual processes can lead to “black box models” that lack transparent and explainable analytic results. Explainable results are crucial when facing questions on the performance of AI algorithms and models. Your customers deserve and are holding your organization accountable to explain reasons for analytics-based decisions.

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DataRobot Blog

DECEMBER 6, 2022

Savvy data scientists are already applying artificial intelligence and machine learning to accelerate the scope and scale of data-driven decisions in strategic organizations. These data science teams are seeing tremendous results—millions of dollars saved, new customers acquired, and new innovations that create a competitive advantage.

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Towards AI

JULY 22, 2023

Last Updated on July 24, 2023 by Editorial Team Author(s): Data Science meets Cyber Security Originally published on Towards AI. Let us go further into the enigmas of Artificial Intelligence, where AI is making waves like never before! So, don’t worry, this is where Explainable AI, also known as XAI, comes in.

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Pickl AI

NOVEMBER 5, 2024

Summary: Data Science and AI are transforming the future by enabling smarter decision-making, automating processes, and uncovering valuable insights from vast datasets. Bureau of Labor Statistics predicts that employment for Data Scientists will grow by 36% from 2021 to 2031 , making it one of the fastest-growing professions.

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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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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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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. Use our guide to help you ask the right questions to get you in.

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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. Often called hallucinations, these problems can be harmful, especially if we blindly trust the AI. Upgrade to access all of Medium.

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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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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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Pickl AI

JULY 1, 2024

Demand forecasting, powered by data science, helps predict customer needs. Optimize inventory, streamline operations, and make data-driven decisions for success. Data Science empowers businesses to leverage the power of data for accurate and insightful demand forecasts.

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ODSC - Open Data Science

AUGUST 14, 2023

“I still don’t know what AI is” If you’re like my parents and think I work at ChatGPT, then you may have to learn a little bit more about AI. Funny enough, you can use AI to explain AI. Most AI-based programs have plenty of good tutorials that explain how to use the automation side of things as well.

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Pickl AI

JANUARY 29, 2024

Summary: In the tech landscape of 2024, the distinctions between Data Science and Machine Learning are pivotal. Data Science extracts insights, while Machine Learning focuses on self-learning algorithms. The collective strength of both forms the groundwork for AI and Data Science, propelling innovation.

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NYU Center for Data Science

JUNE 16, 2023

Motivated by applications in healthcare and criminal justice, Umang studies how to create algorithmic decision-making systems endowed with the ability to explain their behavior and adapt to a stakeholder’s expertise to improve human-machine team performance. By Meryl Phair

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ODSC - Open Data Science

FEBRUARY 13, 2023

Explainable AI: As in the namesake, explainable AI’s purpose is to clearly and in a transparent manner, explain why a machine learning model came to a specific decision. First are two techniques that tackle the question of why predictions are made and explain them. Here are a few that are raising eyebrows.

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IBM Journey to AI blog

MARCH 7, 2023

This required a model with a high level of precision that also is built upon key principles of trustworthy AI including transparency, explainability, fairness, privacy and robustness. The Innocens team needed to build a model that could detect subtle changes in neonates’ vital signs while generating as few false alarms as possible.

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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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Towards AI

FEBRUARY 22, 2024

Our friends at Zoī are hiring their Chief AI Officer. Zoī is at the crossroads of 3 domains: Medical, Data Science, and BeSci. Zoī aims to create personalized user manuals for each member by gathering only the necessary data to provide tailored recommendations based on thousands of factors. Our must-read articles 1.

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ODSC - Open Data Science

FEBRUARY 24, 2023

How to learn more about data exploration tools and uses There are plenty of data exploration tools available and countless ways to use them. For those looking to get more out of their data, whether you’re new to data science or you’re a seasoned pro, getting hands-on training with these tools is the best way to learn how they work.

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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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Mlearning.ai

JANUARY 26, 2023

When it comes to implementing any ML model, the most difficult question asked is how do you explain it. Suppose, you are a data scientist working closely with stakeholders or customers, even explaining the model performance and feature selection of a Deep learning model is quite a task. How can we explain it in simple terms?

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ODSC - Open Data Science

OCTOBER 24, 2023

Figure 1: Synthetic data (left) versus real (right), Telecom dataset The main hyperparameter vector specifies the number of quantile intervals to use for each feature (one per feature). Indeed, the whole technique epitomizes explainable AI. It is easy to fine-tune, allowing for auto-tuning.

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Unite.AI

JUNE 24, 2024

At Astronomer, he spearheads the creation of Apache Airflow features specifically designed for ML and AI teams and oversees the internal data science team. Can you share some information about your journey in data science and AI, and how it has shaped your approach to leading engineering and analytics teams?

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