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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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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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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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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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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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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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

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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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

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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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

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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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

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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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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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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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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Marktechpost

MAY 12, 2024

SEON SEON is an artificial intelligence fraud protection platform that uses real-time digital, social, phone, email, IP, and device data to improve risk judgments. It is based on adjustable and explainable AI technology.

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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

FEBRUARY 22, 2024

Andre Franca | CTO | connectedFlow Join this session to demystify the world of Causal AI, with a focus on understanding cause-and-effect relationships within data to drive optimal decisions. By the end, you will be ready to harness the platform for advanced spatial analysis and the development of sophisticated AI models.

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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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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

JUNE 8, 2023

The week was filled with engaging sessions on top topics in data science, innovation in AI, and smiling faces that we haven’t seen in a while. Some of our most popular in-person sessions were: Data Science Software Acceleration at the Edge: Audrey Reznik Guidera | Sr.

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ODSC - Open Data Science

MAY 30, 2023

Within the European COST Action «Fintech and AI in Finance», he leads the working group «Transparency into Investment Product Performance for Clients». Originally posted on OpenDataScience.com Read more data science articles on OpenDataScience.com , including tutorials and guides from beginner to advanced levels!

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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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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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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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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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DataRobot Blog

JUNE 23, 2022

When that happens, the data science team needs to move forward with a Phase 2. Modeling these decisions to create a business blueprint of the current decision-making approach is key, as it builds a shared understanding between those who must make changes (business and IT) and those doing the analysis (data science).

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Heartbeat

NOVEMBER 27, 2023

Fine-tuning, a process where pre-trained models are further trained on task-specific data, allows the model to adapt and refine its representations to the specific medical imaging domain. Interpretability and Explainability One challenge with deep learning models in medical image analysis is their black-box nature.

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DataRobot Blog

MARCH 15, 2022

In our previous healthcare blog , Sally Embrey explained how the integration of health and care services is gathering pace globally and how the creation of Integrated Care Systems (ICSs) by England’s National Health Service (NHS) is the latest example of services being organized around a local population.

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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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Dlabs.ai

MAY 22, 2022

Machine Learning for Data Science and Analytics Authors: Ansaf Salleb-Aouissi, Cliff Stein, David Blei, Itsik Peer Associate, Mihalis Yannakakis, Peter Orbanz If you ever dreamt of attending classes at Columbia University but never had the chance, this artificial intelligence course focused on ML is the next best thing.

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Defined.ai blog

AUGUST 10, 2023

Regular data audits and implementing robust data governance practices can help maintain data quality. Skills Gap Leveraging AI for financial data analysis requires specialized skills. Building a team with AI, data science, and finance expertise is crucial.

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Marktechpost

AUGUST 24, 2023

With expertise in computer science, machine learning, robotics, mathematics, and other fields, this community is home to academics and engineers developing and utilizing this interdisciplinary topic. r/datascience It features the latest content and discussions on Data Science and its related fields.

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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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